Salt lake resource dynamic monitoring system based on Kriging interpolation method

Abstract

The invention provides a salt lake resource dynamic monitoring system based on a Kriging interpolation method. Comprising a drilling deployment module, a resource reserve initial exploration module, a multi-source data acquisition and preprocessing module, a spatial interpolation algorithm module and a three-dimensional visual modeling and rendering module. According to the method, the Kriging interpolation method and the Co-Kriging interpolation method are combined, the main and auxiliary variable spatial correlation is utilized to improve the reserve estimation accuracy, the limitation of dependence on a large number of samples traditionally is broken through, center encryption and peripheral control drill hole arrangement is combined with uncertainty evaluation to optimize the exploration cost, comprehensive evaluation is supported by a small number of samples, the model is dynamically updated by the system monthly, and the exploration efficiency is improved. The problem of traditional data lag is solved, the resource dynamic state is visually presented through the three-dimensional visual modeling and rendering module, a basis is provided for decision making, the resource utilization rate is improved, and premature exhaustion is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource dynamic monitoring and smart mine, and particularly relates to a salt lake resource dynamic monitoring system based on a Kriging interpolation method. BACKGROUND

[0002] As an important carrier of mineral resources, salt lakes are rich in strategic elements such as lithium, potassium, boron and magnesium. Especially under the background of the rapid development of the new energy industry, the demand for lithium resource development has surged. However, the dynamic nature, complexity and environmental sensitivity of salt lake resources make it difficult to estimate the amount of resources, resulting in frequent reserve estimation deviations, low resource utilization, and even premature depletion during the development process. The influencing factors mainly include four dimensions of natural factors, technical limitations, mining management and economic impacts: 1. Natural factors lead to dynamic changes in resources: The formation and enrichment of salt lake resources are highly dependent on regional climate and hydrological conditions. Factors such as evaporation, precipitation and groundwater recharge directly affect the volume, concentration and mineral grade of brine. At the same time, salt lake resources are not uniformly distributed, but show vertical stratification and horizontal zonation. This heterogeneity makes resource assessment dependent on a large amount of sampling data, but actual exploration often suffers from sparse sampling points due to cost constraints, resulting in large errors in reserve estimation.

[0003] 2. Limitations of monitoring and evaluation technology: Current salt lake resource assessment mainly relies on drilling sampling, geophysical exploration (such as electromagnetic method, seismic exploration) and chemical analysis, but there are the following problems: Data lag: Drilling and laboratory analysis have a long cycle (usually several months), making it difficult to reflect the dynamic changes of salt lake resources in a timely manner; Insufficient spatial coverage: Point sampling is difficult to fully depict the distribution of salt lake resources, especially for deep brine or resources under salt crust.

[0004] 3. Improper mining management exacerbates resource depletion risk: Enterprises tend to exploit high-concentration brine areas, while low-grade areas are ignored, resulting in low overall resource utilization. If brine is not properly recharged or lacks fresh water replenishment after extraction, it may disrupt the salt lake water chemical balance and accelerate resource depletion. Moreover, salt lakes are mostly located in arid regions, and excessive exploitation may affect the surrounding groundwater system, leading to ecological degradation (such as wetland shrinkage and soil salinization).

[0005] 4. Economic and strategic impact: If resource assessment is overly optimistic, enterprises may over-invest in lithium extraction facilities, but the actual recoverable amount is insufficient, resulting in idle production capacity. Moreover, since lithium and potassium resources are significantly affected by market price fluctuations, inaccurate reserve estimates may prevent enterprises from adjusting production strategies flexibly. SUMMARY

[0006] The present application aims to at least solve one of the technical problems existing in the prior art, and provides a salt lake resource dynamic monitoring system based on Kriging interpolation method.

[0007] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a salt lake resource dynamic monitoring system based on Kriging interpolation method, comprising a drilling deployment module, a resource reserve preliminary exploration module, a multi-source data acquisition and preprocessing module, a spatial interpolation algorithm module, and a three-dimensional visualization modeling and rendering module. The drilling deployment module plans the drilling distribution of exploration and sampling through a grid arrangement scheme of "central encryption and peripheral control". The resource reserve preliminary exploration module detects the brine distribution spatial data through geophysical prospecting tools, determines the brine layer depth and thickness, constructs a preliminary three-dimensional geological model in combination with the Leapfrog Geo three-dimensional modeling tool, and divides the preliminary three-dimensional geological model into multiple basic spatial units by equal ratio sectioning. The multi-source data acquisition and preprocessing module carries out targeted data acquisition in multiple basic spatial units through a drilling sampling data acquisition unit and a real-time water level data acquisition unit, and pre-processes the collected data through a data preprocessing unit to generate a data set meeting the requirements of geological statistical modeling. The spatial interpolation algorithm module uses Kriging interpolation method to perform spatial interpolation on the main variables of the data set, and uses Co-Kriging interpolation method to introduce auxiliary variables to improve the estimation accuracy of the main variables, and outputs interpolation result data. The three-dimensional visualization modeling and rendering module fills the interpolation result data into the attribute field of the corresponding basic spatial unit to realize fine assignment of the preliminary three-dimensional geological model, visualizes the salt lake resource spatial distribution characteristics and the dynamic evolution process of exploitation with discretized grid units as the minimum display unit, and supports monthly model update based on new data.

[0008] In some possible embodiments, the grid arrangement scheme includes a central area, a transition area and a peripheral area, wherein the grid density of the central area is 100m x 100m, the grid density of the transition area is 200m x 200m, and the grid density of the peripheral area is 400m x 400m.

[0009] In some possible embodiments, the borehole sampling data acquisition unit acquires conventional ion concentration and physical property data based on the borehole deployment module through titration or a Z-9 Liquidator real-time assay device; and the real-time water level data acquisition unit acquires water level data monitored by an in-situ sensor network in real time.

[0010] In some possible embodiments, the data preprocessing unit pre-processes the acquired data, including outlier rejection, normality test and spatial structure analysis.

[0011] In some possible embodiments, the main variables include Li + concentration and K + concentration; and the auxiliary variables include brine density, pH value and water level change rate.

[0012] In some possible embodiments, the spatial interpolation algorithm module further includes an uncertainty evaluation unit, which calculates the average grade and resource amount of each block segment by dividing the salt lake into regular grids, and outputs the confidence interval of the interpolation result data by Kriging variance analysis, to guide the supplementary sampling work.

[0013] In some possible embodiments, the salt lake resource types presented by the three-dimensional visualization modeling and rendering module include liquid LiCl, liquid KCl, liquid B2O3, liquid MgCl2 and solid NaCl.

[0014] In a second aspect, the present application provides an electronic device, comprising: one or more processors; a storage unit configured to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the salt lake resource dynamic monitoring system based on the Kriging interpolation method as described above.

[0015] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, enables the processor to implement the salt lake resource dynamic monitoring system based on the Kriging interpolation method as described above.

[0016] The salt lake resource dynamic monitoring system based on the Kriging interpolation method has the following advantages: The application can control the resource reserve estimation error within ±8% by combining the Kriging interpolation method with the Co-Kriging interpolation method, fully utilizing the spatial correlation between the main variable and the auxiliary variable, reducing the resource reserve estimation error by 15%-30% compared with the traditional method, breaking through the technical limitation of the traditional interpolation method which relies on a large number of samples, and improving the accuracy of resource evaluation to a certain extent. The system of the application supports dynamic model updating based on new data every month, can timely reflect the dynamic change trend of the salt lake resource grade, and solves the problem of data lag in the traditional evaluation method. The application adopts a drilling grid arrangement scheme of 'central encryption and peripheral control', combined with an uncertainty evaluation unit, can accurately identify the area that needs to be supplemented sampling, reduces the number of invalid drillings, optimizes the exploration cost while ensuring the evaluation accuracy, and can achieve the technical effect of supporting comprehensive evaluation with a small amount of sample data (such as limited drilling sample and in-situ sensor data). The three-dimensional visualization modeling and rendering module of the application directly presents the resource spatial distribution characteristics and the dynamic evolution process under the influence of mining, provides quantitative basis for mining scheme formulation, resource sustainable development planning and ecological protection decision, helps to improve resource utilization rate, and is also helpful to avoid premature depletion of resources. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 It is a structure schematic view of an example electronic device of the salt lake resource dynamic monitoring system based on the Kriging interpolation method of the application. Figure 2 It is a structure schematic view of an example electronic device of the salt lake resource dynamic monitoring system based on the Kriging interpolation method of the application. Figure 3 It is a first visualization schematic view of the salt lake resource dynamic monitoring system based on the Kriging interpolation method of the application. Figure 4 It is a second visualization schematic view of the salt lake resource dynamic monitoring system based on the Kriging interpolation method of the application. Figure 5 It is an example distribution table of the ore bed-block section level resource reserve part in the salt lake resource dynamic monitoring system based on the Kriging interpolation method of the application. DETAILED DESCRIPTION

[0018] The technical solutions of the application will be described clearly and completely in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0019] Figure 1It is a structural schematic diagram of an example electronic device for implementing a salt lake resource dynamic monitoring system based on Kriging interpolation method of the present application. As shown in Figure 1 The electronic device 100 includes one or more processors 110, one or more storage devices 120, one or more input devices 130, one or more output devices 140, etc., which are interconnected through a bus system 150 and / or other forms of connection mechanism. It should be noted that, Figure 1 The components and structure of the electronic device shown are only exemplary and not limiting, and the electronic device can also have other components and structures as needed.

[0020] The processor 110 can be a central processing unit (CPU), or can be other forms of processing units composed of multiple processing cores, or having data processing capability and / or instruction execution capability, and can control other components in the electronic device 100 to perform desired functions.

[0021] The storage device 120 can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer readable storage medium, and the processor can run the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present disclosure described below and / or other desired functions. Various application programs and various data, such as various data used and / or generated by the application programs, etc., can also be stored in the computer readable storage medium.

[0022] The input device 130 can be a device used by a user to input instructions, and can include one or more of a keyboard, a mouse, a microphone, and a touch screen, etc.

[0023] The output device 140 can output various information (such as images or sounds) to the outside (such as a user), and can include one or more of a display, a speaker, etc.

[0024] Figure 2 It is a structural schematic diagram of an example electronic device for implementing a salt lake resource dynamic monitoring system based on Kriging interpolation method of the present application. As shown in Figure 2As shown, a salt lake resource dynamic monitoring system based on Kriging interpolation method includes a drilling deployment module 201, a resource reserve preliminary exploration module 202, a multi-source data acquisition and preprocessing module 203, a spatial interpolation algorithm module 204, and a three-dimensional visualization modeling and rendering module 205; the drilling deployment module 201 plans the drilling distribution of exploration and sampling through the grid arrangement scheme of "central encryption, peripheral control"; the resource reserve preliminary exploration module 202 detects the brine distribution spatial data through geophysical tools, determines the brine layer depth and thickness, constructs a preliminary three-dimensional geological model in combination with the Leapfrog Geo three-dimensional modeling tool, and divides the preliminary three-dimensional geological model into multiple basic spatial units by equal ratio section; the multi-source data acquisition and preprocessing module 203 carries out targeted data acquisition in multiple basic spatial units through a drilling sampling data acquisition unit and a real-time water level data acquisition unit, and pre-processes the collected data through a data preprocessing unit to generate a data set meeting the requirements of geological statistical modeling; the spatial interpolation algorithm module 204 performs spatial interpolation on the main variables of the data set by using the Kriging interpolation method (Kriging interpolation method), and simultaneously uses the Co-Kriging interpolation method (Co-Kriging interpolation method) to introduce auxiliary variables to improve the estimation accuracy of the main variables, and outputs interpolation result data; the three-dimensional visualization modeling and rendering module 205 fills the interpolation result data into the attribute field of the corresponding basic spatial unit to realize fine assignment of the preliminary three-dimensional geological model, visualizes the spatial distribution characteristics of salt lake resources and the dynamic evolution process of exploitation with the discretized grid unit as the minimum display unit, and supports monthly model update based on new data.

[0025] Specifically, the geophysical tool includes but is not limited to WIREBMR borehole magnetic resonance logging instrument or geological radar.

[0026] Specifically, when the preliminary three-dimensional geological model is divided into multiple basic spatial units by equal ratio section, it needs to be carried out under the premise of ensuring data accuracy.

[0027] Specifically, the "Kriging interpolation method" in the present application is Kriging interpolation, which is a spatial interpolation method based on regionalized variable theory, which is used for three-dimensional modeling and dynamic monitoring of salt lake resources in the present technical solution. It determines the variogram function of the regionalized variable through the analysis of the known sampling point data, and then optimally estimates the attribute value of the unknown point; Variogram function construction (spatial correlation quantification): the variogram function is a key tool for describing the spatial variation structure of regionalized variables. The present technical solution uses a spherical semivariogram function to describe the spatial variation structure of Li + Concentration, K + Concentration, etc. The calculation formula is:

[0028] wherein h is the distance between sample points (lag distance), (covariance) is used to reflect the measurement error or micro-scale random variation, c (semi-variogram) is used to reflect the spatial structural variation, a (range) is the maximum distance of spatial autocorrelation (correlation disappears when h > a), (semi-variogram) is the limit value of the total variation degree of the variable; Kriging estimation: for the estimated value of unknown point , it can be obtained by linear combination of known sample points:

[0029] wherein, is the data of known sample points, is the weight coefficient, which satisfies to ensure the unbiasedness of the estimation, the determination of the weight coefficient needs to be realized by solving the Kriging equation set, the Kriging equation set is constructed based on the variogram function, so that the variance of the estimation error is minimized, in the process of mathematical derivation, according to the principle of Lagrange multiplier method, the Lagrange multiplier ψ is introduced to deal with the optimization problem with constraint condition (here refers to This unbiasedness constraint), the Lagrange function is constructed, and the partial derivatives of the function with respect to and ψ are calculated, and they are equal to 0, so a group of equations are obtained, after these equations are arranged, the Kriging equation set (Kriging equation set) is formed:

[0030] wherein ψ is the Lagrange multiplier, is the value of the variogram function between sample points, is the value of the variogram function between the sample point and the unknown point.

[0031] Specifically, the Co-Kriging interpolation method is an extension of the Kriging interpolation method, which not only uses the information of the main variable itself, but also introduces the auxiliary variable related to the main variable in space to improve the interpolation accuracy. When the auxiliary variable is easier to obtain and has strong spatial correlation with the main variable, the Co-Kriging interpolation method can significantly improve the estimation effect of the main variable; Variable correlation: there needs to be a certain spatial correlation between the main variable and the auxiliary variable, which can be quantified by the cross variogram function, the calculation formula of the cross variogram function is:

[0032] wherein,​ the cross variogram value of the primary variable and the secondary variable at distance h, the number of sample point pairs at distance h; Co-Kriging estimation: the primary variable estimate value of unknown point is obtained by linear combination of known sample points of the primary variable and the secondary variable:

[0033] wherein, is the primary variable estimate value of unknown point x0, is the weight coefficient of the primary variable sample point, is the measured value of the primary variable sample point, is the weight coefficient of the secondary variable sample point, is the measured value of the secondary variable sample point, n is the number of primary variable sample points, and m is the number of secondary variable sample points, and The corresponding unbiasedness condition needs to be met, and the weight coefficient is determined by solving the Co-Kriging equation set to minimize the variance of the estimation error.

[0034] Specifically, the attribute fields of the basic spatial units include but are not limited to the primary variables (Li + concentration and K + concentration, etc.), secondary variables (brine density, pH value, and water level change rate, etc.), block average grade (average content of Li + , K + resources in each discrete grid unit), block resource amount (resource reserves such as liquid LiCl and KCl reserves calculated based on the volume and grade of the grid unit), uncertainty evaluation parameters (Kriging variance), and resource type classification (distinguishing the spatial distribution of liquid resources and solid resources).

[0035] Specifically, the model is updated based on new data every month, that is, after the new data is processed by the data preprocessing unit to remove outliers, normality test, and spatial structure analysis, the data set is input and updated, and the system driving spatial interpolation algorithm module 204 is recalculated. Finally, the monthly iteration of the model is realized through the three-dimensional visualization modeling and rendering module 205, ensuring the real-time and accuracy of the resource distribution characteristics and the evolution process of the exploitation; wherein, the new data every month includes but is not limited to ion concentration data, physical property data, and water level data obtained from the multi-source data acquisition and preprocessing module 203 through continuous monitoring and periodic sampling.

[0036] ​In some embodiments, the grid arrangement scheme includes a central zone, a transition zone and a peripheral zone, wherein the central zone has a grid density of 100 m x 100 m, the transition zone has a grid density of 200 m x 200 m, and the peripheral zone has a grid density of 400 m x 400 m, which reflects that the arrangement density of sensors (including but not limited to in-situ sensors) is higher in the central zone (high-grade resource zone), and is moderately sparse in the transition zone and the peripheral zone, which is conducive to balancing monitoring accuracy and deployment cost; specifically, the borehole aperture in the embodiment of the technical solution needs to be calibrated in combination with the size of the exploration equipment; it should be noted that the grid densities of the central zone, the transition zone and the peripheral zone in the above grid arrangement scheme are only exemplary and not restrictive, and the grid densities of the central zone, the transition zone and the peripheral zone need to be adjusted accordingly according to actual conditions.

[0037] In some embodiments, the borehole sampling data acquisition unit obtains conventional ion concentration and physical property data based on the borehole deployment module through titration or Z-9Liquidator real-time assay equipment; the real-time water level data acquisition unit acquires water level data monitored by the in-situ sensor network in real time.

[0038] Specifically, the conventional ions include but are not limited to Li + , K + , Na + , Mg 2+ , Ca 2+ , Cl - and SO4 2- , etc.; the physical property data include but are not limited to brine density, viscosity and pH value, etc.

[0039] In some embodiments, the data preprocessing unit pre-processes the collected data, including outlier rejection, normality test and spatial structure analysis, to ensure that the data quality meets the requirements of geostatistical modeling.

[0040] Specifically, the outlier rejection can adopt a standard deviation method or a boxplot method. In the standard deviation method, the data beyond the range of [μ-kσ, μ+kσ] is regarded as an outlier (generally k is 3), and the principle is based on normal distribution. In the normal distribution, about 99.7% of the data falls within the range of the mean plus or minus 3 times the standard deviation. In the boxplot method, the lower quartile Q1 and the upper quartile Q3 of the data are calculated, and the data less than Q1-1.5IQR or greater than Q3+1.5IQR is determined as an outlier. For the identified outliers, it is necessary to judge whether they are real outliers or not according to professional knowledge. If they are caused by measurement errors, they are rejected. If they are real special values, they need to be handled carefully. They can be retained and marked separately in subsequent analysis. The surface brine layer of the salt lake (affected by evaporation, with many outliers) is preferably analyzed by the boxplot method, and the intercrystalline brine layer of the salt lake (stable data) is analyzed by the standard deviation method to retain real outlier data (such as high-concentration points of tectonic fissures).

[0041] Specifically, the normality test can adopt Shapiro-Wilk test, Kolmogorov-Smirnov test or graphical method. Shapiro-Wilk test is suitable for small sample data (usually the sample size is less than 50). The calculation formula of the test statistic W is relatively complex. The normality of the data is judged by calculating the degree of conformity of the sample data to the normal distribution. When the p-value is greater than a given significance level (such as 0.05), it is considered that the data conforms to the normal distribution. Kolmogorov-Smirnov test is suitable for large sample data (usually the sample size is greater than or equal to 50). The maximum difference between the empirical distribution function of the sample and the theoretical normal distribution function is calculated as the test statistic. If the p-value is greater than the significance level (such as 0.05), the assumption that the data conforms to the normal distribution is accepted. The graphical method specifically refers to drawing a histogram. If the data approximately conforms to the normal distribution, the histogram presents a symmetrical bell-shaped distribution. A Q-Q plot is drawn. If the data points are roughly on a straight line, it indicates that the data is close to the normal distribution. When the brine concentration data is not normal, methods such as logarithmic transformation or square root transformation can be used to process the data to make it closer to the normal distribution, so as to meet the requirements of Kriging interpolation method and Co-Kriging interpolation method for data distribution.

[0042] Specifically, the spatial structure analysis includes semi-variogram cloud map drawing and directional variation analysis. The semi-variogram cloud map drawing specifically refers to calculating the semi-variation value between all sample point pairs: (wherein, is the value of the variogram at a distance h, is the number of sample point pairs with a distance of h, and Regionalized variables in and The values ​​at each point are plotted, and a scatter plot of "distance-semivariance" is drawn with the distance between points as the horizontal axis and the semivariance as the vertical axis. By observing the "distance-semivariance" scatter plot, we can preliminarily determine whether there is spatial correlation in the data. If the semivariance shows a certain trend as the distance increases (such as increasing first and then stabilizing), it indicates that the data has spatial structured characteristics. Directional variability analysis calculates the semivariogram in different directions (such as 0°, 45°, 90°, 135°, etc.) and plots the variability curves in different directions. If there are significant differences between the variability curves in different directions, it indicates that the data is anisotropic. If the differences are small, it is isotropic. This analysis result helps in the selection of subsequent variogram models and parameter determination, and improves the accuracy of interpolation.

[0043] In some embodiments, the main variable includes Li + Concentration and K + Concentration, specifically, Li + and K + It is a key indicator of strategic resources such as lithium and potassium in salt lakes; auxiliary variables include brine density, pH value and water level change rate. Specifically, auxiliary variables can effectively reflect the intrinsic relationship between the physicochemical properties of brine and the main variables. It should be noted that the above main variables and auxiliary variables are only exemplary and not restrictive, and can be adjusted accordingly according to actual needs.

[0044] Specifically, since the main variables and auxiliary variables may have different dimensions and orders of magnitude, data standardization is necessary to facilitate subsequent correlation analysis and model building. A commonly used standardization method is min-max standardization, with the following formula:

[0045] in, The value is the standardized value (ranging from [0, 1]). Let x be the i-th sample value in the original data, min(x) be the minimum value of the variable among all samples, and max(x) be the maximum value of the variable among all samples.

[0046] In some embodiments, the spatial interpolation algorithm module 204 further includes an uncertainty assessment unit. After the spatial interpolation algorithm module 204 completes spatial interpolation, the uncertainty assessment unit calculates the average grade and resource quantity of each block by dividing the salt lake into regular grids, and uses Kriging variance analysis to output the confidence interval of the interpolation result data to guide supplementary sampling work.

[0047] Specifically, the methods for guiding supplementary sampling work include, but are not limited to: generating a list of sampling point coordinates, marking high uncertainty areas in the three-dimensional geological model, and sending operation instructions to the drilling control system. All of the above methods are triggered based on the confidence interval results of Kriging variance analysis, that is, when the confidence interval of the output interpolation result data exceeds the preset threshold.

[0048] Specifically, Kriging variance is the core metric for measuring the accuracy of Kriging interpolation estimation. It quantifies the mean squared error between the estimated value and the true value, directly reflecting the uncertainty of the interpolation result. The calculation formula is as follows:

[0049] in, Unknown point Kriging variance at the location, These are the weighting coefficients obtained by solving the Kriging equations. For regionalized variables at sample points and unknown points The value of the variogram at point ψ is the Lagrange multiplier in the Kriging equations, used to ensure the unbiasedness of the estimate; Influencing factors include: sample point distribution density: the denser the sample points in the salt lake, the stronger the spatial constraint on the unknown points, and the smaller the Kriging variance; distance between sample points and unknown points: the closer the distance, the better the representativeness of the sample points to the unknown points, and the smaller the variance. Within the same mining area, the variance of the unknown area close to the known mining point is even lower. The structure of the variogram: the smaller the nugget value, the weaker the variation of the variable at a small scale, and the relatively smaller the variance; the larger the sill value and the reasonable range, the stronger the spatial correlation of the variable and the more stable the variance.

[0050] In some embodiments, such as Figure 3 , Figure 4 and Figure 5 As shown, the 3D visualization modeling and rendering module 205 presents salt lake resource types including liquid LiCl, liquid KCl, liquid B2O3, liquid MgCl2, and solid NaCl. It should be noted that the salt lake resource types listed above are only exemplary and not restrictive. Salt lake resource types can be adjusted accordingly based on actual needs.

[0051] The dynamic monitoring system for salt lake resources based on Kriging interpolation in this invention has the following advantages: The application can control the resource reserve estimation error within ±8% by combining the Kriging interpolation method with the Co-Kriging interpolation method, fully utilizing the spatial correlation between the main variable and the auxiliary variable, reducing the resource reserve estimation error by 15%-30% compared with the traditional method, breaking through the technical limitation of the traditional interpolation method which relies on a large number of samples, and improving the accuracy of resource evaluation to a certain extent. The system of the application supports dynamic model updating based on new data every month, can timely reflect the dynamic change trend of the salt lake resource grade, and solves the problem of data lag in the traditional evaluation method. The application adopts the drilling grid arrangement scheme of 'central encryption and peripheral control', combined with the uncertainty evaluation unit, can accurately identify the area needing supplementary sampling, reduces the number of invalid drillings, optimizes the exploration cost while ensuring the evaluation accuracy, and can achieve the technical effect of supporting comprehensive evaluation with a small amount of sample data (such as limited drilling sample and in-situ sensor data). The three-dimensional visualization modeling and rendering module 205 of the application directly presents the resource spatial distribution characteristics and the dynamic evolution process under the influence of mining, provides quantitative basis for mining scheme formulation, resource sustainable development planning and ecological protection decision, helps to improve the resource utilization rate, and is also helpful to avoid premature depletion of resources.

[0052] In another aspect of the embodiment of the application, a computer readable storage medium is provided, which stores a computer program, and the computer program can realize the method according to the foregoing description when executed by a processor.

[0053] The computer readable medium can be included in the device, equipment or system of the present disclosure, or can exist independently.

[0054] The computer readable storage medium can be any tangible medium containing or storing a program, which can be an electrical, magnetic, optical, electromagnetic, infrared, semiconductor system, device or equipment, and more specific examples include but are not limited to: an electrical connection with one or more wires, a portable computer diskette, a hard disk, an optical fiber, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0055] The computer readable storage medium can also include a data signal propagating in a baseband or as a carrier wave in a propagated signal, which carries the computer readable program code, and more specific examples include but are not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof.

[0056] Parameter description: All the parameter values involved in the specification are not subjective, but the comprehensive result of the safety / efficiency requirements of the application scene, the requirements of industry standards and specifications, and the experience threshold of industry practice. In actual application, the parameters will be fine-tuned according to the relevant scene.

[0057] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features. Such modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A salt lake resource dynamic monitoring system based on Kriging interpolation method, characterized in that, The drilling deployment module, the resource reserve preliminary exploration module, the multi-source data acquisition and preprocessing module, the spatial interpolation algorithm module, and the three-dimensional visualization modeling and rendering module; The drilling deployment module plans the drilling distribution for exploration and sampling through a grid arrangement scheme of "central encryption and peripheral control". The resource reserve preliminary exploration module detects brine distribution spatial data through geophysical tools, determines the depth and thickness of the brine layer, constructs a preliminary three-dimensional geological model in combination with the Leapfrog Geo three-dimensional modeling tool, and divides the preliminary three-dimensional geological model into multiple basic spatial units in a geometric progression. The multi-source data acquisition and preprocessing module carries out targeted data acquisition in multiple basic spatial units through a drilling sampling data acquisition unit and a real-time water level data acquisition unit, and pre-processes the collected data through a data preprocessing unit to generate a data set meeting the requirements of geological statistical modeling. The spatial interpolation algorithm module uses the Kriging interpolation method to perform spatial interpolation on the main variables of the data set, and uses the Co-Kriging interpolation method to introduce auxiliary variables to improve the estimation accuracy of the main variables, and outputs the interpolation result data. The three-dimensional visualization modeling and rendering module fills the interpolation result data into the attribute field of the corresponding basic spatial unit to realize fine assignment of the preliminary three-dimensional geological model, visualizes the salt lake resource spatial distribution characteristics and the dynamic evolution process of exploitation with discretized grid units as the smallest display unit, and supports monthly model update based on new data. 2.The salt lake resource dynamic monitoring system based on Kriging interpolation method according to claim 1, characterized in that, The grid arrangement scheme includes a central area, a transition area, and a peripheral area, wherein the grid density of the central area is 100m x 100m, the grid density of the transition area is 200m x 200m, and the grid density of the peripheral area is 400m x 400m. 3.The Kriging interpolation-based salt lake resource dynamic monitoring system according to claim 1, characterized in that, The drilling sampling data acquisition unit obtains conventional ion concentration and physical property data based on the drilling deployment module through titration or Z-9 Liquidator real-time testing equipment; the real-time water level data acquisition unit acquires water level data monitored by an in-situ sensor network in real time. 4.The salt lake resource dynamic monitoring system based on Kriging interpolation method of claim 1, wherein, The data preprocessing unit pre-processes the collected data, including outlier rejection, normality test, and spatial structure analysis.

5. The Kriging interpolation method-based salt lake resource dynamic monitoring system according to claim 1, characterized in that, The main variables include Li + concentration and K + concentration; the auxiliary variables include brine density, pH value and water level change rate. 6.The salt lake resource dynamic monitoring system based on Kriging interpolation method of claim 1, wherein, The spatial interpolation algorithm module further includes an uncertainty evaluation unit, which calculates the average grade and resource quantity of each block by dividing the salt lake into regular grids, and outputs the confidence interval of the interpolation result data using Kriging variance analysis to guide supplementary sampling work. 7.The Kriging interpolation-based salt lake resource dynamic monitoring system according to claim 1, characterized in that, The three-dimensional visualization modeling and rendering module presents salt lake resource types including liquid LiCl, liquid KCl, liquid B2O3, liquid MgCl2, and solid NaCl.

8. An electronic device, comprising: comprise: one or more processors; A storage unit is configured to store one or more programs, which when executed by the one or more processors, enable the one or more processors to implement the Kriging interpolation method-based dynamic monitoring system for salt lake resources according to any one of claims 1 to 7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, enables the Kriging interpolation method-based dynamic monitoring system for salt lake resources according to any one of claims 1 to 7.

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