Rock corrosion rate determination method and system

Abstract

The invention provides a rock corrosion rate determination method and system, and relates to the technical field of rock attribute determination, and the method comprises the following steps: obtaining a variable condition continuous value used for determining a rock sample; sampling the variable condition continuous value through a spontaneous chaos traversal sampling mode to obtain a plurality of variable condition discrete values; respectively measuring the rock sample according to the discrete value of each variable condition to obtain the corrosion rate; calculating the corrosion rate change rate between the corrosion rates corresponding to the discrete values of the adjacent variable conditions; taking two variable condition discrete values corresponding to the maximum corrosion rate change rate as endpoint reset variable condition continuous values, and returning to sample again; fitting each measured data pair through a B spline fitting algorithm, and establishing a corrosion rate fitting curve; and outputting the corrosion rate fitting curve to obtain a corrosion rate measurement result of the rock sample. The determination result accuracy is improved and the determination cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of rock property determination, and particularly to a method and system for determining the rock dissolution rate. Background Art

[0002] The rock dissolution rate refers to the rate at which the mass or volume of a rock decreases over time due to chemical dissolution under specific environmental conditions. It is an important indicator for measuring the rock's resistance to dissolution and reflects the intensity of the chemical interaction between the rock and the surrounding fluid. The dissolution rate is usually expressed as the amount of rock loss per unit area or volume per unit time.

[0003] The determination of the rock dissolution rate is the core basis for geological engineering, environmental protection, and disaster prevention in karst areas. For example, in the construction of water conservancy projects (such as dams and tunnels), the dissolution rate data can predict the long-term stability of the rock mass structure and avoid safety accidents caused by dissolution collapse; in the development of karst groundwater, the dissolution rate affects the permeability of the aquifer, and it is necessary to optimize water resource utilization through measurement; in addition, the study of the carbon cycle in the karst ecosystem also relies on the quantification of the dissolution rate. Traditional experimental methods are time-consuming and costly, while the intelligent determination technology proposed in this patent significantly reduces the number of experiments through chaotic sampling and adaptive iteration. At the same time, the accuracy is improved by improving the B-spline fitting, providing an efficient solution for quickly obtaining a reliable continuous curve of the dissolution rate, which has important practical value for engineering safety design, geological disaster warning, and ecological environmental protection.

[0004] However, in the existing process of determining the rock dissolution rate, the selection of variable conditions is mostly based on subjective human decision-making, resulting in uneven distribution of sampling points for variable conditions, being easily affected by subjective factors, difficult to cover the complex and changeable actual environment, and the current determination experiment process often has too long an experimental period, increasing the time cost. Summary of the Invention

[0005] In order to solve the technical problems in the existing process of determining the rock dissolution rate, where the selection of variable conditions is mostly based on subjective human decision-making, resulting in uneven distribution of sampling points for variable conditions, being easily affected by subjective factors, difficult to cover the complex and changeable actual environment, and the current determination experiment process often has too long an experimental period, increasing the time cost, the present invention provides a method and system for determining the rock dissolution rate.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] In the first aspect

[0008] A method for measuring the rock dissolution rate provided by an embodiment of the present invention is applied to a rock dissolution rate measuring device. The rock dissolution rate measuring device includes a temperature-controlled water storage device, a pressure pump, a water flow rate meter, and a reactor connected in sequence. The reactor is connected to the temperature-controlled water storage device. Each component with a connection relationship in the rock dissolution rate measuring device is communicated through a pipeline to form a water circulation system. Among them, the reactor is used to place rock samples. The method includes:

[0009] S1: Obtain continuous values of variable conditions for measuring rock samples;

[0010] S2: Sample the continuous values of variable conditions by the self-organized chaotic traversal sampling method to obtain multiple discrete values of variable conditions;

[0011] S3: In the rock dissolution rate measuring device, measure the rock samples respectively according to each discrete value of variable conditions, and obtain the dissolution rate of the rock samples under the corresponding discrete values of variable conditions. Among them, each discrete value of variable conditions and the corresponding dissolution rate form a measurement data pair;

[0012] S4: Calculate the dissolution rate change rate between the dissolution rates corresponding to adjacent discrete values of variable conditions;

[0013] S5: Use the two discrete values of variable conditions corresponding to the maximum dissolution rate change rate as endpoints to reset the continuous values of variable conditions, and return to step S2 until the number of iterations is greater than the preset number of iterations;

[0014] S6: Fit each measurement data pair through the B-spline fitting algorithm to establish a dissolution rate fitting curve;

[0015] S7: Output the dissolution rate fitting curve to obtain the measurement result of the rock sample dissolution rate.

[0016] Second aspect

[0017] A rock dissolution rate measuring system provided by an embodiment of the present invention includes:

[0018] A processor;

[0019] A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the rock dissolution rate measuring method described in the first aspect is implemented.

[0020] Third aspect

[0021] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored. When the program is executed by a processor, the rock dissolution rate measuring method described in the first aspect is implemented.

[0022] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0023] In the embodiments of the present invention, first, intelligent sampling based on chaotic mapping generates discrete variable conditions with strong ergodicity through self-generated chaotic traversal sampling, breaking through the subjectivity and locality of manual selection and achieving global and efficient coverage of multiple variables such as temperature and flow rate; second, the adaptive iteration mechanism dynamically identifies the sensitive interval with the largest change rate of the dissolution rate, automatically focuses on the key experimental area and resets the variable range, improving the accuracy of the measurement results; finally, the B-spline fitting algorithm is used to perform high-precision modeling on the non-linear response of the dissolution rate, and can extrapolate and predict the change trend of the dissolution rate in the unmeasured range. On the basis of ensuring the stability, reliability and accuracy of the measurement results, the experimental period is greatly shortened and the time cost is significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 It is a schematic flowchart of a method for measuring the rock dissolution rate provided by the embodiments of the present invention;

[0026] Figure 2 It is a schematic structural diagram of a device for measuring the rock dissolution rate provided by the embodiments of the present invention;

[0027] Figure 3 It is a schematic structural diagram of a system for measuring the rock dissolution rate provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following will describe the technical solutions in the present invention with reference to the drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0030] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0031] Refer to the attached specificationFigure 1 , showing a schematic flow chart of a method for measuring the rock dissolution rate provided by an embodiment of the present invention.

[0032] An embodiment of the present invention provides a method for measuring the rock dissolution rate, which can be applied to a rock dissolution rate measuring device. The rock dissolution rate measuring device includes a temperature-controlled water storage tank, a pressure pump, a water flow velocity meter, and a reactor connected in sequence. The reactor is connected to the temperature-controlled water storage tank. Each component with a connection relationship in the rock dissolution rate measuring device is connected through a pipeline to form a water circulation system. Among them, the reactor is used to place rock samples.

[0033] It should be noted that the device can accurately control the water temperature through the temperature-controlled water storage tank, the pressure pump drives the water flow and the flow velocity is monitored in real time by the flow velocity meter. The reactor carries the rock samples and is in continuous contact with the circulating water, forming a closed-loop water circulation system, which can dynamically simulate the real environment, realize the automation of the dissolution experiment under different variable conditions, and ensure the data reliability and experiment repeatability.

[0034] The processing flow of the method for measuring the rock dissolution rate may include the following steps:

[0035] S1: Obtain continuous values of variable conditions for measuring rock samples.

[0036] Among them, the continuous values of variable conditions refer to the continuous change range of environmental parameters that need to be controlled in the rock dissolution experiment, such as the value ranges of key conditions affecting the dissolution rate, such as temperature, water flow velocity, pH value, etc.

[0037] In a possible implementation manner, the continuous values of variable conditions include continuous values of temperature conditions, continuous values of flow velocity conditions, and continuous values of pH value conditions.

[0038] It should be noted that the continuous values of variable conditions need to conform to the laws of nature, which means that the preset ranges of parameters such as temperature, flow velocity, and pH must be based on the possible values in the actual geological or engineering environment, avoiding the laboratory conditions from deviating from the real scenario, ensuring that the measurement results can truly reflect the dissolution behavior of rocks in the natural environment or engineering applications, and improving the practicality and prediction reliability of the data.

[0039] Among them, the rock samples include pure limestone rock samples, limestone rock samples, and marl rock samples.

[0040] S2: Sample the continuous values of variable conditions by the self-organized chaotic traversal sampling method to obtain multiple discrete values of variable conditions.

[0041] Among them, the self-generated chaotic traversal sampling method is a globally efficient sampling strategy based on a chaotic system (such as the Logistic map). Its core is to generate discrete sampling points that are uniformly distributed and cover the entire space within the continuous value range of variable conditions through the ergodicity and pseudo-randomness of the chaotic sequence, avoiding the subjective bias of artificial empirical selection. The discrete values of variable conditions are representative experimental parameter combinations extracted from the continuous variable range. For example, from the continuous range of temperature from 10 to 50 °C, discrete points such as 20 °C, 28 °C, and 42 °C are selected through chaotic sampling as the specific conditions for actually measuring the corrosion rate. By breaking through the limitations of artificial experience through chaotic traversal sampling, a small number of highly representative discrete points are used to cover the variable action interval, reducing the number of experiments while ensuring the global reliability of the data.

[0042] In one possible implementation, S2 specifically includes:

[0043] S201: Randomly select the initial value of the chaotic sequence within the range of 0 to 1.

[0044] S202: Based on the initial value of the chaotic sequence, generate the chaotic sequence of each continuous value of the variable condition through the Logistic chaotic mapping algorithm:

[0045]

[0046] where x k and x k+1 represent the k-th iteration value and the (k + 1)-th iteration value of the chaotic sequence respectively, tanh represents the arctangent function, λ represents the chaotic gain factor for adjusting the change amplitude of the chaotic sequence, σ represents the steepness control parameter for adjusting the traversal speed of the chaotic sequence, and γ represents the boundary repulsion coefficient for avoiding the concentration of the chaotic sequence.

[0047] Among them, the Logistic chaotic mapping algorithm is a method for generating a chaotic sequence through a non-linear equation, which can be used to simulate the pseudo-random behavior of complex systems. This algorithm generates representative discrete conditions (such as temperature, flow rate) through a small number of iterations, breaks through the limitations of artificial experience, and at the same time ensures the effective coverage of extreme conditions (such as low temperature and strong acid), laying a data foundation for a high-precision corrosion model.

[0048] It should be noted that based on the ergodicity of the Logistic chaotic mapping, a small number of sampling points are evenly distributed to cover the entire parameter space of multiple variables such as temperature and flow rate, avoiding local blind spots caused by artificial selection.

[0049] In one possible implementation, the chaotic gain factor is 4, the steepness control parameter is 0.1, and the boundary repulsion coefficient is 1000.

[0050] Optionally, the chaotic gain factor, the steepness control parameter, and the boundary repulsion coefficient can be adjusted according to actual needs during actual application.

[0051] S203: Add a buffer amount to the boundary parameters in the chaotic sequence to prevent the boundary parameters from being within a predetermined boundary range:

[0052] [min'(x k ), max'(x k )] ← [min(x k ) - Δ, max(x k ) + Δ]

[0053] Δ = 0.05 · std(x k )

[0054] where ← represents the update direction, min'(x k ), max'(x k ) represent the minimum and maximum values in the chaotic sequence after adding the buffer amount Δ respectively, and std(x k ) represents the standard deviation of each parameter x k in the chaotic sequence.

[0055] It should be noted that the buffer amount Δ is dynamically adjusted through the standard deviation to prevent the chaotic sequence from over - aggregating near the physical boundary and ensure effective sampling under extreme conditions.

[0056] S204: Map the chaotic sequence after adding the buffer amount to the discrete values of variable conditions representing the actual physical range, where each discrete value of variable conditions is within the constraints of the actual physical range:

[0057]

[0058] where T k represents the k - th discrete value of variable conditions obtained after mapping, and T max and T min represent the maximum value of the actual physical range and the minimum value of the actual physical range corresponding to the variable conditions respectively.

[0059] In a possible implementation, the variable conditions include temperature conditions, flow rate conditions, and pH value conditions.

[0060] It should be noted that through the Logistic chaotic mapping, discrete values with strong ergodicity and uniform distribution are generated. Combining with the dynamic buffer amount adjustment to avoid boundary aggregation, and then through the physical range mapping to ensure that the experimental conditions meet the actual requirements. This method comprehensively covers the collaborative action interval of multiple variables such as temperature and flow rate with a small number of sampling points, breaks through the limitations of manual experience, improves the data representativeness without increasing the number of experiments, and can especially effectively capture the erosion mutation law under extreme conditions, laying a foundation for high - precision modeling.

[0061] In a possible implementation, S2 further includes:

[0062] Truncate the obtained discrete values of the variable conditions according to the actual physical range constraints, where the actual physical range constraints are specifically:

[0063] Clip(T k ,T min ,T max )

[0064] where Clip represents the truncation limit operator.

[0065] Specifically, for clip(x,a,b), if x is less than the minimum value a, the result is a. If x is greater than the maximum value b, the result is b. If x is within the range [a,b], the result is x itself.

[0066] It should be noted that by using the Clip function, the discrete values of the variable conditions are forced to be strictly limited within the actual physical range, avoiding the generation of invalid or unachievable experimental conditions, ensuring the physical rationality of the experimental data and the safety of the equipment. At the same time, it eliminates the interference of abnormal data caused by the boundary overflow of the chaotic sequence, and improves the accuracy and engineering practicability of the subsequent fitting model.

[0067] S3: Measure the rock samples respectively according to the discrete values of each variable condition, and obtain the dissolution rate of the rock samples under the corresponding discrete values of the variable conditions. Among them, each discrete value of the variable condition and the corresponding dissolution rate form a measurement data pair.

[0068] It should be noted that the rock dissolution rate measurement equipment automatically adjusts the environmental parameters and executes the experiment according to the discrete values of the variable conditions generated in step S2, accurately measures the dissolution rate of the rock samples under each discrete condition (such as mass loss or volume change), and finally combines each discrete condition with its corresponding dissolution rate into a measurement data pair, providing key experimental data support for the subsequent fitting curve.

[0069] S4: Calculate the dissolution rate change rate between the dissolution rates corresponding to adjacent discrete values of the variable conditions.

[0070] S5: Use the two discrete values of the variable conditions corresponding to the maximum dissolution rate change rate as endpoints to reset the continuous value of the variable conditions, and return to step S2 until the number of iterations is greater than the preset number of iterations.

[0071] It should be noted that by dynamically identifying the most sensitive interval with the largest dissolution rate change rate and resetting the variable range, the adaptive optimization allocation of experimental resources is realized. The sampling density is automatically increased in the key area (such as the dissolution rate mutation section), avoiding the inefficiency and redundancy caused by uniform sampling. At the same time, through iterative focusing, the non-linear response law between the environmental conditions and the dissolution rate is quickly locked, and a high-precision fitting model is constructed with the least number of experiments.

[0072] It should be noted that those skilled in the art can set the size of the preset number of iterations according to actual needs, and the present invention does not make any limitations in this regard.

[0073] S6: Fit each pair of measured data by using the B-spline fitting algorithm to establish a dissolution rate fitting curve.

[0074] Among them, the B-spline fitting algorithm is a curve fitting method based on piecewise polynomial functions, which smoothly connects discrete data points by constructing locally supported basis functions. The characteristics of B-splines lie in local adjustability (modifying a certain curve segment does not affect the whole) and high-order continuity, which can flexibly adapt to the non-linear changes of the dissolution rate. By fitting each pair of measured data with the B-spline fitting algorithm to establish a dissolution rate fitting curve, the dissolution rate curves of different rock samples can be accurately fitted, and a reliable continuous dissolution rate curve can be obtained through a small number of measurement experiments, reducing the measurement duration and measurement.

[0075] In a possible implementation manner, S6 specifically includes:

[0076] S601: Remove the abnormal data points in each pair of measured data.

[0077] Among them, the abnormal data points include the abnormal data points corresponding to the pairs of measured data with unqualified discrete values of variable conditions and the abnormal data points corresponding to the pairs of measured data with dissolution rates not conforming to the rock characteristics.

[0078] In a possible implementation manner, the abnormal data points include kinetic contradiction points and pseudo-trend points.

[0079] Among them, the kinetic contradiction points refer to the trend abnormal data points in the experimental data that are significantly inconsistent with the dissolution kinetic theory model or empirical law. For example: in an acidic environment (pH < 7), the dissolution rate should increase with the reaction time (in line with the proton-promoted dissolution mechanism), but a certain data point shows an abnormal decreasing trend and cannot be reasonably explained through experimental conditions or error analysis.

[0080] Among them, the pseudo-trend points refer to the false trends caused by noise, interference or human error, which need to be excluded through physical logic or statistical tests. For example: the dissolution rate drops by 80% within 5 minutes and then quickly recovers, which does not conform to the dissolution kinetics of carbonate rocks in an acidic environment (the reaction rate is controlled by the surface reaction and cannot be instantaneously reversed).

[0081] S602: Segment each of the remaining data points to obtain multiple data point intervals.

[0082] S603: Construct B-spline basis functions including all data point intervals:

[0083]

[0084] Among them, B j (T k ) represents the basis function corresponding to the j-th data point interval generated based on the discrete values of the variable conditions, where j = 1, 2, …, m, and m represents the number of data point intervals. c j represents the coefficient of B j (T k ), and S represents the corrosion rate fitting curve.

[0085] S604: Optimize each coefficient by the least squares method:

[0086]

[0087] Among them, R k represents the corrosion rate corresponding to the k-th discrete value of the variable condition T k , where k = 1, 2, …, K, and K represents the total number of discrete values of the variable conditions. represents the coefficient c that makes the function take the minimum value.

[0088] S605: Substitute the optimized coefficients into the basis functions of the corresponding data point intervals to obtain the corrosion rate fitting curve.

[0089] Specifically, the method first eliminates abnormal data (such as invalid points with a flow rate of 0 but a non-zero corrosion rate) to ensure data reliability. Subsequently, it adaptively segments according to the data distribution density (such as subdividing nodes in the corrosion rate mutation area), constructs B-spline basis functions with local support, optimizes the coefficients by the least squares method to minimize the fitting error, and finally generates a smooth and continuous corrosion rate curve. By eliminating abnormal points and performing segmented fitting, the problem of easy inaccuracy of traditional methods in sparse data or mutation regions is solved. The local adjustability and global continuity of B-splines enable the model to capture non-linear mutations while avoiding overfitting, and high-precision prediction of the corrosion rate can be achieved with only a small number of experimental points or measurement data pairs.

[0090] S7: Output the corrosion rate fitting curve to obtain the measurement result of the corrosion rate of the rock sample.

[0091] In the actual application process, first, set the continuous ranges of environmental variables such as temperature and flow rate, and use chaotic traversal sampling to generate representative discrete experimental conditions that uniformly cover the entire space, breaking through the limitations of manual experience. Subsequently, measure the corrosion rates under each discrete condition through automated equipment and form data pairs. Dynamically identify the sensitive interval with the largest change rate of the corrosion rate through iterative iteration, and adaptively increase the sampling density in the key area. Finally, combine the improved B-spline algorithm to eliminate outliers and perform piecewise fitting to generate a high-precision continuous curve. Without increasing the number of experiments, ensure the accuracy of the measurement results and the global reliability of the data. Through adaptive iteration, accurately focus on the non-linear mutation area, and combine B-spline fitting to achieve a continuous prediction model with low error under sparse data, significantly shortening the experimental period and reducing manual intervention, providing efficient and reliable technical support for the stability evaluation of geotechnical engineering.

[0092] The beneficial effects brought by the technical solution provided in the embodiment of the present invention at least include:

[0093] In the embodiment of the present invention, first, the intelligent sampling based on chaotic mapping generates discrete variable conditions with strong ergodicity through the self-generated chaotic traversal sampling method, breaking through the subjectivity and locality of manual selection, and achieving global and efficient coverage of multiple variables such as temperature and flow rate. Secondly, the adaptive iteration mechanism dynamically identifies the sensitive interval with the largest change rate of the corrosion rate, automatically focuses on the key experimental area and resets the variable range, improving the accuracy of the measurement results. Finally, through the B-spline fitting algorithm, a high-precision model is established for the non-linear response of the corrosion rate, and the change trend of the corrosion rate in the unmeasured range can be extrapolated and predicted. On the basis of ensuring the stability, reliability and accuracy of the measurement results, the experimental period is greatly shortened, and the time cost is significantly reduced.

[0094] Refer to the attached Figure 3 illustrates a schematic structural diagram of a rock corrosion rate measurement system provided by the present invention.

[0095] The present invention also provides a rock corrosion rate measurement system 20, which is applied to the above-mentioned rock corrosion rate measurement method, and includes:

[0096] A processor 201.

[0097] A memory 202, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor 201, the rock corrosion rate measurement method as in the method embodiment is realized.

[0098] The rock corrosion rate measurement system 20 provided by the present invention can execute the above-mentioned rock corrosion rate measurement method and achieve the same or similar technical effects. To avoid repetition, the present invention will not be elaborated herein.

[0099] The beneficial effects brought by the technical solution provided in the embodiment of the present invention at least include:

[0100] In the embodiment of the present invention, first, intelligent sampling based on chaotic mapping generates discrete variable conditions with strong ergodicity through the self-generated chaotic traversal sampling method, breaking through the subjectivity and locality of manual selection and achieving global and efficient coverage of multiple variables such as temperature and flow rate. Secondly, the adaptive iteration mechanism dynamically identifies the sensitive interval with the largest change rate of the dissolution rate, automatically focuses on the key experimental area and resets the variable range, improving the accuracy of the measurement results. Finally, the B-spline fitting algorithm is used to perform high-precision modeling on the non-linear response of the dissolution rate, and can extrapolate and predict the change trend of the dissolution rate in the unmeasured range. On the basis of ensuring the stability, reliability and accuracy of the measurement results, the experimental period is greatly shortened and the time cost is significantly reduced.

[0101] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0102] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0103] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be solid-state drives.

[0104] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0105] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0106] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0107] Those of ordinary skill in the art will realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0108] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0112] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0113] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the rock corrosion rate measurement method as described in the method embodiment.

[0114] The computer-readable storage medium provided by the present invention can implement the steps and effects of the rock corrosion rate measurement method in the above method embodiment. To avoid repetition, the present invention will not elaborate further.

[0115] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0116] In the embodiment of the present invention, first, intelligent sampling based on chaotic mapping generates discrete variable conditions with strong ergodicity through a self-generated chaotic traversal sampling method, breaking through the subjectivity and locality of manual selection and achieving global and efficient coverage of multiple variables such as temperature and flow rate; second, the adaptive iteration mechanism dynamically identifies the sensitive interval with the largest change rate of the corrosion rate, automatically focuses on the key experimental area and resets the variable range, improving the accuracy of the measurement results; finally, the B-spline fitting algorithm is used to perform high-precision modeling on the non-linear response of the corrosion rate, and can extrapolate and predict the change trend of the corrosion rate in the unmeasured range. On the basis of ensuring the stability, reliability, and accuracy of the measurement results, the experimental period is greatly shortened, and the time cost is significantly reduced.

[0117] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0118] The following points need to be explained:

[0119] (1) The accompanying drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the general design.

[0120] (2) For clarity, in the accompanying drawings used to describe the embodiments of the present invention, the thickness of layers or regions is enlarged or reduced, that is, these drawings are not drawn to actual scale. It can be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be intervening elements.

[0121] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0122] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for determining rock dissolution rate, characterized in that the method include: Obtaining continuous values ​​of variable conditions for measuring the rock sample; The variable condition continuous value is sampled by a spontaneous chaotic ergodic sampling method to obtain a plurality of variable condition discrete values; The rock samples are measured according to the discrete values ​​of the variable conditions respectively, and the dissolution rate of the rock samples under the corresponding discrete values ​​of the variable conditions is obtained, wherein each discrete value of the variable conditions and the corresponding dissolution rate constitute a measurement data pair; Calculate the change rate of dissolution rate between dissolution rates corresponding to discrete values ​​of adjacent variable conditions; Resetting the variable condition continuous value by using the two variable condition discrete values ​​corresponding to the maximum dissolution rate change rate as endpoints, and returning to resample until the number of iterations is greater than the preset number of iterations; Fitting each of the measured data pairs using a B-spline fitting algorithm to establish a dissolution rate fitting curve; The dissolution rate fitting curve is output to obtain the dissolution rate measurement result of the rock sample.

2. The rock dissolution rate determination method according to claim 1, characterized in that: The variable condition continuous values ​​include temperature condition continuous values, flow rate condition continuous values ​​and pH value condition continuous values.

3. The rock dissolution rate determination method according to claim 1, characterized in that: The variable condition continuous value is sampled by the spontaneous chaotic ergodic sampling method to obtain a plurality of variable condition discrete values, specifically including: Randomly select the initial value of the chaotic sequence in the range of 0 to 1; Based on the initial value of the chaotic sequence, a chaotic sequence of conditional continuous values ​​of each variable is generated by a Logistic chaotic mapping algorithm: Among them, x k and x k+1 They represent the kth iteration value and the k+1th iteration value of the chaotic sequence respectively, tanh represents the inverse tangent function, λ represents the chaotic gain factor for adjusting the amplitude of the chaotic sequence, σ represents the steepness control parameter for adjusting the traversal speed of the chaotic sequence, and γ represents the boundary exclusion coefficient to avoid the concentration of the chaotic sequence; Adding a buffer to the boundary parameters in the chaotic sequence to prevent the boundary parameters from being within a predetermined boundary range: [min′(x k ),max′(x k )]←[min(x k )-Δ,max(x k )+D]; Δ=0.05·std(x k ); Among them, ← represents the update direction, min′(x k ),max′(x k ) represent the minimum and maximum values ​​of the chaotic sequence after adding the buffer Δ, std(x k ) represents the x of each parameter in the chaotic sequence k The standard deviation of The chaotic sequence after adding the buffer is mapped to the variable conditional discrete value representing the actual physical range, wherein each of the variable conditional discrete values ​​is within the actual physical range constraint: Among them, T k represents the conditional discrete value of the kth variable obtained after mapping, T max and T min They respectively represent the actual maximum value and the actual minimum value of the physical range corresponding to the variable conditions.

4. The rock dissolution rate determination method according to claim 3, characterized in that: The chaos gain factor is 4, the steepness control parameter is 0.1, and the boundary rejection coefficient is 1000.

5. The rock dissolution rate determination method according to claim 3, characterized in that: The variable conditions include temperature conditions, flow rate conditions and pH value conditions.

6. The method for measuring rock dissolution rate according to claim 3, characterized in that: The method of sampling the variable condition continuous value by a spontaneous chaotic ergodic sampling method to obtain a plurality of variable condition discrete values ​​also includes: The obtained variable conditional discrete values ​​are truncated according to the actual physical range constraints, which are specifically: Clip(T k ,T min ,T max ); Among them, Clip represents the truncation limiter.

7. The rock dissolution rate determination method according to claim 1, characterized in that: The method of fitting each of the measured data pairs by a B-spline fitting algorithm to establish a dissolution rate fitting curve specifically includes: removing abnormal data points in each of the measured data pairs; Segment each retained data point to obtain multiple data point intervals; Construct a B-spline basis function that includes all data points: Among them, B j (T k ) represents the basis function corresponding to the jth data point interval generated based on the variable conditional discrete value, j = 1, 2, ..., m, m represents the number of data point intervals, c j Indicates B j (T k ), S represents the coefficient of the dissolution rate fitting curve; The coefficients are optimized by the least square method: Among them, R k Represents the conditional discrete value T of the kth variable k The corresponding dissolution rate, k = 1, 2, ..., K, K represents the total number of discrete values ​​of the variable condition, It represents the coefficient c that makes the function take the minimum value; Substitute the coefficients obtained by optimization into the basis functions of the corresponding data point interval to obtain the dissolution rate fitting curve.

8. The rock dissolution rate determination method according to claim 7, characterized in that: The abnormal data points include dynamic contradiction points and pseudo trend points.

9. A rock dissolution rate measuring system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the rock dissolution rate determination method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the rock dissolution rate determination method as described in any one of claims 1 to 8 is implemented.

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