Multi-scale method for calculating probability distribution of mechanical properties of planet rocks

Abstract

The invention belongs to the technical field of planet rock mechanics, and provides a multi-scale method for calculating probability distribution of planet rock mechanical properties. On the basis of a rock sample, a probability distribution function of planet rock micromechanics parameters is obtained through a rock micromechanics test and a reliability principle; on the basis of the reliability principle, a probability distribution function of the precise elastic modulus of the planet rock is obtained in combination with a precise crystal model and an elastic modulus upscaling method; on the basis of a reliability principle, an energy method, a fracture toughness upscaling method based on nanoindentation and mechanical property correlation are combined, and a probability distribution function of fracture toughness, tensile strength and compressive strength of the planet rock is obtained; based on the mineral image, deep learning and the macro-mechanical property of the planetary rock, constructing a prediction model of the macro-mechanical property of the planetary rock; establishing a planet rock discrete element model; based on an accurate planet rock-soil discrete element drilling and planet exploration vehicle walking simulation model, an evaluation method for researching rock-mechanical mechanics interaction and the like are researched.

Description

Technical Field

[0001] The present invention belongs to the technical field of rock mechanics, and in particular, relates to a multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks. Background Art

[0002] Human exploration activities in outer space, such as planetary science research, mineral resource mining, space observatories, and space base construction, cannot be separated from the support of theories related to planetary geomechanics and engineering machinery. However, there are significant differences between planetary rocks and Earth rocks in terms of mineral composition, microstructure, and mechanical properties. Current research on planetary rocks mainly focuses on their chemical composition, mineral composition, density, porosity, etc. Due to the limitations of experimental technology, there are currently few studies on the mechanical properties of planetary rock mineral crystals, and there are currently few studies on the quantification of the anisotropy of rock mineral mechanics. Therefore, establishing an accurate and feasible probability distribution function test method for the macroscopic mechanical properties of planetary rocks is of great significance to the smooth progress of space construction missions.

[0003] The drilling of planetary rocks and the walking process of planetary exploration vehicles are important foundations for the construction of planetary bases. The geological characteristics of the planetary surface are complex. Rock faults are usually the result of early meteorite impacts. The large temperature difference between day and night on the planet may affect the stress inside the rock due to the uneven cooling of rock particles. The lack of knowledge about the geological information and mechanical properties of planetary rocks and soils makes the drilling and sampling process face great uncertainty. Researching the smooth walking of planetary exploration vehicles and the drillability of planetary rocks is of great significance to the smooth progress of missions such as the exploration of outer space planets and the construction of bases. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks to solve the above problems.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks, comprising the following steps:

[0007] Step S1: Based on the rock sample and according to the rock micromechanics test, a probability distribution function of the micromechanics parameters of the rock is obtained, wherein the micromechanics parameters include Young's modulus of various minerals and mineral cements in the rock;

[0008] Step S2: According to the micromechanical parameter probability distribution function, a Monte Carlo method is used in combination with an elastic modulus upscaling method to obtain an upscaled elastic modulus probability distribution function; a precise mineral crystal model is established based on multiple groups of experiments to obtain the correlation between the precise elastic modulus and the elastic modulus obtained by upscaling the elastic modulus, and a precise elastic modulus probability distribution function is obtained based on the correlation;

[0009] Step S3: According to the micromechanical experimental results combined with the energy method, a micro fracture toughness probability density distribution function is obtained; through a fracture toughness upscaling method based on nanoindentation and mechanical property correlation, the fracture toughness, tensile strength, and compressive strength probability distribution functions of planetary rocks are obtained;

[0010] Step S4: Obtain a large amount of data sets according to steps S1-3, and establish a prediction model for macroscopic mechanical properties of planetary rocks based on mineral structure and deep learning;

[0011] Step S5: establishing a planetary rock discrete element model according to the mean of the rock mechanical property distribution function obtained above and the particle set characteristics obtained by scanning CT;

[0012] Step S6: Establish a rock discrete element model based on the microscopic parameters determined in step S5, add currently known planetary soils or planetary soil simulants, and establish an accurate planetary rock and soil discrete element model for simulating drilling on the planetary surface, walking of planetary exploration vehicles, etc.

[0013] In one embodiment, the specific method of step S1 is as follows:

[0014] Step S1.1. According to the rock sample, the mineral composition of the planetary rock is identified by an integrated mineral analyzer TIMA, n microscopic mineral images of the rock are obtained by X-ray micro-computed tomography CT, the microstructure of the three-dimensional rock sample is obtained by three-dimensional image reconstruction of the CT result, and the Young's modulus of various minerals and mineral cements in the rock sample are tested by nanoindentation and atomic force microscopy AFM, wherein the microstructure of the three-dimensional rock sample includes the structure, composition, shape and porosity of the rock;

[0015] Step S1.2. Fit the above microscopic Young's modulus through common distribution functions, and obtain the test statistic (D n ), D n The function with the smallest value is the best fitting probability distribution function of Young's modulus, among which the commonly used distribution functions include normal distribution, lognormal distribution, gamma distribution, Weibull distribution, etc.

[0016] Step S1.3. The structure of the three-dimensional rock sample is used to establish an accurate mineral crystal model in step S2.

[0017] In one embodiment, the specific method of step S2 is as follows:

[0018] Step S2.1. According to the micromechanical parameter probability distribution function, the micromechanical parameter probability distribution function is used to generate n groups of elastic moduli containing each mineral using the Monte Carlo method, the elastic modulus of each mineral in the n groups is substituted into the elastic modulus upscaling method, and the n groups of upscaled elastic moduli are obtained by this method, and the above n groups of upscaled elastic moduli are fitted by the commonly used distribution function, and the test statistic (D) of each commonly used function is obtained by the KS test. n ), D n The function with the smallest value is the best fitting upscaling elastic modulus distribution function, among which the elastic modulus upscaling methods include Mori-Tanaka, Self-consistent method, etc.

[0019] Step S2.2. Randomly select m groups of elastic moduli of each mineral from the elastic moduli of each mineral in the above n groups, substitute the elastic moduli of each mineral in the m groups into the elastic modulus upscaling method and the precise mineral crystal model respectively, obtain the m groups of upscaled elastic moduli and precise elastic moduli through this method, establish a precise mineral crystal model based on the results of the m groups to obtain the correlation between the precise elastic modulus and the upscaled elastic modulus obtained by elastic modulus upscaling, and obtain the precise elastic modulus probability distribution function based on the correlation and the upscaled elastic modulus distribution function in step S2.1.

[0020] In one embodiment, the accurate mineral crystal model method in step S2.2 is as follows: first, a model consistent with the structure size of the three-dimensional rock sample is established in finite element software and a cube grid is divided. Then, the rock micromechanical parameters are assigned to the model through a one-to-one mapping with the minerals of the three-dimensional rock structure, and mineral cements are inserted between different minerals to obtain an accurate mineral crystal model. Finally, based on the accurate mineral crystal model, boundary conditions are added to perform uniaxial compression to obtain the accurate elastic modulus of the rock.

[0021] In one embodiment, the specific method of step S3 is as follows:

[0022] Step S3.1. According to the load-displacement curve of each indentation point obtained from the micromechanical experiment, the fracture toughness and critical energy release rate of each nanoindentation point are calculated by the energy method, the fracture toughness and critical energy release rate are fitted by the common distribution function, and the test statistic (D n ), D nThe function with the smallest value is the best fitting probability distribution function of microscopic fracture toughness and critical energy release rate. Substitute the above minimum critical energy release rate probability distribution function and the precise elastic modulus probability distribution function in step 2.2 into the fracture toughness upscaling method based on nanoindentation to obtain the macroscopic fracture toughness distribution function. Among them, the fracture toughness upscaling method based on nanoindentation was obtained by some scholars through macroscopic mechanical experiments on terrestrial rocks. The specific equation is as follows:

[0023]

[0024] Among them, K IC is the fracture toughness, G min is the minimum critical energy release rate of the mineral, and E is the exact elastic modulus.

[0025] Step S3.2. According to the above macroscopic fracture toughness distribution function, the tensile strength probability distribution function of the planetary rock is obtained through the mechanical property correlation equation between fracture toughness and tensile strength, and the compressive strength probability distribution function of the planetary rock is obtained through the mechanical property correlation equation between tensile strength and compressive strength. Among them, the mechanical property correlation equation between fracture toughness, tensile strength and compressive strength is obtained by many scholars through statistics of a large number of macroscopic mechanical tests on different terrestrial rocks. The selection of the mechanical property correlation equation needs to be based on different rock types (sedimentary rock, igneous rock, metamorphic rock), etc. The specific equation is as follows:

[0026]

[0027] TS = 6.88K IC (3)

[0028] TS= 9.35K IC -2.53 (4)

[0029] UCS = 12.38*TS 1.0725 (5)

[0030] UCS=10.61*TS (6)

[0031] Among them, TS is the tensile strength, UCS is the compressive strength, K IC is the fracture toughness. The above formula (1) is obtained through experiments on marble and granite, and formulas (2)-(6) are obtained through experiments on various rock types.

[0032] In one embodiment, the specific method of step S4 is as follows:

[0033] Step S4.1. Select planetary rock samples of arbitrary shapes and sizes, and perform steps S1 to S3 for each planetary rock sample in sequence to obtain the probability distribution function of the three-dimensional rock sample structure and macroscopic mechanical properties (elastic modulus, fracture toughness, tensile strength, compressive strength);

[0034] Step S4.2. Using the three-dimensional rock sample structure (minerals, pores, microcracks) as data features and the macroscopic mechanical properties probability distribution function as data labels, a prediction model for macroscopic mechanical properties of planetary rocks based on mineral structure and deep learning is established;

[0035] In one embodiment, the specific method of step S5 is as follows: according to the scanning CT result obtained in step S1, grain identification is performed through image processing to obtain the characteristics of the planetary rock particle set, and discrete element particles are generated in a discrete element model by a method of consolidating multiple particles into clusters in one-to-one correspondence with the particle set characteristics, and a bonding contact model is selected to bond the planetary rock particle clusters into a whole to generate straight crack Brazilian disk, uniaxial tension, and uniaxial compression discrete element simulation models, and the macroscopic parameters are adjusted so that the macroscopic mechanical properties obtained by the discrete element simulation and the mean of the macroscopic mechanical properties obtained in steps S2-4 are within the allowable error range, thereby completing the establishment of the discrete element model of planetary rock.

[0036] In one embodiment, the specific method of step S6 is as follows:

[0037] Step S6.1. Based on the planetary rock discrete element model obtained in S5, and in combination with the existing published mechanical properties and particle grading parameters of the planetary weathering layer soil, establish an accurate discrete element model of planetary rock and soil drilling and planetary exploration vehicle walking;

[0038] Step S6.2. Based on the accurate planetary rock and soil discrete element drilling and planetary exploration vehicle walking simulation model, the evaluation method of rock-machine mechanical interaction is studied to provide a theoretical basis and technical support for the evaluation of the drilling process in planetary exploration and base construction.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] (1) The present invention uses CT, TIMA, AFM, nanoindentation, reliability principle, finite element and discrete element to obtain the probability distribution function of the micromechanical parameters of planetary rocks through micromechanical experiments; based on the reliability principle, combined with the precise crystal model and elastic modulus upscaling method, the probability distribution function of the elastic modulus of planetary rocks is obtained; based on the reliability principle, combined with the energy method, the fracture toughness upscaling method based on nanoindentation, and the correlation of mechanical properties, the probability distribution function of the fracture toughness, tensile strength and compressive strength of planetary rocks is obtained; based on mineral images, deep learning and mechanical properties, a prediction model for the macroscopic mechanical properties of planetary rocks is constructed; based on mineral images and deep learning, a prediction model for the macroscopic mechanical properties of planetary rocks is established. Compared with the traditional prediction of macroscopic mechanical properties based on the determined value or average value of microscopic rock mechanical properties, this method fully considers the uncertainty and dispersion of rock mechanics. The method provided by the present invention can obtain the probability distribution function of the macroscopic mechanical properties of rocks based on a small amount of planetary rocks, which has lower use costs and also ensures the reliability of the results;

[0041] (2) The method provided by the present invention studies the evaluation method of rock-machine mechanical interaction through accurate planetary rock and soil discrete element drilling and planetary exploration vehicle walking simulation model, providing theoretical basis and technical support for the evaluation of drilling process in planetary exploration and base construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the process of Example 1 of the present invention. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to have a clearer understanding and knowledge of the present invention, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described below are only used to explain the present invention, and are convenient for understanding. The technical solutions provided by the present invention are not limited to the technical solutions provided by the following embodiments, and the technical solutions provided by the embodiments should not limit the protection scope of the present invention.

[0044] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner. Therefore, the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the form, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0045] Example 1

[0046] like Figure 1 As shown in Figure 1, the specific steps of a multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks are as follows:

[0047] Step S1: Based on the rock sample and according to the rock micromechanics test, a probability distribution function of the micromechanics parameters of the rock is obtained, wherein the micromechanics parameters include Young's modulus of various minerals and mineral cements in the rock;

[0048] Step S2: According to the micromechanical parameter probability distribution function, a Monte Carlo method is used in combination with an elastic modulus upscaling method to obtain an upscaled elastic modulus probability distribution function; a precise mineral crystal model is established based on multiple groups of experiments to obtain the correlation between the precise elastic modulus and the elastic modulus obtained by upscaling the elastic modulus, and a precise elastic modulus probability distribution function is obtained based on the correlation;

[0049] Step S3: According to the micromechanical experimental results combined with the energy method, a micro fracture toughness probability density distribution function is obtained; through a fracture toughness upscaling method based on nanoindentation and mechanical property correlation, the fracture toughness, tensile strength, and compressive strength probability distribution functions of planetary rocks are obtained;

[0050] Step S4: Obtain a large amount of data sets according to steps S1-3, and establish a prediction model for macroscopic mechanical properties of planetary rocks based on mineral structure and deep learning;

[0051] Step S5: establishing a planetary rock discrete element model according to the mean of the rock mechanical property distribution function obtained above and the particle set characteristics obtained by scanning CT;

[0052] Step S6: Establish a rock discrete element model based on the microscopic parameters determined in step S5, add currently known planetary soils or planetary soil simulants, and establish an accurate planetary rock and soil discrete element model for simulating drilling on the planetary surface, walking of planetary exploration vehicles, etc.

[0053] The specific method of step S1 is as follows:

[0054] Step S1.1. According to the rock sample, the mineral composition of the planetary rock is identified by an integrated mineral analyzer TIMA, n microscopic mineral images of the rock are obtained by X-ray micro-computed tomography CT, the microstructure of the three-dimensional rock sample is obtained by three-dimensional image reconstruction of the CT result, and the Young's modulus of various minerals and mineral cements in the rock sample are tested by nanoindentation and atomic force microscopy AFM, wherein the microstructure of the three-dimensional rock sample includes the structure, composition, shape and porosity of the rock;

[0055] Step S1.2. Fit the above microscopic Young's modulus through common distribution functions, and obtain the test statistic (D n ), D nThe function with the smallest value is the best fitting probability distribution function of Young's modulus, among which the commonly used distribution functions include normal distribution, lognormal distribution, gamma distribution, Weibull distribution, etc.

[0056] Step S1.3. The structure of the three-dimensional rock sample is used to establish an accurate mineral crystal model in step S2.

[0057] The specific method of step S2 is as follows:

[0058] Step S2.1. According to the micromechanical parameter probability distribution function, the micromechanical parameter probability distribution function is used to generate n groups of elastic moduli containing each mineral using the Monte Carlo method, the elastic modulus of each mineral in the n groups is substituted into the elastic modulus upscaling method, and the n groups of upscaled elastic moduli are obtained by this method, and the above n groups of upscaled elastic moduli are fitted by the commonly used distribution function, and the test statistic (D) of each commonly used function is obtained by the KS test. n ), D n The function with the smallest value is the best fitting upscaling elastic modulus distribution function, among which the elastic modulus upscaling methods include Mori-Tanaka, Self-consistent method, etc.

[0059] Step S2.2. Randomly select m groups of elastic moduli of each mineral from the elastic moduli of each mineral in the above n groups, substitute the elastic moduli of each mineral in the m groups into the elastic modulus upscaling method and the precise mineral crystal model respectively, obtain the m groups of upscaled elastic moduli and precise elastic moduli through this method, establish a precise mineral crystal model based on the results of the m groups to obtain the correlation between the precise elastic modulus and the upscaled elastic modulus obtained by elastic modulus upscaling, and obtain the precise elastic modulus probability distribution function based on the correlation and the upscaled elastic modulus distribution function in step S2.1.

[0060] The method of the accurate mineral crystal model in step S2.2 is as follows: first, a model consistent with the structure size of the three-dimensional rock sample is established in the finite element software and a cube grid is divided. Then, the rock micromechanical parameters are assigned to the model through a one-to-one mapping with the minerals of the three-dimensional rock structure, and mineral cements are inserted between different minerals to obtain an accurate mineral crystal model. Finally, based on the accurate mineral crystal model, boundary conditions are added to perform uniaxial compression to obtain the accurate elastic modulus of the rock.

[0061] The specific method of step S3 is as follows:

[0062] Step S3.1. According to the load-displacement curve of each indentation point obtained from the micromechanical experiment, the fracture toughness and critical energy release rate of each nanoindentation point are calculated by the energy method, the fracture toughness and critical energy release rate are fitted by the common distribution function, and the test statistic (D n ), D n The function with the smallest value is the best fitting probability distribution function of microscopic fracture toughness and critical energy release rate. Substitute the above minimum critical energy release rate probability distribution function and the precise elastic modulus probability distribution function in step 2.2 into the fracture toughness upscaling method based on nanoindentation to obtain the macroscopic fracture toughness distribution function. Among them, the fracture toughness upscaling method based on nanoindentation was obtained by some scholars through macroscopic mechanical experiments on terrestrial rocks. The specific equation is as follows:

[0063]

[0064] Among them, K IC is the fracture toughness, G min is the minimum critical energy release rate of the mineral, and E is the exact elastic modulus.

[0065] Step S3.2. According to the above macroscopic fracture toughness distribution function, the tensile strength probability distribution function of the planetary rock is obtained through the mechanical property correlation equation between fracture toughness and tensile strength, and the compressive strength probability distribution function of the planetary rock is obtained through the mechanical property correlation equation between tensile strength and compressive strength. Among them, the mechanical property correlation equation between fracture toughness, tensile strength and compressive strength is obtained by many scholars through statistics of a large number of macroscopic mechanical tests on different terrestrial rocks. The selection of the mechanical property correlation equation needs to be based on different rock types (sedimentary rock, igneous rock, metamorphic rock), etc. The specific equation is as follows:

[0066]

[0067] TS = 6.88K IC (3)

[0068] TS= 9.35K IC -2.53 (4)

[0069] UCS = 12.38*TS 1.0725 (5)

[0070] UCS=10.61*TS (6)

[0071] Among them, TS is the tensile strength, UCS is the compressive strength, K IC is the fracture toughness. The above formula (1) is obtained through experiments on marble and granite, and formulas (2)-(6) are obtained through experiments on various rock types.

[0072] The specific method of step S4 is as follows:

[0073] Step S4.1. Select planetary rock samples of arbitrary shapes and sizes, and perform steps S1 to S3 for each planetary rock sample in sequence to obtain the probability distribution function of the three-dimensional rock sample structure and macroscopic mechanical properties (elastic modulus, fracture toughness, tensile strength, compressive strength);

[0074] Step S4.2. Using the three-dimensional rock sample structure (minerals, pores, microcracks) as data features and the macroscopic mechanical properties probability distribution function as data labels, a prediction model for macroscopic mechanical properties of planetary rocks based on mineral structure and deep learning is established;

[0075] The specific method of step S5 is as follows: according to the scanning CT result obtained in step S1, the grain identification is performed through image processing to obtain the characteristics of the planetary rock particle set, and the discrete element particles are generated in a one-to-one correspondence with the particle set characteristics by a method of consolidating multiple particles into clusters in the discrete element model, and the bonding contact model is selected to bond the planetary rock particle clusters into a whole, and the straight crack Brazilian disk, uniaxial tension, and uniaxial compression discrete element simulation models are generated. By adjusting the mesoscopic parameters, the macroscopic mechanical properties obtained by the discrete element simulation and the mean of the macroscopic mechanical properties obtained in steps S2-4 are within the allowable error range, thereby completing the establishment of the planetary rock discrete element model.

[0076] The specific method of step S6 is as follows:

[0077] Step S6.1. Based on the planetary rock discrete element model obtained in S5, and in combination with the existing published mechanical properties and particle grading parameters of the planetary weathering layer soil, establish an accurate discrete element model of planetary rock and soil drilling and planetary exploration vehicle walking;

[0078] Step S6.2. Based on the accurate planetary rock and soil discrete element drilling and planetary exploration vehicle walking simulation model, the evaluation method of rock-machine mechanical interaction is studied to provide a theoretical basis and technical support for the evaluation of the drilling process in planetary exploration and base construction.

[0079] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks, characterized in that: The following steps are involved: Step S1: Based on the rock sample and according to the rock micromechanics test, a probability distribution function of the micromechanics parameters of the rock is obtained, wherein the micromechanics parameters include Young's modulus of various minerals and mineral cements in the rock; Step S2: According to the micromechanical parameter probability distribution function, a Monte Carlo method is used in combination with an elastic modulus upscaling method to obtain an upscaled elastic modulus probability distribution function; a precise mineral crystal model is established based on multiple groups of experiments to obtain the correlation between the precise elastic modulus and the elastic modulus obtained by upscaling the elastic modulus, and a precise elastic modulus probability distribution function is obtained based on the correlation; Step S3: According to the micromechanical experimental results combined with the energy method, a micro fracture toughness probability density distribution function is obtained; through a fracture toughness upscaling method based on nanoindentation and mechanical property correlation, the fracture toughness, tensile strength, and compressive strength probability distribution functions of planetary rocks are obtained; Step S4: Obtain a large amount of data sets according to steps S1-3, and establish a prediction model for macroscopic mechanical properties of planetary rocks based on mineral structure and deep learning; Step S5: establishing a planetary rock discrete element model according to the mean of the rock mechanical property distribution function obtained above and the particle set characteristics obtained by scanning CT; Step S6: Establish a rock discrete element model based on the microscopic parameters determined in step S5, add currently known planetary soils or planetary soil simulants, and establish an accurate planetary rock and soil discrete element model for simulating drilling on the planetary surface, walking of planetary exploration vehicles, etc.

2. A multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks according to claim 1, characterized in that: The specific method of step S1 is as follows: Step S1.

1. According to the rock sample, the mineral composition of the planetary rock is identified by an integrated mineral analyzer TIMA, n microscopic mineral images of the rock are obtained by X-ray micro-computed tomography CT, the microstructure of the three-dimensional rock sample is obtained by three-dimensional image reconstruction of the CT result, and the Young's modulus of various minerals and mineral cements in the rock sample are tested by nanoindentation and atomic force microscopy AFM, wherein the microstructure of the three-dimensional rock sample includes the structure, composition, shape and porosity of the rock; Step S1.

2. Fit the above microscopic Young's modulus through common distribution functions, and obtain the test statistic (D n ), D n The function with the smallest value is the best fitting probability distribution function of Young's modulus, among which the commonly used distribution functions include normal distribution, lognormal distribution, gamma distribution, Weibull distribution, etc. Step S1.

3. The structure of the three-dimensional rock sample is used to establish an accurate mineral crystal model in step S2.

3. A multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks according to claim 1, characterized in that: The specific method of step S2 is as follows: Step S2.

1. According to the micromechanical parameter probability distribution function, the micromechanical parameter probability distribution function is used to generate n groups of elastic moduli containing each mineral using the Monte Carlo method, the elastic modulus of each mineral in the n groups is substituted into the elastic modulus upscaling method, and the n groups of upscaled elastic moduli are obtained by this method, and the above n groups of upscaled elastic moduli are fitted by the commonly used distribution function, and the test statistic (D) of each commonly used function is obtained by the KS test. n ), D n The function with the smallest value is the best fitting upscaling elastic modulus distribution function, among which the elastic modulus upscaling methods include Mori-Tanaka, Kuster- Self-consistent method, etc. Step S2.

2. Randomly select m groups of elastic moduli of each mineral from the elastic moduli of each mineral in the above n groups, and substitute the elastic moduli of each mineral in the m groups into the elastic modulus upscaling method and the precise mineral crystal model respectively. Obtain m groups of upscaled elastic moduli and precise elastic moduli through this method. According to the results of the m groups, establish a precise mineral crystal model to obtain the correlation between the precise elastic modulus and the upscaled elastic modulus obtained by elastic modulus upscaling. According to the correlation and the upscaled elastic modulus distribution function in step S2.1., obtain the precise elastic modulus probability distribution function.

4. A multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks according to claim 1, characterized in that: The method for the accurate mineral crystal model in step S2.2 is as follows: first, a model consistent with the structure size of the three-dimensional rock sample is established in the finite element software and a cube grid is divided. Then, the rock micromechanical parameters are assigned to the model through a one-to-one mapping with the minerals of the three-dimensional rock structure, and mineral cements are inserted between different minerals to obtain an accurate mineral crystal model. Finally, based on the accurate mineral crystal model, boundary conditions are added to perform uniaxial compression to obtain the accurate elastic modulus of the rock.

5. The multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks according to claim 1, characterized in that: The specific method of step S3 is as follows: Step S3.

1. According to the load-displacement curve of each indentation point obtained from the micromechanical experiment, the fracture toughness and critical energy release rate of each nanoindentation point are calculated by the energy method, the fracture toughness and critical energy release rate are fitted by the common distribution function, and the test statistic (D n ), D n The function with the smallest value is the best fitting probability distribution function of microscopic fracture toughness and critical energy release rate. Substitute the above minimum critical energy release rate probability distribution function and the accurate elastic modulus probability distribution function in step 2.2 into the fracture toughness upscaling method based on nanoindentation to obtain the macroscopic fracture toughness distribution function. Among them, the fracture toughness upscaling method based on nanoindentation was obtained by some scholars through macroscopic mechanical experiments on terrestrial rocks. The specific equation is as follows: Among them, K IC is the fracture toughness, G min is the minimum critical energy release rate of the mineral, and E is the exact elastic modulus. Step S3.

2. According to the above macroscopic fracture toughness distribution function, the tensile strength probability distribution function of the planetary rock is obtained through the mechanical property correlation equation between fracture toughness and tensile strength, and the compressive strength probability distribution function of the planetary rock is obtained through the mechanical property correlation equation between tensile strength and compressive strength. Among them, the mechanical property correlation equation between fracture toughness, tensile strength and compressive strength is obtained by many scholars through statistics of a large number of macroscopic mechanical tests on different terrestrial rocks. The selection of the mechanical property correlation equation needs to be based on different rock types (sedimentary rock, igneous rock, metamorphic rock), etc. The specific equation is as follows: TS=6.88K IC (3) TS= 9.35K IC -2.53 (4) UCS = 12.38*TS 1.0725 (5) UCS=10.61*TS (6) Among them, TS is the tensile strength, UCS is the compressive strength, K IC is the fracture toughness. The above formula (1) is obtained through experiments on marble and granite, and formulas (2)-(6) are obtained through experiments on various rock types.

6. A multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks according to claim 1, characterized in that: The specific method of step S4 is as follows: Step S4.

1. Select planetary rock samples of arbitrary shapes and sizes, and perform steps S1 to S3 for each planetary rock sample in sequence to obtain the probability distribution function of the three-dimensional rock sample structure and macroscopic mechanical properties (elastic modulus, fracture toughness, tensile strength, compressive strength); Step S4.

2. Use the above three-dimensional rock sample structure (minerals, pores, microcracks) as data features and the above macroscopic mechanical properties probability distribution function as data labels to establish a macroscopic mechanical properties prediction model for planetary rocks based on mineral structure and deep learning.

7. A multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks according to claim 1, characterized in that: The specific method of step S5 is as follows: according to the scanning CT result obtained in step S1, the crystal grains are identified by image processing to obtain the characteristics of the planetary rock particle set, and discrete element particles are generated in a discrete element model in one-to-one correspondence with the particle set characteristics by a method of consolidating multiple particles into clusters, and the bonding contact model is selected to bond the planetary rock particle clusters into a whole, and a straight crack Brazilian disk, uniaxial tension, and uniaxial compression discrete element simulation model is generated. By adjusting the mesoscopic parameters, the macroscopic mechanical properties obtained by the discrete element simulation and the mean of the macroscopic mechanical properties obtained in steps S2-4 are within the allowable error range, thereby completing the establishment of the planetary rock discrete element model.

8. The multi-scale method for calculating the probability distribution of mechanical properties of planetary rocks according to claim 1, characterized in that: The specific method of step S6 is as follows: Step S6.

1. Based on the planetary rock discrete element model obtained in S5, and in combination with the existing published mechanical properties and particle grading parameters of the planetary weathering layer soil, establish an accurate discrete element model of planetary rock and soil drilling and planetary exploration vehicle walking; Step S6.

2. Based on the accurate planetary rock and soil discrete element drilling and planetary exploration vehicle walking simulation model, the evaluation method of rock-machine mechanical interaction is studied to provide a theoretical basis and technical support for the evaluation of the drilling process in planetary exploration and base construction.

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