Lunar landing area assessment method based on the lunar south pole landing area site selection model

Abstract

The present invention discloses a lunar landing area assessment method based on the lunar South Pole landing area site selection model. The method proposes a lunar South Pole site selection model combined with fuzzy cognitive maps (FCMs). The model includes membership functions, fuzzy rules, aggregate defuzzification, and simulation testing. It iteratively analyzes five factors, including the area of ​​the permanent shadow area, the slope of the landing area, rock abundance, light intensity, and average maximum temperature, to screen out areas that meet the conditions. In addition, the method can consider multiple engineering constraints and can be used for the quantitative assessment of future alternative landing areas on the moon and Mars.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing image processing, and in particular relates to a lunar landing area assessment method based on a lunar South Pole landing area site selection model. Background Art

[0002] Qiao Le and others studied the albedo of the permanent shadow area at the South Pole of the Moon, selected the Amundsen crater at the South Pole of the Moon as the landing area for future exploration, and gave a brief introduction to it.

[0003] Lemelin used a multi-parameter analysis to select the best landing sites to sample H2O and volatile-rich chemicals from the polar regions. The most likely polar regions for future exploration were qualified by (1) distance from permanently shadowed regions, (2) hydrogen abundance (greater than 150 ppm), (3) annual maximum, minimum, and mean temperatures, and (4) shallow slopes (less than 25° for rover movement constraints). They found two such sites in the South Pole (Shoemaker and Faustini craters) and two in the North Pole (Peary crater and an area between Hermite and Rozhdestvenskiy W craters). Because the results were very limited, by changing the thresholds of the qualification criteria, five additional sites were identified in the South Pole (Haworth, Deglacie, and Cabos craters, and an area between Shoemaker and Faustini craters and north of Amundsen crater) and three additional sites in the North Pole (Lenard, Hermite, and Rozhdestvenskiy W craters).

[0004] NASA commissioned the LEAG (VSAT–Volatile Specific Action Team) team to provide landing site recommendations for future missions and added restrictions on visibility from the Sun and the Earth. The H abundance estimated from the Lunar Prospector Neutron Spectrometer (LPNS) data must be higher than 150 ppm, as well as other criteria (annual surface temperature >110 K, moderate slope <10, close to the PSR (<1 km)). A region of interest (ROI) near Cabeus and Shoemaker in the South Pole was proposed.

[0005] However, the constraints on landing site selection at the lunar south pole primarily involve the area of ​​the permanently shadowed region, the slope of the landing area, rock abundance, light intensity, and average maximum temperature. These factors all affect landing safety and the mobility of the lunar rover. Currently, no systematic analysis of the constraints affecting landing site selection at the lunar south pole has been conducted domestically or internationally. Furthermore, no quantitative mathematical evaluation model has been established for selecting a landing site at the lunar south pole. Instead, subjective evaluations of different landing site options are typically conducted through multiple rounds of meetings based on remote sensing data and expert opinions. Without a comprehensive evaluation model, objective assessments of landing site selection at the lunar south pole are impossible. Summary of the Invention

[0006] To address the above-mentioned problems, the present invention proposes a lunar landing area assessment method based on the lunar South Pole landing area site selection model. The method evaluates the lunar landing area by constructing a lunar South Pole landing area site selection model that combines multi-source data. The model combines fuzzy cognitive maps (FCMs), specifically including membership functions, fuzzy rules, aggregate defuzzification, and simulation testing. It can iteratively analyze five factors, including the area of ​​the permanent shadow area, the slope of the landing area, rock abundance, light intensity, and average maximum temperature, to screen out areas that meet the conditions. In addition, the present invention takes into account multiple engineering constraints and can be used for the quantitative assessment of future alternative landing areas on the moon and Mars.

[0007] The technical solution to realize the present invention is:

[0008] A lunar landing area assessment method based on the lunar South Pole landing area site selection model is characterized by comprising the following steps:

[0009] Step 1: Generate an Antarctic slope map using the Antarctic lunar digital elevation map and select areas with slopes <8°;

[0010] Step 2: Calculate the permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature index factors within the selectable area;

[0011] Step 3: Construct a lunar South Pole site selection model combined with fuzzy cognitive maps;

[0012] Step 4: Use the site selection model to iteratively analyze the distribution of permanent shadow areas, slope, rock abundance, light intensity, and average maximum temperature indicators to evaluate the quality of the landing area site.

[0013] Furthermore, the specific steps of step 2 include:

[0014] Step 201: Calculate the lunar surface slope

[0015] Calculated from the slope of each cell of the lunar DEM raster:

[0016]

[0017] Where Cellsize represents the resolution of each grid point; m and n are the row and column numbers in the lunar DEM grid, respectively; θ w-e ,θ s-n They represent the slope in the east-west direction and the slope in the north-south direction respectively;

[0018] And for a suitable lunar station site, the slope constraint is:

[0019] |θ w-e |<θ max ,θ s-n <θ max (3)

[0020] Among them, θ max is the maximum slope, set to 8°;

[0021] Step 202: Calculate rock abundance

[0022] Use CraterTools and Crater Helper Tools to locate and count rock abundance on Lunar Reconnaissance Orbiter Narrow Angle Camera images:

[0023] R m (D)=me -(0.5648+0.01285 / m) / D (4)

[0024] Where m represents rock abundance, R m (D) represents the area fraction of rocks with diameters greater than D;

[0025] Step 203: Calculate light intensity and permanent shadow areas

[0026] The maximum terrain height angle method is used to detect whether a specific location on the moon is illuminated, thereby obtaining the distribution of permanent shadow areas and the illumination intensity.

[0027] Step 204: Calculate the average maximum temperature

[0028] Using a lunar radiometer, the average maximum temperature of the lunar south pole was calculated based on the cumulative temperature of the lunar south pole over the past decade.

[0029] Furthermore, the specific steps of step 203 include:

[0030] Step 2031: Obtain libration information, attitude and position, and sun position from the JPL ephemeris;

[0031] Step 2032: Read the libration information, attitude and position, and solar position value at that moment, and convert the coordinates to the lunar center celestial coordinate system J2000;

[0032] Step 2033: Calculate the solar apparent radius and solar altitude angle through geometric relationships;

[0033] Step 2034: Determine the solar altitude angle Is it greater than the maximum terrain elevation angle? If so, it means there is sunlight and it is a sunny area. If less than, it means there is no sunlight and it is a shadow area.

[0034] Step 2035: Calculate the light intensity at different times in the sunlight area, and obtain the light rate through the light intensity at different times.

[0035] Furthermore, the lunar South Pole site selection model combined with the fuzzy cognitive map established in step 3 includes membership function, fuzzy rules, aggregated fuzzy membership function, and defuzzification. Its construction steps are as follows:

[0036] Step 301: Define fuzzy membership function

[0037] The influencing factors are defined as: permanent shadow area (C1), landing area slope (C2), rock abundance (C3), light intensity (C4), and average maximum temperature (C5). Each factor includes four triangular membership functions, namely {highly feasible (HF), moderately feasible (IF), low feasible (LF), and not feasible (LN)}. Each factor is analyzed using the membership function.

[0038] Obtain fuzzy cognitive map: take C1, C2, C3, C4, C5 as concept nodes and construct relationship weight matrix W = [W ij ] 5×5 , through the relationship weight matrix to form a directed connection, the fuzzy cognitive concept map of the lunar South Pole site selection is obtained;

[0039] Step 302: Define fuzzy membership rules

[0040] In the process of analyzing various factors using the triangular membership function, the central latitude of the landing area, the coverage rate of baseline slope less than 7°, the area of ​​the permanent shadow area, and the proportion of rock abundance were used as input parameters to evaluate the influence of slope S, maximum temperature T, light intensity I, and rock distribution R, and establish the Mamdani minimum fuzzy implication rule:

[0041] μ R (x,y)=minμ A (x),μ B (y) (10)

[0043] Among them, μ R(x,y) represents the activation membership function, μ A (x) and μ B (y) represents the membership value x of language item A and the membership value y of language item B respectively;

[0044] The Mamdani minimum fuzzy implication rule is used to activate the corresponding triangular membership function;

[0045] Step 303: Aggregate membership function

[0046] The family Einstein Sum function is used to aggregate the membership functions, aggregating the triangular membership functions activated in step 302, and the family Einstein Sum function is:

[0047]

[0048] Step 304: Defuzzify the aggregate membership function

[0049] Based on formula (11), the defuzzified values ​​of all concept pairs are calculated and the fuzzy values ​​obtained by reasoning are converted into precise values;

[0050] Step 305: Simulation calculation

[0051] The model parameters of a given FCM are checked by simulating its behavior over discrete simulation steps. In each simulation step, the concept value is updated according to the inference formula. The inference concept is:

[0052]

[0053] in, is the value of concept j at simulation step t, W ji is the causal influence of concept j on concept i.

[0054] Furthermore, the membership function defined for each factor in step 301 is:

[0055] 1) The membership function of the permanent shadow area C1 is:

[0056]

[0057] Where D represents the distance between the site and the permanent shadow area, D max For the maximum distance, set it to 8km;

[0058] 2) The membership function of the slope C2 of the landing area is:

[0059]

[0060] 3) The membership function of rock abundance C3 is:

[0061]

[0062] Among them, K represents the rock abundance of the site. The flatter the terrain, the better. max is the maximum rock abundance, set to 0.65;

[0063] 4) The membership function of light intensity C4 is:

[0064]

[0065] Among them, T is the total time window, a (0≤a≤1) is the scale coefficient corresponding to T, b (0≤b≤a) is the scale coefficient that best suits the time, t s Indicates the light time coverage;

[0066] 5) The membership function of the average maximum temperature C5 is:

[0067]

[0068] Where R is the temperature, R max is the annual maximum temperature, set to 110K.

[0069] Furthermore, the specific steps of step 4 include:

[0070] Step 401: Input permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature indicators;

[0071] Step 402: Analyze the permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature using fuzzy membership functions.

[0072] Step 403: Activate the membership function through the minimum fuzzy implication rule to obtain the activated membership function of the permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature factor;

[0073] Step 404: Aggregate the activated membership function obtained in step 403 by applying the family EinsteinSum aggregation fuzzy membership function;

[0074] Step 405: Defuzzify the membership functions aggregated in step 404 to obtain the causal weights of various factors and analyze their impact on site selection.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] First, this paper analyzes the main factors influencing site selection based on multi-source lunar remote sensing data. In response to the global surge in lunar exploration, this paper combines multi-source remote sensing data, including Chang'e-2 digital elevation data, digital orthophoto data, Lunar Orbiter Laser Altimeter (LRO LOLA) data, Narrow Angle Camera (NAC) data, and Diviner data, to comprehensively consider various factors.

[0077] Second, the model proposed in this invention is a multi-factor fuzzy comprehensive cognition and selection model. This model iteratively analyzes five factors, including the area of ​​the permanent shadow zone, the slope of the landing area, rock abundance, light intensity, and average maximum temperature, to screen out areas that meet the conditions. This method can also perform mathematical quantitative analysis, avoiding subjective evaluation, and can be used for future alternative landing areas on the moon and Mars. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Flowchart for site selection for the lunar south pole landing area;

[0079] Figure 2 This is a schematic diagram of DEM grid calculation;

[0080] Figure 3 Flowchart for light intensity calculation;

[0081] Figure 4 A fuzzy cognitive map of the factors for site selection at the lunar South Pole;

[0082] Figure 5 is the membership value curve of light intensity;

[0083] Figure 6 is the membership value curve of light intensity under Mamdani minimum fuzzy implication rule;

[0084] Figure 7 is the membership value curve obtained by aggregating the membership function of light intensity;

[0085] Figure 8 The curves of the initial values ​​and stable values ​​of the five factors changing with the number of iteration steps. DETAILED DESCRIPTION

[0086] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0087] The core concept of this invention is as follows: First, a South Pole slope map is generated using the Chang'e-2 lunar digital elevation map (DEM). Combined with the Chang'e series of missions, this map is used to identify potential areas with slopes less than 8°. Secondly, the distribution of permanent shadow areas, rock abundance, slope, light intensity, and average maximum temperature within a feasible range are calculated. Finally, based on these constraint factors, membership functions, fuzzy rules, aggregated fuzzy membership functions, and defuzzification are established. Fuzzy reasoning is then used to derive evaluation metrics for each landing area, enabling landing site selection.

[0088] Combined with attachment Figure 1 The process of selecting the landing area at the lunar south pole is shown in the figure. It can be seen that it is divided into the following steps:

[0089] Step 1: Acquisition of lunar South Pole images

[0090] The Chang'e-2 digital elevation model (DEM) is generated based on stereo images obtained by the CE-2 stereo camera CCD at an orbital altitude of 100 km. It can be downloaded from the Lunar Exploration Data Release Center (https: / / moon.bao.ac.cn / ). The CE-220m DEM was selected for analysis above 80°S in Antarctica.

[0091] Step 2: Calculation of indicator factors

[0092] 1. Calculation of lunar surface slope

[0093] In selecting the landing site for China's lunar mission, the average terrain slope of the landing area should not exceed 8°, and the area with a slope less than 8° should account for a relatively large proportion. Figure 2 As shown, the surface slope can be calculated from the slope of each cell of the lunar DEM grid:

[0094]

[0095] Where Cellsize represents the resolution of each grid point; subscripts m and n are the row and column numbers in the lunar DEM grid, respectively; θ w-e ,θ s-n They represent the slope in the east-west direction and the slope in the north-south direction respectively.

[0096] For a suitable lunar station site, the slope should be subject to the following constraints:

[0097] |θ w-e |<θ max ,θ s-n <θ max (3)

[0098] Among them, θ max is the maximum slope, set to 8°;

[0099] 2. Lighting model calculation

[0100] The study of lunar polar lighting conditions is of great significance to human landing on the moon. It also provides a basis for the design of probe sensors, the selection of lunar surface bases, and further research on the existence of water ice. This paper uses the maximum terrain elevation angle method to detect whether a specific location on the moon is illuminated. The calculation process is shown in the attached figure. Figure 3 As shown, the specific steps include:

[0101] S1: Obtain libration information, attitude and position, and sun position from the JPL ephemeris;

[0102] S2: Read the libration information, attitude and position, and solar position value at that moment, and convert the coordinates to the lunar center celestial coordinate system J2000;

[0103] S3, calculate the solar apparent radius and solar altitude angle through geometric relationships;

[0104] S3: Determine the solar altitude angle Is it greater than the maximum terrain elevation angle? If so, it means there is sunlight and it is a sunny area. If less than, it means there is no sunlight and it is a shadow area.

[0105] S4: Obtain the illumination rate by calculating the illumination intensity at different times.

[0106] 3. Rock abundance model

[0107] The smallest rocks that can be confidently identified in Lunar Reconnaissance Orbiter Narrow Angle Camera (NAC) images are between 1 and 2 meters, depending on the image resolution. These rocks were located and counted on NAC images using CraterTools and Crater Helper Tools developed in ArcGIS.

[0108] R m (D)=me -(0.5648+0.01285 / m) / D (4)

[0109] Where m represents rock abundance, R m (D) represents the area fraction of rocks with diameters greater than D; D represents the diameter of the rock, and the area fraction can be obtained by setting a threshold;

[0110] 4. Temperature calculation

[0111] Since July 2009, the Lunar Diviner radiometer aboard the US Reconnaissance Orbiter has mapped the Moon's infrared radiation using seven spectral channels ranging from 7.55 to 400 μm, with a spatial resolution of 200 meters. Using this radiometer data, the average maximum temperature of the Moon's south pole over the past decade has been calculated, which in turn provides a polar stereographic projection of the maximum temperature distribution at the lunar south pole.

[0112] Step 3: Construction of the lunar South Pole site selection model combined with fuzzy cognitive map

[0113] To address the site selection problem for the lunar South Pole landing site, this paper proposes a multi-factor fuzzy comprehensive cognition and site selection model that combines the distribution of permanent shadow areas (C1), lunar surface slope (C2), rock distribution (C3), light intensity (C4), and maximum temperature (C5). Specifically, this model includes the following steps:

[0114] (1) Define the fuzzy membership function

[0115] Each factor variable C1, C2, C3, C4, and C5 includes four triangular membership functions, namely {highly feasible (HF), moderately feasible (IF), lowly feasible (LF), and not feasible (LN)}. For example, for factor C1, the four categories are HF1, IF1, LF1, and LN1;

[0116] Again, with these five factors E=={C1, C2, C3, C4, C5} as concept nodes, establish the relationship weight matrix W=[W ij ] 5×5 , forming a directed connection through the relationship weight matrix, and constructing the following Figure 4 The fuzzy cognitive map of the lunar South Pole site selection is shown; Figure 4 The relationship weights between nodes are shown, and the concept values ​​and arc weights are fuzzy values.

[0117] Based on the previous analysis of the constraints and combined with planetary science expertise, the importance ranking of the constraint indicator factors is obtained, as shown in Table 1.

[0118] Table 1. Order of importance of constraints

[0119]

[0120] For Antarctic exploration missions, due to terrain restrictions, the closer to the permanent shadow area, the better. The fuzzy membership function of C1 can be expressed as:

[0121]

[0122] Where D represents the distance between the site and the permanent shadow area, D maxThe maximum distance is set to 8km.

[0123] For the lunar surface, the flatter the terrain, the better the field of view. max The position of is not a suitable position, so the fuzzy membership function of C2 can be expressed as:

[0124]

[0125] According to the Chang'e mission, the slope should not exceed 8°, so θ max It should be set to 8°, so

[0126] The fuzzy membership function of C3 can be expressed as:

[0127]

[0128] Among them, K represents the rock abundance of the site. The flatter the terrain, the better. max is the maximum rock abundance, set to 0.65.

[0129] The fuzzy membership function of C4 can be expressed as:

[0130]

[0131] Among them, T is the total time window, a (0≤a≤1) is the scale coefficient corresponding to T, b (0≤b≤a) is the scale coefficient that best suits the time, t s Indicates the light time coverage;

[0132] The fuzzy membership function of C5 can be expressed as:

[0133]

[0134] Where R is the temperature, Rmax is the annual maximum temperature, which is set to 110K;

[0135] Each factor variable contains four triangular membership functions, which makes the transition between functions smoother and more stable. Figure 5 As shown in the figure, taking the light intensity factor as an example, the feasible landing points are divided into {highly feasible (HF), moderately feasible (IF), low feasible (LF) and not feasible (LN)}, and the membership value curve of light intensity is shown in the figure. Figure 5 shown.

[0136] (2) Define fuzzy membership rules

[0137] In the process of using triangular membership function for analysis, the central latitude of the landing area, the coverage rate of baseline slope less than 7°, the area of ​​permanent shadow area, and the proportion of rock abundance were used as input parameters. Combined with the influence importance of slope (S), maximum temperature (T), light intensity (I), and rock distribution (R) obtained in Table 1, the Mamdani minimum fuzzy implication rule was established. Similarly, taking light intensity as an example, feasible landing sites were divided into {highly feasible (HF), moderately feasible (IF), low feasible (LF), and infeasible (LN)}. The minimum fuzzy implication rule was used to "activate" the corresponding membership function, and the membership value curve of light intensity was obtained, as shown in the attached figure. Figure 6 As shown, the minimum fuzzy implication rule is:

[0138] μ R (x,y)=minμ A (x),μ B (y) (10)

[0140] Among them, μ R (x,y) represents the activation membership function, μ A (x) and μ B (y) represent the membership value x of language item A and the membership value y of language item B respectively. For example, C1 and C 2， C1 and C3, etc.;

[0141] (3) Aggregate fuzzy membership function

[0142] Use the family Einstein Sum function to aggregate the activation membership function obtained from the previous step. Taking light intensity as an example, we can get the membership value curve of light intensity after aggregation as shown in the attached figure. Figure 7 As shown, and the family EinsteinSum function:

[0143]

[0144] (4) Defuzzification of aggregate membership functions

[0145] The crisp value (defuzzified value) is calculated based on the aggregate membership function (11), and the fuzzy value obtained by reasoning is converted into an exact value.

[0146] (5) Simulation calculation

[0147] Check the model parameters of a given FCM by simulating its behavior over discrete simulation steps. The discrete simulation step involves taking the initial state vector and the FCM weight matrix (also called the connectivity matrix) and applying one of the above update functions (see Figure 8Output concepts can be specified by providing a list of these concepts to the corresponding output concept parameter, and the inference concept is shown in Equation 8. If the output concept parameter is not specified, all concepts in the FCM are considered output concepts, and the simulation stops when the change of all concepts between two consecutive steps is less than a threshold.

[0148] At each simulation step, the concept values ​​are updated according to the defined inference formulas.

[0149] Reasoning formula:

[0150]

[0151] in, is the value of concept j at simulation step t, W ji is the causal influence of concept j on concept i;

[0152] Through the above steps, the five factor values ​​of different areas of interest in Antarctica are input, and the model can be used to analyze and evaluate the scores, which can quantitatively evaluate the advantages and disadvantages of the landing area site selection.

[0153] Example

[0154] The initial values ​​of the concept nodes permanent shadow area distribution C1, lunar surface slope C2, rock distribution C2, light intensity C4, and maximum temperature C5 are input into the FCM model of the lunar south pole landing area. The number of inference iterations is set to 8. After 8 iterations of inference using formula (12), the site selection model reaches equilibrium, and each concept node shows a stable value. Then the data values ​​of the model iteration are derived to obtain the stable value in the equilibrium state, as shown in Table 2. At the same time, the initial value and stable value can also be obtained from Figure 8 see.

[0155] Table 2 Statistics of initial values ​​and stable values

[0156]

[0157] from Figure 8 The simulation results for the conceptual nodes of the eight iterations show that C1 represents the distribution of permanent shadow areas; C2 represents the lunar surface slope; C3 represents the rock distribution; C4 represents the light intensity; and C5 represents the average maximum temperature. The figure shows that the location of the lunar base is closely related to these five factors. C1 and C5 tend to stabilize after increasing, while C2, C3, and C4 tend to stabilize after decreasing. Furthermore, the model stabilized after the sixth iteration, with a significant correlation between the distribution of permanent shadow areas (C1) and the maximum temperature (C5), and the stability weight of C4 reaching 0.714. Based on the above analysis, the final conclusion is that the site should be selected in an area with good light intensity (C4) and lunar surface slope (C2).

[0158] The contents not described in detail in this specification belong to the prior art known to those skilled in the art. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A lunar landing area assessment method based on the lunar south pole landing area site selection model, characterized in that: The following steps are involved: Step 1: Generate an Antarctic slope map using the Antarctic lunar digital elevation map and select areas with slopes <8°; Step 2: Calculate the permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature index factors within the selectable area; Step 3: Construct a lunar South Pole site selection model combined with fuzzy cognitive maps; Step 4: Use the site selection model to iteratively analyze the distribution of permanent shadow areas, slope, rock abundance, light intensity, and average maximum temperature to evaluate the quality of the landing area site; The lunar South Pole site selection model combined with the fuzzy cognitive map established in step 3 includes membership functions, fuzzy rules, aggregated fuzzy membership functions, and defuzzification. Its construction steps are as follows: Step 301: Define fuzzy membership function The influencing factors are defined as: permanent shadow area (C1), landing area slope (C2), rock abundance (C3), light intensity (C4), and average maximum temperature (C5). Each factor includes four triangular membership functions, namely {highly feasible (HF), moderately feasible (IF), low feasible (LF), and not feasible (LN)}. Each factor is analyzed using the membership function. Obtain fuzzy cognitive map: take C1, C2, C3, C4, C5 as concept nodes and construct relationship weight matrix W = [W ij ] 5×5 , through the relationship weight matrix to form a directed connection, the fuzzy cognitive concept map of the lunar South Pole site selection is obtained; Step 302: Define fuzzy membership rules In the process of analyzing various factors using the triangular membership function, the central latitude of the landing area, the coverage rate of baseline slope less than 7°, the area of ​​the permanent shadow area, and the proportion of rock abundance were used as input parameters to evaluate the influence of slope S, maximum temperature T, light intensity I, and rock distribution R, and establish the Mamdani minimum fuzzy implication rule: m R (x,y)=minμ A (x),μ B (y) (10) Among them, μ R (x,y) represents the activation membership function, μ A (x) and μ B (y) represents the membership value x of language item A and the membership value y of language item B respectively; The Mamdani minimum fuzzy implication rule is used to activate the corresponding triangular membership function; Step 303: Aggregate membership function The family Einstein Sum function is used to aggregate the membership functions, aggregating the triangular membership functions activated in step 302, and the family Einstein Sum function is: Step 304: Defuzzify the aggregate membership function Based on formula (11), the defuzzified values ​​of all concept pairs are calculated and the fuzzy values ​​obtained by reasoning are converted into precise values; Step 305: Simulation calculation The model parameters of a given FCM are checked by simulating its behavior over discrete simulation steps. In each simulation step, the concept value is updated according to the inference formula. The inference concept is: in, is the value of concept j at simulation step t, W ji is the causal influence of concept j on concept i.

2. The lunar landing area assessment method based on the lunar South Pole landing area site selection model according to claim 1, characterized in that: The specific steps of step 2 include: Step 201: Calculate the lunar surface slope Calculated from the slope of each cell of the lunar DEM raster: Where Cellsize represents the resolution of each grid point; m and n are the row and column numbers in the lunar DEM grid, respectively; θ w-e ,θ s-n They represent the slope in the east-west direction and the slope in the north-south direction respectively; And for a suitable lunar station site, the slope constraint is: |θ w-e |<θ max ,i s-n <θ max (3) Among them, θ max is the maximum slope, set to 8°; Step 202: Calculate rock abundance Use CraterTools and Crater Helper Tools to locate and count rock abundance on Lunar Reconnaissance Orbiter Narrow Angle Camera images: R m (D)=me -(0.5648+0.01285 / m) / D (4) Where m represents rock abundance, R m (D) represents the area fraction of rocks with diameters greater than D; Step 203: Calculate light intensity and permanent shadow areas The maximum terrain height angle method is used to detect whether a specific location on the moon is illuminated, thereby obtaining the distribution of permanent shadow areas and the illumination intensity. Step 204: Calculate the average maximum temperature Using a lunar radiometer, the average maximum temperature of the lunar south pole was calculated based on the cumulative temperature of the lunar south pole over the past decade.

3. The lunar landing area assessment method based on the lunar South Pole landing area site selection model according to claim 2, wherein step 203 comprises: Step 2031: Obtain libration information, attitude and position, and sun position from the JPL ephemeris; Step 2032: Read the current libration information, attitude and position, and solar position value, and convert the coordinates to the lunar center celestial coordinate system J2000; Step 2033: Calculate the solar apparent radius and solar altitude angle through geometric relationships; Step 2034: Determine the solar altitude angle Is it greater than the maximum terrain elevation angle? If so, it means there is sunlight and it is a sunny area. If less than, it means there is no sunlight and it is a shadow area. Step 2035: Calculate the light intensity at different times in the sunlight area, and obtain the light rate through the light intensity at different times.

4. The lunar landing area assessment method based on the lunar South Pole landing area site selection model according to claim 1, characterized in that: The membership function defined for each factor in step 301 is: 1) The membership function of the permanent shadow area C1 is: Where D represents the distance between the site and the permanent shadow area, D max For the maximum distance, set it to 8km; 2) The membership function of the slope C2 of the landing area is: 3) The membership function of rock abundance C3 is: Among them, K represents the rock abundance of the site. The flatter the terrain, the better. max is the maximum rock abundance, set to 0.65; 4) The membership function of light intensity C4 is: Where T is the total time window; a is the scale coefficient corresponding to T, and 0≤a≤1; b is the scale coefficient that best fits the time, and 0≤b≤a; t s Indicates the light time coverage; 5) The membership function of the average maximum temperature C5 is: Where R is the temperature, R max is the annual maximum temperature, set to 110K.

5. The lunar landing area assessment method based on the lunar South Pole landing area site selection model according to claim 1, characterized in that: The specific steps of step 4 include: Step 401: Input permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature indicators; Step 402: Analyze the permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature using fuzzy membership functions. Step 403: Activate the membership function through the minimum fuzzy implication rule to obtain the activated membership function of the permanent shadow area distribution, slope, rock abundance, light intensity, and average maximum temperature factor; Step 404: Aggregate the activated membership function obtained in step 403 by applying the family Einstein Sum aggregation fuzzy membership function; Step 405: Defuzzify the membership functions aggregated in step 404 to obtain the causal weights of various factors and analyze their impact on site selection.

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