Mars landing point selection method based on multi-agent decision

Abstract

The invention relates to a Mars landing point selection method based on multi-agent decision, and the method comprises the following steps: building a Mars surface virtual environment model based on multi-source remote sensing data, abstracting a scientific target into a space entity through building a space grid, and defining an engineering constraint parameter as a space attribute; setting four types of agent roles: a manager, an engineer, a scientist and an evaluator, and defining a corresponding behavior decision rule for each type of agents to realize agent modeling; and each type of intelligent agent senses virtual environment information and performs dynamic interaction and collaborative decision with other intelligent agents according to respective behavior decision rules, so that a landing point selection process is optimized, and an optimal candidate landing area is determined. Compared with the prior art, the Mars landing point automatic selection method can realize Mars landing point automatic selection under multi-target and multi-constraint conditions, and has the advantages of high intelligent degree, strong task change adaptation capability, scientific and reasonable site selection result and the like.

Description

Technical Field

[0001] The present invention relates to the field of deep space exploration, and in particular to a Mars landing site selection method based on multi-agent decision-making. Background Art

[0002] Scientific exploration of Mars is crucial for uncovering scientific questions such as the possibility of life, planetary evolution, and the development of extraterrestrial resources. Landing site selection for Mars exploration missions not only impacts engineering safety but also directly influences the acquisition and output of scientific research. It is a critical step in mission planning and potentially a decisive factor in the success or failure of future missions.

[0003] CN113033462A proposes a method and system for determining a Mars landing site based on the Martian dust devil yield rate. The method includes: acquiring high-resolution Mars remote sensing time-series image pairs of the same area at different times; identifying newly generated dust devil tracks within the Mars landing area from the acquired Mars remote sensing time-series image pairs; calculating the dust devil yield rate based on the overlapping area of ​​the Mars landing area, the observation time interval, and the number of newly generated dust devil objects observed in the image pairs; calculating the coverage area of ​​the newly generated dust devil object tracks within the landing area image pairs per Martian day; and then calculating the number of Martian days required to cover the entire landing area based on the newly generated dust devil tracks per Martian day, i.e., the cleaning period for the Mars rover's solar panels; and determining the Mars landing site based on the dust devil yield rate and the cleaning period for the Mars rover's solar panels. The method then selects a landing and patrol area with the optimal cleaning period for the subsequent Mars rover patrol mission after a successful Mars landing. However, this method only considers engineering feasibility, limiting the consideration of the landing site.

[0004] The core of selecting a landing site on Mars is to identify a landing / exploration area with high scientific value and high engineering feasibility. This process is complex and highly sophisticated, and usually requires a team of experts from multiple fields such as science and engineering to participate and make collaborative decisions. However, due to differences in target priorities and evaluation criteria among different roles, this process is often accompanied by cognitive conflicts. For example, scientists from different disciplinary backgrounds may have different definitions of scientific goals and assign different priorities to each goal, which in turn leads to inconsistent opinions on site selection. Existing methods are mostly based on hierarchical analysis or multi-index weighting for comprehensive evaluation. Although it has improved decision-making efficiency to a certain extent, it still has obvious shortcomings in dealing with complex group collaboration and dynamic priority adjustments, and it is difficult to fully reflect the game and interaction characteristics in the actual site selection process. Summary of the Invention

[0005] The purpose of this invention is to provide a Mars landing site selection method based on multi-agent decision-making to resolve the goal conflicts and coordination problems caused by the participation of multiple teams in the Mars landing site selection process, and to achieve efficient identification and optimization of potential landing areas with both high scientific value and high engineering safety.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A Mars landing site selection method based on multi-agent decision-making includes the following steps:

[0008] Step 1) Build a virtual environment model of the Martian surface based on multi-source remote sensing data. By establishing a spatial grid, abstract the scientific objectives into spatial entities and define the engineering constraint parameters as spatial attributes.

[0009] Step 2) Set up four types of intelligent agent roles: manager, engineer, scientist, and evaluator, and define corresponding behavioral decision rules for each type of agent to implement intelligent agent modeling. Among them, the manager agent is responsible for coordinating planning tasks. Its behaviors include perceiving environmental information, forming preferences, and guiding scientists to explore areas of interest. The engineer agent assesses regional engineering safety. Its behaviors include defining engineering constraints based on expert knowledge and performing engineering assessments on the observed environment to divide safe areas and risk areas. The scientist agent focuses on exploring scientific goals. Its behaviors are dynamically adjusted based on various perceptions. The objects of perception include the areas of interest searched by the manager agent, the safe areas defined by engineers, scientific target information, and information about neighboring scientist agents. The evaluator agent evaluates the suitability of candidate areas based on the specific shapes and sizes of the inputs. Its behaviors include perception, movement, and evaluation.

[0010] Step 3) Each intelligent agent perceives the virtual environment information and dynamically interacts and makes collaborative decisions with other intelligent agents based on their own behavioral decision-making rules to optimize the landing point selection process and determine the optimal candidate landing area.

[0011] The establishment of the spatial grid specifically includes: defining the grid unit as the discrete two-dimensional space where the intelligent agent is located, which serves as the basic unit of the intelligent agent's perception, decision-making and movement. The unit size of the spatial grid corresponds to the resolution of the raster data.

[0012] The scientific targets include geological structures, hydrous minerals and hydromorphology. The scientific targets are imported from external vector data and mapped onto the constructed spatial grid in the form of discrete points to form spatial entities.

[0013] The engineering constraint parameters include digital elevation model, slope, albedo, thermal inertia and dust cover index. Characteristic values ​​will be assigned to each grid cell based on the raster data of the engineering constraint parameters to define spatial attributes.

[0014] The manager agent generates preferences based on the environmental information it perceives and determines the target area of ​​interest. Its decision-making behavior is defined as follows:

[0015] A man ={Perc man ,Pref man ,Guid man}

[0016]

[0017] Where A man To manage the behavior of the agent, Perc man 、Pref man and Guid man They are perception, preference and guiding behavior, env is the environment, E M is the environmental information, (x, y) is the grid position, Perc is the environmental perception action, Pref is the preference generation action, U M represents preference, ROI represents region of interest, and τ is the preset preference threshold.

[0018] The decision-making behavior of the engineer agent is defined as follows:

[0019] A eng ={Const eng ,Perc eng ,Asses eng}

[0020]

[0021] Where A eng For the behavior of the engineer agent, Const eng 、Perc eng and Assess eng They are respectively the definition of constraints, the perception of the environment and the engineering evaluation behavior, eng is the engineering constraint related information, Know is the expert knowledge acquisition action, Θ is the set of multiple constraints, env_eng is the engineering related environmental information, Perc is the environmental perception action, E E is the engineering-related information in the environment perceived by the engineer agent, θ e is an engineering constraint in the multi-constraint set Θ, F e To judge the action, judge the perceived information E E Whether the constraint θ is satisfied e , if satisfied then is 1, otherwise, is 0, is the engineering safety index, Rsafe is the safe area, R risky is the risk area, and n is the number of constraints.

[0022] The decision-making behavior of the scientist agent is defined as follows:

[0023] A sci ={Perc sci ,Expl sci ,Move sci}

[0024]

[0025] Where A sci For the behavior of the scientist agent, Perc sci 、Expl sci and Move sci They are perception, exploration and movement behaviors, R safe is the safe area, R risky is the risk area, T S For scientific goals, S S is a neighbor scientist, ROI is a region of interest, Perc is an environmental perception action, C S is the perception result of the scientist agent, T i All the targets found for scientists, Target T i type, type S is the type of scientist S, S i For the i-th scientist, is the target set found by the i-th scientist, F is the action selected by the mobile strategy, P S (x,y) is the position where the scientist moves.

[0026] The decision-making behavior of the evaluator agent is defined as follows:

[0027] A eva ={Perc eva ,Eval eva ,Move eva}

[0028]

[0029] Where A eva To evaluate the behavior of the agent, Perc eva 、Eval eva and Move eva They are perception, evaluation and movement behaviors, Perc is the environmental perception action, R safe is the safe area, R riskyis the risk area, indicators are evaluation indicators, E l is the information perceived by the evaluator agent, w i is the weight of the i-th evaluation index, P E (x, y) is the moving position of the evaluator; P t P is the proportion of scientific objectives within the assessment scope to the global proportion, tt is the global proportion of scientific target types, P s is the proportion of scientists in the world, P st is the proportion of scientist types in the world, D t is the uniformity of scientific target distribution, D s is the uniformity of scientist distribution, Ma is the matching score, and is defined as follows:

[0030]

[0031] Where N t and N tt is the number and type of scientific objectives within the assessment scope, N t,total and N tt,total is the total number and type of scientific objectives for the entire region; N s and N st is the number and type of scientists within the assessment scope, N s,total and N st,total is the total number and type of scientists in the region; σ t and σ s are the standard deviations of the distribution of scientific targets and scientists, respectively; d max is the maximum theoretical distance; N t,i ,N s,i and N s,i are the number of scientific objectives of type i and the corresponding scientists, respectively; I and U represent the intersection and union of scientific objective types and scientist types, and |U| represents the number of elements in the union.

[0032] Specifically, step 3) includes the following steps: managers set target areas of interest based on available environmental information and their own preferences, and guide other agents to conduct targeted regional exploration; engineers judge engineering safety and identify safe areas by observing the environment; scientists explore scientific goals in safe areas, interact and share information with other scientists, and collaboratively propose potential candidate sites; and evaluators conduct suitability assessments on the candidate areas proposed by scientists, providing a scientific basis for the final site selection decision.

[0033] The specific shape and size are the ellipse size and inclination set according to the engineering task requirements, wherein the ellipse size includes the major axis and the minor axis, and the inclination is the direction angle relative to the longitude and latitude lines.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] This paper proposes the use of a multi-agent system to simulate the collaborative decision-making behavior of various participants in the Mars landing site selection process, defines four types of agents: managers, engineers, scientists, and evaluators, and their behavioral rules, to achieve efficient identification of potential landing sites with both high scientific value and high engineering safety, and provide reliable candidate landing area plans for Mars landing site selection and sample return missions, which has important practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the method of the present invention;

[0037] Figure 2 Environmental modeling results for a certain area of ​​paleo-coastline;

[0038] Figure 3 This is a diagram of the multi-agent decision convergence process;

[0039] Figure 4 This is the candidate ellipse distribution map of the ancient coastline area in a certain region. DETAILED DESCRIPTION

[0040] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0041] This embodiment provides a Mars landing site selection method based on multi-agent decision-making, such as Figure 1 As shown, the following steps are included:

[0042] Step 1) Build a virtual environment model of the Martian surface based on multi-source remote sensing data. By establishing a spatial grid, abstract the scientific objectives into spatial entities and define the engineering constraint parameters as spatial attributes.

[0043] Step 11) Define the grid cells

[0044] The grid cell is the discrete two-dimensional space where the agent is located. As the basic unit of the agent's perception, decision-making and movement, the cell size of the spatial grid corresponds to the resolution of the raster data.

[0045] Step 12) Define spatial entities

[0046] Spatial entities refer to various scientific targets on the Martian surface, including geological structures, hydrous minerals, and hydromorphological features. These entities are imported from external vector data and mapped onto the constructed spatial grid in the form of discrete points.

[0047] Step 13) Define space properties

[0048] Based on external raster data (including DEM, slope, albedo, thermal inertia, dust cover index and other engineering constraint parameters), characteristic values ​​are assigned to each grid cell to define spatial attributes.

[0049] The environmental modeling results of the ancient coastline area in a certain area are as follows: Figure 2 As shown, the base map is DEM data.

[0050] Step 2) Set four types of intelligent agent roles: manager, engineer, scientist and evaluator, and define corresponding behavioral decision rules for each type of intelligent agent to achieve intelligent agent modeling.

[0051] Step 21) Agent Definition

[0052] The manager agent is responsible for coordinating planning tasks. Its behaviors include perceiving environmental information, forming preferences, and guiding scientists to explore areas of interest. The engineer agent assesses regional engineering safety. Its behaviors include defining engineering constraints based on expert knowledge and performing engineering assessments on the observed environment to divide safe areas (meeting engineering constraints) and risky areas (not meeting engineering constraints). The scientist agent focuses on exploring scientific goals. Its behaviors are dynamically adjusted based on various perceptions. The objects of perception include the areas of interest searched by the manager agent, the safety areas defined by engineers, scientific target information, and information from adjacent scientist agents. The evaluator agent evaluates the suitability of candidate areas based on the specific shapes and sizes of the inputs. Its behaviors include perception, movement, and evaluation.

[0053] Step 22) Agent behavior definition

[0054] The four different types of intelligent agents have independent decision-making capabilities and each exhibits different behavioral characteristics.

[0055] The manager agent generates preferences based on the environmental information it perceives and determines the target region of interest (ROI), which is represented by the manager agent perceiving the environmental information E M On this basis, a preference U is formed for each unit in the grid M (x, y), and then proposes a region of interest (ROI) based on the established preferences. Specifically, its decision behavior is defined as follows:

[0056] A man ={Perc man ,Pref man ,Guid man} (1)

[0057]

[0058] Where Aman To manage the behavior of the agent, Perc man 、Pref man and Guid man They are perception, preference and guiding behavior, enc is the environment, E M is the environmental information, (x, y) is the grid position, Perc is the environmental perception action, Pref is the preference generation action, U M represents preference, ROI represents region of interest, and τ is the preset preference threshold.

[0059] The engineer agent defines engineering constraints based on expert knowledge and then performs engineering evaluation on the perceived surrounding environment. Specifically, the engineer acquires expert knowledge to define engineering constraint thresholds, constructs a multi-constraint set Θ, and perceives engineering-related information E in the environment. E , and evaluate engineering safety based on engineering constraints and environmental engineering information Then divide the area into safety zones safe and danger zone risky Specifically, its decision-making behavior is defined as follows:

[0060] A eng ={Const eng ,Perc eng ,Asses eng} (3)

[0061]

[0062] Where A eng For the behavior of the engineer agent, Const eng 、Perc eng and Assess eng They are respectively the definition of constraints, the perception of the environment and the engineering evaluation behavior, eng is the engineering constraint related information, Know is the expert knowledge acquisition action, Θ is the set of multiple constraints, env_eng is the engineering related environmental information, Perc is the environmental perception action, E E is the engineering-related information in the environment perceived by the engineer agent, θ e is an engineering constraint in the multi-constraint set Θ, F e To judge the action, judge the perceived information E E Whether the constraint θ is satisfied e , if satisfied then is 1, otherwise, is 0, is the engineering safety index, R safe is the safe area, R risky is the risk area, and n is the number of constraints.

[0063] The scientist agent explores and moves based on the perceived environmental information and dynamic information from other agents. Specifically, the scientist senses whether it is in the safe area defined by the engineer, obtains the distribution of scientific targets, the distribution of neighboring scientists, and the distribution of ROI, and explores the same specific type of scientific targets T as itself. s and neighbor scientist S who has found the scientific goal s The position and quantity of the mobile device, and select the corresponding moving strategy. The moving position P S (x,y) depends on prioritizing areas with high concentrations of scientific targets, areas with nearby scientists discovering resources, areas of manager-specified interest, and random movement. Furthermore, the number of scientist agents is determined by the type and distribution of scientific targets, with each type of scientist corresponding to a specific type of scientific target. Specifically, their decision-making behavior is defined as follows:

[0064] A sci ={Perc sci ,Expl sci ,Move sci} (5)

[0065]

[0066] Where A sci For the behavior of the scientist agent, Perc sci 、Expl sci and Move sci They are perception, exploration and movement behaviors, R safe is the safe area, R risky is the risk area, T s For scientific goals, S s is a neighbor scientist, ROI is a region of interest, Perc is an environmental perception action, C S is the perception result of the scientist agent, T i All the targets found for scientists, Target T i type, type S is the type of scientist S, S i For the i-th scientist, is the target set found by the i-th scientist, F is the action selected by the mobile strategy, P s (x,y) is the position where the scientist moves.

[0067] The evaluator agent evaluates the suitability of the surrounding areas it passes through during movement. Specifically, the evaluator considers the engineering safety of the current area, the distribution of scientific objectives, and the location of scientists, and gives a comprehensive evaluation score (Score). It then continues to move towards locations with higher scores. The decision-making behavior is defined as follows:

[0068] A eva ={Perc eva ,Eval eva ,Move eva} (7)

[0069]

[0070] Where A eva To evaluate the behavior of the agent, Perc eva 、Eval eva and Move eva They are perception, evaluation and movement behaviors, Perc is the environmental perception action, R safe is the safe area, R risky is the risk area, indicators are evaluation indicators, E l is the information perceived by the evaluator agent, w i is the weight of the i-th evaluation index, P E (x, y) is the moving position of the evaluator; P t P is the proportion of scientific objectives within the assessment scope to the global proportion, tt is the global proportion of scientific target types, P s is the proportion of scientists in the world, P st is the proportion of scientist types in the world, D t is the uniformity of scientific target distribution, D s is the uniformity of scientist distribution, Ma is the matching score, and is defined as follows:

[0071]

[0072] Where N t and N tt is the number and type of scientific objectives within the assessment scope, N t,total and N tt,total is the total number and type of scientific objectives for the entire region; N s and N st is the number and type of scientists within the assessment scope, N s,total and N st,total is the total number and type of scientists in the region; σ t and σ s are the standard deviations of the distribution of scientific targets and scientists, respectively; d maxis the maximum theoretical distance; N t,i ,N s,i and N s,i are the number of scientific objectives of type i and the corresponding scientists, respectively; I and U represent the intersection and union of scientific objective types and scientist types, and |U| represents the number of elements in the union.

[0073] During the decision-making process of the evaluator agent, a specific shape and size are defined. This specific shape and size is used to limit the size and shape of the generated candidate area. Specifically, according to the specific engineering task requirements, the parameters of the target landing ellipse are set, including the ellipse's dimensions (major and minor axes) and inclination (the angle relative to the longitude and latitude). Therefore, the resulting candidate landing area is also represented as an elliptical area.

[0074] Step 3) Each intelligent agent perceives the virtual environment information and dynamically interacts and makes collaborative decisions with other intelligent agents based on their own behavioral decision-making rules to optimize the landing point selection process and determine the optimal candidate landing area.

[0075] Four types of agents perceive the environment and dynamically interact with other agents according to their own behavioral rules to select the optimal landing site on Mars. The main tasks are: managers set target areas of interest based on their environmental information and preferences, and guide other agents to conduct targeted regional exploration; engineers observe the environment to determine engineering safety and identify safe areas; scientists conduct scientific exploration within safe areas, interact with other scientists, share information, and collaboratively propose potential candidate sites; and evaluators conduct suitability assessments on the candidate sites proposed by scientists, providing a scientific basis for the final site selection decision.

[0076] The above-mentioned perception-interaction-evaluation mechanism among multiple agents will be run in an iterative optimization manner until all agents reach a stable state or converge on a consistent location decision in the collaborative decision-making process, such as Figure 3 The candidate landing ellipse of the ancient coastline area of ​​a certain region is shown as Figure 4 shown.

[0077] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A Mars landing site selection method based on multi-agent decision making, characterized by: The following steps are involved: Step 1) Build a virtual environment model of the Martian surface based on multi-source remote sensing data. By establishing a spatial grid, abstract the scientific objectives into spatial entities and define the engineering constraint parameters as spatial attributes. Step 2) Set up four types of agent roles: manager, engineer, scientist, and evaluator. Define corresponding behavioral decision rules for each type of agent to implement agent modeling. The manager agent is responsible for coordinating planning tasks. Its behaviors include perceiving environmental information, forming preferences, and guiding scientists to explore areas of interest. The engineer agent assesses regional engineering safety. Its behaviors include defining engineering constraints based on expert knowledge and performing engineering assessments on the observed environment to divide safe areas into risky areas. The scientist agent focuses on exploring scientific objectives, and its behavior is dynamically adjusted based on various perceptions, including the area of ​​interest searched by the manager agent, the safety zone defined by the engineer, information about scientific objectives, and information about neighboring scientist agents. The evaluator agent evaluates the suitability of candidate areas based on the specific shape and size of the input, and its behavior includes perception, movement, and evaluation. Step 3) Each intelligent agent perceives the virtual environment information and dynamically interacts and makes collaborative decisions with other intelligent agents based on their own behavioral decision-making rules to optimize the landing point selection process and determine the optimal candidate landing area.

2. The Mars landing site selection method based on multi-agent decision-making according to claim 1 is characterized in that: The establishment of the spatial grid specifically includes: defining the grid unit as the discrete two-dimensional space where the intelligent agent is located, which serves as the basic unit of the intelligent agent's perception, decision-making and movement. The unit size of the spatial grid corresponds to the resolution of the raster data.

3. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: The scientific targets include geological structures, hydrous minerals and hydromorphology. The scientific targets are imported from external vector data and mapped onto the constructed spatial grid in the form of discrete points to form spatial entities.

4. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: The engineering constraint parameters include digital elevation model, slope, albedo, thermal inertia and dust cover index. Characteristic values ​​will be assigned to each grid cell based on the raster data of the engineering constraint parameters to define spatial attributes.

5. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: The manager agent generates preferences based on the environmental information it perceives and determines the target area of ​​interest. Its decision-making behavior is defined as follows: A man ={Perc man ,Pref man ,Guid man } Where A man To manage the behavior of the agent, Prec man 、Pref man and Guid man They are perception, preference and guiding behavior, env is the environment, E M is the environmental information, (x, y) is the grid position, Perc is the environmental perception action, Pref is the preference generation action, U M represents preference, ROI represents region of interest, and τ is the preset preference threshold.

6. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: The decision-making behavior of the engineer agent is defined as follows: A eng ={Const eng ,Perc eng ,Asses eng } Where A eng For the behavior of the engineer agent, Const eng 、Perc eng and Assess eng They are respectively the definition of constraints, the perception of the environment and the engineering evaluation behavior, eng is the engineering constraint related information, Know is the expert knowledge acquisition action, Θ is the set of multiple constraints, env_eng is the engineering related environmental information, Perc is the environmental perception action, E E is the engineering-related information in the environment perceived by the engineer agent, θ e is an engineering constraint in the multi-constraint set Θ, F e To judge the action, judge the perceived information E E Whether the constraint θ is satisfied e , if satisfied then is 1, otherwise, is 0, is the engineering safety index, R safe is the safe area, R risky is the risk area, and n is the number of constraints.

7. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: The decision-making behavior of the scientist agent is defined as follows: A sci ={Perc sci ,Expl sci ,Move sci } Where A sci For the behavior of the scientist agent, Perc sci 、Expl sci and Move sci They are perception, exploration and movement behaviors, R safe is the safe area, R risky is the risk area, T S For scientific goals, S S is a neighbor scientist, ROI is a region of interest, Perc is an environmental perception action, C S is the perception result of the scientist agent, T i All the targets found for scientists, Target T i type, type S is the type of scientist S, S i For the i-th scientist, is the target set found by the i-th scientist, F is the action selected by the mobile strategy, P S (x,y) is the position where the scientist moves.

8. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: The decision-making behavior of the evaluator agent is defined as follows: A eva ={Perc eva ,Eval eva ,Move eva } Where A evq To evaluate the behavior of the agent, Perc eva 、Eval eva and Move eva They are perception, evaluation and movement behaviors, Perc is the environmental perception action, R safe is the safe area, R risky is the risk area, indicators are evaluation indicators, E I is the information perceived by the evaluator agent, w i is the weight of the i-th evaluation index, P E (x, y) is the moving position of the evaluator; P t P is the proportion of scientific objectives within the assessment scope to the global proportion, tt is the proportion of scientific target types in the global total, P s is the proportion of scientists in the world, P st is the proportion of scientist types in the world, D t is the uniformity of scientific target distribution, D s is the uniformity of scientist distribution, Ma is the matching score, and is defined as follows: Where N t and N tt is the number and type of scientific objectives within the assessment scope, N t,total and N tt,to1al is the total number and type of scientific objectives for the entire region; N s and N st is the number and type of scientists within the assessment scope, N s,total and N st,total is the total number and type of scientists in the region; σ t and σ s are the standard deviations of the distribution of scientific targets and scientists, respectively; d max is the maximum theoretical distance; N t,i ,N s,i and N s,i are the number of scientific objectives of type i and the corresponding scientists, respectively; I and U represent the intersection and union of scientific objective types and scientist types, and |U| represents the number of elements in the union.

9. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: Step 3) specifically includes: the manager sets a target area of ​​interest based on the environmental information and personal preferences, and guides other agents to conduct targeted regional exploration; the engineer judges the engineering safety by observing the environment and identifies the safe area; Scientists explore scientific goals in safe areas, interact and share information with other scientists, and jointly propose potential candidate site areas; evaluators are responsible for conducting suitability assessments on the candidate areas proposed by scientists to provide a scientific basis for the final site selection decision.

10. The Mars landing site selection method based on multi-agent decision-making according to claim 1, characterized in that: The specific shape and size are the ellipse size and inclination set according to the engineering task requirements, wherein the ellipse size includes the major axis and the minor axis, and the inclination is the direction angle relative to the longitude and latitude lines.

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