Multi-attribute sensing reliable routing method for lunar complex environment

Abstract

The invention relates to a multi-attribute sensing reliable routing method oriented to a lunar complex environment. The method comprises the following steps: each node in a network periodically broadcasts HELLO messages to all neighbor nodes in a communication range of the node; starting from a source node, each current node screens out neighbor nodes meeting a preset condition from neighbor nodes of the current node through a geometric pre-screening mechanism to serve as candidate nodes; calculating the energy utility of the candidate node according to the residual energy of the candidate node; calculating the load utility of the candidate node according to the queue occupation condition of the candidate node; calculating the SINR utility of the candidate node according to the SINR index of the candidate node; calculating the weight of each utility of the candidate node based on a weight distribution mechanism of an attribute deviation maximization principle, and fusing the energy utility, the load utility and the SINR utility of the candidate node according to the weight of each utility of the candidate node to obtain a reliability value of the candidate node; and selecting a next hop node from the candidate nodes by using a roulette algorithm according to the reliability values of the candidate nodes until the next hop node is a destination node. The method can be widely applied to lunar surface wireless ad hoc network communication, and high-reliability, low-delay and safe transmission of detection task data is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and in particular relates to a multi-attribute-aware reliable routing method for complex lunar environments. Background Technology

[0002] With the ongoing manned lunar landing and long-term lunar residency programs, building a large-scale, distributed, and self-organizing wireless network for the moon has become a core requirement for providing high-performance, autonomous communication services. Compared to terrestrial networks, the lunar communication environment has significant unique characteristics: the absence of an atmosphere, complex terrain, wide temperature differences, strong space radiation, and unstable energy supply are all extreme conditions that pose enormous challenges to the deployment and maintenance of communication links.

[0003] Especially during lunar exploration activities, mobile nodes (such as lunar rovers and landers) and fixed nodes (such as research modules and energy relay stations) constitute a complex and resource-heterogeneous communication system. The connections between nodes continuously change with mission deployment and environmental disturbances, resulting in a network topology exhibiting significant dynamism and uncertainty. In this context, routing strategies relying on static configuration or centralized control are prone to sharp declines in overall communication performance due to factors such as link failures and node outages. Furthermore, since most lunar communication nodes rely on solar power, and the lunar day-night cycle lasts for 14 Earth days, node operational capacity is limited. Communication decisions must be made under extreme energy constraints, making it difficult to directly migrate and apply traditional high-energy-consumption, high-redundancy terrestrial routing algorithms.

[0004] At the level of physical characteristics of the links, the lunar surface has drastic topography, making channel propagation susceptible to obstruction and scattering interference. The electromagnetic wave propagation environment is far more complex than on Earth, exhibiting higher signal attenuation and channel instability. Simultaneously, due to the lack of atmospheric shielding, lunar node equipment must withstand solar radiation far exceeding that on Earth, leading to frequent communication link disconnections. These intertwined adverse factors necessitate that lunar communication networks simultaneously consider multiple dynamic constraints during routing decisions, including link availability, node energy consumption, and limited resources.

[0005] Currently, domestic and international research on lunar surface communication mainly focuses on three types of routing schemes: ① store-and-forward algorithms based on Delay Tolerant Networks (DTN); ② shortest hop count algorithms that are simplified and directly ported from terrestrial AODV / OLSR; and ③ energy-aware improved algorithms for extreme energy consumption. However, these algorithms generally assume that node topology and link parameters are approximately static within the task window, rely on pre-set access time slots or fixed weights, and lack adaptive mechanisms for dynamic node movement and frequent link interruptions, thus making it difficult to guarantee the reliability and real-time performance of routing selection. Summary of the Invention

[0006] To address the problems existing in the background art, this invention provides a multi-attribute-aware reliable routing method for complex lunar environments, comprising:

[0007] S1: Each node in the network periodically broadcasts a HELLO message to all neighboring nodes within its communication range. The HELLO message includes: the node's remaining energy, queue occupancy status, and SINR index.

[0008] S2: Starting from the source node, each current node selects neighboring nodes that meet preset conditions as candidate nodes through a geometric pre-screening mechanism.

[0009] S3: Calculate the energy utility of candidate nodes based on their remaining energy; calculate the load utility of candidate nodes based on their queue occupancy; calculate the SINR utility of candidate nodes based on their SINR index.

[0010] S4: The weight allocation mechanism based on the principle of maximizing attribute deviation calculates the weight of each utility of the candidate node, and the energy utility, load utility and SINR utility of the candidate node are fused according to the weight of each utility of the candidate node to obtain the reliability value of the candidate node.

[0011] S5: Based on the reliability value of the candidate nodes, use the roulette wheel algorithm to select the next hop node from the candidate nodes until the next hop node is the destination node.

[0012] Another aspect of the present invention provides a multi-attribute-aware reliable routing system for complex lunar environments, the system comprising a memory and a processor; the memory is used to store an application program; the processor is used to run the application program and execute the multi-attribute-aware reliable routing method for complex lunar environments.

[0013] Another aspect of the present invention provides a computer storage medium storing a remote monitoring program, which, when executed by a processor, implements the aforementioned multi-attribute-aware reliable routing method for complex lunar environments.

[0014] The present invention has at least the following beneficial effects

[0015] This invention achieves real-time perception through periodic broadcasting of node status information, narrows the candidate range through geometric pre-screening, quantifies node performance through multi-dimensional utility calculation, integrates reliability through dynamic weight allocation, and selects the next-hop node using a roulette wheel algorithm. This forms a complete routing decision system that can effectively adapt to extreme environments such as the lunar surface's lack of atmosphere, complex terrain, unstable energy supply, and susceptibility to link interference. It also takes into account multiple dynamic constraints such as link availability, node energy consumption, and limited resources. This solves the problems of poor adaptability and insufficient reliability of traditional routing algorithms in the lunar environment, ensuring high reliability, low latency, and secure transmission of probe mission data in lunar wireless ad hoc networks, and improving the adaptability of the routing system to dynamic topology and extreme conditions. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0017] Figure 2 This is a schematic diagram of the lunar surface communication network routing scenario of the present invention. Detailed Implementation

[0018] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0019] Please see Figure 1 and Figure 2 This invention models the lunar communication network as an undirected connected graph containing n nodes. ,in , represents the set of all nodes in the network. , representing the set of all links between nodes. Considering the group mobility of lunar nodes (such as lunar rovers, astronauts, etc.), the network topology exhibits certain dynamic changes. Based on the reference point group movement model, the relative offset of member nodes in a group to their reference point is small within a time slot, which can be approximated as locally statically stable, facilitating the calculation of link costs; however, between time slots, the positions of nodes are significantly updated with the overall migration of the group, resulting in substantial changes in link states.

[0020] like Figure 2 The diagram shows a local lunar surface communication network topology and hop-by-hop data transmission over three consecutive time slots, including the source node. , destination node And other lunar nodes. If there is no direct link between the source and destination nodes, data transmission must be completed via relay nodes through a multi-hop approach. Due to the complexity of the lunar environment, link breaks may occur due to node failures or link quality deterioration, thus affecting data transmission. Therefore, it is necessary to dynamically select the optimal relay path under the conditions of node movement and link changes.

[0021] Please see Figure 1 This invention provides a multi-attribute-aware reliable routing method for complex lunar environments, comprising:

[0022] S1: Each node in the network periodically broadcasts a HELLO message to all neighboring nodes within its communication range. The HELLO message includes: the node's remaining energy, queue occupancy status, and SINR index.

[0023] In this embodiment, step S1 is the basic information collection step of the lunar surface communication network routing method. Its core content is that all nodes in the network (including mobile nodes such as lunar rovers and fixed nodes such as research modules) send HELLO messages to all neighboring nodes within their communication coverage area at a fixed period (adapting to the dynamic changes of the lunar surface network). The messages carry three types of key status information: remaining energy, queue occupancy status, and SINR index.

[0024] S2: Starting from the source node, each current node selects neighboring nodes that meet preset conditions as candidate nodes through a geometric pre-screening mechanism.

[0025] Preferably, the preset condition includes: the distance from the neighboring node to the destination node is less than the distance from the current node to the destination node.

[0026] In this embodiment, step S2 refers to starting from the source node, each current node selects neighboring nodes that meet preset conditions (i.e., the distance from the neighboring node to the destination node is less than the distance from the current node to the destination node) as candidate nodes through a geometric pre-screening mechanism. This step performs preliminary screening of neighboring nodes through the geometric pre-screening mechanism, which can quickly narrow down the range of candidate nodes, effectively reduce the computational load of subsequent utility calculations and routing selections, and at the same time ensure that the selected candidate nodes are geographically closer to the destination node, which helps to shorten the data transmission path, reduce transmission latency and energy consumption caused by excessively long paths, adapt to the characteristics of node movement and dynamic topology changes under the complex terrain of the lunar surface, and provide a more targeted candidate set for subsequent refined routing decisions based on multiple attributes such as energy, load, and SINR. It is an important link in improving routing efficiency and reliability.

[0027] S3: Calculate the energy utility of candidate nodes based on their remaining energy; calculate the load utility of candidate nodes based on their queue occupancy; calculate the SINR utility of candidate nodes based on their SINR index.

[0028] In this embodiment, to ensure the continuous operation and stable communication of nodes in the lunar communication network, it is necessary to comprehensively evaluate the remaining energy status of the nodes. Based on the lunar environment, a node energy consumption model oriented towards the task execution process is constructed. This invention models node energy consumption from three aspects: activity energy consumption, communication energy consumption, and computational energy consumption. In multi-attribute routing evaluation, to unify the dimensions and evaluation criteria of each attribute, this invention converts the node's remaining energy into a utility value ranging from 0 to 1.

[0029] Preferably, the calculation of the energy utility of the candidate nodes includes:

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] in, Indicates candidate nodes Energy utility; Indicates candidate nodes The initial energy; Indicates candidate nodes The minimum remaining energy; Indicates candidate nodes The remaining energy; Indicates candidate nodes Mobile energy consumption; Indicates candidate nodes Communication energy consumption; Indicates candidate nodes The computational energy consumption; This represents the number of bits of data processed by candidate node i; Indicates candidate nodes Power consumed in processing a unit bit of data; Indicates data processing time; Indicates candidate nodes Power consumption per unit of data transmission; Indicates candidate nodes The power consumption of the basic circuit per unit time; k represents the candidate node. The number of bits of data sent and received; Candidate nodes The time required to send and receive kbits of data; Indicates candidate nodes The quality; Represents gravitational acceleration; and Indicates candidate nodes The altitude of the starting and ending points when performing a task; Indicates candidate nodes The energy consumption per unit friction; Indicates candidate nodes Movement speed; Indicates the slope angle corresponding to the starting point; Indicates the slope angle corresponding to the endpoint.

[0036] When the number of data packets in a node's buffer queue accumulates and cannot be processed or forwarded in a timely manner, queue overflow is highly likely to occur, leading to data packet loss and severely impacting routing stability and communication reliability. Therefore, this invention references queuing theory... The structure is used to quantitatively model the load utility of node queues.

[0037] Preferably, the load utility of the computational candidate node includes:

[0038]

[0039]

[0040]

[0041]

[0042] in, Indicates candidate nodes The load utility Indicates candidate nodes Data processing service rate; Indicates candidate nodes The shortest theoretical waiting time; This indicates that data has arrived at the candidate node. The rate; Indicates candidate nodes Service intensity; Indicates candidate nodes The maximum capacity of the buffer; This indicates that the service package is in the candidate node. The probability of being discarded due to timeout or buffer overflow; Indicates candidate nodes Size of received service packets; Indicates candidate nodes The number of queued business data packets; Indicates candidate nodes Business data packets.

[0043] Due to the complex lunar environment and the severe impact of electromagnetic interference and surface reflection on the channel, wireless signals experience path loss, multipath effects, and irregular attenuation during propagation, leading to fluctuations in communication quality and even link interruptions. Therefore, to more accurately assess the possibility of establishing stable communication between two nodes, this invention constructs a composite path loss model encompassing free-space propagation, reflection propagation, and diffraction propagation based on various typical wireless propagation scenarios, and derives link availability accordingly.

[0044] Free space propagation model

[0045] When the communication environment is unobstructed and free from interference, signal propagation can be considered as ideal free-space propagation, and path loss is a function of propagation distance. The path loss under this condition is calculated as follows:

[0046]

[0047] Reflection propagation model

[0048] If there are terrain undulations or obstacles in the signal propagation path, the signal may be reflected within the Fresnel region. In this case, reflection loss must be considered in addition to the signal propagation. Based on the phase difference between the reflected path and the direct path, the reflection loss can be expressed as:

[0049]

[0050] Diffraction propagation model

[0051] When communication nodes are obstructed by obstacles and an effective reflection path cannot be formed, the signal propagates through diffraction. This invention uses the method recommended in ITU-R P.526-15 to calculate the additional path loss caused by diffraction. First, an approximate contour line, called the profile, is introduced, which connects the transmitting and receiving nodes and consists of multiple obstacle vertices, to estimate the diffraction effect that occurs when the signal propagates along it.

[0052] Preferably, the SINR utility of the calculated candidate nodes includes:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] in, Indicates candidate nodes SINR utility; Indicates the current node in terms of power To candidate nodes SINR of the transmitted data packet; The reference SINR value required for the link quality to reach its optimal state; This represents the minimum SINR threshold required for effective communication via the link. Indicates noise power density; This represents the path loss during free space propagation. This represents the reflection propagation path loss (at the candidate node, the direct path is the path of the signal directly from the source node to the candidate node, while the reflection propagation path is the path of the signal after being reflected by an obstacle to reach the candidate node). This represents the path loss during diffraction propagation. Indicates the wavelength of a radio signal; Represents pi; Indicates the distance from the current node to the candidate node. The path distance; Indicates the reflection coefficient; This indicates the phase difference between the two paths; It is the specular reflection coefficient; Indicates the roughness of the mirror surface; The scattering factor of a mirror; This indicates the path length from the current node to the reflection point; Indicates the distance from the reflection point to the candidate node. Path length; This represents the diffraction angle from the current node to the highest point of the profile; the highest point of the profile refers to the contour line formed by connecting multiple obstacle vertices between the current node and the candidate node, discretizing the profile into s sampling points, and the highest point of the sampling points is the highest point of the profile. Indicates the height of the current node; Indicates candidate nodes Height; Indicates the initial slope of the profile; This represents the distance from the current node to the highest point of the profile. Indicates the effective lunar curvature; Indicates the first section The height of each sampling point; Indicates the distance from the current node to the first node on the profile. The distance between each sampling point; This represents the function that takes the maximum value.

[0063] In this embodiment, step S3 refers to the process where, starting from the source node, after each current node determines candidate nodes through a geometric pre-screening mechanism, its energy utility is calculated based on the remaining energy of the candidate nodes (this utility calculation involves the candidate node's initial energy, minimum remaining energy, remaining energy, as well as mobility energy consumption, communication energy consumption, computing energy consumption, etc., covering parameters such as the number of bits processed by the node, power consumption per bit, transmission power consumption, basic circuit power consumption, mobility-related mass, gravitational acceleration, altitude difference, friction energy consumption, speed, and slope angle). The load utility of the candidate nodes is calculated based on their queue occupancy (involving data processing service rate, shortest theoretical waiting time, data arrival rate, service intensity, maximum buffer capacity, packet drop probability, received packet size, and existing queue size, etc.). Finally, the SINR utility of the candidate nodes is calculated based on their SINR index. (This involves parameters such as reference SINR value, minimum SINR threshold, noise power density, free space / reflection / diffraction propagation path loss, as well as wavelength, path distance, reflection coefficient, phase difference, specular reflection coefficient, roughness, scattering factor, reflection point path length, diffraction tilt angle, node height, profile slope, effective lunar curvature, sampling point height and distance, etc.); This step quantifies the three key attributes of candidate nodes—energy, load, and SINR—transforming the dynamic state of the nodes into comparable utility values. This provides a precise quantitative basis for subsequent weight allocation and reliability value fusion based on the principle of maximizing attribute deviation. It comprehensively considers the core constraints of node energy constraints, load congestion, and unstable link quality under lunar conditions, and achieves scientific measurement of multiple attributes, laying a key foundation for improving the adaptability and reliability of routing decisions.

[0064] S4: The weight allocation mechanism based on the principle of maximizing attribute deviation calculates the weight of each utility of the candidate node, and the energy utility, load utility and SINR utility of the candidate node are fused according to the weight of each utility of the candidate node to obtain the reliability value of the candidate node.

[0065] After completing the normalization modeling of the three attribute values, this invention further proposes a weight allocation mechanism based on the principle of maximizing attribute deviation, so as to dynamically determine the relative importance of each decision attribute in the comprehensive score.

[0066] Preferably, the weights for calculating the utility of each candidate node include:

[0067] S41: Let the candidate node be... The weights of each utility attribute are And it needs to meet the following constraints: ;

[0068] S42: Calculate the weighted deviation of candidate nodes in each utility attribute:

[0069]

[0070] in, Indicates candidate nodes In the Weighted deviation of each utility attribute; Indicates candidate nodes The Individual utility value; Indicates the candidate node number The weight of each utility attribute; Indicates candidate nodes The Individual utility value;

[0071] S42: Calculate the weighted sum of deviations for all candidate nodes across all utility attributes:

[0072]

[0073] in, All candidate nodes are in the The weighted sum of deviations for each utility attribute;

[0074] S43: Construct an optimization model by maximizing the weighted sum of deviations of all candidate nodes across all utility attributes:

[0075]

[0076] S43: To simplify the solution, we introduce the difference of squares instead of the difference of absolute values, and construct the Lagrangian function of the optimization model as follows:

[0077]

[0078] in, Represents the Lagrangian function of the optimization model; Represents the Lagrange multipliers;

[0079] S44: Weights and Lagrange multipliers Taking the partial derivatives separately and setting them to zero, we obtain the analytical expression for the utility attribute weights:

[0080]

[0081]

[0082] in, Indicates weight The partial derivative; Represents Lagrange multipliers The partial derivative; Indicates utility attribute The weight parsing expression;

[0083] S45: Standardize the analytical expression for the utility attribute weights described above to obtain the weights of the utility attributes:

[0084]

[0085] in, Indicates utility attribute The weight.

[0086] Preferably, the reliability value of the candidate node includes:

[0087]

[0088] in, Indicates candidate nodes Reliability value; express of Power; Indicates candidate nodes Energy utility; express The weights; express of Power; Indicates candidate nodes The load utility; express The weights; express of Power; Indicates candidate nodes SINR utility; express The weight.

[0089] In this embodiment, step S4 refers to calculating the weights of each utility based on the weight allocation mechanism of the attribute deviation maximization principle after determining the candidate nodes and their energy utility, load utility, and SINR utility. This includes setting constraints on the weight of the j-th utility attribute, calculating the weighted deviation of the candidate node in each utility attribute and the sum of the weighted deviations of all candidate nodes in each utility attribute, constructing an optimization model by maximizing the total weighted deviation, introducing the squared difference to construct the Lagrange function, taking partial derivatives with respect to the weights and Lagrange multipliers and setting the derivatives to zero to obtain the analytical expression for the weights, and then standardizing the expression to obtain the weights of each utility attribute. Based on these weights, the energy utility, load utility, and SINR utility of the candidate nodes are then... The reliability values ​​of candidate nodes are obtained by fusion (by adding the products of each utility and its weight to the power of a formula). This step dynamically and scientifically allocates the weights of each utility attribute, fully considering the differences in energy, load, and SINR attributes among different candidate nodes. This allows the fused reliability values ​​to comprehensively and accurately reflect the overall performance of the candidate nodes, effectively adapting to the dynamic changes in node status and link quality in the lunar environment. This provides a quantitative and comprehensive decision-making basis for the subsequent selection of the next-hop node using the roulette wheel algorithm, improving the adaptability and reliability of routing decisions in the complex lunar environment.

[0090] S5: Based on the reliability value of the candidate nodes, use the roulette wheel algorithm to select the next hop node from the candidate nodes until the next hop node is the destination node.

[0091] Preferably, the step of selecting the next-hop node from the candidate nodes using the roulette wheel algorithm includes: calculating the probability of each candidate node being the next-hop node using the roulette wheel algorithm, and selecting the next-hop node from the candidate nodes based on the probability of each candidate node being the next-hop node, wherein the probability of each candidate node being the next-hop node includes:

[0092]

[0093] in, Indicates candidate nodes The probability of being the next hop node; Indicates candidate nodes Reliability value; Indicates candidate nodes Reliability value; This indicates the number of candidate nodes.

[0094] In this embodiment, step S5 refers to selecting the next-hop node from the candidate nodes using a roulette wheel algorithm after obtaining the reliability value of the candidate nodes, until the next-hop node is the destination node. The roulette wheel algorithm calculates the probability of each candidate node being the next-hop node (i.e., the ratio of the reliability value of candidate node i to the sum of the reliability values ​​of all candidate nodes) and selects the next-hop node based on this probability. This step, based on the comprehensive reliability value of the candidate nodes, uses the roulette wheel algorithm to prioritize the selection of highly reliable nodes while retaining a certain degree of randomness to avoid getting trapped in local optima. It can adapt to the dynamic changes in node status and link quality in the lunar environment, ensuring that a better relay node can be selected at each hop, continuously optimizing the data transmission path until the data reaches the destination node. This effectively guarantees the high reliability and low latency transmission of probe mission data in the lunar wireless ad hoc network and improves the flexibility and adaptability of routing selection.

[0095] Another aspect of the present invention provides a multi-attribute-aware reliable routing system for complex lunar environments, the system comprising a memory and a processor; the memory is used to store an application program; the processor is used to run the application program and execute the multi-attribute-aware reliable routing method for complex lunar environments.

[0096] Another aspect of the present invention provides a computer storage medium storing a remote monitoring program, which, when executed by a processor, implements the aforementioned multi-attribute-aware reliable routing method for complex lunar environments.

[0097] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0098] In summary, this invention achieves real-time perception through periodic broadcasting of node status information, narrows the candidate range through a geometric pre-screening mechanism, quantifies node performance through multi-dimensional utility calculation, integrates reliability through dynamic weight allocation, and selects the next-hop node using a roulette wheel algorithm. This forms a complete routing decision system that can effectively adapt to extreme environments such as the lunar surface's lack of atmosphere, complex terrain, unstable energy supply, and susceptibility to link interference. It also takes into account multiple dynamic constraints such as link availability, node energy consumption, and limited resources. This solves the problems of poor adaptability and insufficient reliability of traditional routing algorithms in the lunar environment, ensuring high reliability, low latency, and secure transmission of probe mission data in lunar wireless ad hoc networks, and improving the adaptability of the routing system to dynamic topology and extreme conditions.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-attribute-aware reliable routing method for complex lunar environments, characterized in that, include: S1: Each node in the network periodically broadcasts a HELLO message to all neighboring nodes within its communication range. The HELLO message includes: the node's remaining energy, queue occupancy status, and SINR index. S2: Starting from the source node, each current node selects neighboring nodes that meet preset conditions as candidate nodes through a geometric pre-screening mechanism. S3: Calculate the energy utility of candidate nodes based on their remaining energy; calculate the load utility of candidate nodes based on their queue occupancy; calculate the SINR utility of candidate nodes based on their SINR index. S4: The weight allocation mechanism based on the principle of maximizing attribute deviation calculates the weight of each utility of the candidate node, and the energy utility, load utility and SINR utility of the candidate node are fused according to the weight of each utility of the candidate node to obtain the reliability value of the candidate node. S5: Based on the reliability value of the candidate nodes, use the roulette wheel algorithm to select the next hop node from the candidate nodes until the next hop node is the destination node.

2. The multi-attribute-aware reliable routing method for complex lunar environments according to claim 1, characterized in that, The preset conditions include: the distance from the neighboring node to the destination node is less than the distance from the current node to the destination node.

3. The multi-attribute-aware reliable routing method for complex lunar environments according to claim 1, characterized in that, The energy utility of the calculated candidate nodes includes: in, Indicates candidate nodes Energy utility; Indicates candidate nodes The initial energy; Indicates candidate nodes The minimum remaining energy; Indicates candidate nodes The remaining energy; Indicates candidate nodes Mobile energy consumption; Indicates candidate nodes Communication energy consumption; Indicates candidate nodes The computational energy consumption; This represents the number of bits of data processed by candidate node i; Indicates candidate nodes Power consumed in processing a unit bit of data; Indicates data processing time; Indicates candidate nodes Power consumption per unit of data transmission; Indicates candidate nodes The power consumption of the basic circuit per unit time; k represents the candidate node. The number of bits of data sent and received; Candidate nodes The time required to send and receive k bits of data; Indicates candidate nodes The quality; Represents gravitational acceleration; and Indicates candidate nodes The altitude of the starting and ending points when performing a task; Indicates candidate nodes The energy consumption per unit friction; Indicates candidate nodes Movement speed; Indicates the slope angle corresponding to the starting point; Indicates the slope angle corresponding to the endpoint.

4. The multi-attribute-aware reliable routing method for complex lunar environments according to claim 1, characterized in that, The load utility of the computational candidate nodes includes: in, Indicates candidate nodes The load utility Indicates candidate nodes Data processing service rate; Indicates candidate nodes The shortest theoretical waiting time; This indicates that data has arrived at the candidate node. The rate; Indicates candidate nodes Service intensity; Indicates candidate nodes The maximum capacity of the buffer; This indicates that the service package is in the candidate node. The probability of being discarded due to timeout or buffer overflow; Indicates candidate nodes Size of received service packets; Indicates candidate nodes The number of queued business data packets; Indicates candidate nodes Business data packets.

5. A multi-attribute-aware reliable routing method for complex lunar environments according to claim 1, characterized in that, The SINR utility of the calculated candidate nodes includes: in, Indicates candidate nodes SINR utility; Indicates the current node in terms of power To candidate nodes SINR of the transmitted data packet; The reference SINR value required for the link quality to reach its optimal state; This represents the minimum SINR threshold required for effective communication via the link. Indicates noise power density; This represents the path loss during free space propagation. Indicates the reflection propagation path loss; This represents the path loss during diffraction propagation. Indicates the wavelength of a radio signal; Represents pi; Indicates the distance from the current node to the candidate node. The path distance; Indicates the reflection coefficient; This indicates the phase difference between the two paths; It is the specular reflection coefficient; Indicates the roughness of the mirror surface; The scattering factor of a mirror; This indicates the path length from the current node to the reflection point; Indicates the distance from the reflection point to the candidate node. Path length; This represents the diffraction angle from the current node to the highest point of the profile; the highest point of the profile refers to the contour line formed by connecting multiple obstacle vertices between the current node and the candidate node, discretizing the profile into s sampling points, and the highest point of the sampling points is the highest point of the profile. Indicates the height of the current node; Indicates candidate nodes Height; Indicates the initial slope of the profile; This represents the distance from the current node to the highest point of the profile. Indicates the effective lunar curvature; Indicates the first section on the cross-section The height of each sampling point; Indicates the distance from the current node to the first node on the profile. The distance between each sampling point; This represents the function that takes the maximum value.

6. The multi-attribute-aware reliable routing method for complex lunar environments according to claim 1, characterized in that, The weights for calculating the utility of each candidate node include: S41: Let the candidate node be... The weights of each utility attribute are And it needs to meet the following constraints: ； S42: Calculate the weighted deviation of candidate nodes in each utility attribute: in, Indicates candidate nodes In the Weighted deviation of each utility attribute; Indicates candidate nodes The Individual utility value; Indicates the candidate node number The weight of each utility attribute; Indicates candidate nodes The Individual utility value; S42: Calculate the weighted sum of deviations for all candidate nodes across all utility attributes: in, All candidate nodes are in the The weighted sum of deviations for each utility attribute; S43: Construct an optimization model by maximizing the weighted sum of deviations of all candidate nodes across all utility attributes: S43: To simplify the solution, we introduce the difference of squares instead of the difference of absolute values, and construct the Lagrangian function of the optimization model as follows: in, Represents the Lagrangian function of the optimization model; Represents the Lagrange multipliers; S44: Weights and Lagrange multipliers Taking the partial derivatives separately and setting them to zero, we obtain the analytical expression for the utility attribute weights: in, Indicates weight The partial derivative; Represents Lagrange multipliers The partial derivative; Indicates utility attribute The weight parsing expression; S45: Standardize the analytical expression for the utility attribute weights described above to obtain the weights of the utility attributes: in, Indicates utility attribute The weight.

7. A multi-attribute-aware reliable routing method for complex lunar environments according to claim 1, characterized in that, The reliability values ​​of the candidate nodes include: in, Indicates candidate nodes Reliability value; express of Power; Indicates candidate nodes Energy utility; express The weights; express of Power; Indicates candidate nodes The load utility; express The weights; express of Power; Indicates candidate nodes SINR utility; express The weight.

8. A multi-attribute-aware reliable routing method for complex lunar environments according to claim 1, characterized in that, The step of selecting the next-hop node from candidate nodes using the roulette wheel algorithm includes: calculating the probability of each candidate node being the next-hop node using the roulette wheel algorithm, and selecting the next-hop node from the candidate nodes based on the probability of each candidate node being the next-hop node, wherein the probability of each candidate node being the next-hop node includes: in, Indicates candidate nodes The probability of being the next hop node; Indicates candidate nodes Reliability value; Indicates candidate nodes Reliability value; This indicates the number of candidate nodes.

9. A multi-attribute-aware reliable routing system for complex lunar environments, characterized in that, The system includes a memory and a processor; the memory is used to store an application program; the processor is used to run the application program and execute a multi-attribute-aware reliable routing method for complex lunar environments as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that, The computer storage medium stores a remote monitoring program, which, when executed by the processor, implements a multi-attribute-aware reliable routing method for complex lunar environments as described in any one of claims 1 to 8.

Images