Low earth orbit satellite energy management and low energy dynamic routing method based on network calculus

Abstract

The application relates to the technical field of satellite communication, in particular to a low-orbit satellite energy management and low-energy-consumption dynamic routing method based on network calculus. The method comprises the following steps: determining an actual output voltage of a battery; determining a transmitting power according to the actual output voltage of the battery; determining a maximum channel capacity; determining a network calculus arrival envelope according to a maximum burst data volume and a data average arrival rate; colliding the network calculus arrival envelope with a dynamic service curve to obtain a maximum instantaneous queuing backlog; calculating a deterministic network calculus delay upper limit according to the maximum instantaneous queuing backlog and the maximum channel capacity; and determining a comprehensive routing weight value according to the network calculus delay upper limit and a preset basic anti-loop hop penalty. The application can make the path searching process naturally avoid low power and potential congestion links, and finally output an optimal transmission path with the minimum global cumulative delay cost.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, and in particular to a low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus. Background Technology

[0002] With the development of sixth-generation mobile communication networks, low-Earth orbit satellite networks have become an important component in building a global interconnected network due to their advantages of low latency, wide coverage, and high throughput. However, due to the limited computing power and battery energy of satellites, as well as the complexity of the space environment, the routing and scheduling of large-scale low-Earth orbit satellite networks face severe challenges of energy constraints and network congestion. Summary of the Invention

[0003] To address the aforementioned issues, embodiments of this application provide a low-Earth orbit satellite energy management and low-energy dynamic routing method based on network computation, which can solve at least one of the aforementioned technical problems.

[0004] According to one aspect of the embodiments of this application, a method for energy management and low-energy dynamic routing of low-Earth orbit satellites based on network calculus is proposed, the method comprising: Determine the DC internal resistance of the battery, the open-circuit voltage of the battery, and the discharge current of the communication payload for the low-orbit satellite with its current remaining power. The actual output voltage of the battery is determined based on the battery's DC internal resistance, the battery's open-circuit voltage, and the communication load's discharge current. The actual usable transmit power of the radio frequency front-end of the communication system of the low-orbit satellite is determined based on the actual output voltage of the battery. Determine the physical bandwidth and channel gain of the communication system; The maximum channel capacity that the inter-satellite link between the low-Earth orbit satellite and other low-Earth orbit satellites can provide is determined based on the transmission power, the physical bandwidth, and the channel gain. The maximum channel capacity varies with the current remaining battery power. Determine the maximum burst data volume and average data arrival rate of the inter-satellite link, and determine the network-calculated arrival envelope based on the maximum burst data volume and the average data arrival rate; A dynamic service curve is determined based on the maximum channel capacity, which varies with time and current remaining battery power. The maximum instantaneous queue backlog is obtained by colliding the network calculation arrival envelope with the dynamic service curve. The upper limit of deterministic network computation delay is calculated based on the maximum instantaneous queue backlog and the maximum channel capacity; Based on the network computation delay limit and the preset basic anti-loop hop count penalty, a comprehensive routing weight is determined. The comprehensive routing weight is then input into a preset shortest path planning algorithm to obtain the optimal transmission path with the minimum cumulative delay. Power consumption of the low-orbit satellites in the inter-satellite link is deducted based on the optimal transmission path.

[0005] In the above scheme, determining the DC internal resistance of the low-orbit satellite's battery with its current remaining power includes: Determine polynomial coefficients related to the underlying battery characteristics of the low-Earth orbit satellite's battery, the polynomial coefficients including a first coefficient, a second coefficient, a third coefficient, a fourth coefficient, and a fifth coefficient; The DC internal resistance of the battery is determined based on the first coefficient, the second coefficient, the third coefficient, the fourth coefficient, the fifth coefficient, and the current remaining power. The DC internal resistance of the battery is calculated using the following formula; ; In the formula, The DC internal resistance of the battery is... The coefficients of the polynomial are... For the first coefficient, The second coefficient, For the third coefficient, For the fourth coefficient, This refers to the fifth coefficient.

[0006] In the above scheme, determining the actual output voltage of the battery based on the battery's DC internal resistance, the battery's open-circuit voltage, and the communication load's discharge current includes: The first result is obtained by multiplying the battery's DC internal resistance by the communication load's discharge current. The battery open-circuit voltage is taken as the second result, and the difference between the second result and the first result is taken as the actual output voltage of the battery.

[0007] In the above scheme, determining the actual usable transmit power of the low-Earth orbit satellite's communication system radio frequency front-end based on the actual output voltage of the battery includes: Determine the power amplifier conversion efficiency and the antenna equivalent load impedance of the communication system. The transmit power is determined based on the actual output voltage of the battery, the conversion efficiency of the power amplifier, and the equivalent load impedance of the antenna.

[0008] In the above scheme, the maximum channel capacity is calculated using the following formula: ; In the formula, The maximum channel capacity, The physical bandwidth, The channel gain is mentioned. The transmission power, This represents noise power.

[0009] In the above scheme, the network arrival envelope is determined by the following formula: ; In the formula, The network is calculated to reach the envelope. t represents the average arrival rate of the data, b represents the maximum burst data volume, and t represents the current time.

[0010] In the above scheme, the maximum instantaneous queue backlog is determined by the following formula: ; In the formula, The maximum instantaneous queue backlog is, The network is calculated to reach the envelope. This refers to the dynamic service curve.

[0011] In the above scheme, the step of calculating the upper limit of deterministic network computation delay based on the maximum instantaneous queue backlog and the maximum channel capacity includes: Determine the total size of the data packets in the communication system, and determine the physical distance between the low-Earth orbit satellite and any other low-Earth orbit satellite; Divide the total size of the data packets by the maximum channel capacity to obtain the transmission delay used to characterize the data packet transmission. Dividing the maximum instantaneous queue backlog by the maximum channel capacity yields the queuing delay used to characterize the burst data volume. Divide the physical distance by the speed of light to obtain the propagation delay caused by the physical distance; The transmission delay, queuing delay, and propagation delay are superimposed to obtain the upper limit of the deterministic network computation delay.

[0012] The beneficial effects of this application are as follows: This application converts the current remaining power of the low-Earth orbit satellite at the transmitting end into the currently supported transmission power (the actual usable transmission power of the radio frequency front-end of the communication system), and thereby derives the maximum channel capacity (i.e., the maximum dynamic data service rate) that the inter-satellite link can provide at this moment. This cross-layer mapping mechanism makes the actual transmission capacity of the inter-satellite link objectively decrease in real time as the power of the low-Earth orbit satellite is consumed. Subsequently, traffic characteristic parameters such as the average data arrival rate and the maximum burst data volume are determined, and the maximum instantaneous queuing backlog caused by the collision between the network calculation arrival envelope and the dynamic service curve is accurately calculated using network calculus theory. Based on this maximum instantaneous queuing backlog, a deterministic network calculation delay upper limit is further derived. The system adds this deterministic delay upper limit to a preset basic anti-loop hop count penalty to generate the current comprehensive routing weight of the inter-satellite link, and performs topology routing iteration using a preset shortest path planning algorithm starting from the traffic source satellite. Since low power will cause a significant reduction in data service rate (maximum channel capacity) and induce nonlinear amplification of queuing delay, this pathfinding process naturally avoids low power and potentially congested links, and finally outputs an optimal transmission path with the minimum global cumulative delay cost. Attached Figure Description

[0013] Figure 1 A flowchart illustrating the low-Earth orbit satellite energy management and low-energy dynamic routing method based on network computation provided in this application embodiment; Figure 2 A specific implementation logic diagram of the low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus provided in the embodiments of this application; Figure 3 A diagram showing the relationship between SOC, battery DC internal resistance, and channel capacity provided in the embodiments of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] It should be noted that while some processes described in the specification, claims, and accompanying drawings include multiple steps appearing in a specific order, it should be clearly understood that these steps may not be performed in the order they appear herein, or may be performed in parallel. The step numbers are merely used to distinguish different steps and do not themselves represent any execution order. Furthermore, descriptions such as "first," "second," or "objective" in this document are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. "Multiple" in this document refers to at least two.

[0016] It is worth noting that in the specific embodiments of this application, data such as power consumption, internal resistance, voltage, and data transmission packets are involved. When the above embodiments of this application are applied to specific products or technologies, permission or consent from the target object is required, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards. For example, when an embodiment of this application needs to obtain data such as power consumption, internal resistance, voltage, and data transmission packets, separate permission or consent from the target object can be obtained through pop-up windows or redirection to a confirmation page. After obtaining the separate permission or consent from the target object, the relevant data such as power consumption, internal resistance, voltage, and data transmission packets used to enable the embodiment of this application to operate normally can then be obtained.

[0017] With the development of sixth-generation mobile communication networks, low-Earth orbit satellite networks have become an important component in building a global interconnected network due to their advantages of low latency, wide coverage, and high throughput. However, due to the limited computing power and battery energy of satellites, as well as the complexity of the space environment, the routing and scheduling of large-scale low-Earth orbit satellite networks face severe challenges of energy constraints and network congestion.

[0018] First, there are the issues of satellite energy constraints and lifespan degradation. A reliable satellite power system is crucial for the long-term stable operation of satellite optical networks. This system mainly consists of solar panels and onboard batteries. When the satellite is in sunlight, it relies on solar panels for power generation; however, when the satellite enters the Earth's shadow, it relies entirely on battery power. The satellite's power consumption is closely related to the traffic load it carries; the higher the traffic load, the greater the energy consumption. The satellite's operational lifespan largely depends on its battery life, which is primarily affected by the depth of discharge. Depth of discharge refers to the ratio of energy consumed to the total battery capacity. If a satellite in the shadow continuously handles high-load data forwarding tasks, it will cause the battery to over-cycle and exacerbate the depth of discharge, thus significantly shortening the satellite's overall operational lifespan and even leading to premature satellite failure and loss of inter-satellite communication capabilities.

[0019] Furthermore, large-scale satellite networks face challenges such as lagging state awareness and sudden traffic congestion. Large-scale low Earth orbit satellite networks are characterized by a massive number of nodes, a large spatial scale, and high dynamism, leading to severe link state awareness lag, and the awareness overhead increases significantly with the number of satellites. In addition, due to the geographical imbalance of terrestrial users, the traffic transmitted by satellite networks exhibits significant regional distribution differences. Under the impact of massive and sudden terrestrial service traffic, some inter-satellite links are prone to overload, resulting in a rapid increase in transmission latency and packet loss rate. Existing satellite network routing algorithms have the following technical shortcomings in addressing these challenges.

[0020] Defect 1: Traditional routing algorithms ignore the physical energy status of nodes, exacerbating network lifespan degradation. Existing traditional load balancing or shortest path algorithms primarily aim to reduce network congestion by offloading traffic from high-load satellites to other satellites. However, these algorithms do not adequately consider the battery status of other satellite nodes. Blindly offloading high-load traffic to satellites in shadow areas with low battery levels accelerates the lifespan depletion of these vulnerable satellites. While directly putting shadow area satellites into hibernation mode can extend lifespan, it increases operational overhead and fails to meet the typical performance requirements of optical satellite networks.

[0021] Defect 2: Lack of network congestion prediction, making it difficult to handle sudden traffic surges. Existing distributed routing algorithms mainly rely on frequent link-state interactions for route calculation, which not only suffers from perception lag but also merely passively avoids congestion, failing to fundamentally solve the problem of load imbalance. When dealing with large amounts of unpredictable terrestrial network traffic, such as variable bit rate (VBR) traffic with strong burst characteristics, these algorithms are prone to causing regional link congestion and routing instability. They cannot quantitatively assess instantaneous queue backlog depth, cannot provide deterministic latency upper limit guarantees, and are prone to buffer overflows under heavy load conditions. In summary, there is an urgent need for a satellite network routing method that can comprehensively consider physical layer battery status and network layer traffic characteristics, provide congestion prediction, and has low overhead characteristics.

[0022] The purpose of this application is to overcome the shortcomings of existing low Earth orbit satellite network routing technologies in two aspects: the disconnect between energy sensing and physical layer link capacity, and the lack of a forward-looking queuing backlog prediction mechanism in the face of sudden traffic. This application aims to establish a quantitative mapping relationship between the underlying physical remaining power, the dynamic transmission capacity of inter-satellite links, and the queuing delay of the upper-layer network, thereby achieving forward-looking prediction of link congestion status and energy consumption. This will ensure high-reliability transmission of sudden traffic while achieving dynamic load balancing across the entire network and significantly extending the service life of satellite network node equipment.

[0023] The following provides a detailed description of the specific implementation methods of the embodiments of this application: Please see Figure 1 , Figure 1 This is a flowchart illustrating the low-Earth orbit satellite energy management and low-energy dynamic routing method based on network computation provided in this application embodiment. Figure 1 The network-based algorithm-based energy management and low-energy dynamic routing method for low-Earth orbit satellites shown includes: Step 210: Determine the DC internal resistance of the battery, the open-circuit voltage of the battery, and the discharge current of the communication payload for the low-orbit satellite with its current remaining power. Step 220: Determine the actual output voltage of the battery based on the battery's DC internal resistance, the battery's open-circuit voltage, and the communication load's discharge current; Step 230: Determine the actual usable transmit power of the radio frequency front-end of the communication system of the low-orbit satellite based on the actual output voltage of the battery; Step 240: Determine the physical bandwidth and channel gain of the communication system; Step 250: Determine the maximum channel capacity that the inter-satellite link between the low-Earth orbit satellite and other low-Earth orbit satellites can provide based on the transmission power, the physical bandwidth, and the channel gain. The maximum channel capacity varies with the current remaining power. Step 260: Determine the maximum burst data volume and average data arrival rate of the inter-satellite link, and determine the network calculation arrival envelope based on the maximum burst data volume and the average data arrival rate; Step 270: Determine the dynamic service curve that varies with time and current remaining power based on the maximum channel capacity; Step 280: Collide the network calculation arrival envelope with the dynamic service curve to obtain the maximum instantaneous queue backlog. Step 290: Calculate the upper limit of deterministic network computation delay based on the maximum instantaneous queue backlog and the maximum channel capacity; Step 300: Based on the network calculation delay limit and the preset basic anti-loop hop count penalty, determine the comprehensive routing weight, input the comprehensive routing weight into the preset shortest path planning algorithm to obtain the optimal transmission path with the minimum cumulative delay, and deduct the power consumption of the low-orbit satellites of the inter-satellite link according to the optimal transmission path.

[0024] The following combination Figure 2 and Figure 3 The specific embodiments of this application are explained in detail below: first, Figure 2 This is a logic diagram illustrating the specific implementation of network-based energy management and low-energy dynamic routing for low-Earth orbit satellites in this application. Figure 3This is a graph showing the relationship between the SOC (State of Charge) of a low-Earth orbit satellite, the DC internal resistance of its battery, and the channel capacity.

[0025] 1. Cross-layer quantization mapping of physical layer to link transmission capability based on polynomial fitting The core innovation of this application lies in establishing a rigorous transmission mechanism from the physical battery internal resistance to the upper-layer network bandwidth. The system first calculates the battery's DC internal resistance at the current remaining charge (SOC) based on a dynamic internal resistance model derived from high-frequency pulse power characteristic experiments. The relationship between internal resistance and SOC conforms to polynomial fitting characteristics: ; In the formula, The coefficients are polynomials determined by the underlying battery characteristics. Referring to existing authoritative theories on dynamic modeling of DC internal resistance during lithium battery discharge, the nonlinear variation of internal resistance with SOC can typically keep the root mean square error within 1 mΩ when fitted using a fourth-order polynomial, resulting in high estimation accuracy. Therefore, this application preferably uses a fourth-order polynomial, which ensures accurate characterization of the battery's nonlinear degradation features while meeting the low computational complexity requirement of low-Earth orbit satellites for routing algorithms.

[0026] Subsequently, according to the battery equivalent circuit model, the nonlinear increase in internal resistance will lead to a decrease in the actual output voltage of the battery. A fall occurred: ; In the formula, Battery open circuit voltage, This is the discharge current for the communication load.

[0027] After obtaining the actual output voltage, the system further derives the actual usable transmit power of the communication system's radio frequency front-end. Based on the electric power formula and power amplifier efficiency, the derivation of the transmission power is as follows: ; In the formula, For power amplifier conversion efficiency, Let be the equivalent load impedance of the antenna. This formula clearly states that the transmit power is strictly limited by the output voltage drop due to the surge in internal resistance. .

[0028] Ultimately, the system will be subject to physically constrained transmission power. Substituting Shannon's theorem, we can derive the maximum achievable rate (maximum channel capacity) that this inter-satellite link can actually provide at this moment. : ; In the formula, For physical bandwidth, The channel gain is used to comprehensively characterize the free space path loss and multipath fading characteristics of electromagnetic signals during spatial transmission. This is the noise power. Through the aforementioned rigorous mathematical conduction chain (SOC decrease) Dynamic internal resistance surge Output voltage fall Transmit power attenuation Service speed (Reduction), this application objectively quantifies the physical weakening effect of satellite low battery status on the actual data transmission capability of the network.

[0029] 2. Calculation of instantaneous queue backlog under sudden flow injection To obtain the aforementioned dynamic service rate That is, the maximum channel capacity Subsequently, network computation theory is used to model the backlog behavior of traffic with bursty characteristics at satellite nodes. Let the network computation arrival envelope of the currently arriving service request be... (in For the largest burst of data, (This refers to the average data arrival rate), while the dynamic service curve that a node can provide under the current power level is... Based on the vertical deviation theorem in network computation, the system calculates the maximum instantaneous queue backlog caused by the collision between the two entities. The maximum supremacy of the arrival curve and the service curve: ; When the system is in a steady state (i.e., the maximum channel capacity is greater than the average data arrival rate, and...) When a burst of input is injected, the maximum backlog is limited by the burst boundary of the input.

[0030] 3. Derivation of the upper limit of delay and comprehensive routing cost in deterministic network calculus Based on the instantaneous accumulation amount obtained above With limited service rate The system further derives the upper limit of deterministic network computation delay for this inter-satellite link. This upper limit of latency is determined by the transmission latency of the data packet itself (…). ), queueing delays caused by sudden events ( ) and the propagation delay caused by physical distance ( Together they are strictly accumulated and constituted: ; In the formula, Total data packet size The physical distance between the two satellites. The speed of light. This set of formulas clearly demonstrates the core weight generation logic of this invention for avoiding congestion: when When the satellite's battery power is reduced due to insufficient power, the denominator in the formula will result in a queuing delay component. This is significantly amplified, thus creating a penalty for low-power nodes in the routing cost.

[0031] Finally, the system applies this deterministic delay limit to the preset base anti-loop hop count penalty ( The data is then merged to generate the final integrated routing weights. ; The system will use this comprehensive weight as a benchmark to drive the shortest path planning algorithm to select the globally optimal transmission path.

[0032] 4. Shortest path planning oriented towards comprehensive routing costs and security constraints After obtaining the combined routing weights of the aforementioned inter-satellite links, the system uses these weights as link costs and initiates a preset shortest path planning algorithm (i.e., ...) within the secure topology. Figure 2 The Dijkstra algorithm shown solves for the path with the minimum cumulative delay from the source satellite node to the destination satellite node. Specifically, let the secure topology of the satellite network (the communication system described in this application) at time t be a directed graph. .in, The set of available satellite nodes after filtering out nodes that have run out of power (low-Earth orbit satellites of the inter-satellite link described in this application); This represents the set of inter-satellite links (including multiple inter-satellite links, with one inter-satellite link between every two adjacent low-Earth orbit satellites). For any inter-satellite link in the diagram... Its link weight is the comprehensive routing weight calculated in the preceding steps. For a given source satellite node and target satellite node System definition For the source node To the node The current known minimum cumulative path cost. The iterative solution for the shortest path uses edge relaxation operations based on the following formula: ; In the formula, The node that has been visited and has the lowest cost. For nodes The system iterates through and updates all relevant nodes, then outputs the optimal transmission path with the minimum cumulative delay cost. This path effectively guarantees end-to-end transmission latency while ensuring physical power safety.

[0033] 5. Node dynamic energy consumption quantification and cross-layer state adaptive feedback update In the optimal transmission path After allocation, the system performs quantitative calculations of physical power consumption for each traffic request and synchronously updates the state matrix in the global topology graph to drive the routing planning for the next concurrent request, forming an adaptive feedback loop.

[0034] When a business traffic request arrives, the system parses the data stream characteristics of the request to determine the effective data transfer rate. Specifically, if the request is a constant bit rate steady-state traffic, then the preset target transmission rate will be directly used as the target rate. If the request is a burst of traffic with a variable bit rate, then the mathematical expectation of the base guaranteed rate and the burst peak rate of that traffic is used as... This reasonably reflects the overall average transmission pressure under real network fluctuations. In determining the optimal path... Subsequently, the system adopts a unified single-hop forwarding nonlinear power consumption model. For the path... Each satellite node that performs data transmission and forwarding tasks The power consumption of a single communication task This includes both fixed base sustaining power consumption and RF transmission power consumption that dynamically varies with the data rate, specifically calculated using the following formula: ; In the formula, Energy consumption for single-hop basic maintenance and baseband processing For dynamic power consumption coefficient, To characterize the power consumption index that represents the nonlinear stress exerted on the RF front end by burst traffic, typically Subsequently, the system processed the path. Each relay satellite Remaining battery power Deduct in real time: ; In the formula, This is the remaining power after deduction. This represents the maximum physical capacity of the satellite's battery. The function ensures that the charge level does not fall below the minimum threshold during the charging process.

[0035] After completing the above physical power deduction, the system drives a closed-loop update of the entire network topology weights: on the one hand, due to the remaining power of nodes on the path... The decrease in [something] causes the system to reapply the Shannon formula, resulting in a decrease in the maximum channel capacity of the relevant inter-satellite links. Real-time scaling; on the other hand, the system will reduce the effective data transfer rate of this request. Traffic characteristics are dynamically accumulated into the historical congestion packets of relevant links, resulting in the aforementioned maximum instantaneous queuing backlog. Increases in real time. According to the aforementioned formula... The reduction in channel capacity and the increase in queuing backlog will directly lead to a nonlinear amplification of queuing delay. Therefore, when the next concurrent request arrives and the topology is recalculated, the overall routing weight of the previously selected link will be affected. This will automatically increase significantly, thus prompting the shortest path planning algorithm to naturally avoid these links that have just consumed power and accumulated cache, achieving dynamic adaptive load balancing of power consumption and traffic across the entire network.

[0036] In summary, for each incoming network traffic request, the system completes globally optimal path scheduling through an endogenously driven dynamic closed-loop mechanism. For available inter-satellite links retained in the topology graph, the system converts the real-time remaining power of the transmitting satellite into the currently supported transmission power based on the battery discharge model, and derives the actual dynamic data service rate that the link can provide at this moment based on Shannon's theorem. This cross-layer mapping mechanism ensures that the actual transmission capacity of the link objectively decreases in real time as the satellite's power is consumed. Subsequently, the system extracts traffic characteristic parameters such as the total data size, average arrival rate, and maximum burst data volume of the current service request, and uses network calculus theory to inject the burst data volume into the aforementioned limited dynamic data service rate, accurately calculating the maximum instantaneous queuing backlog caused by the collision between the two. Based on this backlog, the system further derives a deterministic network calculus delay upper limit composed of transmission delay, queuing delay, and propagation delay. The system adds this deterministic delay upper limit to the basic anti-loop hop count penalty to generate the current comprehensive routing weight of the inter-satellite link, and uses the shortest path algorithm to perform topology routing iteration starting from the traffic source satellite. Because low power levels significantly reduce data service rates and cause nonlinear amplification of queuing delays, this routing process naturally avoids low power levels and potentially congested links, ultimately outputting an optimal transmission path with the lowest global cumulative delay cost. After determining the optimal path and performing route allocation, the system immediately triggers a joint write-back mechanism of the underlying physical state and the upper-layer network state. The system dynamically accumulates the characteristic parameters of this traffic into the arrival envelope of the network calculation history of each link along the optimal path to accurately update the real-time queuing backlog depth of each link; at the same time, the system deducts the remaining physical power of each relay satellite along the route in real time and accurately based on the communication power cost consumed in transmitting this burst of traffic. The node survival status and link congestion environment of the entire network are thus continuously and dynamically evolved, and serve as the latest and most accurate global environmental benchmark, seamlessly participating in the real-time routing weight generation and routing calculation of the next traffic request in a massive high-concurrency sequence, thus forming an endogenously driven, adaptive feedback, and rigorous cross-layer scheduling closed loop.

[0037] The network-based energy management and low-energy dynamic routing for low-Earth orbit satellites proposed in this application can directly bring the following significant and positive technical effects compared to existing technologies: First, this application achieves endogenous adaptive satellite energy protection, which significantly extends the overall lifespan of the satellite network.

[0038] Secondly, this application creatively establishes a quantitative mapping relationship from the remaining power of satellite nodes and the dynamic transmission capacity of inter-satellite links to the queuing delay of the upper-layer network. This cross-layer mechanism objectively reflects the physical reduction in actual data service rate caused by the decline in satellite power, enabling the routing algorithm to naturally divert traffic from nodes with low power and low transmission capacity when solving for the path with the lowest network computation delay.

[0039] Third, under heavy load conditions with continuous high load requests, this application can stably reduce the total lifespan loss of all satellites in the network by about 10% compared to traditional shortest path algorithms that are not energy-aware. This mechanism effectively achieves dynamic balance of energy consumption across the entire network and completely avoids premature physical failure caused by excessive discharge of local core nodes.

[0040] The above is a detailed description of the embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for energy management and low-energy dynamic routing of low-Earth orbit satellites based on network calculus, characterized in that, The method includes: Determine the DC internal resistance of the battery, the open-circuit voltage of the battery, and the discharge current of the communication payload for the low-orbit satellite with its current remaining power. The actual output voltage of the battery is determined based on the battery's DC internal resistance, the battery's open-circuit voltage, and the communication load's discharge current. The actual usable transmit power of the radio frequency front-end of the communication system of the low-orbit satellite is determined based on the actual output voltage of the battery. Determine the physical bandwidth and channel gain of the communication system; The maximum channel capacity that the inter-satellite link between the low-Earth orbit satellite and other low-Earth orbit satellites can provide is determined based on the transmission power, the physical bandwidth, and the channel gain. The maximum channel capacity varies with the current remaining battery power. Determine the maximum burst data volume and average data arrival rate of the inter-satellite link, and determine the network-calculated arrival envelope based on the maximum burst data volume and the average data arrival rate; A dynamic service curve is determined based on the maximum channel capacity, which varies with time and current remaining battery power. The maximum instantaneous queue backlog is obtained by colliding the network calculation arrival envelope with the dynamic service curve. The upper limit of deterministic network computation delay is calculated based on the maximum instantaneous queue backlog and the maximum channel capacity; Based on the network computation delay limit and the preset basic anti-loop hop count penalty, a comprehensive routing weight is determined. The comprehensive routing weight is then input into a preset shortest path planning algorithm to obtain the optimal transmission path with the minimum cumulative delay. Power consumption of the low-orbit satellites in the inter-satellite link is deducted based on the optimal transmission path.

2. The low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus as described in claim 1, characterized in that, Determining the DC internal resistance of the battery of a low-orbit satellite with its current remaining power includes: Determine polynomial coefficients related to the underlying battery characteristics of the low-Earth orbit satellite's battery, the polynomial coefficients including a first coefficient, a second coefficient, a third coefficient, a fourth coefficient, and a fifth coefficient; The DC internal resistance of the battery is determined based on the first coefficient, the second coefficient, the third coefficient, the fourth coefficient, the fifth coefficient, and the current remaining power. The DC internal resistance of the battery is calculated using the following formula; ； In the formula, The DC internal resistance of the battery is... The coefficients of the polynomial are... For the first coefficient, The second coefficient, For the third coefficient, For the fourth coefficient, This refers to the fifth coefficient.

3. The low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus as described in claim 1, characterized in that, The step of determining the actual output voltage of the battery based on the battery's DC internal resistance, the battery's open-circuit voltage, and the communication load's discharge current includes: The first result is obtained by multiplying the battery's DC internal resistance by the communication load's discharge current. The battery open-circuit voltage is taken as the second result, and the difference between the second result and the first result is taken as the actual output voltage of the battery.

4. The low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus as described in claim 1, characterized in that, The step of determining the actual usable transmit power of the radio frequency front-end of the communication system of the low-Earth orbit satellite based on the actual output voltage of the battery includes: Determine the power amplifier conversion efficiency and the antenna equivalent load impedance of the communication system. The transmit power is determined based on the actual output voltage of the battery, the conversion efficiency of the power amplifier, and the equivalent load impedance of the antenna.

5. The low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus as described in claim 1, characterized in that, The maximum channel capacity is calculated using the following formula: ； In the formula, The maximum channel capacity, The physical bandwidth, The channel gain is mentioned. The transmission power, This represents noise power.

6. The low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus according to claim 1, characterized in that, The arrival envelope of the network calculus is determined by the following formula: ； In the formula, The network is calculated to reach the envelope. t represents the average arrival rate of the data, b represents the maximum burst data volume, and t represents the current time.

7. The low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus according to claim 1, characterized in that, The maximum instantaneous queue backlog is determined by the following formula: ； In the formula, The maximum instantaneous queue backlog is, The network is calculated to reach the envelope. This refers to the dynamic service curve.

8. The low-Earth orbit satellite energy management and low-energy dynamic routing method based on network calculus according to claim 1, characterized in that, The step of calculating the upper limit of deterministic network computation delay based on the maximum instantaneous queue backlog and the maximum channel capacity includes: Determine the total size of the data packets in the communication system, and determine the physical distance between the low-Earth orbit satellite and any other low-Earth orbit satellite; Divide the total size of the data packets by the maximum channel capacity to obtain the transmission delay used to characterize the data packet transmission. Dividing the maximum instantaneous queue backlog by the maximum channel capacity yields the queuing delay used to characterize the burst data volume. Divide the physical distance by the speed of light to obtain the propagation delay caused by the physical distance; The transmission delay, queuing delay, and propagation delay are superimposed to obtain the upper limit of the deterministic network computation delay.

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