Multi-mode satellite terminal power supply adaptive management method

Abstract

The invention provides a multi-mode satellite terminal power supply adaptive management method. The method comprises the following steps: S1, acquiring an operation state and resource sensing data of a satellite terminal power supply; obtaining business demand data; s2, determining a plurality of working modes corresponding to the satellite terminal power supply based on the operation state and the resource sensing data; s3, based on the plurality of working modes, determining total power consumption corresponding to the plurality of working modes; s4, determining a satellite terminal power management optimization strategy based on the total power consumption, the operation state, the resource sensing data and the service demand data; s5, adjusting a satellite terminal power supply control instruction in a corresponding working mode by adopting a satellite terminal power supply management optimization strategy; judging whether the system operation parameters of the satellite terminal power supply meet preset change conditions or not; and if yes, repeating the steps S1-S5. Signal abrupt change is responded in real time through a closed-loop feedback mechanism, the sensing and decision-making process is cyclically updated, and self-adaptive balance of energy efficiency, communication quality and battery life in a complex scene is achieved.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a method for adaptively managing power supply of a multi-mode satellite terminal. Background Art

[0002] The current security protection system has structural weaknesses in ultra-long-distance coverage, heterogeneous network integration, and emergency communication support. Especially in special terrain areas such as high-altitude mountainous areas and desert Gobi, it is urgent to build a global security communication platform with all-weather multi-dimensional perception, intelligent collaborative disposal, and elastic networking capabilities. This requires the integration of advanced technical means such as space-based monitoring, drone patrols, and intelligent sensor arrays to form a three-dimensional, modular, and scalable security communication solution to respond to non-traditional security threats and emergency response needs.

[0003] Low-orbit satellite Internet is an effective means to solve the communication guarantee problem of control in the above-mentioned complex geographical environment. Compared with high-orbit satellites, it has the advantages of wide coverage, strong guarantee capability, small transmission delay, multiple business types, and high security. However, there are certain defects in relying solely on satellite Internet communications, such as high cost and poor communication signals in obstructed areas. With the development of satellite communication technology, multi-mode satellite terminals are increasingly used in various scenarios. Dual-mode communication terminals that integrate low-orbit satellite Internet and ground self-organizing networks can meet the needs of more scenarios such as border control. They have global coverage and high reliability, and can provide a more powerful and reliable communication network.

[0004] Such terminals need to support both satellite network and ground ad hoc network communications and implement dual-channel dynamic switching in complex environments to meet the continuity requirements of diversified services. However, existing technologies still have significant deficiencies in the power management of multi-mode terminals: (1) Energy efficiency contradictions in multi-mode network switching. The power management of traditional satellite terminals is mostly based on fixed rules, which is difficult to adapt to dynamic scenarios where satellite and ground networks frequently switch. For example, in areas with dense ground base stations, the terminal may still keep the satellite module running at full power, resulting in unnecessary energy consumption; when the network signal fluctuates, the static power strategy is prone to cause communication interruption or response delay, making it difficult to balance energy efficiency and performance; (2) Separate management of battery status and network status. Existing solutions usually treat the battery state of charge (SOC) and network status as independent variables and lack a collaborative optimization mechanism. For example, when the battery power is low, the terminal may still force the maintenance of a high-power satellite link, accelerating the depletion of power; or when the ground network is available, the power supply priority is not dynamically allocated in combination with the battery health state (SOH), resulting in low power efficiency; (3) The ambiguity of signal strength judgment. Existing technologies mostly use qualitative descriptions such as "available" or "interrupted" to describe satellite signal quality, lacking quantitative indicators, resulting in insufficient accuracy of power switching strategies. For example, in the critical signal strength area, the terminal may frequently trigger mode switching by mistake, causing power supply fluctuations and reduced communication quality; (4) Algorithm limitations and static strategies. Traditional power management algorithms (such as threshold control and fixed priority scheduling) rely on empirical parameters and cannot adapt to dynamic environments. Especially in the scenario of intermittent parallel operation of satellite-ground networks, existing methods find it difficult to predict changes in network status and adjust power supply strategies in advance, resulting in difficulty in achieving both energy efficiency and real-time performance. Summary of the invention

[0005] In order to overcome the above technical defects, the present application provides a multi-mode satellite terminal power adaptive management method. To achieve the above purpose, the present application is implemented according to the following technical solutions: The present application provides a multi-mode satellite terminal power adaptive management method, including: Step S1: Collecting the operating status and resource perception data of the satellite terminal power supply; Obtain business demand data; Step S2: determining a plurality of operating modes corresponding to the satellite terminal power supply based on the operating status and resource sensing data; Step S3: Based on the multiple working modes, determining the total power consumption corresponding to the multiple working modes; Step S4: determining a satellite terminal power management optimization strategy based on the total power consumption, the operating status and resource perception data, and the service demand data; Step S5: using the satellite terminal power management optimization strategy to adjust the satellite terminal power control instruction in the corresponding working mode; Determining whether the system operating parameters of the satellite terminal power supply meet the preset change conditions; If satisfied, repeat steps S1-S5; Wherein, determining the satellite terminal power management optimization strategy based on the total power consumption, the operating status and resource perception data and the business demand data comprises: Determining a current operating state vector of the satellite based on the operating state and resource data and the service demand data; Based on the total power consumption and the business demand data, a multi-objective optimization function for the current operating system performance of the satellite is established; Based on the multi-objective optimization function and the current operating state vector of the satellite, a satellite terminal power management strategy is determined.

[0006] Optionally, the operating status and resource perception data of the satellite terminal power supply include: satellite signal carrier-to-noise ratio, ground network received signal strength, battery charge state, health status and environmental parameters; the business requirements include voice priority or high-throughput data transmission.

[0007] Optionally, the multiple working modes include: power off mode, standby mode, wake-up mode, and working mode.

[0008] Optionally, determining a satellite terminal power management strategy based on the multi-objective optimization function and the current operating state vector of the satellite comprises: Based on the multi-objective optimization function, generating a set of candidate actions for future operation of the satellite; Determining a future operating state vector of the satellite based on the current operating state vector of the satellite; Based on the satellite future operation state vector and the satellite future operation candidate action set, a satellite terminal power management strategy is determined.

[0009] Optionally, generating a set of candidate actions for future operation of the satellite based on the multi-objective optimization function comprises: The particle swarm algorithm is used to solve the multi-objective optimization function and generate a set of candidate actions for the future operation of the satellite.

[0010] Optionally, the determining the future operation state vector of the satellite based on the current operation state vector of the satellite comprises: The current operating state vector of the satellite is input into the LSTM prediction model, and the future operating state vector of the satellite is output.

[0011] Optionally, the preset change condition includes one of the following: signal mutation: ΔC / N 0>5dB-Hz or ΔRSSI>1dBm; Battery status sudden change: ΔSOC>10% or Tbattery >45℃; Time condition: the time difference from the last power control instruction update meets the first preset time period.

[0012] This application has the following beneficial effects: This application builds a multi-dimensional state perception system by collecting the operating status and resource perception data of satellite terminals in real time; dynamically divides the terminal into four modes: shutdown, standby, wake-up, and working based on quantitative data, and defines the precise control strategy of the power circuit in each mode (such as cutting off the RF module when the satellite signal is weak); formulates differentiated power management strategies for different modes (such as dynamically adjusting the transmission power ratio and supply voltage), combines LSTM to predict future network status and battery attenuation trends, and uses a multi-objective reinforcement learning algorithm to dynamically optimize strategy weights; responds to signal mutations or sharp drops in battery status in real time through a closed-loop feedback mechanism, and cyclically updates the perception and decision-making process to achieve an adaptive balance between energy efficiency, communication quality, and battery life in complex scenarios.

[0013] In addition to the above-described purposes, features and advantages, the present application has other purposes, features and advantages. The present application will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 It is a flow chart of a multi-mode satellite terminal power adaptive management method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The embodiments of the present application are described in detail below with reference to the accompanying drawings; however, the present application can be implemented in many different ways as defined and covered by the claims.

[0016] In order to solve the problems raised by the background technology, such as Figure 1 As shown, the present application provides a multi-mode satellite terminal power adaptive management method, including: Step S1: Collecting the operating status and resource perception data of the satellite terminal power supply; Obtain business demand data; Satellite terminal power supply is a key component for providing energy to satellite terminals. It includes chemical batteries and solar cells. With its high reliability and radiation resistance, it ensures the operation of satellite equipment and is widely used in satellite missions such as communications and remote sensing.

[0017] The operating status and resource perception data of the satellite terminal power supply, including the satellite signal carrier-to-noise ratio (C / N0), ground network received signal strength (RSSI), battery state of charge (SOC), health state (SOH) and environmental parameters (such as geographic location, temperature), construct a multi-dimensional state vector. In this application, multi-source sensors are used to collect terminal operating status and resource perception data in real time.

[0018] Business demand data includes voice priority or high-throughput data transmission. Comprehensive analysis of network availability, remaining battery life, and environmental impact on communication links provides quantitative input for subsequent mode division.

[0019] Step S2: determining a plurality of operating modes corresponding to the satellite terminal power supply based on the operating status and resource sensing data; After obtaining the above operating status and resource perception data, the satellite terminal power supply is divided into four working modes as shown in Table 1 according to the operating status and resource perception data: shutdown mode (cut off the power supply of the satellite module when the ground network is stable to minimize power consumption), standby mode (low-power monitoring signal and fast response switching in the satellite-ground network fusion scenario), wake-up mode (activate the ground network module when the satellite signal is interrupted to balance performance and energy consumption), working mode (full power operation to ensure high-quality communication when the dual networks are continuously parallel), and scene-driven power circuit dynamic control is realized through precise quantitative thresholds and adaptive strategies. Among them, C / N 0 is the satellite carrier-to-noise ratio (dB-Hz), RSSI ground is the ground signal strength (dBm), and SOC is the battery state of charge (%).

[0020] Table 1 Four working modes , Step S3: Based on the multiple working modes, determining the total power consumption corresponding to the multiple working modes; According to the implementation steps of terminal power consumption estimation under different working modes, calculate the total power consumption Ptotal (mW) under different power supply modes and power adjustment parameters in the current working mode.

[0021] (1) In shutdown mode, confirm that the power supply circuit of satellite communication related functional modules has been cut off, and measure the current required to maintain the basic circuit state at this time. Assume that , the supply voltage is known to be , the power consumption in shutdown mode is: (1) However, the power consumption in shutdown mode is extremely small and can be ignored in actual calculations.

[0022] (2) In standby mode, analyze the power consumption of each relevant module in the power supply circuit triggered by the dynamic switching event of satellite and ground communications. For example, the power consumption of the baseband processor By measuring its working current and operating voltage Get, that is: (2) The power consumption of the signal monitoring circuit can be calculated by a similar method ,Right now: (3) The total power consumption in standby mode is calculated as follows: (4) (3) In wake-up mode, according to the characteristics of the intermittent parallel communication scenario between the satellite and ground networks, the power consumption of the satellite communication module and the ground communication module are calculated separately each time the communication is woken up. Power consumption of satellite communication module Including the power consumption of the RF transmission circuit , power consumption of signal processing circuit Etc. Assume that the transmission power of the RF transmission circuit is , working hours are , the power consumption of the signal processing circuit is , then the power consumption of the satellite communication module is: (5) Power consumption of ground communication module The calculation method is similar. At the same time, the power consumption during the communication gap is considered , the total power consumption in wake-up mode is calculated as follows: (6) in, is the number of satellite communications, is the number of communication gaps.

[0023] (4) In working mode, based on the characteristics of the continuous parallel communication scenario between the satellite and ground networks, the power consumption of the satellite communication module and the ground communication module is continuously calculated. Power consumption of satellite communication module , assuming that its RF transmission power is , continuous working time is , the power consumption of the signal processing circuit is , then the power consumption of the satellite communication module is calculated as follows: (7) Power consumption of ground communication module The calculation method is similar. The total power consumption in working mode is calculated as follows: (8) Step S4: determining a satellite terminal power management optimization strategy based on the total power consumption, the operating status and resource perception data, and the service demand data; Differentiated control strategies are designed for each mode, including power circuit start and close rules (turning off non-essential RF modules), dynamic adjustment of supply voltage (DVFS technology), transmission power and bandwidth allocation (scaling according to business demand), to ensure stable and efficient energy supply when switching modes. As shown in Table 2, the corresponding power control instructions in different working modes are explained.

[0024] Table 2 Power control instructions in four working modes , in, It is the nominal value of the hardware and is determined by the terminal model. is the adjustment coefficient obtained by power management optimization, and BER is the current bit error rate.

[0025] Here, the above adjustment coefficient needs to be calculated, that is, the adjustment coefficient involved above needs to be determined through the power management optimization strategy. The specific process is as follows: Firstly, the current operation state vector of the satellite is determined according to the operation state, resource data and business demand data; When obtaining operating status and resource data, that is, battery temperature , current bit error rate BER, satellite carrier-to-noise ratio C / N 0 (dB-Hz), ground signal strength , battery state of charge SOC (%), and then obtain the business demand throughput , and then according to the above data, the current operating state vector of the satellite can be determined ,Right now: (9) After determining the current state vector of the satellite, , determine the satellite's future state vector , that is, the current operating state vector of the satellite , input into the LSTM prediction model, and then output the satellite's future operating state vector The general working process of the LSTM prediction model is as follows: Input: Historical state sequence (5 minute window).

[0026] Output: Future state sequence prediction (1 minute in the future).

[0027] (10) in, is the hidden layer weight, is the input layer weight, is the bias term, is a hidden state.

[0028] Then according to the total power consumption , and business demand data, as well as the current bit error rate, to establish the multi-objective optimization function of the satellite's current operating system performance as follows: (11) Where BER is the bit error rate, is the actual throughput (Mbps), is the business demand (Mbps), , , are the weights of the three optimization objectives respectively. To prevent division by zero, the weight changes adaptively with SOC, temperature, and business needs.

[0029] The constraints of the multi-objective optimization function of the current operating system performance of the above satellite are the comprehensive constraints that meet the power supply operating voltage, continuous working time and communication requirements, as follows: (12) in, Indicates the working voltage of the satellite communication module. and are its lower and upper limits respectively; Indicates the working voltage of the ground communication module. and are its lower and upper limits respectively; Indicates the continuous working time of the satellite communication module. and are its lower and upper limits respectively; Indicates the continuous working time of the ground communication module. and are its lower and upper limits respectively; Indicates the data transmission rate, and are its lower and upper limits respectively; represents the bit error rate, as its upper limit.

[0030] When solving the multi-objective optimization function of the current operating system performance of the above-mentioned satellite, considering the reasonable use of the computing power of the portable terminal, an intelligent optimization algorithm that is as simple and efficient as possible should be used. The particle swarm optimization algorithm is simple and easy to implement, has fast solution and strong global search capability, and in order to avoid falling into the local optimal problem, this embodiment uses the particle swarm optimization algorithm (PSO) to solve the model and generate a set of candidate actions for the future operation of the satellite. .

[0031] Output actions in the candidate action set for future operation of the above satellite Contains two types of instructions: (13) Among them, mode selection∈[off, standby, wake-up, working], adjustment coefficient .

[0032] In simple terms, in the working mode corresponding to the output, the corresponding power supply working adjustment coefficient in the corresponding working mode can be determined.

[0033] After obtaining the candidate action set and the future operating state vector of the satellite, the satellite terminal power management strategy can be determined, that is, the Markov chain is used to calculate the long-term reward of each action. The specific process is as follows: State transition probability: (14) in, is Gaussian noise.

[0034] Instant Rewards: (15) Long-term rewards: (16) in, is the discount factor, To predict the best action.

[0035] Choose the one with the greatest long-term reward , and execute the power control instructions in the corresponding working mode.

[0036] Step S5: using the satellite terminal power management optimization strategy to adjust the satellite terminal power control instruction in the corresponding working mode; Determining whether the system operating parameters of the satellite terminal power supply meet the preset change conditions; If satisfied, repeat steps S1-S5.

[0037] That is, to maximize long-term rewards After that, the corresponding power supply working adjustment coefficient can be determined, and the corresponding power supply control strategy can be adjusted, that is, the satellite terminal power supply control instruction under the corresponding working mode can be adjusted.

[0038] Then, during the operation of the satellite, the satellite may need to adjust the power control instructions. At this time, the power control instructions are determined to be adjusted, that is, a closed-loop feedback mechanism is established: whether the system operating parameters of the satellite terminal power supply meet the preset change conditions. If so, it means that the power control instructions need to be adjusted.

[0039] The preset conditions in this application include one of the following, namely: signal mutation: ΔC / N 0>5dB-Hz or ΔRSSI>1dBm; Battery status sudden change: ΔSOC>10% or T battery >45℃; Time condition: The time difference between the distance and the last power control instruction update time meets the first preset time period. The first preset time period can be specifically set according to actual needs and is not specifically limited here.

[0040] It can be understood that when any of the above conditions is met, the process returns to steps S1-S5 to calculate and update the next round of power control strategy, thereby adjusting the corresponding power control instruction.

[0041] In summary, the present application constructs a multi-dimensional state perception system by real-time collection of the operating status and resource perception data of the satellite terminal; dynamically divides the terminal into four modes: shutdown, standby, wake-up, and working based on quantitative data, and defines the precise control strategy of the power circuit in each mode (such as cutting off the RF module when the satellite signal is weak); formulates differentiated power management strategies for different modes (such as dynamically adjusting the transmission power ratio and the supply voltage), combines LSTM to predict future network status and battery attenuation trends, and uses a multi-objective reinforcement learning algorithm to dynamically optimize the strategy weights; responds to signal mutations or sharp drops in battery status in real time through a closed-loop feedback mechanism, and cyclically updates the perception and decision-making process to achieve an adaptive balance between energy efficiency, communication quality, and battery life in complex scenarios.

[0042] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for adaptive power management of a multi-mode satellite terminal, characterized in that: include: Step S1: Collecting the operating status and resource perception data of the satellite terminal power supply; Obtain business demand data; Step S2: determining a plurality of operating modes corresponding to the satellite terminal power supply based on the operating status and resource sensing data; Step S3: Based on the multiple working modes, determining the total power consumption corresponding to the multiple working modes; Step S4: determining a satellite terminal power management optimization strategy based on the total power consumption, the operating status and resource perception data, and the service demand data; Step S5: using the satellite terminal power management optimization strategy to adjust the satellite terminal power control instruction in the corresponding working mode; Determining whether the system operating parameters of the satellite terminal power supply meet the preset change conditions; If satisfied, repeat steps S1-S5; Wherein, determining the satellite terminal power management optimization strategy based on the total power consumption, the operating status and resource perception data and the business demand data comprises: Determining a current operating state vector of the satellite based on the operating state and resource data and the service demand data; Based on the total power consumption and the service demand data, a multi-objective optimization function for the current operating system performance of the satellite is established; Based on the multi-objective optimization function and the current operating state vector of the satellite, a satellite terminal power management strategy is determined.

2. The method according to claim 1, characterized in that The operating status and resource perception data of the satellite terminal power supply include: satellite signal carrier-to-noise ratio, ground network reception signal strength, battery charge status, health status and environmental parameters; The service requirements include voice priority or high throughput data transmission.

3. The method according to claim 1, characterized in that The multiple working modes include: shutdown mode, standby mode, wake-up mode, and working mode.

4. The method according to claim 1, characterized in that: Determining a satellite terminal power management strategy based on the multi-objective optimization function and the current operating state vector of the satellite; include: Based on the multi-objective optimization function, generating a set of candidate actions for future operation of the satellite; Determining a future operating state vector of the satellite based on the current operating state vector of the satellite; Based on the satellite future operation state vector and the satellite future operation candidate action set, a satellite terminal power management strategy is determined.

5. The method according to claim 4, characterized in that The step of generating a set of candidate actions for future operation of the satellite based on the multi-objective optimization function comprises: The particle swarm algorithm is used to solve the multi-objective optimization function and generate a set of candidate actions for the future operation of the satellite.

6. The method according to claim 4, characterized in that The step of determining the future operation state vector of the satellite based on the current operation state vector of the satellite comprises: The current operating state vector of the satellite is input into the LSTM prediction model, and the future operating state vector of the satellite is output.

7. The method according to claim 1, characterized in that The preset change condition includes one of the following: signal mutation: ΔC / N 0>5dB-Hz or ΔRSSI>1dBm; Battery status sudden change: ΔSOC>10% or T battery >45℃; Time condition: the time difference from the last power control instruction update meets the first preset time period.

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