Heterogeneous dual-frequency satellite communication switching method for marine environment monitoring system

Abstract

The invention provides a heterogeneous dual-frequency satellite communication switching method for a marine environment monitoring system. The method comprises the following steps: constructing a time delay-cost utility function of a service; the SNR of the satellite communication link is calculated according to the rain attenuation value of the satellite communication link; the method comprises the following steps: setting initial values of a static interruption threshold value and a dynamic rollback threshold value of a satellite communication link, and optimizing the static interruption threshold value and the dynamic rollback threshold value through a genetic algorithm according to a time delay-cost utility function; and transferring the communication data of the service from the main link to the standby link, and if the SNR of the main satellite communication link reaches a set dynamic rollback threshold range, switching to the main satellite communication link to transmit the service. According to the method disclosed by the invention, the optimal communication link can be evaluated and selected in real time according to factors such as the current communication environment, the link quality and the cost effectiveness, so that the ship is always kept in an efficient communication state in a complex and changeable ocean environment.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a heterogeneous dual-frequency satellite communication switching method for a marine environment monitoring system. Background Art

[0002] Current satellite communication technology has become the preferred solution for achieving mobility and large-scale communications due to its flexibility in system design and wide coverage, especially for areas lacking ground network infrastructure and geographically dispersed user groups. Compared with high-orbit satellites, low-orbit satellites have the advantage of lower communication latency due to their lower orbital altitude, which enables them to communicate directly with low-power handheld terminal devices. This feature gives low-orbit satellites a unique advantage in providing real-time communication services worldwide.

[0003] The ground coverage pattern of low-orbit satellites presents a multi-cell honeycomb pattern. This coverage method can provide higher service capacity than high-orbit satellites, thus meeting the growing communication needs. However, as an open communication platform, the communication link of the satellite communication system is extremely susceptible to interference from external environmental factors. Especially in high-frequency band communications, the impact of rain attenuation on communication quality is particularly significant, which has become an important factor restricting the performance of satellite communications. In summary, although satellite communications have significant advantages in providing global and highly mobile communication services, the stability and reliability of their communication links still face severe challenges from external conditions, especially rain attenuation.

[0004] During the ocean-going operations of ocean monitoring ships, due to the complex and changeable operating environment, more stringent requirements are placed on communication technology. In view of the limitations of a single communication method in the ocean information monitoring scenario, current ships generally use a combination of multiple communication technologies including satellite communications and cellular networks. However, when the ship is far away from the land base station area, the communication capacity of the cellular network often cannot meet the demand, resulting in communication interruption. In addition, under extreme weather conditions, satellite communication links in a single frequency band may frequently break due to signal attenuation or interference, causing the ship to lose contact temporarily, making it impossible for monitoring information to be transmitted to the ground information center in a timely manner. This not only poses a potential threat to the safety of ship navigation, but also seriously affects the real-time monitoring and protection of the marine environment.

[0005] To solve the above problems and ensure that ships always maintain a stable communication state during ocean voyages, multi-link fusion becomes the key. Multi-link fusion technology can make full use of the advantages of different communication technologies and improve the reliability and stability of the communication system. However, while pursuing communication stability, it is also necessary to take into account the economy of communication, that is, to select cost-effective and good communication links for data transmission.

[0006] Currently, the switching of communication links mainly relies on manual judgment. This method is not only cumbersome to operate but also inefficient, making it difficult to meet the requirements of ocean monitoring ships for rapid and accurate switching of communication links. Therefore, developing an algorithm or system that can automatically and intelligently select and switch communication links has become an important research direction for improving the communication capabilities of ocean monitoring ships.

[0007] The disadvantages of the satellite communication method for ocean monitoring ships in the above-mentioned prior art include:

[0008] (1) When ocean monitoring ships perform the switching of dual-frequency satellite antenna networks, they rely heavily on manual judgment. This method is not only cumbersome and inefficient but also difficult to ensure the accuracy and timeliness of switching. Manual judgment may be affected by various factors, such as the experience level, fatigue degree of operators, and external environment. These factors may lead to deviations in switching decisions, thus affecting communication quality.

[0009] (2) A significant disadvantage of the current method for switching dual-frequency satellite antenna networks during ocean monitoring is that it mainly makes switching decisions based on the current link state and lacks the ability to predict the link quality within a certain period in the future. It usually relies on real-time link quality monitoring data, such as signal strength and bit error rate, to evaluate the state of the current communication link. When these parameters reach the preset thresholds, the system will trigger a switching operation to switch to another seemingly better link. However, this method ignores the complexity and dynamics of the ocean environment, as well as the long-term impact of various factors such as satellite orbits and weather conditions on link quality.

[0010] (3) In ocean monitoring tasks, the decision-making process for switching dual-frequency satellite antenna networks often faces a dilemma: either switching too frequently or too conservatively. When the switching decision is too frequent, the communication system needs to continuously re-establish links and adjust parameters, which not only increases the operating burden of the system but also incurs additional cost consumption. More seriously, frequent switching may damage the integrity and continuity of data, resulting in the loss or delay of important information. In ocean monitoring tasks, the integrity and continuity of data are crucial because they directly relate to the accuracy and reliability of monitoring results. If the data is damaged due to frequent switching during transmission, the value and significance of the entire monitoring task will be greatly reduced. If the switching strategy is too conservative, the system may stay on the current link for a long time, even if the quality of this link has started to decline, and fail to switch to a better link in a timely manner. In this case, the system will miss the best opportunity to switch to a better link, thus wasting precious communication resources.

[0011] (4) Existing dual - frequency satellite antenna network switching methods often lack intelligent switching strategies. In the complex and ever - changing marine environment, the quality of communication links is affected by various factors, such as weather conditions, satellite orbit changes, ocean waves, etc. However, current switching methods often cannot perceive the changes of these factors in real - time and make intelligent switching decisions accordingly. This leads to problems such as frequent disconnections or unstable signals in communication links in extreme weather or satellite orbit changes, thus affecting the real - time transmission of monitoring information. Summary of the Invention

[0012] Embodiments of the present invention provide a heterogeneous dual - frequency satellite communication switching method for a marine environment monitoring system to ensure the satellite communication quality of the marine environment monitoring system.

[0013] To achieve the above object, the present invention adopts the following technical solutions.

[0014] A heterogeneous dual - frequency satellite communication switching method for a marine environment monitoring system includes:

[0015] Classify the services in the marine environment monitoring system, and construct a delay - cost utility function according to the demand characteristics of different services for delay, cost, and reliability;

[0016] Predict the rain attenuation value of the satellite communication link, and calculate the signal - to - interference - plus - noise ratio SNR of the satellite communication link according to the rain attenuation value;

[0017] Set the initial values of the static interruption threshold and the dynamic fallback threshold of the satellite communication link, and optimize the static interruption threshold and the dynamic fallback threshold through a genetic algorithm according to the delay - cost utility function. When the SNR of the primary satellite communication link of a service is lower than the static interruption threshold, transfer the communication data of the service from the primary satellite communication link to the backup satellite communication link;

[0018] Real - time monitor the SNR of the primary satellite communication link. If the SNR of the primary satellite communication link does not reach the set dynamic fallback threshold range, continue to transmit the service through the backup satellite communication link; if the SNR of the primary satellite communication link reaches the set dynamic fallback threshold range, switch to the primary satellite communication link to transmit the service.

[0019] Preferably, the classification of the services in the marine environment monitoring system and the construction of the delay - cost utility function according to the demand characteristics of different services for delay, cost, and reliability include:

[0020] Classify the services in the marine environment monitoring system, and define the delay - cost utility function of the service as:

[0021]

[0022] C = μBτ

[0023] Where R is the total amount of data to be transmitted for the service, μ is the cost-utility constant, B is the bandwidth required for data transmission, τ is the time delay required for the service, t is the time required to complete information transmission, and C is the communication cost.

[0024] Preferably, predicting the rain attenuation value of the satellite communication link and calculating the SNR of the satellite communication link according to the rain attenuation value includes:

[0025] Setting the signal-to-noise ratio of the satellite communication link to be calculated by the following formula:

[0026]

[0027] Where P t is the transmit power, G t is the transmit antenna gain, G r is the receive antenna gain, λ is the signal wavelength, d is the distance between the satellite and the ground terminal, K is the Boltzmann constant, T is the equivalent noise temperature at the receiving end, and B is the communication bandwidth;

[0028] Using the long short-term memory (LSTM) model in the recurrent neural network (RNN) to perform time series learning and prediction on the rain attenuation value of the satellite communication link. The training data of the LSTM model consists of time series data containing N moments. The x i in the time series data is the data at the i-th moment, including the rainfall amount, frequency, antenna elevation angle, and polarization mode data at that moment, and y i is the rainfall attenuation obtained through the rain attenuation formula or actual measurement at that moment. The LSTM model outputs the rain attenuation prediction value y N+1 for the next moment. Using the loss function and the method of gradient descent to iteratively optimize the LSTM model to obtain a trained LSTM model;

[0029] Inputting the rainfall data corresponding to the latest time series into the trained LSTM model, and the trained LSTM model outputs the rain attenuation value for future moments;

[0030] The calculation formula for the SNR of the satellite communication link affected by the rain attenuation phenomenon is:

[0031]

[0032] Where A r represents the predicted rain attenuation value, and SNR0 represents the initial SNR before being affected by the rain attenuation phenomenon.

[0033] Setting initial values for the static interruption threshold and dynamic fallback threshold of a satellite communication link, and optimizing the static interruption threshold and dynamic fallback threshold according to a delay-cost utility function, including:

[0034] Setting initial values for the static interruption threshold and dynamic fallback threshold according to the actual requirements of the transmission task, and dynamically adjusting the dynamic fallback threshold in the following manner:

[0035] r return = r0 + α·Δr

[0036]

[0037] where r return is the dynamically adjusted dynamic fallback threshold, r0 is the initial dynamic fallback threshold, Δr is the change value of the threshold within a certain time period, α > 0 represents the threshold change influence coefficient, indicating that the handover threshold is reduced when the signal-to-noise ratio drops rapidly, and r th is the static interruption threshold;

[0038] Establishing an optimization problem for the static interruption probability and dynamic fallback probability shown in Equation (1):

[0039]

[0040] C th is the set cost limit threshold, τ is the specified delay threshold, t is the communication delay in the delay-cost utility function, C is the communication cost in the delay-cost utility function, P all is the allowed maximum power, P t is the transmission power; the goal of the optimization problem shown in Equation (1) is to minimize the static interruption probability P return and dynamic fallback threshold r th and static interruption threshold r out by adjusting the delay τ, bandwidth B, dynamic fallback threshold r return ;

[0041] Solving the optimization problem shown in Equation (1) through a genetic algorithm to obtain the optimized static interruption threshold r th and dynamic fallback threshold r return .

[0042] The primary satellite communication link includes the Ka band, and the backup satellite communication link includes the L band.

[0043] As can be seen from the technical solutions provided by the embodiments of the present invention above, the method of the present invention can evaluate and select the optimal communication link in real time according to factors such as the current communication environment, link quality, and cost-effectiveness, so as to ensure that the ship always maintains a stable and efficient communication state in the complex and changeable ocean environment. Through this dynamic decision-making process, the comprehensive optimization of the efficiency, stability, and cost-effectiveness of communication in the complex marine environment is effectively realized.

[0044] Additional aspects and advantages of the present invention will be given in part in the following description, and these will become apparent from the following description or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a model diagram of a marine monitoring communication system based on heterogeneous dual-band satellite communication provided by an embodiment of the present invention;

[0047] Figure 2 It is a processing flow chart of a heterogeneous dual-band satellite communication switching method for an ocean environment monitoring system provided by an embodiment of the present invention;

[0048] Figure 3 It is a schematic diagram of the error between the predicted rain attenuation value and the actual rain attenuation value provided by an embodiment of the present invention;

[0049] Figure 4 It is a schematic diagram of a switching algorithm for static interruption threshold and dynamic fallback threshold provided by an embodiment of the present invention;

[0050] Figure 5 It is a relationship diagram between the satellite communication signal-to-noise ratio SNR and the interruption probability P out in the present invention.

[0051] Figure 6 It is a relationship diagram between the satellite communication signal-to-noise ratio and the fallback probability provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following details the embodiments of the present invention. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0053] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The phrase "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0054] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention pertains. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as here.

[0055] For the convenience of understanding the embodiments of the present invention, the following will further explain with several specific embodiments in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0056] A marine monitoring communication system model based on heterogeneous dual - band satellite communication provided by an embodiment of the present invention is as Figure 1 shown. The system consists of an L - band satellite antenna, a Ka - band satellite antenna, a group of marine monitoring vessels and a ground information center. The group of marine monitoring vessels includes multiple vessels, which are responsible for collecting monitoring data in the marine environment and performing data interaction with the ground information center through the corresponding satellite communication links via the L - band antenna / Ka - band satellite antenna, and transmitting the collected data to the ground information center. The ground information center serves as the data - processing core of the entire system and is responsible for receiving, storing and analyzing the data uploaded from the group of marine monitoring vessels.

[0057] In the above marine monitoring communication system, the L-band provides a low-rate communication link with strong anti-interference ability, suitable for adverse weather conditions; the Ka-band supports high-speed data transmission and is suitable for transferring large-capacity data under good conditions. In addition, in terms of traffic costs, the L-band is higher than the Ka-band. Therefore, during communication, the system preferentially communicates with the ground center through the Ka-band (represented by a red solid line). When the Ka-band link is interrupted due to rain fade or other reasons (represented by a black dashed line), it automatically switches to the L-band link to ensure the continuity and reliability of communication. The system can dynamically adjust the communication switch between the Ka-band and the L-band according to the real-time link quality (such as signal-to-noise ratio) to achieve efficient data transmission. At the same time, through the collaborative work of the dual bands, it provides redundancy for the communication link, enhancing the stability and anti-interference ability of the system, so as to be able to cope with the complexity and dynamics of the marine environment and ensure the real-time and accuracy of monitoring data.

[0058] The Ka-band is a microwave band in the electromagnetic spectrum with a frequency range of 26.5 - 40 GHz, and the L-band is a radio wave band with a frequency of 1 - 2 GHz. The Ka-band and the L-band are mainly used for satellite communication. Due to the relatively low transmission rate of the L-band, its communication equipment has a wide coverage range and is suitable for wide-area monitoring. Scarcity of spectrum resources: Although the L-band has a relatively narrow bandwidth, its spectrum is a scarce resource globally. Many key communication services (such as the Global Positioning System GPS, civil aviation communication, military communication, etc.) rely on the L-band, resulting in fierce competition in this frequency band. However, the low transmission rate of the L-band means that it takes a longer transmission time to complete the transmission of a large amount of data. Therefore, its transmission efficiency is low, which may lead to a relatively high cost of bandwidth occupancy, especially increasing the total operating cost in applications with high real-time requirements. In contrast, the Ka-band communication system has a high transmission rate and can significantly improve efficiency when transmitting a large amount of data, thereby reducing the transmission time and bandwidth requirements, which is very important for applications that require fast and real-time transmission of large-scale marine monitoring data. Therefore, the L-band satellite communication system is suitable for marine monitoring applications with high anti-interference requirements and low real-time requirements, but the transmission rate is slow; while the Ka-band system, although having a higher transmission rate and being suitable for scenarios with high real-time requirements, has a relatively high operating cost due to its sensitivity to adverse weather and high equipment cost.

[0059] Based on the above marine monitoring communication system, an embodiment of the present invention provides a processing flow of a heterogeneous dual-band satellite communication switching method for a marine environment monitoring system as Figure 2 shown, including the following processing steps:

[0060] Step S10: The system classifies the services to be processed and constructs a delay-cost utility function according to the demand characteristics of different services for delay, cost, and reliability.

[0061] Step S20: Predict the rain attenuation value of the satellite communication link, and calculate the SNR (Signal to Interference plus Noise Ratio) of the satellite communication link according to the rain attenuation value.

[0062] Step S30: Set the static interruption threshold and dynamic fallback threshold of the satellite communication link according to the delay-cost utility function. When the SNR of the primary satellite communication link of the service is lower than the static interruption threshold, the system considers that the current channel quality no longer meets the service quality requirements, so the handover mechanism is triggered to transfer the communication data of the delay-sensitive service from the primary satellite communication link to the backup satellite communication link. The above primary satellite communication link can be a Ka-band satellite, and the backup satellite communication link can be an L-band satellite.

[0063] After the communication data of the service accesses the backup satellite communication link, the system evaluates the data link quality of the service by real-time monitoring the SNR of the primary satellite communication link. If the SNR of the primary satellite communication link does not reach the set dynamic fallback threshold range, the service continues to be transmitted through the backup satellite communication link; if the SNR of the primary satellite communication link reaches the set dynamic fallback threshold range, the service is switched to the primary satellite communication link for transmission to ensure the reliability and stability of communication.

[0064] Specifically, the above step S10 includes:

[0065] The delay-sensitive services of the satellite communication system in the field of ocean monitoring mainly involve real-time data collection and transmission to support timely decision-making and response. Ocean monitoring tasks such as real-time meteorological monitoring, ocean disaster warning, and ocean environment monitoring have extremely strict requirements for communication delay, because any delay may lead to the delay of important information transmission, affecting the accuracy and timeliness of decision-making. These delay-sensitive services require the satellite system to have a fast response ability to transmit data in a timely manner. Especially in emergency situations, such as disaster warnings like tsunamis or storm surges caused by meteorological changes, the stability and low latency of the communication link are directly related to the monitoring effect and the effectiveness of the warning system. For equipment such as ocean monitoring ships, buoys, and autonomous underwater vehicles, satellite communication needs to provide a low-latency link to ensure efficient and stable information transmission in harsh sea conditions or ocean environments.

[0066] Generally speaking, when selecting a suitable satellite communication frequency band, factors such as the real-time requirements of the monitoring task, data transmission volume, environmental conditions, and cost-benefit need to be comprehensively considered. The satellite transmission cost is mainly proportional to the amount of data transmitted.

[0067] Therefore, the delay-cost utility function of the service is defined as:

[0068]

[0069] C = μBτ

[0070] Where R is the total amount of data to be transmitted for the service, μ is the cost-utility constant, B is the bandwidth required for data transmission, and τ is the time delay required for the service. t is the time required to complete the information transmission, and C is the cost. The delay-cost utility function is used in the process of optimizing the dynamic fallback threshold and the static interruption threshold, and is used in the third constraint condition of the optimization problem later.

[0071] Specifically, the above step S20 includes:

[0072] Evaluate the quality of the communication link of the service, providing a theoretical basis for subsequent link selection, handover, and improvement of transmission quality. The signal-to-noise ratio of the communication link is an important indicator to measure the signal quality, representing the ratio of the received signal power to the noise power. Specifically, SNR (Signal to Interference plus Noise Ratio) is a metric used to evaluate the clarity and stability of the signal, usually in decibels (dB). In satellite communication, the signal needs to be transmitted from the ground station to the satellite and then back to the ground from the satellite. Since the signal is affected by factors such as the atmosphere, rain, clouds, etc. during propagation, the signal received at the receiving end is often superimposed with noise (such as thermal noise, interference, etc.). Generally, the satellite communication signal-to-noise ratio can be expressed by the following formula:

[0073]

[0074] Where P t is the transmit power, G t is the transmit antenna gain, G r is the receive antenna gain, λ is the signal wavelength, d is the distance between the satellite and the ground terminal, K is the Boltzmann constant, T is the equivalent noise temperature at the receiving end, and B is the communication bandwidth.

[0075] The communication link of VSAT (Very Small Aperture Terminal) is easily affected by rainfall weather and generates relatively large attenuation. Moreover, the uncertainty of rainfall in terms of occurrence time and geographical area makes it difficult to accurately quantify the attenuation. The magnitude of the rain attenuation value of the communication link is related to many factors such as the height of rainfall, rainfall intensity, wind speed, and antenna elevation angle. In order to more deeply explore the characteristics of data streams in the time series, the present invention uses the LSTM (Long Short-Term Memory) network in the RNN (Recurrent Neural Network) to learn and predict the rain attenuation value in the time series. Compared with the traditional forward feedback neural network, the RNN introduces a directional loop and can handle the problem of the front-back correlation between inputs. Each influencing factor in the influencing factors of the rain attenuation amount can be grouped and sorted according to the time sequence. Therefore, the RNN can be used to mine the sequence of the rain attenuation amount. The LSTM is a special RNN that mainly solves the problem of "long-term dependence" in sequence data.

[0076] This method is divided into three steps:

[0077] 1) Communication link quality assessment algorithm;

[0078] 2) Mining algorithm for the correlation relationship between rain attenuation factors and channel link quality;

[0079] 3) Algorithm for predicting the channel link quality based on the predicted values of rain attenuation factors.

[0080] Specifically, in the model training of the LSTM, the gradient descent method is used to optimize the network weight matrix W and the offset b. First, m data samples are selected from the training set and preprocessed. Each time series is converted into a vector as the input of the LSTM network. For the convenience of model training, the batch_size is set to 60 and the time step is set to 15, and all the input data is normalized; then W and b are randomly initialized. In the i-th iteration, the difference between the output h(x) of the network and the actual value y is calculated to obtain the loss function J(W, b). By continuously adjusting the sizes of W and b to minimize the loss value, the optimized W and b are finally obtained and used as the parameters of the LSTM model to predict the corresponding target. The algorithm flow is summarized as follows:

[0081]

[0082]

[0083] The training data of the LSTM model consists of time series data, and the x in the time series data iis the data at the i-th moment, including the rainfall, frequency, antenna elevation angle, polarization mode, etc. at this moment, y i is the rainfall attenuation obtained through the rainfall attenuation formula or actual measurement at this moment; output the predicted value of rainfall attenuation at the next moment y N+1 ; perform iterative optimization through the loss function.

[0084] Use the trained LSTM model to predict the rainfall attenuation value. During the prediction process, the input data of the trained LSTM model is the rainfall corresponding to the latest time series, and the output data is the rainfall attenuation value at a future moment.

[0085] Figure 3 This is a schematic diagram of the error between the predicted result of the rainfall attenuation value and the actual rainfall attenuation value provided by the embodiment of the present invention. Among them, the blue line represents the actual rainfall attenuation value, and the red line represents the predicted value. It can be seen from the simulation results that the closer the time series is to the current time, the more accurate the prediction result. When using the data with indexes 5000 - 5800 for training, the prediction result basically coincides with the actual rainfall attenuation value.

[0086] Rainfall attenuation is a signal attenuation phenomenon caused by precipitation in satellite communication, which mainly affects the signal propagation through signal absorption and scattering. The greater the intensity of precipitation, the more significant the impact of rainfall attenuation, especially in higher frequency bands (such as Ku band, Ka band). Since the signals in these frequency bands are more easily absorbed and scattered by precipitation, the signal attenuation is more serious. As the signal strength weakens, the signal-to-noise ratio (SNR) at the receiving end decreases, and the interference of noise to the signal relatively increases, thereby affecting the communication quality, which may lead to an increase in the bit error rate and even cause communication interruption. Especially in the case of heavy rain or strong precipitation, the signal attenuation may reach dozens of dB, and the SNR drops sharply, resulting in the instability or failure of the communication link.

[0087] (3) The impact of rainfall attenuation value on the SNR of the communication link

[0088] The specific impact can be expressed by the following formula:

[0089]

[0090] Among them, A r represents the predicted rainfall attenuation value, and SNR0 represents the initial SNR before being affected by the rainfall attenuation phenomenon.

[0091] Sort the quality of each satellite communication link according to the SNR of each satellite communication link. The higher the SNR, the higher the quality of the satellite communication link.

[0092] Specifically, step S30 includes:

[0093] In the satellite dual - band switching strategy, the stability and reliability of the communication link are key factors in the design. To effectively reduce the ping - pong switching phenomenon that may occur during the switching process and optimize the performance of the communication system, the embodiment of the present invention proposes a satellite dual - band switching algorithm based on an interruption threshold and a dynamic fallback threshold. This algorithm combines the changing trend of the signal - to - noise ratio and realizes adaptive switching decisions under complex channel conditions through the cooperation of static and dynamic thresholds.

[0094] 1. Static interruption threshold

[0095] In this satellite dual - band switching algorithm, the interruption threshold is set as a static threshold, which is used to determine when to switch to the standby communication link. When the signal - to - noise ratio of the satellite communication link is lower than this threshold, the system considers that the quality of the current primary communication link no longer meets the quality - of - service requirements, so it triggers the switching mechanism to transfer the communication load to the standby link. The static interruption threshold, as a fixed parameter, remains unchanged during the entire system operation and is regarded as an important basis for determining whether the decision - signal quality is sufficient to maintain stable communication. The design of this static interruption threshold requires balancing the system's response speed and switching accuracy, avoiding premature switching when the channel has short - term fluctuations and also avoiding communication interruptions caused by hysteresis when the signal deteriorates continuously. By reasonably setting the static interruption threshold, it can be ensured that the switching occurs when the signal quality reaches the optimal balance point, thereby optimizing the communication efficiency of the system.

[0096] 2. Dynamic fallback threshold

[0097] Different from the static interruption threshold, the setting of the dynamic fallback threshold has dynamic characteristics. The dynamic fallback threshold is used to determine when to fallback from the standby link to the primary link. When the signal quality of the standby link recovers to a certain standard, the system will perform a fallback operation and resume communication on the primary link. However, the recovery of the signal quality is dynamically changing, so the dynamic fallback threshold needs to be dynamically adjusted according to the real - time change of the channel quality.

[0098] The dynamic adjustment of the dynamic fallback threshold is based on two key factors: one is the current static interruption threshold, and the other is the predicted trend of signal-to-noise ratio change. Specifically, when the signal-to-noise ratio recovers quickly and the link quality is stable, the dynamic fallback threshold can be moderately increased to ensure that the fallback operation is only performed when the signal quality significantly recovers and remains stable. This strategy can avoid fallback when the signal slightly recovers, prevent the system from frequently switching, and reduce the performance loss caused by overly frequent switching. On the other hand, when the signal-to-noise ratio recovers slowly or there are signal fluctuations, the dynamic fallback threshold will be appropriately reduced, thereby reducing the delay of the fallback operation. This adjustment strategy helps to avoid communication interruptions caused by overly late fallback decisions during the signal recovery process, ensuring that the system can also timely fallback to the main link in an environment with a relatively low but stable signal-to-noise ratio to maintain stable communication. The dynamic adjustment of the dynamic fallback threshold can be carried out in the following ways:

[0099] r return = r0 + α·Δr

[0100]

[0101] where r return is the dynamically adjusted dynamic fallback threshold after adaptive adjustment, r0 is the initial dynamic fallback threshold, Δr is the change value of the threshold within a certain period of time, and α > 0 represents the threshold change influence coefficient, indicating that the switching threshold is reduced when the signal-to-noise ratio rapidly decreases. r th is the static interruption threshold.

[0102] Set the initial values of the static interruption threshold and the dynamic fallback threshold according to the actual requirements of the transmission task. The initial values of the static interruption threshold and the dynamic fallback threshold for different services are different.

[0103] Figure 4 This is a schematic diagram of a switching algorithm for the static interruption threshold and the dynamic fallback threshold provided by an embodiment of the present invention. As Figure 4 shown, the static and dynamic thresholds cooperate to achieve the stability and efficiency of the switching strategy. The design of the dual thresholds effectively prevents unnecessary frequent switching caused by slight signal fluctuations. The static threshold provides a basic switching judgment criterion to ensure the rationality of switching under normal circumstances; the dynamic threshold can be adjusted according to the real-time channel conditions and signal-to-noise ratio changes, further enhancing the adaptability of the algorithm to channel changes. This cooperation makes the algorithm have both a certain degree of stability and flexibility, and can better handle various complex communication scenarios.

[0104] Marine blue carbon surveys are of great significance for the accurate assessment of marine carbon sources and sinks. During the process of blue carbon surveys, it is necessary to use collection equipment to conduct surveys on marine blue carbon in multiple dimensions such as time, longitude and latitude, depth, etc.

[0105] (1) Interruption probability

[0106] In the field of ocean monitoring, the outage probability P of the satellite communication system out refers to the probability that the system fails to receive or transmit data normally due to the link conditions not meeting the communication requirements. The relationship between the outage probability and the signal-to-noise ratio can be defined by the following formula:

[0107]

[0108] Figure 5 is a relationship diagram between the signal-to-noise ratio SNR and the outage probability P provided by an embodiment of the present invention out between them. The overall trend of the two curves is monotonically decreasing. As the signal-to-noise ratio increases, the outage probability decreases significantly. Specifically, it can be observed from the image features that:

[0109] (a) Low signal-to-noise ratio region (SNR < 10dB):

[0110] In this range, the curve slope is large, indicating that the outage probability decreases significantly with the increase of the signal-to-noise ratio, but the outage probability is still at a relatively large value. This region is the key range for system performance improvement.

[0111] (b) Medium signal-to-noise ratio region (10dB ≤ SNR ≤ 20dB):

[0112] In the medium signal-to-noise ratio region, the outage probability is relatively low at this time, less than 10%, but the decreasing trend gradually slows down, indicating that the improvement effect of the signal-to-noise ratio on the outage probability weakens. This shows that the link quality is good at this time, and the possibility of the communication system outage is low;

[0113] (c) High signal-to-noise ratio region (SNR > 20dB):

[0114] In the high signal-to-noise ratio region, the outage probability is close to 0, and the communication link tends to be stable and reliable. At this time, further increasing the signal-to-noise ratio has little effect on the outage probability.

[0115] Therefore, if the outage probability is optimized for the low signal-to-noise ratio region, the reliability of the communication system can be significantly improved. This is because in the low signal-to-noise ratio range, a small increase in the signal-to-noise ratio will cause a rapid decrease in the outage probability, thereby greatly improving the link performance. Figure 5 The outage probability curve shown in Figure [ID] intuitively demonstrates the significant impact of the signal-to-noise ratio on the outage probability, especially in the low signal-to-noise ratio region, where the impact is particularly significant. Optimizing the communication performance in the low signal-to-noise ratio region can not only effectively reduce the occurrence of outage events, but also improve the overall link stability and service quality.

[0116] The dynamic fallback probability refers to the probability that the backup communication link successfully falls back to the primary communication link during the recovery process in a satellite communication system. It reflects the probability that when the system switches back from the backup link to the primary link after the backup link is restored, the signal quality is stable enough to ensure uninterrupted communication. The dynamic fallback probability P return is defined as follows:

[0117]

[0118] where SNR is the signal-to-noise ratio, e is the exponential function, and r return is the dynamic fallback threshold.

[0119] Figure 6 Figure Figure 6 shows the relationship between the signal-to-noise ratio and the fallback probability in a satellite communication system provided by an embodiment of the present invention. The overall trend of the two curves is monotonically increasing. As the signal-to-noise ratio increases, the fallback probability increases significantly. Specifically, it can be observed from the image features that:

[0120] When the SNR is at a low level, the dynamic fallback probability is correspondingly low, indicating that in the case of poor signal quality (i.e., low signal-to-noise ratio), the probability that the backup link successfully switches back to the primary link and ensures uninterrupted communication is small. As the SNR gradually increases, the fallback probability shows a significant upward trend, which means that the improvement of signal quality (increase in signal-to-noise ratio) significantly enhances the ability of the backup link to reliably switch back to the primary link, thereby increasing the probability of uninterrupted communication. However, when the SNR reaches a high value, the growth rate of the fallback probability gradually slows down and approaches 1. This phenomenon indicates that although the fallback probability approaches its maximum value under high signal-to-noise ratio conditions, due to the existence of other factors such as internal interference in the system and the complexity of link switching, the fallback probability cannot reach 100%.

[0121] Based on this, the following framework can be established for the optimization problem of static outage probability and dynamic fallback probability: By designing reasonable link parameters (such as transmit power or system bandwidth), minimize the outage probability under constraint conditions while meeting the power budget, communication delay, and cost limitations of the system. The core objective of this optimization framework is to balance signal quality, system resource utilization, and communication cost to improve the reliability of the communication system in a complex channel environment. The optimization problem is established as follows:

[0122]

[0123] where the objective of the optimization problem is to minimize the static outage probability P return of the communication system and maximize the fallback probability P th by adjusting the dynamic fallback threshold r out , the static outage threshold r return , bandwidth B, and delay τ.The difference is to ensure that the communication link is as reliable as possible. During the optimization process, a series of constraints need to be satisfied, including: the transmit power P t shall not exceed the allowable maximum power P all , the communication delay t must be less than the specified delay threshold τ, and the communication cost C shall not exceed the set cost limit C th .

[0124] As can be seen from the above, this optimization problem has various limitations. The objective function has non - convexity and multimodality, involving the interaction of multiple parameters. For example, P out -P return calculation depends on complex signal - to - noise ratio formulas, etc., resulting in multiple local extreme points. Traditional gradient - based algorithms are prone to falling into local optima. The constraint conditions are complex and may be coupled with each other. The constraints on transmit power and transmission time affect each other, forming a complex feasible region, which brings difficulties to accurate search. At the same time, the variables in the problem may have different physical meanings and value ranges, making it difficult to handle with simple mathematical transformations. The genetic algorithm, with its global search ability, adaptability to complex constraints, and inclusiveness of different variable characteristics, can effectively cope with these limitations and find better solutions.

[0125] The genetic algorithm has significant advantages. In terms of global search ability, it can avoid falling into local optimal solutions. Even if the objective function has multiple local optimal cases, it can explore multiple regions of the solution space simultaneously through population search to find the global optimum. It can also adapt to complex solution spaces and handle non - linear, discontinuous, or complex topological structures. In terms of robustness and adaptability, it does not require special treatment of the objective function and constraint conditions. It can directly operate according to the calculated fitness, and is insensitive to the selection of the initial solution. Even if the quality of the individuals in the initial population is poor, it can converge to a better solution during evolution. In addition, it has the potential for parallel computing and good scalability. It can improve the search efficiency through parallel operations and can easily add or modify optimization variables and constraint conditions, suitable for changing optimization requirements.

[0126] The process of the enhanced genetic algorithm is as follows:

[0127]

[0128]

[0129] By using the enhanced genetic algorithm to solve the optimization problem shown in the above formula (1), the obtained solution results are two thresholds, including the static outage threshold r th and the dynamic fallback threshold r return .

[0130] In summary, the embodiments of the present invention propose a comprehensively innovative and highly intelligent solution, which can eliminate the excessive reliance on human judgment. By integrating advanced automation and intelligent technologies, such as machine learning and artificial intelligence algorithms, etc., the handover decision-making process becomes more accurate and efficient. This can not only significantly improve the accuracy and timeliness of handovers, but also greatly reduce the operation complexity and improve the overall communication efficiency.

[0131] Enhance the link quality prediction ability: Aiming at the problem that the existing methods lack the prediction of future link quality, the present invention is committed to developing a precise prediction system. This system can comprehensively consider the dynamic changes of the ocean environment, the movement rules of satellite orbits, and the real-time updates of weather conditions to predict the link quality with high precision over a long period. This will make the handover decision more forward-looking and effectively avoid communication interruptions caused by sudden changes in link quality.

[0132] Optimize the handover strategy and balance frequency and conservatism: The present invention aims to design an adaptive handover strategy. By dynamically adjusting the handover threshold and strategy parameters, it can avoid both the increase in system burden and cost caused by overly frequent handovers and the missed opportunity to switch to a better link due to overly conservative handovers. This strategy will be based on real-time link quality data, historical handover records, and prediction results to achieve intelligent and dynamic handover decisions.

[0133] Introduce an intelligent handover strategy to enhance the ability to handle complex environments: Facing the complex and changeable ocean environment, the present invention will introduce an intelligent handover strategy, enabling the system to perceive and adapt to the impacts of various factors in real time, such as weather conditions, satellite orbit changes, and ocean fluctuations. Through continuous learning and optimization, the system will be able to more accurately predict and respond to potential communication challenges to ensure the stable and real-time transmission of monitoring information.

[0134] Those of ordinary skill in the art can understand that the drawings are only schematic diagrams of an embodiment, and the modules or processes in the drawings are not necessarily essential for implementing the present invention.

[0135] From the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes contributions to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0136] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the description of the method embodiments. The device and system embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0137] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A heterogeneous dual-frequency satellite communication switching method for a marine environment monitoring system, characterized in that: include: Classify the services in the marine environment monitoring system and construct a delay-cost utility function based on the requirements of different services for delay, cost and reliability; Predict the rain attenuation value of the satellite communication link, and calculate the signal to interference plus noise ratio (SNR) of the satellite communication link based on the rain attenuation value; Setting initial values ​​of a static interruption threshold and a dynamic backoff threshold of a satellite communication link, optimizing the static interruption threshold and the dynamic backoff threshold by a genetic algorithm according to a delay-cost utility function, and transferring communication data of the service from the primary satellite communication link to a backup satellite communication link when the SNR of the primary satellite communication link of the service is lower than the static interruption threshold; Monitor the SNR of the primary satellite communication link in real time. If the SNR of the primary satellite communication link does not reach the set dynamic fallback threshold range, continue to transmit services through the backup satellite communication link. If the SNR of the primary satellite communication link reaches the set dynamic back-off threshold range, the service is switched to the primary satellite communication link for transmission.

2. The method according to claim 1, characterized in that The above-mentioned classification of the services in the marine environment monitoring system and the construction of a delay-cost utility function according to the requirements of different services for delay, cost and reliability include: The services in the marine environment monitoring system are classified, and according to the requirements of different services for delay, cost and reliability, the delay-cost utility function of the service is defined as: C=μBτ Where R is the total amount of data that needs to be transmitted by the service, μ is the cost-utility constant, B is the bandwidth required for data transmission, τ is the delay required by the service, t is the time required to complete the information transmission, and C is the communication cost.

3. The method according to claim 2, characterized in that The method of predicting the rain attenuation value of the satellite communication link and calculating the SNR of the satellite communication link according to the rain attenuation value includes: The signal-to-noise ratio of the satellite communication link is calculated by the following formula: Where P t is the transmission power, G t is the transmitting antenna gain, G r The receiving antenna gain, λ is the signal wavelength, d is the distance between the satellite and the ground terminal, K is the Boltzmann constant, T is the equivalent noise temperature at the receiving end, and B is the communication bandwidth; The long short-term memory (LSTM) model in the recurrent neural network (RNN) is used to learn and predict the rain attenuation value of the satellite communication link in time series. The training data of the LSTM model consists of time series data containing N moments. i is the data at the i-th moment, including the rainfall, frequency, antenna elevation angle and polarization mode data at that moment, y i is the rainfall attenuation obtained by the rain attenuation formula or actual measurement at this moment. The LSTM model outputs the predicted rain attenuation value y at the next moment. N+1 , use the loss function to iteratively optimize the LSTM model using the gradient descent method to obtain a trained LSTM model; The rainfall data corresponding to the latest time series is input into the trained LSTM model, and the trained LSTM model outputs the rain attenuation value at the future moment; The calculation formula for the SNR of a satellite communication link affected by rain attenuation is: Among them A r represents the predicted rain attenuation value, and SNR0 represents the initial SNR before being affected by the rain attenuation phenomenon.

4. The method according to claim 3, characterized in that The initial values ​​of the static interruption threshold and the dynamic backoff threshold of the satellite communication link are set, and the static interruption threshold and the dynamic backoff threshold are optimized by a genetic algorithm according to a delay-cost utility function, including: The initial values ​​of the static interruption threshold and the dynamic backoff threshold are set according to the actual requirements of the transmission task. The dynamic adjustment of the dynamic backoff threshold is performed in the following manner: r return =r0+α·Δr where r return is the dynamic backoff threshold after adaptive adjustment, r0 is the initial dynamic backoff threshold, Δr is the change value of the threshold within a certain period of time, α>0 represents the threshold change influence coefficient, indicating that the switching threshold is reduced when the signal-to-noise ratio drops rapidly, r th is the static interruption threshold; The optimization problem of static outage probability and dynamic backoff probability shown in formula (1) is established: C th is the set cost limit threshold, τ is the specified delay threshold, t is the communication delay in the delay-cost utility function, C is the communication cost in the delay-cost utility function, P all is the maximum power allowed, P t is the transmission power; the goal of the optimization problem shown in formula (1) is to adjust the delay τ, bandwidth B, dynamic backoff threshold r return and the static interruption threshold r th , to minimize the static outage probability P of the communication system out and maximize the retreat probability P return ; The optimization problem shown in formula (1) is solved by genetic algorithm to obtain the optimized static interruption threshold r th and dynamic backoff threshold r return .

5. The method according to claim 1, characterized in that The main satellite communication link includes the Ka band, and the backup satellite communication link includes the L band.

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