Air-sea cross-domain communication method and device

Abstract

The embodiment of the invention provides an air-sea cross-domain communication method and device. The method comprises the following steps: fusing a multi-system communication protocol into a cross-domain communication network protocol through a water surface wireless communication multi-band access technology, and carrying out adaptive networking; the method comprises the following steps: performing dictionary table mapping on original information sent by underwater subsurface buoy sensor equipment to obtain a translated character sequence, constructing a Huffman tree according to the occurrence frequency of each character in the translated character sequence, and generating a Huffman code corresponding to each character according to the Huffman tree; the method comprises the following steps: monitoring channel noise and a channel error rate of an underwater acoustic channel in real time, determining a real-time channel state, determining a channel coding parameter adjustment strategy matched with the real-time channel state through an adaptive algorithm, and dynamically adjusting a coding mode and coding parameters of the channel according to the channel coding parameter adjustment strategy. Uploading the Huffman code to a superior command center through a preset dual channel according to the dynamically adjusted coding mode and coding parameters; according to the invention, the air-sea cross-domain communication efficiency can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an air-sea cross-domain communication method and device. Background Art

[0002] With the rapid development of information technology, the demand for interconnected networks and systems is increasing, especially in fields such as navigation, aerospace, and energy. To effectively safeguard social development and people's livelihoods, satellite communication networks have emerged. Maritime applications are a typical focus of satellite communication network construction. Air-sea cross-domain communication systems, a key means of extending space-based information systems to the ocean, are attracting increasing attention.

[0003] However, cross-domain communication presents a technical challenge and bottleneck due to issues such as protocol incompatibility, security isolation, and data format differences between networks. Research on high-speed networking technology for air-sea cross-domain communication gateways aims to address these technical challenges and provide a high-speed, secure, and reliable cross-domain communication solution. Summary of the Invention

[0004] In response to the problems in the prior art, the present application provides an air-sea cross-domain communication method and device, which can effectively improve the efficiency of air-sea cross-domain communication.

[0005] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides an air-sea cross-domain communication method, which is applied to a heterogeneous wide-area communication network composed of distributed cross-domain communication gateway buoy nodes at sea, wherein the heterogeneous wide-area communication network is used to receive raw information uploaded by underwater submersible sensor devices deployed on the seabed and upload the raw information to a superior command center. The method includes:

[0007] Through the multi-band access technology of surface wireless communication, multi-standard communication protocols are integrated into the cross-domain communication network protocol and adaptive networking is performed;

[0008] Perform dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, construct a Huffman tree based on the frequency of occurrence of each character in the translated character sequence, and generate a Huffman code corresponding to each character based on the Huffman tree;

[0009] Monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time to determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, dynamically adjust the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy, and upload the Huffman code to the superior command center through the preset dual channel according to the dynamically adjusted coding mode and coding parameters.

[0010] Furthermore, the multi-standard communication protocol is integrated into the cross-domain communication network protocol through the multi-band access technology of surface wireless communication and adaptive networking is performed, including:

[0011] Add an autonomous switching unit in the cross-domain communication gateway buoy node;

[0012] Through multi-band access to multi-standard communication links via surface wireless communications, the protocol is integrated into the cross-domain communication network protocol by combining the communication protocol and communication frequency band, and adaptive networking is performed.

[0013] Furthermore, the original information sent by the underwater buoy sensor device is mapped into a dictionary table to obtain a corresponding translated character sequence, including:

[0014] Pre-establishing a dictionary table for underwater buoy sensor equipment, wherein the dictionary table includes various raw data formats that may be generated by the sensor equipment and defines corresponding translation character sequences for each raw data format;

[0015] The translated character sequence corresponding to the original information sent by the underwater buoy sensor device is obtained according to the mapping result of the dictionary table.

[0016] Furthermore, the step of constructing a Huffman tree according to the frequency of occurrence of each character in the translated character sequence, and generating a Huffman code corresponding to each character according to the Huffman tree, includes:

[0017] Counting the frequency of occurrence of each character in the translated character sequence, and constructing a Huffman coding tree according to the frequency of occurrence of the characters, wherein a leaf node of the Huffman tree corresponds to each character, and the weight of the node of the Huffman tree is the frequency of occurrence of the corresponding character;

[0018] The constructed Huffman tree is traversed, and a corresponding Huffman code is generated for each character according to the path code from the root node to the leaf node in the Huffman tree.

[0019] Furthermore, the real-time monitoring of the channel noise and channel bit error rate of the underwater acoustic channel to determine the corresponding real-time channel state includes:

[0020] Deploy underwater acoustic sensors to monitor the channel noise of the underwater acoustic channel in real time, collect the power spectral density of the signal, determine the channel noise based on the power spectral density, and measure the error rate between the signal received by the receiving end and the original transmitted signal;

[0021] The real-time state of the current underwater acoustic channel is determined based on the monitored channel noise and the error rate in combination with preset channel quality evaluation rules.

[0022] Furthermore, the determining a channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjusting the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy, includes:

[0023] Collect historical channel noise and historical bit error rate data and establish a database to analyze the optimal coding parameter configuration under different channel conditions

[0024] An optimal coding parameter configuration matching the real-time channel state is determined by setting an adaptive algorithm, and the coding mode and coding parameters of the channel are dynamically adjusted according to the optimal coding parameter configuration.

[0025] Furthermore, uploading the Huffman code to a superior command center through a preset dual channel according to the dynamically adjusted coding mode and coding parameters includes:

[0026] Packing the Huffman coded data and adding necessary header information and check codes;

[0027] The packaged Huffman coded data is uploaded to the superior command center simultaneously through a preset dual channel, wherein the dual channels can be different physical channels.

[0028] In a second aspect, the present application provides an air-sea cross-domain communication device, which is applied to a heterogeneous wide-area communication network composed of distributed cross-domain communication gateway buoy nodes at sea, wherein the heterogeneous wide-area communication network is used to receive original information uploaded by underwater submersible sensor equipment deployed on the seabed and upload the original information to a superior command center, and the device includes:

[0029] Self-organizing network module, used to integrate multi-standard communication protocols into cross-domain communication network protocols and perform adaptive networking through multi-band access technology of surface wireless communication;

[0030] The coding and compression module is used to perform dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, construct a Huffman tree based on the frequency of occurrence of each character in the translated character sequence, and generate a Huffman code corresponding to each character based on the Huffman tree;

[0031] The channel correction module is used to monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjust the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy. According to the dynamically adjusted coding mode and coding parameters, the Huffman code is uploaded to the superior command center through the preset dual channel.

[0032] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the air-sea cross-domain communication method when executing the program.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the air-sea cross-domain communication method.

[0034] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the air-sea cross-domain communication method.

[0035] It can be seen from the above technical solution that the present application provides an air-sea cross-domain communication method and device, which integrates multi-standard communication protocols into cross-domain communication network protocols through surface wireless communication multi-band access technology and performs adaptive networking; the original information sent by the underwater buoy sensor equipment is mapped to a dictionary table to obtain a translated character sequence, a Huffman tree is constructed according to the frequency of occurrence of each character in the translated character sequence, and the Huffman code corresponding to each character is generated according to the Huffman tree; the channel noise and channel bit error rate of the underwater acoustic channel are monitored in real time to determine the real-time channel state, and a channel coding parameter adjustment strategy matching the real-time channel state is determined through an adaptive algorithm, and the channel coding mode and coding parameters are dynamically adjusted according to the channel coding parameter adjustment strategy, and the Huffman code is uploaded to the superior command center through a preset dual channel according to the dynamically adjusted coding mode and coding parameters, thereby effectively improving the efficiency of air-sea cross-domain communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0037] Figure 1 This is one of the flow charts of the air-sea cross-domain communication method in an embodiment of the present application;

[0038] Figure 2 This is a second flow chart of the air-sea cross-domain communication method in an embodiment of the present application;

[0039] Figure 3 This is the third flow chart of the air-sea cross-domain communication method in the embodiment of the present application;

[0040] Figure 4This is a fourth flow chart of the air-sea cross-domain communication method in an embodiment of the present application;

[0041] Figure 5 This is a fifth flow chart of the air-sea cross-domain communication method in an embodiment of the present application;

[0042] Figure 6 This is a sixth flow chart of the air-sea cross-domain communication method in an embodiment of the present application;

[0043] Figure 7 This is the seventh flow chart of the air-sea cross-domain communication method in the embodiment of the present application;

[0044] Figure 8 This is a structural diagram of the air-sea cross-domain communication device in an embodiment of the present application;

[0045] Figure 9 Schematic diagram of the structure of the electronic device in the embodiment of the present application.

[0046] Reference numerals:

[0047] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0048] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0049] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0050] Taking into account the problems existing in the prior art, the present application provides an air-sea cross-domain communication method and device, which integrates multi-standard communication protocols into cross-domain communication network protocols through surface wireless communication multi-band access technology and performs adaptive networking; the original information sent by the underwater buoy sensor device is mapped into a dictionary table to obtain a translated character sequence, a Huffman tree is constructed according to the frequency of occurrence of each character in the translated character sequence, and the Huffman code corresponding to each character is generated according to the Huffman tree; the channel noise and channel bit error rate of the underwater acoustic channel are monitored in real time to determine the real-time channel state, and a channel coding parameter adjustment strategy matching the real-time channel state is determined through an adaptive algorithm, and the channel coding method and coding parameters are dynamically adjusted according to the channel coding parameter adjustment strategy, thereby effectively improving the efficiency of air-sea cross-domain communication.

[0051] In order to effectively improve the efficiency of air-sea cross-domain communication, the present application provides an embodiment of an air-sea cross-domain communication method, which is applied to a heterogeneous wide-area communication network composed of distributed cross-domain communication gateway buoy nodes at sea. The heterogeneous wide-area communication network is used to receive the original information uploaded by underwater buoy sensor devices deployed on the seabed and upload the original information to the superior command center. Figure 1 The air-sea cross-domain communication method specifically includes the following contents:

[0052] Step S101: integrating the multi-standard communication protocol into the cross-domain communication network protocol through the multi-band access technology of surface wireless communication and performing adaptive networking;

[0053] Optionally, in this embodiment, the overall system architecture consists of three main components: underwater buoy sensor equipment, offshore distributed cross-domain communication gateway buoy nodes, and a higher-level command center. The underwater buoy sensor equipment is deployed on the seabed and is responsible for collecting raw information. The offshore distributed cross-domain communication gateway buoy nodes float on the sea surface, forming a heterogeneous wide-area communication network. This network's primary task is to receive information from the underwater sensors and transmit it to the higher-level command center.

[0054] The key to implementing this system lies in multi-band access technology for surface wireless communications and adaptive networking. Multi-band access technology allows the system to communicate across different frequencies, which is particularly important in marine environments. This is because different frequencies have different propagation characteristics in the ocean. For example, low-frequency signals can travel farther but at lower data rates, while high-frequency signals, while shorter in range, offer higher data rates.

[0055] The specific implementation process can be as follows: First, each buoy node is equipped with communication equipment capable of operating on multiple frequency bands. This equipment can include very short wave (VHF), short wave (HF), satellite communication, and other modes. The system automatically selects the most suitable frequency band and communication protocol based on the current communication needs and environmental conditions.

[0056] For example, when two buoys far apart need to communicate, the system may choose to use shortwave communication, because shortwave signals can be reflected by the ionosphere and propagate over long distances. When a buoy communicates with a nearby ship or shore base station, it may choose to use ultra-shortwave communication, because ultra-shortwave has higher bandwidth and better anti-interference ability in short-range communication.

[0057] Adaptive networking is another key technology. In ocean environments, the positions of buoys constantly change with currents and wind direction, and some buoys may fail for various reasons. Therefore, the network needs to be able to automatically adapt to these changes. Adaptive networking technology allows the network to dynamically adjust its topology to maintain optimal communication.

[0058] Implementing adaptive networking may involve the following steps:

[0059] Each buoy node broadcasts its own location and status information periodically.

[0060] Nodes receive broadcasts from other nodes and build a neighbor table.

[0061] Based on neighbor information, each node calculates the optimal routing path.

[0062] When a change in network topology is detected (such as the addition of a new node or the failure of an old node), the node will recalculate the route.

[0063] This adaptive networking technology makes the network highly robust, and it can continue to operate normally even when some nodes fail.

[0064] Integrating multi-standard communication protocols into a cross-domain communication network protocol is another technical challenge. This requires designing a unified protocol stack that can handle different types of communication protocols. This unified protocol stack can include the following layers:

[0065] Physical layer: handles signal modulation and demodulation in different frequency bands.

[0066] Data Link Layer: handles frame formatting and error detection for different communication protocols.

[0067] Network layer: implements cross-protocol routing and addressing.

[0068] Transport layer: provides end-to-end reliable data transmission.

[0069] Application layer: handles specific application requirements, such as data compression, encryption, etc.

[0070] This converged protocol stack allows the system to seamlessly switch between different communication protocols, maximizing communication efficiency and reliability.

[0071] This technical solution addresses several key technical issues. First, it resolves the problem of unstable communications in marine environments. By utilizing multi-band access technology, the system can select the optimal communication method based on current environmental conditions, significantly improving communication reliability. Second, it addresses the issue of dynamically changing network topologies. Adaptive networking technology enables the network to automatically adjust to changes in node locations and the addition or removal of nodes. Finally, it addresses the compatibility issues between different communication protocols. By integrating multiple protocols into a unified framework, seamless cross-protocol communication is achieved.

[0072] This technical solution achieves several important benefits. First, it significantly improves the reliability and stability of the marine communication network. Even in adverse sea conditions or when some nodes fail, the network can maintain normal operation. Second, it enhances the adaptability and flexibility of the network. The network can automatically select the optimal communication method and routing path based on actual conditions, without the need for human intervention. Third, it improves network communication efficiency. By selecting the most appropriate communication protocol and frequency band, the system can transmit more information within limited bandwidth. Finally, this technology enables real-time transmission and remote control of marine data, which is of great significance to fields such as marine scientific research, environmental monitoring, and resource exploration.

[0073] Let's use a specific example to illustrate the application of this technology. Let's assume we have established an ocean monitoring network in a certain sea area to monitor marine environmental parameters and vessel activity. This network consists of 100 underwater submersible sensors and 50 surface buoys, covering approximately 100,000 square kilometers of ocean.

[0074] Underwater buoy sensors are deployed at various depths to collect data on water temperature, salinity, current velocity, and other parameters, as well as to detect underwater acoustic signals. These sensors transmit this data via acoustic waves to the nearest surface buoy. In addition to receiving data from the underwater sensors, the surface buoy is also equipped with meteorological sensors, AIS receivers, and radar to gather information from the sea and air.

[0075] Under normal circumstances, buoys primarily communicate using very short wave (VHF), which offers a high data rate. However, in severe weather, such as typhoons, VHF communications can be severely disrupted. In such cases, the system automatically switches to short wave (HF) communication mode. While HF has a lower data rate, it provides more stable communication in adverse weather conditions.

[0076] Each buoy is also equipped with a satellite communication module for communicating with a remote command center. If communications in a local area are interrupted, data can be transmitted via satellite link to ensure timely delivery of information.

[0077] The network's adaptive networking capabilities are particularly important in responding to emergencies. For example, when a ship accidentally struck and damaged several buoys, the network automatically reorganized itself, finding new routing paths to transmit data. Even if some nodes failed, the entire network remained operational.

[0078] This system not only monitors the ocean environment in real time but also promptly detects and tracks suspicious vessel activity. For example, if the system detects a vessel without an AIS signal, it automatically adjusts the operating mode of nearby buoys, increases radar scanning frequency, and quickly transmits relevant information to the command center. This real-time monitoring and rapid response capability is crucial for safeguarding maritime rights and interests.

[0079] Furthermore, the system's flexibility allows it to perform ad hoc tasks. For example, when intensive observation of a specific area is needed, the command center can autonomously network some of the buoys to form a small, high-density observation network. This dynamic adjustment capability greatly enhances the system's practicality.

[0080] Overall, this marine communication system, based on multi-band access technology and adaptive networking for surface wireless communications, provides a powerful tool for real-time, comprehensive monitoring and understanding of the marine environment. It not only promotes the development of marine scientific research but also provides important technical support for the sustainable use of marine resources, the protection of the marine environment, and the management of maritime activities.

[0081] Step S102: performing dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, constructing a Huffman tree based on the frequency of occurrence of each character in the translated character sequence, and generating a Huffman code corresponding to each character based on the Huffman tree;

[0082] Optionally, in this embodiment, the raw information sent by the submersible sensor device may contain various complex data formats, such as floating-point numbers, timestamps, and coordinates. The purpose of the dictionary mapping is to convert these complex data formats into simple character sequences to facilitate subsequent compression. This process is similar to translating from one language to another, but here the complex data format is "translated" into a series of predefined characters.

[0083] The specific implementation process can be as follows: First, we need to predefine a dictionary table. This table maps various possible data types and value ranges to specific characters or character combinations. For example, we can map the temperature range of -10°C to 50°C to the letters A to Z, with each letter representing a range of 2.3°C. Similarly, we can define mapping rules for other parameters such as salinity, depth, and current velocity. When receiving raw data, the system searches this dictionary table and converts each data point into the corresponding character. In this way, a complex set of ocean parameter data is converted into a sequence of characters.

[0084] A key advantage of this mapping method is that it can significantly reduce the length of data representation. For example, a 32-bit floating-point number can be mapped to a single character, which significantly improves the efficiency of subsequent compression. Furthermore, this method can provide a certain degree of data privacy protection, as the lack of a dictionary makes it difficult for external observers to understand the meaning of these character sequences.

[0085] Next, we need to build a Huffman tree. A Huffman tree is a specialized binary tree used for data compression that's constructed based on the frequency of each character. The construction process is as follows: First, we count the frequency of each character in the translated character sequence. Then, we create a priority queue containing all characters, with lower-frequency characters taking priority. Each time, we remove the two lowest-frequency nodes from the queue, create a new parent node with the sum of the frequencies of its two children, and then insert this new node into the queue. This process is repeated until there is only one node left in the queue; this node is the root of the Huffman tree.

[0086] The construction process of the Huffman tree can be illustrated with a specific example. Assume that our translated character sequence contains the following characters and their frequencies: A(10), B(15), C(12), D(3), E(4), F(13). We first create leaf nodes containing these characters and then start merging:

[0087] Merge D(3) and E(4) to create a new node DE(7)

[0088] Merge DE(7) and A(10) to create a new node DEA(17)

[0089] Merge C(12) and F(13) to create a new node CF(25)

[0090] Merge B(15) and DEA(17) to create a new node BDEA(32)

[0091] Finally, merge CF(25) and BDEA(32) to create the root node CFBDEA(57)

[0092] In this way, we get a Huffman tree.

[0093] With the Huffman tree, we can generate a Huffman code for each character. The path from the root node to each leaf node defines the code of the character, with going left being 0 and going right being 1. For example, in the tree above, the code for the character A could be 110, while the code for the character F could be 00.

[0094] A key property of Huffman coding is that frequently occurring characters receive shorter codes, while less frequently occurring characters receive longer codes. This ensures that the overall code length is minimized, resulting in efficient data compression.

[0095] This step solves several key technical issues. First, it addresses the complex format and large volume of raw data. Through dictionary mapping, complex data is converted into simple character sequences, greatly simplifying subsequent processing. Second, it addresses the issue of inefficient data transmission and storage. Huffman coding is a lossless compression technology that can significantly reduce data volume without losing information. Finally, it also addresses data security to a certain extent, as the lack of a dictionary and encoding rules makes it difficult for external observers to decipher the data.

[0096] This technical solution achieves several important technical benefits. First, it significantly improves data transmission efficiency. In marine environments, bandwidth is often a scarce resource. Through this compression method, we can transmit more information within a limited bandwidth. Second, it improves storage efficiency. This is particularly important for marine monitoring systems that need to store large amounts of historical data for a long time. Third, it improves the real-time performance of the system. Reducing the amount of data means shortening transmission time, which is very important for some applications that require rapid response (such as tsunami warning systems). Finally, this method also provides a certain degree of data protection, enhancing the security of the system.

[0097] Let's use a specific example to illustrate the application of this technical solution. Suppose we have a network of underwater buoy sensors deployed in a certain ocean to monitor marine environmental parameters. Each sensor collects data once a minute, including parameters such as water temperature, salinity, pressure, and dissolved oxygen.

[0098] The original data can be shown as follows:

[0099] Timestamp: 2023-09-09 10:15:00

[0100] Water temperature: 28.5℃

[0101] Salinity: 35.2 psu

[0102] Pressure: 10.5 bar

[0103] Dissolved oxygen: 6.8 mg / L

[0104] If this data is transmitted directly, it may take hundreds of bytes. However, through our dictionary mapping, this data can be converted into the following character sequence:

[0105] TJLMN

[0106] Among them, T represents time (assuming we only need to transmit minute information), J represents the water temperature range of 28-29℃, L represents the salinity range of 35-36psu, M represents the pressure range of 10-11bar, and N represents the dissolved oxygen range of 6.5-7.0mg / L.

[0107] Next, we count the frequency of these characters over a period of time. Assume that the statistical results are as follows:

[0108] T:20%, J:15%, L:25%, M:30%, N:10%

[0109] Based on these frequencies, we construct the Huffman tree and generate the Huffman code:

[0110] M:0

[0111] L:10

[0112] J:110

[0113] T:111

[0114] N:1110

[0115] Using this encoding, our original TJLMN sequence becomes: 111 110 10 0 1110.

[0116] This binary sequence only requires 11 bits to represent the original 5 characters, with a compression rate of over 90% compared to the original data.

[0117] In practical applications, this compression method can significantly improve data transmission efficiency. For example, if our sensor network covers 100 square kilometers of ocean and consists of 1,000 sensor nodes, each generating data once per minute, using this compression method, we can transmit all this data using only 10% of the original bandwidth. This not only reduces system operating costs but also enables us to increase the frequency of data collection or cover larger areas of ocean.

[0118] This method also facilitates long-term data storage. Marine environmental monitoring often requires the collection and analysis of long-term historical data. Using this compression method, we can store ten times more historical data within the same storage space, which is extremely valuable for identifying long-term trends and conducting predictive analysis.

[0119] It's important to note that while this compression method is highly efficient, it has its limitations. Most notably, it sacrifices some precision. In our example, we mapped a continuous range of values ​​to discrete characters, which means we lost some fine-grained information. However, for most ocean monitoring applications, this loss of precision is acceptable, as we're more interested in overall trends and significant changes than in minor fluctuations.

[0120] Another consideration is that this approach requires regular updates to the dictionary table and Huffman tree. The distribution of data can change over time, for example due to seasonal variations or long-term climate trends. Therefore, the system needs to be able to periodically recalculate character frequencies and update the Huffman tree to maintain optimal compression.

[0121] In general, this data processing method based on dictionary table mapping and Huffman coding provides a powerful tool for marine environmental monitoring. It not only greatly improves data transmission and storage efficiency, but also provides a basis for real-time monitoring and long-term trend analysis.

[0122] Step S103: Monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjust the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy. Upload the Huffman coding to the superior command center through the preset dual channel according to the dynamically adjusted coding mode and coding parameters.

[0123] Optionally, in this embodiment, the underwater acoustic channel is a complex communication environment whose characteristics are affected by multiple factors, such as water temperature, salinity, pressure, ocean currents, and seabed topography. These factors can lead to problems such as signal attenuation, multipath effects, and frequency-selective fading. Therefore, real-time monitoring of channel status is crucial to ensuring communication quality.

[0124] In this step, we primarily monitor two key parameters: channel noise and channel bit error rate (BER). Channel noise can be estimated by measuring the background noise power at the receiver. This can involve using digital signal processing techniques such as the Fast Fourier Transform (FFT) to analyze the spectral characteristics of the received signal. The channel BER can be calculated by periodically sending a known test sequence and comparing the received sequence with the original sequence at the receiver.

[0125] This real-time monitoring process can be performed every few seconds or minutes, depending on the rate of change of the channel and the requirements of the system. The monitoring results will be used to determine the real-time channel status, which can be classified as "good", "fair" or "poor".

[0126] Next, we need to design an adaptive algorithm to determine the channel coding parameter adjustment strategy that matches the real-time channel state. The core idea of ​​this algorithm is to dynamically adjust the coding method and parameters based on the channel state to achieve optimal communication performance under different channel conditions.

[0127] Adaptive algorithms can be based on machine learning techniques, such as reinforcement learning or neural networks. They leverage historical data to learn the optimal coding strategy for different channel conditions. For example, when the channel is good, a coding method with lower redundancy can be used to improve transmission efficiency; when the channel is poor, a coding method with stronger error correction capabilities can be used to ensure reliability.

[0128] Specific coding parameter adjustment strategies may include adjusting the error correction code type (such as switching from convolutional code to turbo code), changing the coding rate, adjusting the interleaving depth, etc. For example, when the channel status changes from "good" to "fair", the system can reduce the coding rate from 1 / 2 to 1 / 3 to increase redundancy; when the status further deteriorates to "poor", the system can switch to a more powerful low-density parity check (LDPC) code and increase the interleaving depth to resist burst errors.

[0129] Based on the determined adjustment strategy, the system will dynamically adjust the channel encoding method and encoding parameters. This process needs to be synchronized between the sender and receiver, so a special signaling mechanism may be needed to exchange encoding parameter information between the communicating parties.

[0130] Finally, the dynamically adjusted encoding method and parameters are used to perform further channel coding on the Huffman-encoded data. This encoded data is then uploaded to the higher-level command center via a pre-set dual-channel transmission. The dual-channel transmission is designed to further enhance communication reliability, as it mitigates the risk of a single channel failure.

[0131] This technical solution addresses several key technical issues. First, it addresses the highly variable and unpredictable nature of underwater acoustic channels. Through real-time monitoring and adaptive adjustment, the system can quickly respond to changes in channel conditions and maintain optimal communication performance. Second, it addresses the issue of ensuring communication reliability in harsh environments. By dynamically adjusting coding parameters and utilizing dual-channel transmission, the system significantly improves communication success rates under various conditions. Finally, it addresses the trade-off between communication efficiency and reliability. Under favorable channel conditions, the system can select a more efficient coding method, while switching to a more reliable method under adverse conditions.

[0132] This technical solution achieves several important benefits. First, it significantly improves the adaptability and robustness of the underwater communication system. The system can maintain stable communication performance in various marine environments, which is crucial for long-term ocean monitoring and offshore operations. Second, it optimizes the use of communication resources. By dynamically adjusting coding parameters based on actual needs, the system avoids unnecessary resource waste and improves overall communication efficiency. Third, it enhances the system's real-time performance. The adaptive algorithm can quickly respond to channel changes, ensuring that critical information is delivered in a timely manner. Finally, this approach also improves the system's scalability, as the adaptive algorithm can adapt to new environments and needs through learning.

[0133] Let's use a specific example to illustrate the application of this technical solution. Consider an underwater acoustic network deployed in a certain ocean to monitor the marine environment and transmit seafloor seismic data. Nodes in the network need to transmit the collected data to a buoy on the surface, which then transmits it to a command center on land via a satellite link.

[0134] Under normal conditions, the underwater acoustic channel is in good condition, with a signal-to-noise ratio (SNR) of approximately 15dB and a bit error rate (BER) below 10^-5. In this case, the system uses a rate 1 / 2 convolutional code with an interleaving depth of 100 symbols. This configuration provides high data rates while ensuring reliability.

[0135] Suddenly, a typhoon hit. Strong winds and huge waves caused the water to churn violently, generating a large number of bubbles and suspended particles, which severely affected sound wave propagation. The system detected that the SNR had dropped to 5dB and the BER had risen to 10^-3. The adaptive algorithm immediately responded, adjusting the coding strategy to:

[0136] Switch the coding method from convolutional code to more powerful turbo code

[0137] Reduce the coding rate to 1 / 3 and increase redundancy

[0138] Increased interleaving depth to 500 symbols for better resistance to burst errors

[0139] Activate dual-channel transmission mode and use two acoustic channels in different frequency bands at the same time

[0140] After these adjustments were quickly implemented, the system was able to maintain a BER below 10^-4 despite the harsh environment, ensuring the reliable transmission of critical data. As the typhoon passed and channel conditions gradually improved, the system gradually relaxed these strict coding parameters, returning to a more efficient transmission mode.

[0141] Throughout the entire process, the adaptive algorithm continuously learns and optimizes. It records the impact of different weather conditions, sea states, seasons, and other factors on channel conditions, and develops the optimal coding strategy accordingly. Over time, the system becomes increasingly intelligent, able to predictively adjust parameters and even prepare for weather deterioration before it occurs.

[0142] Another advantage of this adaptive system is its ability to adapt to different data types and priorities. For example, when transmitting high-priority earthquake warning data, the system can choose a more conservative encoding strategy to ensure absolutely reliable transmission of this critical data, even under good channel conditions. In contrast, for routine environmental monitoring data, the system can adopt a more aggressive strategy to improve transmission efficiency.

[0143] However, this complex adaptive system also faces several challenges. The first is computational complexity. Real-time monitoring of channel conditions, running the adaptive algorithm, and dynamically adjusting coding parameters all require considerable computing resources. In energy-constrained underwater environments, striking a balance between performance and energy consumption is a key issue. Second, there's the issue of system stability. Excessively frequent parameter adjustments can cause system oscillations, degrading overall performance. Therefore, appropriate stability constraints must be considered when designing adaptive algorithms.

[0144] Furthermore, while dual-channel transmission improves reliability, it also increases energy consumption and complexity. In some cases, activating dual-channel mode might be a trade-off. For example, for real-time video streaming data that tolerates a small amount of packet loss, single-channel transmission might be sufficient, leaving the valuable second channel resources for more critical data.

[0145] Overall, this underwater communication solution based on real-time monitoring and adaptive coding provides strong technical support for ocean observation networks. It not only greatly improves the reliability and efficiency of communication, but also provides a more stable and flexible data transmission platform for marine scientific research and marine resource management.

[0146] From the above description, it can be seen that the air-sea cross-domain communication method provided in the embodiment of the present application can integrate multi-standard communication protocols into the cross-domain communication network protocol and perform adaptive networking through surface wireless communication multi-band access technology; the original information sent by the underwater buoy sensor device is mapped to a dictionary table to obtain a translated character sequence, a Huffman tree is constructed according to the frequency of occurrence of each character in the translated character sequence, and the Huffman code corresponding to each character is generated according to the Huffman tree; the channel noise and channel bit error rate of the underwater acoustic channel are monitored in real time to determine the real-time channel state, and the channel coding parameter adjustment strategy that matches the real-time channel state is determined through an adaptive algorithm, and the channel coding method and coding parameters are dynamically adjusted according to the channel coding parameter adjustment strategy, thereby effectively improving the efficiency of air-sea cross-domain communication.

[0147] In one embodiment of the air-sea cross-domain communication method of the present application, see Figure 2 , and can also include the following:

[0148] Step S201: adding an autonomous switching unit in the cross-domain communication gateway buoy node;

[0149] Step S202: Access a multi-standard communication link through multi-band surface wireless communication, integrate the protocol into the cross-domain communication network protocol by combining the communication protocol and the communication frequency band, and perform adaptive networking.

[0150] Optionally, in this embodiment, first, step S201 involves adding an autonomous switching unit to the cross-domain communication gateway buoy node. The core of this step is to enhance the functionality of the buoy node, enabling it to autonomously process and forward communication signals from different domains (such as underwater and surface). The autonomous switching unit can be understood as an intelligent router that can identify, process, and forward different types of communication protocols and data packets.

[0151] The implementation of this autonomous switching unit can include the following key components: 1) a multi-interface communication module for receiving and sending different types of signals; 2) a protocol identification and conversion module for identifying the protocol type of incoming signals and performing protocol conversion when necessary; 3) an intelligent routing decision module for determining the optimal forwarding path based on the current network status and packet priority; and 4) a cache module for temporarily storing data packets to cope with network congestion or disconnection.

[0152] The core technologies of the autonomous switching unit are software-defined networking (SDN) and network function virtualization (NFV). Through SDN, the switching unit can dynamically configure and manage network resources, implementing flexible routing strategies. NFV allows various network functions, such as firewalls and load balancing, to be implemented on general-purpose hardware, thereby improving system flexibility and scalability.

[0153] This step addresses several key technical challenges in cross-domain communication. First, it resolves the issue of protocol incompatibility between different communication domains. Through the autonomous switching unit, data from the underwater acoustic network can be seamlessly converted into a format suitable for surface wireless transmission. Second, it addresses the issue of dynamically changing network topology. Buoy nodes may move with ocean currents, and the autonomous switching unit can adjust routing strategies in real time to ensure network connectivity. Finally, it addresses the issue of communication optimization in resource-constrained environments. Through intelligent routing decisions, the system can achieve optimal communication within limited bandwidth and energy resources.

[0154] Next, step S202 describes a complex multi-band, multi-standard communication integration process. The goal of this step is to achieve diverse connection methods through surface wireless communication and integrate these different communication protocols and frequency bands into a unified cross-domain communication network.

[0155] The technical process for achieving this step involves the following stages: First, multi-band, multi-standard wireless communication modules must be installed on the buoy nodes. These can include communication equipment for different frequency bands, such as VHF, UHF, microwave, and satellite, as well as modules for different communication standards, such as GSM, CDMA, LTE, and Wi-Fi. Each module requires a corresponding antenna system.

[0156] Secondly, an intelligent communications management system needs to be developed. This system can monitor the status of each communication link in real time, including parameters such as signal strength, latency, and bandwidth. Based on this information, the system can dynamically select the optimal communication link. For example, in near-shore areas, LTE networks can be prioritized, while in offshore areas, satellite communications can be switched.

[0157] Next, we need to design a protocol conversion and fusion layer. This layer converts different communication protocols into a unified, system-defined, cross-domain communication network protocol. This process may involve operations such as message format conversion, address mapping, and QoS parameter adjustment. The core idea of ​​protocol fusion is to extract the commonalities between various protocols and design a sufficiently flexible meta-protocol that is compatible with and encapsulates various existing protocols.

[0158] Finally, the system needs to implement adaptive networking capabilities. This includes processes such as network topology discovery, routing table updates, and link quality assessment. The goal of adaptive networking is to enable the entire network to automatically adjust its structure and parameters based on environmental changes and communication needs to achieve optimal performance.

[0159] The technical principles of this step involve multiple fields, including software-defined radio (SDR), cognitive radio, protocol engineering, and self-organizing networks. SDR technology allows for the implementation of different communication standards through software definition on the same hardware platform, laying the foundation for multi-standard communications. Cognitive radio technology enables the system to intelligently perceive and utilize available spectrum resources. The principles of protocol engineering are used to design flexible and scalable network protocols. The theory of self-organizing networks provides the theoretical foundation for adaptive networking.

[0160] This step addresses several key technical challenges. First, it addresses the issue of insufficient communication coverage in marine environments. By integrating multiple communication methods, the system significantly expands its coverage and reliability. Second, it addresses the issue of interoperability between different communication systems. Through protocol convergence, data from different systems can be transmitted within a unified network. Finally, it addresses network adaptability, enabling the system to respond to dynamically changing marine environments and communication requirements.

[0161] This technical solution achieves several important benefits. First, it significantly improves the reliability and robustness of the ocean communication system. Even if some communication links fail, the system can still maintain connectivity through other means. Second, it improves communication efficiency. By intelligently selecting the optimal link and adaptively networking, the system can achieve maximum communication throughput with limited resources. Third, it enhances the system's scalability. New communication technologies and protocols can be relatively easily integrated into existing systems. Finally, this approach also improves the coverage and data collection capabilities of the ocean observation network, providing strong technical support for marine scientific research and resource exploration.

[0162] Let's use a specific example to illustrate the application of this technical solution. Suppose we have deployed a large-scale ocean observation network in a certain sea, consisting of hundreds of underwater sensor nodes, dozens of buoy nodes, and several shore-based base stations. This network's mission is to collect marine environmental data, monitor marine ecosystems, and support maritime search and rescue, among other functions.

[0163] In traditional systems, subnetworks with different functions can use different communication methods, making seamless information sharing difficult. For example, an environmental monitoring network may rely primarily on satellite communications, while a search and rescue system may rely more heavily on VHF radio. This fragmented communication architecture limits the overall effectiveness of the system.

[0164] We are now applying new cross-domain communication technologies. Each buoy node is equipped with an autonomous switching unit and a multi-band, multi-standard communication module. These buoys can simultaneously support underwater acoustic communications, VHF / UHF radio, 4G / 5G cellular networks, satellite communications, and other methods.

[0165] When an underwater sensor detects an unusual change in water temperature, it first transmits the data to the nearest buoy via an acoustic signal. Upon receiving this acoustic signal, the buoy's autonomous relay unit immediately performs protocol conversion, converting it into data packets suitable for wireless transmission. The relay unit then evaluates the status of each communication link and determines which method to use for data transmission.

[0166] Assuming the buoy is located offshore with a strong 4G signal, the system will transmit the data via the 4G network to the shore-based base station. Simultaneously, the system will notify other nearby buoy nodes of the abnormal event. These nodes will adjust their monitoring parameters, forming a localized, encrypted observation network to obtain more detailed data.

[0167] If the temperature anomaly persists and threatens nearby fishing activities, the system automatically upgrades communication priority. It can simultaneously activate satellite communications and maritime VHF broadcasts to issue alerts to a wider area. All of this is done automatically, without human intervention.

[0168] At the same time, the system's adaptive networking capabilities continuously optimize the network structure. For example, if it detects that certain buoys are experiencing performance degradation due to low battery levels, the network automatically adjusts routing to reduce reliance on these nodes. If new buoys are deployed, the system automatically integrates them into the existing network structure, expanding coverage.

[0169] This integrated communications system not only improves the reliability and efficiency of data transmission but also significantly enhances the functionality of the entire ocean observation network. For example, it enables real-time big data analysis of the ocean environment, enabling earlier prediction and response to marine disasters. During maritime search and rescue operations, information from different subsystems can be quickly integrated, significantly improving search and rescue efficiency.

[0170] In one embodiment of the air-sea cross-domain communication method of the present application, see Figure 3 , and can also include the following:

[0171] Step S301: Pre-establishing a dictionary table for underwater buoy sensor equipment, wherein the dictionary table includes various raw data formats that can be generated by the sensor equipment and defines corresponding translation character sequences for each raw data format;

[0172] Step S302: Obtaining a translated character sequence corresponding to the original information sent by the underwater buoy sensor device according to the mapping result of the dictionary table.

[0173] Optionally, in this embodiment, step S301 first involves pre-establishing a dictionary table specific to the underwater buoy sensor device. This dictionary table is the foundation of the entire data processing system. It contains the various raw data formats that the sensor device can generate, as well as the corresponding translated character sequences for each format. Implementing this step requires a deep understanding of the operating principles and data characteristics of underwater sensors.

[0174] The technical process for achieving this step can include the following stages: First, a comprehensive survey and analysis of various underwater sensors is required. This includes temperature sensors, pressure sensors, salinity sensors, dissolved oxygen sensors, flow rate sensors, etc. Each sensor can have different manufacturers and models, so all possible data formats need to be collected.

[0175] Secondly, a unified format for translating character sequences needs to be designed. This format should be flexible enough to accommodate a variety of data types, while also being concise enough to accommodate the limited bandwidth of underwater communications. The translating character sequence can include information such as data type identifier, timestamp, measurement value, unit, and precision.

[0176] Next, you need to define corresponding translation rules for each raw data format. This may involve operations such as parsing the data format, unit conversion, and precision adjustment. For example, some sensors may use a proprietary binary format, and you need to define how to convert this format into a standard ASCII character sequence.

[0177] Finally, consolidate all this information into a structured dictionary table. This dictionary table can be in XML, JSON, or other suitable data structure format. The dictionary table should be easy to update and maintain, as it may need to be continuously expanded as new sensors are introduced.

[0178] The technical principles of this step involve data standardization, protocol design, and information coding theory. Data standardization principles are used to design a unified format for translating character sequences. Protocol design principles are used to define the structure and fields of data packets. Information coding theory is used to optimize the efficiency of translating character sequences and minimize redundant information.

[0179] Step S301 solves several key technical issues. First, it addresses the diverse data formats of underwater sensors. Sensors from different manufacturers and models can use different data formats, which poses challenges for data integration and processing. By establishing a unified dictionary table, the system can convert all these different formats into a standardized form.

[0180] Secondly, it addresses the limited bandwidth of underwater communications. By carefully designing the translation character sequence, the system can minimize the amount of data required to be transmitted while retaining the necessary information. This is crucial for improving the efficiency of underwater communications.

[0181] Thirdly, it lays the foundation for the system's scalability and compatibility. With the introduction of new sensors, only the dictionary table needs to be updated without modifying the entire system architecture. This greatly improves the system's flexibility and adaptability.

[0182] Step S302 describes how to use the dictionary table established in step S301 to process the actual sensor data. The core of this step is to map the received raw data format into a corresponding translated character sequence.

[0183] The technical process for implementing this step can include the following stages: First, the system needs to receive raw data from the underwater buoy sensor. This can involve receiving data, buffering it, and performing preliminary error checking.

[0184] Second, the system needs to identify the original format of the received data. This may involve analyzing features such as the data header, length, and checksum. Once the data format is determined, the system can search the dictionary table for the corresponding translation rules.

[0185] The system then processes the raw data according to the translation rules. This can include data parsing, format conversion, unit unification, and timestamp addition. The result of this processing is a standardized sequence of translated characters.

[0186] Finally, the system may need to validate the translated data to ensure that no errors were introduced during the translation process. This can include range checks, consistency checks, etc.

[0187] The technical principles of this step involve data parsing, pattern matching, and information transformation theory. Data parsing principles are used to extract useful information from raw data. Pattern matching principles are used to identify data formats and select appropriate translation rules. Information transformation theory guides how to accurately convert between different representations.

[0188] Step S302 addresses the issue of real-time data processing. In underwater environments, real-time data is often very important. By using predefined translation rules, the system can quickly convert raw data into a standard format for subsequent processing and analysis.

[0189] In addition, this step also solves the problem of data quality control. During the translation process, the system can perform preliminary data verification and promptly detect and mark possible outliers or erroneous data.

[0190] Together, these two steps achieve several important technical benefits. First, they significantly improve the efficiency and reliability of underwater data processing. By standardizing the data format, the system can process data from different sensors more quickly and accurately.

[0191] Secondly, this approach improves the scalability and flexibility of the entire underwater observation system. New sensor types can be easily integrated into the system by updating the dictionary table without requiring large-scale modifications to the entire system.

[0192] Thirdly, this approach lays the foundation for underwater big data analysis and real-time monitoring. Standardized data formats make it easier to integrate and compare data from different sources, facilitating the discovery of deeper ocean phenomena and patterns.

[0193] Finally, this approach also improves system interoperability. Standardized data formats make it easier for different research institutions or projects to share and exchange data, promoting cooperation and development in marine scientific research.

[0194] Let's use a specific example to illustrate the application of this technical solution. Suppose we have deployed a large-scale underwater observation network in a certain ocean, including hundreds of submersible buoys, each equipped with multiple sensors to monitor parameters such as seawater temperature, salinity, dissolved oxygen, and flow rate.

[0195] In traditional systems, each sensor uses its own proprietary data format. For example, a temperature sensor might output a string like "-2.5|C" to represent -2.5 degrees Celsius, while a salinity sensor might output "35.1PSU" to represent 35.1 practical salinity units. This inconsistent format creates challenges for data integration and analysis.

[0196] Now, let's apply the new data processing method. First, we create a comprehensive dictionary table. This dictionary table can contain the following:

[0197] Temperature data:

[0198] Original format: "-2.5|C"

[0199] Translation sequence: "T:-2.5,U:C,TS:1633046400"

[0200] Salinity data:

[0201] Original format: "35.1PSU"

[0202] Translation sequence: "S:35.1,U:PSU,TS:1633046400"

[0203] Dissolved oxygen data:

[0204] Original format: "6.5mg / L"

[0205] Translation sequence: "DO:6.5, U:mg / L, TS:1633046400"

[0206] In this translation sequence, we have unified the format: parameter type: value, unit: unit, timestamp: Unix timestamp. This format is concise and contains all the necessary information.

[0207] When the system receives raw data from a beacon, it translates it according to the dictionary. For example, if it receives temperature data "-2.5|C," the system recognizes it as temperature data and then searches for the corresponding translation rules, generating a standardized translation sequence of "T:-2.5,U:C,TS:1633046400." This process is similar for all types of sensor data.

[0208] This standardized data format offers several benefits. First, it greatly simplifies subsequent data processing. Regardless of the sensor type, the processing program can parse and use the data in a unified manner.

[0209] Second, it improves data transmission efficiency. In bandwidth-constrained underwater environments, a compact data format can significantly reduce transmission time and energy consumption. For example, we can further optimize the translation sequence to use shorter codes to represent parameter types and units, such as "T" for temperature and "C" for degrees Celsius.

[0210] Again, this approach improves the scalability of the system. If we want to add a new sensor, we only need to add the corresponding entry in the dictionary table without having to modify the entire system code.

[0211] Finally, this standardized format makes real-time data analysis much easier. For example, we can quickly compare temperature data at different depths or locations because all temperature data are in a unified format.

[0212] In one embodiment of the air-sea cross-domain communication method of the present application, see Figure 4 , and can also include the following:

[0213] Step S401: Counting the frequency of occurrence of each character in the translated character sequence, and constructing a Huffman coding tree according to the frequency of occurrence of the characters, wherein a leaf node of the Huffman tree corresponds to each character, and the weight of the node of the Huffman tree is the frequency of occurrence of the corresponding character;

[0214] Step S402: traverse the constructed Huffman tree, and generate a corresponding Huffman code for each character according to the path code from the root node to the leaf node in the Huffman tree.

[0215] Optionally, in this embodiment, steps S401 and S402 describe a Huffman encoding process for optimizing data transmission from underwater buoy sensors. This process aims to further compress the translated character sequence to accommodate bandwidth-constrained communication conditions in underwater environments. By implementing these two steps, the system can significantly reduce the amount of data required for transmission while maintaining data integrity and recoverability.

[0216] In step S401, the system first needs to perform a statistical analysis on the translated character sequence. This process involves the technical principle of character frequency statistics. Specifically, the system will traverse the entire character sequence and count the number of times each character appears. This can be achieved by using a hash table or array, with each character as the key and the number of occurrences as the value. For example, if the translated character sequence is "TAABBCCCC", the statistical result may be: A: 2, B: 2, C: 4, T: 1.

[0217] Once the statistics are complete, the system constructs a Huffman coding tree based on this frequency information. The Huffman tree construction process is based on the principles of a greedy algorithm. First, the system creates a priority queue and places all characters and their frequencies as leaf nodes in the queue. Then, the system repeats the following steps until only one node remains in the queue: it removes the two lowest-frequency nodes in the queue, creates a new internal node with a frequency equal to the sum of the frequencies of these two nodes, and then places this new node back into the queue. This process ultimately forms a tree-like structure, known as the Huffman tree.

[0218] The construction of the Huffman tree solves a key technical problem: how to assign codes of different lengths to characters of different frequencies. In this way, characters that appear frequently are assigned shorter codes, while characters that appear less frequently are assigned longer codes, thereby minimizing the overall code length.

[0219] In step S402, the system traverses the constructed Huffman tree and generates a corresponding Huffman code for each character. This process is based on the technical principle of tree traversal. Specifically, the system starts from the root node, adds a "0" when moving to the left child node, and adds a "1" when moving to the right child node, until it reaches a leaf node. The path corresponding to the leaf node is the Huffman code of the character. These two steps together achieve several important technical effects. First, they significantly reduce the amount of data that needs to be transmitted. In underwater environments, data transmission is a major bottleneck, and reducing the amount of data can directly improve the efficiency and response speed of the system. This optimization can have a critical impact, especially for application scenarios that require real-time monitoring, such as tsunami warning systems.

[0220] Secondly, this encoding method maintains data integrity. Although the data is compressed, the Huffman tree allows the receiver to fully restore the original character sequence without any information loss. This is crucial for the accuracy of scientific research data.

[0221] Thirdly, this approach improves the system's adaptability. Different datasets can have varying character frequency distributions, and Huffman coding can automatically adapt to these changes, consistently providing optimal compression. This allows the system to flexibly switch between different observation tasks without redesigning the data transmission protocol.

[0222] Let's use a specific example to illustrate the application of this technical solution. Suppose we have deployed an underwater observation network in a certain ocean to monitor seawater temperature, salinity, and dissolved oxygen. After the previous steps, we obtain a series of standardized translation character sequences, as follows:

[0223] "T:25.5,U:C,TS:1633046400; S:35.1,U:PSU,TS:1633046400; DO:6.5,U:mg / L,TS:1633046400"

[0224] In step S401, the system first counts the frequency of occurrence of each character in the sequence. The statistical results can be as follows:

[0225] T:3,::6,2:2,5:3,,:6,U:3,C:1,S:6,3:4,1:2,4:1,0:6,;:2,D:1,O:1,6:2,.:3,P:1,m:1,g:1, / :1,L:1

[0226] The system then constructs a Huffman tree based on these frequencies. In this process, the most frequently occurring characters (such as ":", "", "S", and "0") are placed near the root of the tree, while less frequently occurring characters (such as "C", "D", "O", etc.) are placed deeper in the tree.

[0227] In step S402, the system traverses the Huffman tree and generates a corresponding Huffman code for each character. For example, the most frequent character ":" can be encoded as "00", while the less frequent character "C" can be encoded as "111000".

[0228] Ultimately, the entire character sequence is converted into a string of binary data. For example, "T:25.5" can be encoded as "1010001101111001100111". This binary sequence is much shorter than the original ASCII encoding, which can significantly reduce the amount of data transmitted.

[0229] At the receiving end, the system only needs to know the Huffman tree structure used to decode the binary sequence back to the original character sequence. This process ensures the integrity and accuracy of the data.

[0230] This encoding method is particularly well-suited for underwater environments. Acoustic communication is the primary method of data transmission underwater, but its bandwidth is far lower than wireless communications on land. Huffman coding allows us to transmit more information within this limited bandwidth. For example, if the original data would take 10 seconds to transmit, Huffman coding can reduce it to just 5 seconds. This not only improves data transmission efficiency but also reduces energy consumption, extending the operating time of underwater equipment.

[0231] Furthermore, this approach is highly adaptable. Different types of observation data can have different character distributions. For example, temperature data can contain many digits and decimal points, while chemical composition data can contain more letters. Huffman coding can automatically adapt to these different distributions, consistently providing optimal compression. This means that even if we change the observation content or add new sensors, the system can automatically adjust to maintain efficient data transmission.

[0232] In one embodiment of the air-sea cross-domain communication method of the present application, see Figure 5 , and can also include the following:

[0233] Step S501: deploying an underwater acoustic sensor to monitor the channel noise of the underwater acoustic channel in real time, collecting the power spectrum density of the signal, determining the channel noise based on the power spectrum density, and measuring the error rate between the signal received by the receiving end and the original transmitted signal;

[0234] Step S502: Determine the real-time status of the current underwater acoustic channel based on the monitored channel noise and the error rate in combination with preset channel quality evaluation rules.

[0235] Optionally, in this embodiment, steps S501 and S502 describe a process for real-time monitoring and evaluation of underwater acoustic channel conditions. This process is crucial for optimizing the performance of underwater communication systems, as the complexity and variability of the underwater environment causes channel conditions to rapidly change over time and space. By implementing these two steps, the system can promptly adjust communication strategies to adapt to current channel conditions, thereby improving communication reliability and efficiency.

[0236] In step S501, the system first deploys underwater acoustic sensors to monitor the underwater acoustic channel in real time. These sensors are typically hydrophones, which capture underwater acoustic signals. Hydrophone deployment requires consideration of multiple factors, such as water depth, currents, and seabed topography. Typically, multiple hydrophones are deployed at varying depths and locations to obtain comprehensive channel information.

[0237] The raw signal captured by the underwater acoustic sensor requires a series of processing steps to extract useful information. First, the system performs analog-to-digital conversion, converting the analog signal into a digital signal. Then, using algorithms such as the Fast Fourier Transform (FFT), the signal's power spectral density (PSD) is calculated. The power spectral density reflects the energy distribution of the signal at different frequencies and is an important tool for analyzing channel noise.

[0238] Determining channel noise involves signal processing and statistical analysis techniques. The system analyzes power spectral density characteristics, such as average energy level and peak frequency, to identify and quantify different types of noise. For example, ship noise typically exhibits increased energy in low-frequency bands, while biological noise can exhibit periodic energy fluctuations in specific frequency bands.

[0239] The system also needs to measure the error rate between the received signal and the original transmitted signal. This is typically achieved by sending a known test sequence. The receiver compares the received signal with the original sequence and calculates the bit error rate (BER) or symbol error rate (SER). This process involves fundamental principles of digital communications and information theory.

[0240] Step S501 addresses the technical challenge of accurately measuring channel status in complex underwater environments. Underwater environments contain a variety of noise sources, such as the sounds of marine life, noise from distant ships, and background noise from surface waves. The intensity and characteristics of these noise sources constantly change, posing a significant challenge to measuring channel status. Through real-time monitoring and analysis, the system can promptly capture these changes, providing a basis for subsequent adjustments to communication strategies.

[0241] In step S502, the system determines the real-time status of the underwater acoustic channel based on the monitored channel noise and error rate, combined with preset channel quality evaluation rules. This process involves the technical principles of pattern recognition and decision theory.

[0242] Preset channel quality assessment rules are typically developed based on experience and theoretical analysis. For example, we might categorize channel status into three levels: "good," "fair," and "poor." A rule might include: if the signal-to-noise ratio (SNR) is above a certain threshold and the bit error rate is below a certain threshold, the channel status is considered "good"; if the SNR is at a moderate level or the bit error rate is slightly high, the channel status is considered "fair"; and if the SNR is very low or the bit error rate is very high, the channel status is considered "poor."

[0243] In practice, the system might use fuzzy logic or machine learning algorithms to process these rules. These approaches are better able to handle the uncertainty and complexity of underwater environments. For example, the system might consider the combined effects of multiple parameters rather than simply comparing a single parameter against a threshold.

[0244] Step S502 addresses the technical challenge of converting complex channel measurement results into actionable channel status assessments. The state of an underwater acoustic channel is a multi-dimensional concept, and a single parameter is often insufficient to fully reflect the channel's actual conditions. By comprehensively considering multiple factors, such as noise characteristics and error rates, the system can make more accurate and reliable assessments.

[0245] Together, these two steps achieve several important technical benefits. First, they provide real-time, accurate assessment of the underwater acoustic channel status. This is crucial for optimizing the performance of underwater communication systems. For example, when the system detects deteriorating channel conditions, it can immediately adjust transmit power, modulation scheme, or coding strategy to maintain communication quality.

[0246] Secondly, this approach improves the adaptability and robustness of underwater communication systems. The underwater environment is highly dynamic and unpredictable, making traditional fixed-parameter communication solutions difficult to handle. By using real-time monitoring and evaluation, the system can dynamically adapt to environmental changes, significantly improving communication reliability.

[0247] Finally, this approach provides a foundation for adaptive routing and topology control in underwater networks. In multi-node underwater networks, the channel status of each link directly impacts the overall network performance. By monitoring the status of each link in real time, the system can optimize data transmission paths, avoiding links with poor performance, and thus improving the efficiency of the entire network.

[0248] Let's use a specific example to illustrate the application of this technical solution. Suppose we deploy an underwater acoustic communication network in a certain sea for marine environmental monitoring and seabed resource exploration. The network consists of multiple fixed underwater nodes and some mobile underwater robots.

[0249] At some point, the system detected a sudden drop in communication quality between two fixed nodes. In step S501, the system first analyzed the underwater acoustic sensor data on this link. Power spectral density analysis revealed strong noise interference in the 50-100 Hz frequency band. Simultaneously, the bit error rate suddenly increased from 10^-5 to 10^-3.

[0252] Based on this information, in step S502, the system starts the preset channel quality evaluation rules. Assume that the rules are as follows:

[0253] If the noise power in the 50-100 Hz frequency band exceeds the background noise by 10 dB and the bit error rate is higher than 10^-4, the channel status is judged to be "bad".

[0254] If the noise power in the 50-100 Hz frequency band exceeds the background noise by 5 dB but less than 10 dB, or if the bit error rate is between 10^-5 and 10^-4, the channel status is judged to be "Fair".

[0255] In other cases, the channel status is determined to be "good".

[0256] Based on these rules, the system determines the current channel status as "bad".

[0257] This judgment triggers a series of adaptive measures. First, the system increases transmit power to improve the signal-to-noise ratio. Simultaneously, it switches to a more robust modulation scheme (for example, from QAM to BPSK) and increases the redundancy of the error correction coding. These measures immediately improve communication quality, reducing the bit error rate to an acceptable level.

[0258] At the same time, the system also broadcasts the link status information to other nodes in the network, which allows the network's routing algorithm to temporarily avoid this link and redistribute traffic to other paths with better status.

[0259] The system further analyzed the noise's characteristics and speculated that a large vessel might be passing through the area. It relayed this information to the control center on the surface, which then dispatched a drone for surface reconnaissance, confirming that a large cargo ship was indeed sailing nearby.

[0260] This example demonstrates how real-time channel monitoring and assessment can optimize the performance of underwater communication networks at multiple levels. It not only enables rapid adjustments at the link level, but also optimizes routing at the network level, and even provides valuable information for higher-level decision-making.

[0261] Overall, this underwater acoustic channel management approach based on real-time monitoring and evaluation provides a powerful and flexible solution for underwater communication networks.

[0262] In one embodiment of the air-sea cross-domain communication method of the present application, see Figure 6 , and can also include the following:

[0263] Step S601: Collect historical channel noise and historical bit error rate data and establish a database to analyze the optimal coding parameter configuration under different channel conditions

[0264] Step S602: determining an optimal coding parameter configuration that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjusting the channel coding mode and coding parameters according to the optimal coding parameter configuration.

[0265] Optionally, in this embodiment, steps S601 and S602 describe an adaptive process for optimizing the underwater communication system's coding strategy. This process aims to enable the communication system to maintain optimal performance in a constantly changing underwater environment through historical data analysis and real-time adjustments. By implementing these two steps, the system can automatically select the most appropriate coding method and parameters based on current channel conditions, significantly improving communication reliability and efficiency.

[0266] In step S601, the system first collects historical channel noise and bit error rate data and builds a comprehensive database. This process involves the technical principles of big data collection and management. Specifically, the system records the noise characteristics and corresponding bit error rates under various channel conditions over a long period of time. This data can include information on multiple dimensions, such as signal-to-noise ratio, multipath effect intensity, Doppler shift, water temperature, and salinity. The database design needs to take into account the diversity and time series characteristics of the data. Technologies such as time series databases or column-based storage may be used to optimize query and analysis efficiency.

[0267] After collecting sufficient historical data, the system needs to conduct in-depth analysis to identify the optimal coding parameter configuration for different channel conditions. This analysis process involves the technical principles of data mining and machine learning. For example, the system may use clustering algorithms to identify typical channel state patterns and then use methods such as regression analysis or decision trees to determine the optimal coding parameters for each state. These parameters may include the coding method (such as convolutional code, LDPC code, Turbo code, etc.), code rate, interleaving depth, modulation method, etc.

[0268] Step S601 addresses the technical challenge of extracting useful knowledge from massive amounts of historical data and applying this knowledge to actual communication systems. The complexity and variability of underwater communication environments make traditional fixed parameter solutions difficult to adapt to all situations. By establishing a database and conducting in-depth analysis, the system builds an "experience library" that provides a reliable basis for subsequent adaptive adjustments.

[0269] In step S602, the system needs to design an adaptive algorithm to select the optimal coding parameter configuration based on the current real-time channel state. This process involves the technical principles of real-time systems and decision theory. The core of the adaptive algorithm is to be able to quickly and accurately match the current channel state with patterns in historical data and select the most suitable parameter configuration.

[0270] In specific implementations, the system may employ a variety of technical approaches. For example, a rule-based expert system may be used to encode the knowledge analyzed in S601 into a series of if-then rules. Alternatively, more advanced machine learning methods, such as support vector machines (SVMs) or deep neural networks, may be employed. These methods are capable of handling more complex nonlinear relationships and may provide better performance in certain situations.

[0271] Another important consideration is the real-time nature of the algorithm. Underwater communication systems typically have strict latency requirements, so the adaptive algorithm needs to be able to make decisions in a very short time. This can require the use of optimization techniques such as pre-computation and caching to accelerate the decision-making process.

[0272] Once the optimal encoding parameter configuration is determined, the system needs to dynamically adjust the actual encoding method and parameters. This process involves the technical principles of communication protocols and hardware control. The system must be able to smoothly switch encoding methods or adjust parameters without interrupting communication. This may require the design of a special signaling mechanism to ensure that the sender and receiver can make adjustments synchronously.

[0273] Step S602 addresses the technical challenge of achieving rapid and accurate adaptive adjustments in a real-time system. The underwater communication environment can change significantly over a short period of time, and the system must be able to respond promptly to these changes. By designing an efficient adaptive algorithm and dynamic adjustment mechanism, the system can quickly react to channel state changes and maintain communication quality.

[0274] These two steps together achieve several important technical benefits. First, they significantly improve the adaptability and robustness of the underwater communication system. Through historical data analysis and real-time adaptation, the system is able to maintain excellent performance in a variety of complex underwater environments. This is of great significance for underwater observation networks that require long-term stable operation or for emergency communication systems that are temporarily deployed.

[0275] Secondly, this approach improves the system's resource utilization efficiency. Traditional fixed-parameter solutions often require worst-case scenarios, which can lead to resource waste under good channel conditions. Through dynamic adjustment, the system can maintain optimal resource utilization under varying conditions, ensuring both communication quality and energy efficiency.

[0276] Again, this adaptive method provides a powerful tool for performance evaluation and optimization of underwater communication systems. By collecting and analyzing data over a long period of time, researchers can better understand the characteristics and changing patterns of underwater channels, providing guidance for future system design and optimization.

[0277] Let's use a specific example to illustrate the application of this technical solution. Suppose we deploy an underwater acoustic communication network in the East China Sea for marine environmental monitoring and submarine earthquake early warning. This network must maintain stable and reliable communication under various complex environmental conditions.

[0278] In step S601, the system collects long-term channel noise and bit error rate data in the area. After analysis, researchers found that the channel status in the area can be roughly divided into four categories:

[0279] Shallow water layer in summer: Affected by the thermocline, the signal propagation path is complex and the multipath effect is strong.

[0280] Shallow water layer in winter: The temperature is uniform and the channel is relatively stable, but the background noise is large.

[0281] Storm weather: Sea surface noise increases dramatically and channels become extremely unstable.

[0282] Normal sea conditions: moderate levels of noise and multipath.

[0283] For each state, the researchers determined the optimal encoding parameter configuration through data analysis:

[0284] Summer shallow water layer: Use LDPC code, low code rate (such as 1 / 2), deep interleaving, and QPSK modulation.

[0285] Shallow water layer in winter: Use convolutional code, medium code rate (such as 2 / 3), medium interleaving depth, and 8PSK modulation.

[0286] Stormy weather: Use Turbo code, extremely low bit rate (such as 1 / 3), very deep interleaving, and BPSK modulation.

[0287] Normal sea conditions: Use LDPC codes, high code rate (such as 3 / 4), shallow interleaving, and 16QAM modulation.

[0288] These configurations are stored in the system's database and serve as a reference for the adaptive algorithm.

[0289] In step S602, the system designs an adaptive algorithm based on fuzzy logic. This algorithm receives real-time channel state parameters, including signal-to-noise ratio, multipath strength, and Doppler shift, and then calculates the similarity between the current state and four typical states. Based on the weighted similarities, the algorithm selects an optimal coding parameter configuration or, if necessary, interpolates and generates a new configuration.

[0290] For example, one day the system detects the following channel status:

[0291] Signal-to-noise ratio: 5dB

[0292] Multipath strength: Medium

[0293] Doppler shift: Slight

[0294] Water temperature: 22℃

[0295] Wind speed: 10m / s

[0296] The adaptive algorithm analyzes this state and concludes that it is most similar to "normal sea conditions," but also has some characteristics of "stormy weather." Therefore, it decides to use the following encoding configuration:

[0297] Coding method: LDPC code

[0298] Bit rate: 2 / 3 (between normal sea conditions and stormy weather)

[0299] Weave Depth: Medium (to account for increased wind speeds)

[0300] Modulation: 8PSK (lower than normal sea conditions, but not as high as BPSK)

[0301] The system then begins dynamically adjusting the encoding parameters. It first sends a special signaling message to the receiver, notifying it of the impending parameter adjustment. Then, it switches to the new encoding configuration at the beginning of the next data frame. Upon receiving the signaling message, the receiver adjusts its decoding parameters accordingly.

[0302] This adjustment process was completed within seconds, without interruption in communication. After the adjustment, the system's bit error rate was reduced from 10^-3 to 10^-5, effectively coping with changing channel conditions.

[0303] The system continues to monitor channel status and communication performance. If performance falls short of expectations, it fine-tunes parameters or tries alternative configurations. This status information and adjustment results are recorded in a database for further refinement of the adaptive algorithm.

[0304] This example demonstrates how to optimize the performance of an underwater communication system through historical data analysis and real-time adaptation. It can not only cope with various complex channel conditions, but also continuously learn and improve, ensuring long-term stable underwater communications.

[0305] Overall, this coding optimization method based on historical data analysis and real-time adaptation provides a powerful and flexible solution for underwater communication systems.

[0306] In one embodiment of the air-sea cross-domain communication method of the present application, see Figure 7 , and can also include the following:

[0307] Step S701: Packing the Huffman coded data and adding necessary header information and checksums;

[0308] Step S702: Uploading the packaged Huffman coded data to the upper command center simultaneously through the preset dual channels, wherein the dual channels may be different physical channels.

[0309] Optionally, in this embodiment, steps S701 and S702 describe a process of packaging the Huffman-coded data, adding necessary information, and uploading it to the higher-level command center via dual channels. This process involves data encapsulation, error detection, and multi-channel transmission technology, aiming to improve the reliability and efficiency of data transmission.

[0310] In step S701, the system first packages the Huffman-encoded data. Huffman coding is a lossless data compression algorithm that assigns codes of varying lengths to each character based on its frequency. Frequent characters receive shorter codes, thereby compressing the overall data volume. The main purpose of the packaging process is to organize these codes, which can vary in length, into a structured data packet for easier transmission and subsequent processing.

[0311] Packaging can involve splitting the encoded data into fixed-size chunks, each of which can contain multiple encoded characters. This chunking helps improve transmission efficiency by allowing the system to begin decoding once it has received enough data, rather than waiting for the entire file to be transferred. Chunking also facilitates error detection and correction; if a chunk is damaged during transmission, only that chunk needs to be retransmitted, rather than the entire file.

[0312] Next, the system needs to add the necessary header information. This header typically includes, but is not limited to, the packet's sequence number, timestamp, source and destination addresses, data type identifier, and a Huffman tree structure (or a pointer to the location where the Huffman tree is stored). This information is crucial for the receiver to correctly parse and process the data. For example, the sequence number can be used to reassemble packets that may have arrived out of order; the timestamp can be used to determine the timeliness of the data; and the Huffman tree structure is essential for the decoding process.

[0313] Adding a checksum is a critical step in ensuring data integrity. Common checksums include cyclic redundancy check (CRC) and checksums. CRC is widely used due to its strong error detection capabilities. Choosing a checksum requires a balance between error detection capability, computational complexity, and overhead. In scenarios requiring extremely high reliability, more complex forward error correction codes (FEC), such as Reed-Solomon codes, may be used. They not only detect errors but also correct them to a certain extent.

[0314] Step S701 solves the technical problem of how to organize the compressed data into a format suitable for transmission and provide the necessary information and protection mechanisms. By properly packaging and adding necessary information, the system can improve the reliability and efficiency of data transmission and facilitate processing at the receiving end.

[0315] In step S702, the system uploads the packaged data simultaneously to the higher-level command center via the pre-set dual channels. The key technical principle here is multipath transmission. Multipath transmission significantly improves transmission reliability and throughput by utilizing multiple different physical paths to transmit the same data simultaneously.

[0316] Dual-channel transmission can take many forms. For example, it can use both a wired network (such as fiber optics) and a wireless network (such as satellite communications) simultaneously, or use two independent physical links within the same type of network. The primary purpose of this redundant design is to improve system availability and interference resistance. If one channel fails or is interfered with, the other channel can still ensure data transmission.

[0317] Implementing dual-channel transmission requires resolving several technical challenges. The first is data distribution and synchronization. The system must decide whether to copy the entire data packet and transmit it over both channels, or to split the packet and transmit it separately over each channel. The former provides full redundancy, while the latter offers higher transmission speeds.

[0318] The second issue is data reassembly at the receiving end. Because the transmission characteristics of the two channels can differ, the order and timing of data packets arriving can vary. The receiving end needs to be able to correctly reassemble these packets, which may arrive out of order. This is typically achieved by adding sequence numbers in step S701.

[0319] Another issue to consider is how to handle inconsistent data transmitted across two channels. This can be caused by transmission errors or interference. The system can employ various strategies, such as selecting the first arriving packet or performing packet-level checksum comparisons to select the correct packet.

[0320] Step S702 solves the technical problem of how to improve the reliability and efficiency of data transmission. By utilizing dual-channel transmission, the system can maintain the continuity and reliability of communication in a harsh or unstable network environment.

[0321] These two steps together achieve several important technical benefits. First, they significantly improve data transmission reliability. By adding checksums and using dual-channel transmission, the system can effectively cope with errors and interference that may occur during transmission. Even if one channel fails completely, the other channel can still ensure data transmission.

[0322] Secondly, this method improves the efficiency of data transmission. Huffman coding itself is an efficient compression method. Combined with reasonable packaging and dual-channel transmission, the system can transmit more information under limited bandwidth conditions.

[0323] Again, this approach improves the flexibility and adaptability of the system. By adding rich header information to the data packet, the system can dynamically adjust the transmission strategy as needed, such as selecting the optimal transmission path based on network conditions.

[0324] Finally, this method provides the basis for secure data transmission. Although encryption is not explicitly mentioned in the steps, the added header information and checksum facilitate the implementation of encryption and authentication.

[0325] In order to effectively improve the efficiency of air-sea cross-domain communication, the present application provides an embodiment of an air-sea cross-domain communication device for implementing all or part of the contents of the air-sea cross-domain communication method, see Figure 8 The air-sea cross-domain communication device specifically includes the following contents:

[0326] The self-organizing network module 10 is used to integrate the multi-standard communication protocol into the cross-domain communication network protocol and perform adaptive networking through the multi-band access technology of surface wireless communication;

[0327] The coding compression module 20 is used to perform dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, construct a Huffman tree based on the frequency of occurrence of each character in the translated character sequence, and generate a Huffman code corresponding to each character based on the Huffman tree;

[0328] The channel correction module 30 is used to monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjust the channel coding method and coding parameters according to the channel coding parameter adjustment strategy. According to the dynamically adjusted coding method and coding parameters, the Huffman code is uploaded to the superior command center through the preset dual channel.

[0329] From the above description, it can be seen that the air-sea cross-domain communication device provided in the embodiment of the present application can integrate multi-standard communication protocols into the cross-domain communication network protocol and perform adaptive networking through surface wireless communication multi-band access technology; perform dictionary table mapping on the original information sent by the underwater buoy sensor device to obtain a translated character sequence, construct a Huffman tree according to the frequency of occurrence of each character in the translated character sequence, and generate the Huffman code corresponding to each character according to the Huffman tree; monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state through an adaptive algorithm, and dynamically adjust the channel coding method and coding parameters according to the channel coding parameter adjustment strategy, thereby effectively improving the efficiency of air-sea cross-domain communication.

[0330] From a hardware perspective, in order to effectively improve the efficiency of air-sea cross-domain communication, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the air-sea cross-domain communication method, and the electronic device specifically includes the following contents:

[0331] Processor (processor), memory (memory), communication interface (Communications Interface) and bus; wherein, the processor, memory, and communication interface complete communication with each other through the bus; the communication interface is used to realize information transmission between the air-sea cross-domain communication device and related equipment such as the core business system, user terminal and related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., and this embodiment is not limited to this. In this embodiment, the logic controller can be implemented with reference to the embodiment of the air-sea cross-domain communication method in the embodiment, and the embodiment of the air-sea cross-domain communication device, the contents of which are incorporated herein, and the repeated parts are not repeated.

[0332] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0333] In practical applications, part of the air-sea cross-domain communication method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capabilities of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0334] The client device may include a communication module (i.e., a communication unit) that can establish a communication connection with a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0335] Figure 9 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 9 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0336] In one embodiment, the air-sea cross-domain communication method function may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0337] Step S101: integrating the multi-standard communication protocol into the cross-domain communication network protocol through the multi-band access technology of surface wireless communication and performing adaptive networking;

[0338] Step S102: performing dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, constructing a Huffman tree based on the frequency of occurrence of each character in the translated character sequence, and generating a Huffman code corresponding to each character based on the Huffman tree;

[0339] Step S103: Monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjust the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy. Upload the Huffman coding to the superior command center through the preset dual channel according to the dynamically adjusted coding mode and coding parameters.

[0340] From the above description, it can be seen that the electronic device provided in the embodiment of the present application integrates multi-standard communication protocols into cross-domain communication network protocols and performs adaptive networking through multi-band access technology of surface wireless communication; performs dictionary table mapping on the original information sent by the underwater buoy sensor device to obtain a translated character sequence, constructs a Huffman tree according to the frequency of occurrence of each character in the translated character sequence, and generates the Huffman code corresponding to each character according to the Huffman tree; monitors the channel noise and channel bit error rate of the underwater acoustic channel in real time, determines the real-time channel state, determines the channel coding parameter adjustment strategy that matches the real-time channel state through an adaptive algorithm, and dynamically adjusts the channel coding method and coding parameters according to the channel coding parameter adjustment strategy, thereby effectively improving the efficiency of air-sea cross-domain communication.

[0341] In another embodiment, the air-sea cross-domain communication device can be configured separately from the central processor 9100. For example, the air-sea cross-domain communication device can be configured as a chip connected to the central processor 9100, and the air-sea cross-domain communication method function can be implemented under the control of the central processor.

[0342] like Figure 9 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 9 In addition, the electronic device 9600 may also include all components shown in Figure 9 For components not shown, reference may be made to the prior art.

[0343] like Figure 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0344] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0345] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0346] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), or a SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 by the central processing unit 9100.

[0347] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0348] The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0349] Based on different communication technologies, multiple communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing typical telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0350] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the air-sea cross-domain communication method in the above-mentioned embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all steps of the air-sea cross-domain communication method in the above-mentioned embodiment, where the execution subject is a server or a client, are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0351] Step S101: integrating the multi-standard communication protocol into the cross-domain communication network protocol through the multi-band access technology of surface wireless communication and performing adaptive networking;

[0352] Step S102: performing dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, constructing a Huffman tree based on the frequency of occurrence of each character in the translated character sequence, and generating a Huffman code corresponding to each character based on the Huffman tree;

[0353] Step S103: Monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjust the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy. Upload the Huffman coding to the superior command center through the preset dual channel according to the dynamically adjusted coding mode and coding parameters.

[0354] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application integrates the multi-standard communication protocol into the cross-domain communication network protocol and performs adaptive networking through the multi-band access technology of surface wireless communication; the original information sent by the underwater buoy sensor device is mapped to a dictionary table to obtain a translated character sequence, a Huffman tree is constructed according to the frequency of occurrence of each character in the translated character sequence, and the Huffman code corresponding to each character is generated according to the Huffman tree; the channel noise and channel bit error rate of the underwater acoustic channel are monitored in real time to determine the real-time channel state, and the channel coding parameter adjustment strategy that matches the real-time channel state is determined through an adaptive algorithm, and the channel coding method and coding parameters are dynamically adjusted according to the channel coding parameter adjustment strategy, thereby effectively improving the efficiency of air-sea cross-domain communication.

[0355] The embodiments of the present application also provide a computer program product capable of implementing all steps of the air-sea cross-domain communication method in the above-mentioned embodiment, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the air-sea cross-domain communication method are implemented. For example, the computer program / instruction implements the following steps:

[0356] Step S101: integrating the multi-standard communication protocol into the cross-domain communication network protocol through the multi-band access technology of surface wireless communication and performing adaptive networking;

[0357] Step S102: performing dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, constructing a Huffman tree based on the frequency of occurrence of each character in the translated character sequence, and generating a Huffman code corresponding to each character based on the Huffman tree;

[0358] Step S103: Monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjust the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy. Upload the Huffman coding to the superior command center through the preset dual channel according to the dynamically adjusted coding mode and coding parameters.

[0359] From the above description, it can be seen that the computer program product provided in the embodiment of the present application integrates multi-standard communication protocols into cross-domain communication network protocols and performs adaptive networking through multi-band access technology of surface wireless communication; performs dictionary table mapping on the original information sent by the underwater buoy sensor device to obtain a translated character sequence, constructs a Huffman tree according to the frequency of occurrence of each character in the translated character sequence, and generates the Huffman code corresponding to each character according to the Huffman tree; monitors the channel noise and channel bit error rate of the underwater acoustic channel in real time, determines the real-time channel state, determines the channel coding parameter adjustment strategy that matches the real-time channel state through an adaptive algorithm, and dynamically adjusts the channel coding method and coding parameters according to the channel coding parameter adjustment strategy, thereby effectively improving the efficiency of air-sea cross-domain communication.

[0360] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0361] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0362] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0363] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0364] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. An air-sea cross-domain communication method, characterized in that: Applied to a heterogeneous wide area communication network composed of distributed cross-domain communication gateway buoy nodes at sea, the heterogeneous wide area communication network is used to receive original information uploaded by underwater submersible sensor equipment deployed on the seabed, and upload the original information to a superior command center, the method includes: Through the multi-band access technology of surface wireless communication, multi-standard communication protocols are integrated into the cross-domain communication network protocol and adaptive networking is performed; Perform dictionary mapping on the original information sent by the underwater buoy sensor device to obtain a corresponding translated character sequence, construct a Huffman tree according to the frequency of occurrence of each character in the translated character sequence, and generate a Huffman code corresponding to each character according to the Huffman tree; Monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, dynamically adjust the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy, and upload the Huffman code to the superior command center through the preset dual channel according to the dynamically adjusted coding mode and coding parameters.

2. The air-sea cross-domain communication method according to claim 1, characterized in that: The method of integrating the multi-standard communication protocol into the cross-domain communication network protocol and performing adaptive networking through the multi-band access technology of surface wireless communication includes: Adding autonomous switching units in the cross-domain communication gateway buoy nodes; Through multi-band access to multi-standard communication links via surface wireless communications, the protocol is integrated into the cross-domain communication network protocol in combination with the communication protocol and communication frequency band, and adaptive networking is performed.

3. The air-sea cross-domain communication method according to claim 1, characterized in that: The original information sent by the underwater buoy sensor device is mapped into a dictionary table to obtain a corresponding translated character sequence, including: Pre-establishing a dictionary table for underwater buoy sensor equipment, wherein the dictionary table includes various raw data formats that may be generated by the sensor equipment and defines corresponding translation character sequences for each raw data format; The translated character sequence corresponding to the original information sent by the underwater buoy sensor device is obtained according to the mapping result of the dictionary table.

4. The air-sea cross-domain communication method according to claim 1, characterized in that: The step of constructing a Huffman tree according to the frequency of occurrence of each character in the translated character sequence, and generating a Huffman code corresponding to each character according to the Huffman tree, comprises: Counting the frequency of occurrence of each character in the translated character sequence, and constructing a Huffman coding tree according to the frequency of occurrence of the characters, wherein a leaf node of the Huffman tree corresponds to each character, and the weight of the node of the Huffman tree is the frequency of occurrence of the corresponding character; The constructed Huffman tree is traversed, and a corresponding Huffman code is generated for each character according to the path code from the root node to the leaf node in the Huffman tree.

5. The air-sea cross-domain communication method according to claim 1, characterized in that: The real-time monitoring of the channel noise and the channel bit error rate of the underwater acoustic channel to determine the corresponding real-time channel state includes: Deploy an underwater acoustic sensor to monitor the channel noise of the underwater acoustic channel in real time, collect the power spectrum density of the signal, determine the channel noise based on the power spectrum density, and measure the error rate between the signal received by the receiving end and the original transmitted signal; The real-time state of the current underwater acoustic channel is determined based on the monitored channel noise and the error rate in combination with preset channel quality evaluation rules.

6. The air-sea cross-domain communication method according to claim 1, characterized in that: The step of determining a channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjusting the channel coding mode and coding parameters according to the channel coding parameter adjustment strategy, includes: Collect historical channel noise and historical bit error rate data and establish a database to analyze the optimal coding parameter configuration under different channel conditions; An optimal coding parameter configuration matching the real-time channel state is determined by setting an adaptive algorithm, and the coding mode and coding parameters of the channel are dynamically adjusted according to the optimal coding parameter configuration.

7. The air-sea cross-domain communication method according to claim 1, characterized in that: The method of uploading the Huffman code to the superior command center through a preset dual channel according to the dynamically adjusted coding mode and coding parameters includes: Packing the Huffman coded data and adding necessary header information and checksums; The packaged Huffman coded data is uploaded to the superior command center simultaneously through a preset dual channel, wherein the dual channels may be different physical channels.

8. An air-sea cross-domain communication device, characterized in that: Applied to a heterogeneous wide area communication network composed of distributed cross-domain communication gateway buoy nodes at sea, the heterogeneous wide area communication network is used to receive original information uploaded by underwater submersible sensor equipment deployed on the seabed, and upload the original information to a superior command center, the device includes: Self-organizing network module, used to integrate multi-standard communication protocols into cross-domain communication network protocols and perform adaptive networking through multi-band access technology of surface wireless communication; The coding compression module is used to map the original information sent by the underwater buoy sensor device into a dictionary table to obtain a corresponding translated character sequence, construct a Huffman tree according to the frequency of occurrence of each character in the translated character sequence, and generate a Huffman code corresponding to each character according to the Huffman tree; The channel correction module is used to monitor the channel noise and channel bit error rate of the underwater acoustic channel in real time, determine the corresponding real-time channel state, determine the channel coding parameter adjustment strategy that matches the real-time channel state by setting an adaptive algorithm, and dynamically adjust the channel coding method and coding parameters according to the channel coding parameter adjustment strategy, and upload the Huffman code to the superior command center through the preset dual channel according to the dynamically adjusted coding method and coding parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the air-sea cross-domain communication method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the air-sea cross-domain communication method described in any one of claims 1 to 7 are implemented.

Images