A cross-protocol adaptive conversion and dynamic link optimization system

Abstract

The application relates to the technical field of unmanned aerial vehicle communication, in particular to a cross-protocol adaptive conversion and dynamic link optimization system. The system comprises a general protocol conversion gateway, a link management module and a protocol-link adaptation module. The general protocol conversion gateway is used for automatically identifying unmanned aerial vehicle communication protocols and realizing bidirectional conversion with a general protocol of a nest; the link management module is used for monitoring and predicting the stability of multiple heterogeneous communication links and performing dynamic switching of the links; and the protocol-link adaptation module is used for generating matched transmission parameter configuration strategies according to the identified protocol types to guide link selection and configuration. The application realizes the cross-over from passive response to active prevention and from general transmission to fine optimization according to protocol characteristics through integration of protocol automatic identification and conversion, link stability evaluation and switching based on prediction and protocol-aware link adaptation, and significantly improves the compatibility, reliability, security and overall service quality of heterogeneous unmanned aerial vehicle cluster communication.

Description

Technical Field

[0001] This invention relates to the field of technical fields, and specifically to a cross-protocol adaptive conversion and dynamic link optimization system. Background Technology

[0002] With the rapid development and widespread application of drone technology, its operational modes in fields such as inspection, surveying, logistics, and emergency rescue are evolving from single-machine, single-point operations to multi-machine collaboration, and from line-of-sight operations to beyond-line-of-sight long-range operations. In this process, stable, reliable, and efficient unified management and remote communication of cross-brand drones have become crucial support. However, current drone communication systems face two major challenges. First, protocol barriers are prominent. Different brands of drones typically use their own proprietary, non-open communication protocols. These protocols differ significantly in data frame structure, command sets, encoding methods, and even encryption mechanisms, making it difficult for drones of different brands to connect to a unified drone pod or ground control station, creating "information silos" and severely restricting the collaborative operation capabilities of heterogeneous drone swarms. Second, existing communication link optimization technologies have limitations. Traditional multipath transmission or link aggregation technologies primarily focus on improving bandwidth or achieving failover by utilizing the redundancy of multiple physical links under a single protocol. Their optimization decisions are typically based on simple current or historical network metrics (such as signal strength and packet loss rate), switching only after a link quality degradation is detected, resulting in significant switching delays and data interruption risks. More importantly, existing solutions generally lack the ability to perceive the characteristics of upper-layer application protocols, failing to differentiate link selection and parameter configuration based on different protocol requirements (such as extremely low latency for control commands, high bandwidth for image transmission data, and high computing power requirements for secure communication). This leads to coarse-grained network resource allocation, making it difficult to meet the refined communication quality requirements of diverse UAV services. Therefore, there is an urgent need for a system that can deeply integrate adaptive protocol conversion and intelligent link optimization to overcome protocol compatibility challenges and achieve a leap from "connectivity is sufficient" to "high-quality connectivity."

[0003] Therefore, the existing technology still needs further development. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a cross-protocol adaptive conversion and dynamic link optimization system to solve the problems existing in the prior art.

[0005] To achieve the above technical objectives, this invention provides a cross-protocol adaptive conversion and dynamic link optimization system, comprising: A universal protocol conversion gateway is used to communicate with drones and drone nests to automatically identify the communication protocols used by the drones and perform corresponding protocol conversions. The link management module is used to connect to multiple heterogeneous communication networks, evaluate the stability parameters of each communication link in real time, and dynamically select a communication link for the UAV based on the evaluation results. The protocol-link adaptation module is connected to the general protocol conversion gateway and the link management module respectively. It is used to select the corresponding link transmission parameter configuration strategy for the link management module according to the protocol type identified by the general protocol conversion gateway, thereby realizing the coordinated control of protocol conversion and link optimization.

[0006] Specifically, the universal protocol conversion gateway includes a programmable protocol parsing chip, a protocol identification module, and a protocol conversion engine; The programmable protocol parsing chip is used to capture the initial communication handshake signal and subsequent business data stream between the UAV and the UAV nest. The protocol identification module is connected to the programmable protocol parsing chip and is used to perform deep packet analysis on the captured initial communication handshake signal and the service data stream, extract protocol fingerprints including frame structure features, handshake timing features and specific instruction set features, and match the protocol fingerprints with the built-in extensible protocol feature library to determine the communication protocol type and version information used by the UAV. The protocol conversion engine is connected to the protocol identification module. It has a pre-built syntax rule library and semantic mapping rules for different protocol types. Based on the output of the protocol identification module, it calls the corresponding rules to parse the private protocol data stream of the UAV into an intermediate abstract representation, and then serializes the intermediate abstract representation into a general control protocol data stream supported by the UAV's nest, thus realizing bidirectional protocol conversion.

[0007] Specifically, the deep packet analysis performed by the protocol identification module includes static feature matching and dynamic behavior learning; The static feature matching is used to compare the bit pattern, data packet length distribution, and port number information of the fixed fields in the initial communication handshake signal; The dynamic behavior learning is used to continuously monitor the business data stream after communication is established. By analyzing the data packet sending interval pattern, confirmation mechanism, retransmission behavior, and statistical characteristics of payload content, a dynamic behavior profile of the UAV communication protocol is constructed. The scalable protocol feature library supports remote updates. When the protocol identification module cannot match known protocol features, the protocol fingerprint and corresponding dynamic behavior profile are marked as new protocol samples and uploaded to the cloud analysis platform. After the cloud analysis platform confirms that it is a new protocol through a clustering algorithm, a new protocol feature description file is generated and distributed to the scalable protocol feature library, realizing the online evolution of protocol identification capabilities.

[0008] Specifically, the protocol conversion engine includes a protocol parsing submodule, a semantic mapping submodule, and a serialization submodule; The protocol parsing submodule performs lexical and syntactic analysis on the input private protocol data stream based on the syntax rule library, and constructs a structured syntax tree that represents its control instructions, state data and parameter information; The semantic mapping submodule maps the node elements in the structured syntax tree to intermediate operation instructions and intermediate data objects with standardized semantic definitions that are independent of specific protocols, according to the semantic mapping rules. The semantic mapping rules define the conversion functions from specific fields of the source protocol to intermediate representations and context-dependent conditional mapping logic. The serialization submodule rearranges and encapsulates the intermediate operation instructions and intermediate data objects into data packets conforming to the target protocol format according to the data format specifications of the general control protocol supported by the UAV nest, thus completing the protocol conversion. At the same time, the protocol conversion engine records the latency and resource consumption indicators during the conversion process to optimize the scheduling of subsequent conversion tasks.

[0009] Specifically, the link management module includes a link monitoring unit, a stability prediction unit, and a handover decision unit; The link monitoring unit continuously collects stability parameters, including real-time signal strength, signal-to-noise ratio, data packet round-trip time, packet loss rate, and available bandwidth, from the multiple heterogeneous communication network interfaces. The stability prediction unit has a built-in time series prediction model. It receives the historical and real-time stability parameter sequences collected by the link monitoring unit, predicts the change trend of various stability parameters of each communication link in the future preset time window, and outputs the prediction results including the predicted stability score and potential quality degradation time points. The handover adjudication unit receives the prediction results from the stability prediction unit and the transmission parameter configuration strategy from the protocol-link adaptation module. Based on the preset adjudication strategy, the adjudication strategy takes into account at least the predicted stability score, the link attribute matching degree required by the transmission parameter configuration strategy, and the overhead brought by the handover itself. Before predicting the current link quality degradation, it actively triggers the link handover command to forward the UAV's data stream to the optimal target link selected by the handover adjudication unit.

[0010] Specifically, the protocol-link adaptation module maintains a protocol feature-link configuration mapping table, which records the preferred link attribute set and transmission parameter configuration strategy associated with different protocol types and their dynamic behavior profiles; After the protocol identification module completes the protocol identification, the protocol-link adaptation module queries the mapping table according to the identified protocol type to obtain the corresponding transmission parameter configuration strategy. The transmission parameter configuration strategy includes the packet fragmentation size, forward error correction coding scheme, heartbeat packet interval, service quality level identifier, and encryption algorithm suite recommended for the protocol type. The protocol-link adaptation module distributes the transmission parameter configuration strategy to the handover decision unit of the link management module, so that when the handover decision unit performs link selection or parameter configuration, it can prioritize matching communication links that meet the preferred link attribute set and apply the transmission parameter configuration strategy to the selected link, thereby achieving deep coupling optimization of protocol characteristics and link performance.

[0011] Specifically, the decision-making strategy executed by the switching decision-making unit includes: First, based on the transmission parameter configuration strategy issued by the protocol-link adaptation module, the candidate links are initially screened to exclude links that do not meet the minimum bandwidth requirements, maximum latency tolerance, or specified encryption algorithm support capabilities. Secondly, the links that pass the initial screening are sorted according to the predicted stability score output by the stability prediction unit. Then, the estimated handover cost from the current active link to each candidate link is calculated, and the handover cost includes link reconstruction delay, packet reordering cost and signaling cost. Finally, a comprehensive evaluation function is constructed. The input variables of this function include the predicted stability score of the candidate link, the matching weight with the protocol preferred link attribute, and the negative weight of the handover overhead. The handover decision unit selects the candidate link with the highest value of the comprehensive evaluation function as the target link and triggers a seamless handover process one protection time interval before the potential quality degradation time point.

[0012] Specifically, the system also includes a security adaptation module, which is integrated into the general protocol conversion gateway and works in conjunction with the protocol conversion engine and the protocol-link adaptation module. The security adaptation module is used to automatically detect the encryption method used by the UAV communication data stream. The detection method includes analyzing the payload entropy value of the data packet, identifying specific encryption protocol handshakes, and parsing encryption-related protocol fields. After identifying the encryption method, the security adaptation module dynamically loads the corresponding decryption algorithm component and performs a secure key negotiation process with the UAV to obtain or generate a decryption key, thereby decrypting the encrypted data for protocol parsing by the protocol conversion engine. At the same time, the security adaptation module synchronizes the identified encryption method strength information to the protocol-link adaptation module. When generating the transmission parameter configuration strategy, the protocol-link adaptation module considers the encryption method strength as one of the factors in selecting a communication link that supports the corresponding computing power.

[0013] Specifically, the general protocol conversion gateway adopts a distributed deployment architecture, including a master gateway node and at least one slave gateway node. The master gateway node is directly connected to the UAV nest, and the slave gateway nodes are deployed at the network edge or in different geographical locations. The master gateway node is responsible for maintaining the global protocol feature library, protocol conversion rule library, and status information of each UAV session. When the movement of a UAV causes a degradation in the link quality between it and the currently serving gateway node, the switching decision unit of the link management module can trigger inter-gateway session migration while deciding to switch the network link. The master gateway node coordinates the synchronization of the current UAV's protocol identification context, protocol conversion session status, and security key materials to the target slave gateway node, which then takes over the subsequent protocol conversion and forwarding tasks, realizing the continuity of cross-geographical region protocol adaptive conversion and link optimization services.

[0014] Specifically, the link management module also includes a resource reservation submodule, which is used to interact with the network controller corresponding to the target link and perform bandwidth reservation based on the protocol type before the handover decision unit determines that a handover to a new communication link is required. The resource reservation submodule initiates a resource reservation request to the network infrastructure of the target link based on the transmission parameter configuration strategy provided by the protocol-link adaptation module, particularly regarding the quality of service level identifier and estimated bandwidth requirements of the protocol used by the UAV. This request carries the reserved bandwidth value, maximum latency constraint, and protocol priority identifier. After the network infrastructure confirms successful resource reservation, the handover decision unit performs the final link handover operation, thereby ensuring that after the handover is completed, the UAV's data stream can immediately obtain a transmission channel with guaranteed quality of service that conforms to its protocol characteristics, avoiding performance jitter caused by resource contention in the initial handover phase.

[0015] Beneficial effects: The cross-protocol adaptive conversion and dynamic link optimization system provided by this invention offers several significant advantages over existing technologies. First, the system achieves automatic identification and seamless conversion of various proprietary and general protocols through a universal protocol conversion gateway, fundamentally solving the problem of uniformity in heterogeneous UAV access. The gateway's integrated static feature matching and dynamic behavior learning mechanisms enable it not only to identify known protocols but also to adapt to unknown or updated protocols through learning and evolution. This greatly improves the system's compatibility and lifespan, breaks down technical barriers between brands, and lays a solid foundation for achieving truly "plug-and-play" UAV swarm management.

[0016] Secondly, through the collaboration of the link management module and the protocol-link adaptation module, the system achieves a leap from passive response to proactive prediction, and from general optimization to on-demand customization. The link stability prediction function based on a time-series prediction model can anticipate link quality degradation trends in advance and complete a smooth switch before communication interruption occurs, completely avoiding the risk of command loss or control interruption caused by switching delays in traditional solutions, significantly improving the reliability and continuity of the UAV control link. Simultaneously, the protocol-aware link adaptation mechanism enables fine-grained configuration of network resources according to protocol characteristics. For example, it automatically allocates low-latency links and configures strong error correction for high-frequency control commands, and allocates high-bandwidth links for high-definition image transmission, achieving dual optimization of communication quality and resource utilization efficiency.

[0017] Furthermore, by introducing a security adaptation module and a distributed gateway architecture, the system further enhances the robustness, security, and scalability of the overall solution. The security adaptation module achieves automatic identification and adaptation of encrypted communication, eliminating the need for manual key pre-setting, thus improving usability while ensuring data security. It feeds back encryption strength to the link selection strategy, avoiding computational latency caused by high-strength encryption and achieving a balance between security and performance. The distributed architecture supports stateless migration of sessions between gateways, enabling continuous protocol conversion and optimization services for UAVs moving over a wide area, effectively supporting long-endurance, large-scale operational tasks. Finally, the resource reservation submodule ensures the quality of service after the switch by pre-applying for guaranteed resources on the target link before switching, achieving truly seamless, zero-aware switching. In summary, this invention provides highly reliable, high-performance, and highly secure intelligent communication assurance for heterogeneous UAV clusters through multi-layered, modular, and deep collaboration. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system composition of the cross-protocol adaptive conversion and dynamic link optimization system provided in a specific embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0020] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0021] Please see Figure 1 This invention provides a cross-protocol adaptive conversion and dynamic link optimization system, comprising: A universal protocol conversion gateway is used to communicate with drones and drone nests to automatically identify the communication protocols used by the drones and perform corresponding protocol conversions. The link management module is used to connect to multiple heterogeneous communication networks, evaluate the stability parameters of each communication link in real time, and dynamically select a communication link for the UAV based on the evaluation results. The protocol-link adaptation module is connected to the general protocol conversion gateway and the link management module respectively. It is used to select the corresponding link transmission parameter configuration strategy for the link management module according to the protocol type identified by the general protocol conversion gateway, thereby realizing the coordinated control of protocol conversion and link optimization.

[0022] It should be further explained that this system integrates protocol conversion and link optimization to address the issue of different brands of drones being unable to uniformly access the universal drone hub due to incompatible proprietary communication protocols. The system's three core modules collaborate efficiently through an internal data bus and message queue. The universal protocol conversion gateway, as the core processing unit at the protocol level, is responsible for direct or indirect communication with drones, and its primary task is to overcome protocol barriers. The link management module, as the core processing unit at the network level, is responsible for connecting to access devices in various heterogeneous communication networks such as 4G / 5G public networks, WiFi hotspots, and dedicated wireless networks, focusing on ensuring the quality of the transmission channel. The protocol-link adaptation module acts as the "smart glue" connecting the two core modules. Based on the protocol identification results, it dynamically guides the link management module on how to "tailor-make" a transmission strategy for the specific protocol, rather than using a one-size-fits-all approach, thus achieving end-to-end adaptive optimization from protocol parsing to network transmission.

[0023] Understandably, this system breaks away from the limitations of traditional solutions where protocol conversion and link management are independent. By introducing a protocol-link adaptation module, the system can understand the differentiated requirements of different protocols for link characteristics (for example, some proprietary protocols are extremely sensitive to latency, while others have high bandwidth requirements), and make precise link resource configuration and switching decisions accordingly. This collaborative control mechanism enables drones to not only achieve communication connectivity when accessing cross-brand and cross-protocol systems, but also obtain high-quality, high-reliability communication services that match the characteristics of their protocols, thus improving the overall intelligence level and communication efficiency of heterogeneous drone swarm management.

[0024] Specifically, the universal protocol conversion gateway includes a programmable protocol parsing chip, a protocol identification module, and a protocol conversion engine; The programmable protocol parsing chip is used to capture the initial communication handshake signal and subsequent business data stream between the UAV and the UAV nest. The protocol identification module is connected to the programmable protocol parsing chip and is used to perform deep packet analysis on the captured initial communication handshake signal and the service data stream, extract protocol fingerprints including frame structure features, handshake timing features and specific instruction set features, and match the protocol fingerprints with the built-in extensible protocol feature library to determine the communication protocol type and version information used by the UAV. The protocol conversion engine is connected to the protocol identification module. It has a pre-built syntax rule library and semantic mapping rules for different protocol types. Based on the output of the protocol identification module, it calls the corresponding rules to parse the private protocol data stream of the UAV into an intermediate abstract representation, and then serializes the intermediate abstract representation into a general control protocol data stream supported by the UAV's nest, thus realizing bidirectional protocol conversion.

[0025] It should be further explained that the universal protocol conversion gateway is the core hardware and logical carrier for achieving protocol adaptation, and its specific design includes: (1) Programmable protocol parsing chips, such as those based on FPGA (Field Programmable Gate Array), have the advantage of being able to reconfigure their packet capture and preprocessing logic through hardware description languages, thereby efficiently and with low latency processing raw bit streams of various unknown or proprietary protocol formats. It can capture all data frames uploaded from the physical layer at line speed, including handshake signals at the beginning of connection establishment (such as SYN / ACK sequences, specific beacon frames) and continuous service data streams after connection establishment.

[0026] (2) The protocol identification module is the "brain" of the gateway. Its deep packet analysis process is divided into two stages: a) Static Feature Matching Phase: This phase primarily analyzes the initial handshake signal. The module extracts static features from the data packets, such as: specific bit patterns in the preamble / synchronization header (e.g., 0xAA55), packet length (fixed at 128 bytes or variable), source / destination port numbers (e.g., dedicated port 5600), and fixed timing intervals for handshake message exchanges (e.g., a 100ms interval between request and response). These features are combined into an initial "protocol fingerprint."

[0027] b) Dynamic Behavior Learning Phase: After communication is established, the module continuously monitors the business data flow and analyzes its dynamic behavior characteristics. This includes: statistically analyzing the transmission interval patterns of command and telemetry data packets (e.g., commands every 50ms, telemetry every 20ms), observing confirmation and retransmission mechanisms (e.g., stop-and-wait protocols or sliding windows), and analyzing the entropy distribution of data payload content and the value range of specific fields. These dynamic characteristics are supplemented into the protocol fingerprint, forming a more accurate protocol profile.

[0028] (3) The protocol conversion engine is the "translator" that performs specific conversion tasks. Its workflow is as follows: First, the protocol parsing submodule performs lexical analysis (identifying field boundaries and delimiters) and syntax analysis (constructing a structured syntax tree representing "takeoff command, parameter: altitude = 50 meters" based on the syntax rule base) on the input stream.

[0029] Then, the semantic mapping submodule maps the syntax tree nodes to intermediate representations according to the semantic mapping rules. For example, it maps "takeoff instruction" to the standard opcode OP_TAKEOFF and the parameter "altitude=50" to the intermediate data object {"altitude":50, "unit":"m"}.

[0030] Finally, the serialization submodule encodes the intermediate representation into a binary stream and outputs it according to the specifications of a common nesting protocol (such as a format based on the MAVLink extension).

[0031] Furthermore, the extensible protocol feature library, syntax rule library, and semantic mapping rules all support online updates. When an unknown protocol is identified, the system can upload its fingerprint and behavior logs to the cloud analysis platform. The platform then uses machine learning clustering analysis to generate new feature and rule description files, which are then distributed to the gateway, achieving a closed-loop evolution of "learning-recognition-conversion" capabilities.

[0032] Understandably, by combining programmable chips with software modules, the gateway can not only recognize known proprietary protocols (such as DJI SkyPort and Autel), but also adapt to unknown or slightly modified protocols through behavioral learning. Decomposing the conversion process into three steps—parsing, mapping, and serialization—and introducing an intermediate abstract representation greatly improves the system's scalability and maintainability. When adding support for a new protocol, only a set of syntax and semantic rules needs to be added, without modifying the core conversion logic, effectively solving the problem of protocol compatibility across multiple drone brands.

[0033] Specifically, the deep packet analysis performed by the protocol identification module includes static feature matching and dynamic behavior learning; The static feature matching is used to compare the bit pattern, data packet length distribution, and port number information of the fixed fields in the initial communication handshake signal; The dynamic behavior learning is used to continuously monitor the business data stream after communication is established. By analyzing the data packet sending interval pattern, confirmation mechanism, retransmission behavior, and statistical characteristics of payload content, a dynamic behavior profile of the UAV communication protocol is constructed. The scalable protocol feature library supports remote updates. When the protocol identification module cannot match known protocol features, the protocol fingerprint and corresponding dynamic behavior profile are marked as new protocol samples and uploaded to the cloud analysis platform. After the cloud analysis platform confirms that it is a new protocol through a clustering algorithm, a new protocol feature description file is generated and distributed to the scalable protocol feature library, realizing the online evolution of protocol identification capabilities.

[0034] It should be further explained that the protocol identification process, especially the dynamic behavior learning and online evolution mechanism, is described in detail in this implementation as follows: (1) Specific parameters for static feature matching include, but are not limited to: preamble fixed bit pattern (e.g., 8 bytes 0xFF00AA55FF00AA55), handshake phase packet length sequence (e.g., [64, 128, 64]), and the UDP / TCP port number used (e.g., 5560, 14550). These features constitute the "hard" features of the protocol fingerprint, which are used for rapid initial screening.

[0035] (2) Dynamic behavior learning is key to improving recognition robustness, especially for protocols where static features are obfuscated or encrypted. Specific learning content includes: a) Transmission interval patterns: Statistical analysis of the transmission periods and jitter of different message types, such as control commands, telemetry data, and heartbeat packets. For example, the heartbeat packet interval is identified as 1 second ± 10 ms, and the telemetry reporting interval is identified as 100 ms ± 5 ms.

[0036] b) Acknowledgment and retransmission behavior: Analyze whether an acknowledgment (ACK) is given for each data packet, what the estimated timeout retransmission time (RTO) is (e.g., 200ms), and whether the selective repeat (SACK) mechanism is used.

[0037] c) Payload Statistical Characteristics: Calculate the Shannon entropy of the data payload portion; high entropy values ​​may indicate encryption or compression. Analyze the numerical range of specific offset positions; for example, the 2 bytes starting from the 5th byte may represent altitude, with a value range typically between 0 and 5000 (meters). Through continuous learning (e.g., a learning window of 1000 data packets), a dynamic behavioral profile of the protocol is constructed, which can be represented as a feature vector.

[0038] (3) The specific process of the online evolution mechanism is as follows: When the local feature library fails to match (similarity is lower than the threshold, such as 85%), the protocol identification module packages the static fingerprint and dynamic profile feature vector of the session into a new protocol sample and uploads it to the cloud analysis platform through a secure channel. The cloud platform collects unknown samples from all network gateways and analyzes them using an unsupervised clustering algorithm (such as DBSCAN). The DBSCAN algorithm has two key parameters: neighborhood radius eps and minimum number of samples min_samples. Experimental verification shows that for the protocol feature vector in this scenario, setting eps=0.3 (based on the Euclidean distance after feature normalization) and min_samples=5 is a better choice. The reasons are as follows: eps=0.3 can effectively distinguish different protocols with obvious feature differences, while avoiding misjudging new categories due to minor differences caused by normal communication jitter; min_samples=5 means that only when at least 5 gateways report highly similar unknown protocol samples is it considered to be a new protocol with a certain degree of universality, rather than noise from individual devices, which improves the reliability of new protocol discovery. When a clustering algorithm generates a new cluster, and the center distance of this cluster to all known protocol clusters is greater than eps. At step 2 (i.e., 0.6), the platform confirms the discovery of a new protocol. Subsequently, the platform analyzes samples within the cluster and, automatically or after expert review, generates a protocol feature description file (in JSON or XML format) containing static features, dynamic behavior patterns, and suggested syntax and semantic mapping rules. This file is then pushed to all online gateway devices to update their local scalable protocol feature library and related rule library.

[0039] Understandably, by combining static and dynamic features, the accuracy and anti-interference capability of protocol identification are significantly enhanced. The introduction of a cloud-based online evolution mechanism enables the system to possess "lifelong learning" capabilities, allowing it to cope with the constant emergence of new or improved proprietary protocols. This greatly extends the system's effective lifespan, reduces the risk of system failure due to protocol updates, and achieves a leap from passive adaptation to proactive learning.

[0040] Specifically, the protocol conversion engine includes a protocol parsing submodule, a semantic mapping submodule, and a serialization submodule; The protocol parsing submodule performs lexical and syntactic analysis on the input private protocol data stream based on the syntax rule library, and constructs a structured syntax tree that represents its control instructions, state data and parameter information; The semantic mapping submodule maps the node elements in the structured syntax tree to intermediate operation instructions and intermediate data objects with standardized semantic definitions that are independent of specific protocols, according to the semantic mapping rules. The semantic mapping rules define the conversion functions from specific fields of the source protocol to intermediate representations and context-dependent conditional mapping logic. The serialization submodule rearranges and encapsulates the intermediate operation instructions and intermediate data objects into data packets conforming to the target protocol format according to the data format specifications of the general control protocol supported by the UAV nest, thus completing the protocol conversion. At the same time, the protocol conversion engine records the latency and resource consumption indicators during the conversion process to optimize the scheduling of subsequent conversion tasks.

[0041] It should be further explained that the protocol conversion engine is the core of achieving lossless semantic conversion. This implementation method further details its internal working mechanism and optimizations as follows: (1) Protocol parsing submodule working process: The syntax rule base defines the syntax of each private protocol in a form similar to Backus normal form (BNF). The lexical analyzer divides the byte stream into meaningful "lexical units" (Tokens) according to the delimiters (such as commas and spaces), fixed-length fields, and identifier rules defined in the rules. For example, for the data stream "CMD,TAKEOFF,ALT,50", the lexical analysis can obtain Tokens: [type:CMD], [instruction:TAKEOFF], [parameter:ALT], [value:50]. The syntax analyzer then organizes these Tokens into a syntax tree according to the syntax rules (such as <instruction>::=<instruction type><instruction name><parameter pair list>). The tree nodes contain the original value of the field, the data type, and its position in the data packet.

[0042] (2) Semantic Mapping Submodule Working Process: The intermediate representation defines a standardized set of operation instructions (such as takeoff, land, goTo) and data structures (such as GPS coordinates {lat, lon, alt}). Semantic mapping rules exist in the form of rule tables or scripts. The conversion function is responsible for direct value conversion. For example, dividing the integer value (unit: 0.1 meters) of the source protocol altitude field by 10 yields a floating-point number in meters in the intermediate representation. The conditional mapping logic handles more complex cases. For example, if the value of the "flight mode" field in the syntax tree is "2" and the previous instruction is "unlock", it is mapped to the intermediate instruction takeoff; otherwise, it is mapped to setMode(altitude_hold). This ensures the accurate translation of protocol semantics.

[0043] (3) Serialization Submodule Operation Process: The general control protocol of the nest (e.g., a custom binary protocol) has a fixed data packet format: packet header (2-byte synchronization header) + command ID (1 byte) + data length (1 byte) + payload (N bytes) + checksum (2 bytes). The serialization submodule packages intermediate operation instructions (e.g., takeoff mapped to command ID 0x01) and intermediate data objects (e.g., height value 50.0 encoded as a 4-byte floating-point number) according to this format and calculates the CRC checksum and fills it. Finally, the target protocol data packet is generated.

[0044] (4) Performance monitoring and optimization: After each conversion, the protocol conversion engine records key metrics, including: single packet conversion latency t_conv, CPU utilization c_usage, and memory usage increment m_delta. These metrics are aggregated and used to dynamically adjust the conversion task scheduling strategy. For example, when the system detects a backlog of conversion tasks for a high-latency protocol (t_conv>5ms), it can automatically reduce the parsing depth of the protocol's data stream or assign it a higher processing priority to ensure the real-time performance of the overall system.

[0045] Understandably, through the decoupled design of "parsing-mapping-serialization," the protocol conversion engine possesses a high degree of modularity and scalability. Introducing an intermediate presentation layer decouples the system's core logic from specific protocols; adding support for new protocols only requires adding the corresponding front-end parsing rules and back-end serialization templates, without affecting the core mapping logic. Simultaneously, by monitoring conversion performance and performing feedback optimization, the system can adaptively balance conversion accuracy and processing efficiency, maintaining stable and efficient processing capabilities when facing massive, heterogeneous UAV data streams, ensuring the real-time nature of control commands.

[0046] Specifically, the link management module includes a link monitoring unit, a stability prediction unit, and a handover decision unit; The link monitoring unit continuously collects stability parameters, including real-time signal strength, signal-to-noise ratio, data packet round-trip time, packet loss rate, and available bandwidth, from the multiple heterogeneous communication network interfaces. The stability prediction unit has a built-in time series prediction model. It receives the historical and real-time stability parameter sequences collected by the link monitoring unit, predicts the change trend of various stability parameters of each communication link in the future preset time window, and outputs the prediction results including the predicted stability score and potential quality degradation time points. The handover adjudication unit receives the prediction results from the stability prediction unit and the transmission parameter configuration strategy from the protocol-link adaptation module. Based on the preset adjudication strategy, the adjudication strategy takes into account at least the predicted stability score, the link attribute matching degree required by the transmission parameter configuration strategy, and the overhead brought by the handover itself. Before predicting the current link quality degradation, it actively triggers the link handover command to forward the UAV's data stream to the optimal target link selected by the handover adjudication unit.

[0047] It should be further explained that the link management module is the core of realizing dynamic link optimization. Its key lies in "predictive" switching, rather than traditional "reactive" switching. The specific design is as follows: (1) The link monitoring unit polls each network interface at a fixed frequency (e.g., 10Hz). The parameters collected include: a) Physical layer parameters: Received Signal Strength Indicator (RSSI), in dBm; Signal-to-Noise Ratio (SNR), in dB.

[0048] b) Network layer parameters: Round-trip time (RTT), measured by sending ICMPPing or specific probe packets, in milliseconds; Loss rate, calculated based on the sent sequence number, in %; Available bandwidth, estimated using tools such as Path Chirp, in Mbps. These parameters are encapsulated as time-series data S(t) = {RSSI, SNR, RTT, Loss, BW}.

[0049] (2) The prediction model built into the stability prediction unit preferably adopts a Long Short-Term Memory (LSTM) network. The model input is the link parameter sequence {S(tN),...,S(t-1)} of the past N time steps. After experimentation, N=10 was selected, that is, using the past 1 second (10 The model uses historical data (0.1s) to predict the future. The model outputs predicted values ​​for each parameter over the next M time steps: {S'(t), S'(t+1), ..., S'(t+M-1)}. Setting M=20 means predicting the trend over the next 2 seconds. The model structure is a two-layer LSTM with 128 hidden units per layer, followed by a fully connected layer to output the predicted values. The model is trained using historical real-world link data, and the loss function is mean squared error (MSE).

[0050] (3) Prediction result processing: Based on the predicted future sequence S'(t)...S'(t+M-1), a comprehensive prediction stability score Score_pred is calculated.

[0051] Furthermore, one feasible calculation method is: Score_pred=w1 f1(RSSI')+w2 f2(1 / RTT')+w3 f3(1 / Loss')+w4 f4(BW'), where RSSI', RTT', Loss', and BW' are the average or minimum values ​​of the predicted sequence, f1, f2, f3, and f4 are normalization functions, and w1, w2, w3, and w4 are weight coefficients, and w1+w2+w3+w4=1. At the same time, by analyzing the time point when any key parameter (such as RSSI) in the predicted sequence first falls below a set threshold (such as -100dBm), the potential quality degradation time point T_degrade is determined. If no degradation occurs within the prediction window, then T_degrade is set to t+M (i.e., the end time of the prediction window). (4) The switching adjudication unit is the decision center. The core of its adjudication strategy is a multi-objective optimization function used to calculate the comprehensive utility value U_i of each candidate link i: U_i=α (Score_pred_i / Score_pred_max)+β (MatchDegree_i / MatchDegree_max)-γ (Cost_switch_i / Cost_switch_max) Where, Score_pred_i is the predicted stability score of candidate link i; Score_pred_max is the maximum predicted score among all candidate links, used to maximize future link quality. MatchDegree_i is the degree of matching between the attributes of candidate link i and the transmission parameter configuration strategy issued by the protocol-link adaptation module. For example, if the strategy requires low latency, then the link with a smaller RTT has a higher matching degree, used to meet specific protocol requirements. Cost_switch_i is the estimated cost of switching from the current link to candidate link i, including handover latency, signaling overhead, and data retransmission loss, used to minimize handover cost. α, β, and γ are weighting coefficients, representing the degree of importance attached to stability, protocol matching degree, and handover cost, respectively, and can be dynamically adjusted according to the mission phase (e.g., α is high during the cruise phase, β is high during the execution of critical instructions). The link with the highest U_i is selected as the target link. The handover trigger timing is T_degrade_current - T_protect, which is the predicted degradation time of the current link T_degrade_current minus a protection advance T_protect (the preferred value is 300ms, to allow time for handover operation and network convergence). This achieves "handover 1-2 seconds in advance".

[0052] Understandably, by introducing an LSTM model for link quality prediction, the system can proactively detect link attenuation trends, triggering handover before actual communication quality deteriorates. This avoids the "handover blackout period" and control command loss risks associated with traditional threshold-based handover, significantly improving the continuity and reliability of UAV control. The handover decision-making strategy comprehensively considers future link quality, protocol requirements, and handover costs. The decision is no longer simply "choosing the strongest signal," but rather an optimal economic choice based on the mission context, achieving a shift from "brainless handover" to "intelligent path selection."

[0053] Specifically, the protocol-link adaptation module maintains a protocol feature-link configuration mapping table, which records the preferred link attribute set and transmission parameter configuration strategy associated with different protocol types and their dynamic behavior profiles; After the protocol identification module completes the protocol identification, the protocol-link adaptation module queries the mapping table according to the identified protocol type to obtain the corresponding transmission parameter configuration strategy. The transmission parameter configuration strategy includes the packet fragmentation size, forward error correction coding scheme, heartbeat packet interval, service quality level identifier, and encryption algorithm suite recommended for the protocol type. The protocol-link adaptation module distributes the transmission parameter configuration strategy to the handover decision unit of the link management module, so that when the handover decision unit performs link selection or parameter configuration, it can prioritize matching communication links that meet the preferred link attribute set and apply the transmission parameter configuration strategy to the selected link, thereby achieving deep coupling optimization of protocol characteristics and link performance.

[0054] It should be further explained that the protocol-link adaptation module is the bridge connecting the protocol world and the network world, and its core is the protocol feature-link configuration mapping table. This table is a dynamically configurable knowledge base, and each record contains at least the following fields: [Protocol Type ID], [Dynamic Behavioral Profile Features (such as command frequency, data volume)], [Preferred Link Attribute Set], and [Transmission Parameter Configuration Strategy]. The specific design is as follows: (1) Optimize the set of link attributes: This is a requirement for the "quality" of the link. For example: a) For protocols that send high-frequency, small-sized control commands (such as the protocol of a certain brand of racing drone, with a command frequency >100Hz), the preferred attributes are "extremely low latency (RTT<20ms)" and "high stability (packet loss rate <0.1%)", while the bandwidth requirement is not high.

[0055] b) For protocols used to transmit high-definition video transmission data, the preferred attributes are "high bandwidth (>10Mbps)" and "moderate latency (RTT<100ms)", which can tolerate a certain amount of instantaneous jitter.

[0056] c) For certain proprietary protocols with extremely high security requirements, preferred attributes also include "support for hardware encryption acceleration".

[0057] (2) Transmission parameter configuration strategy: This is the configuration of the link "use". Specific parameters include: a) Message Fragmentation Size (MTU): Set according to the typical message size of the protocol. For example, for small instruction protocols, a smaller MTU (such as 300 bytes) can be set to reduce fragmentation and reassembly latency; for large block data transmission protocols, a standard MTU (1500 bytes) can be set to improve throughput.

[0058] b) Forward Error Correction (FEC) Coding Scheme: The tolerance for packet loss is set according to the protocol. For control commands, a more redundant FEC (such as RS(10,7)) is used to ensure that the command can be reached to the greatest extent; for image transmission data, a less redundant FEC or only retransmission can be used.

[0059] c) Heartbeat message interval: The frequency of external keep-alive probes is dynamically adjusted according to the protocol's own heartbeat mechanism to avoid waste caused by repeated transmissions.

[0060] d) Quality of Service (QoS) Level Labeling: Different DSCP / TOS labels are assigned to different types of data streams to enable network devices to prioritize them. For example, control commands are labeled EF (Accelerated Forwarding), telemetry data is labeled AF41, and image transmission data is labeled AF31.

[0061] e) Encryption Algorithm Suite: Specifies the security algorithms to be applied on the selected link, such as AES-256-GCM for encryption and ECDHE for key exchange. Once the protocol identification module identifies the protocol type, the protocol-link adaptation module performs a joint query not only based on the protocol type but also on the dynamic behavioral profile observed in the current session (such as actual command frequency) to obtain the most accurate strategy. For example, the matching strategy may be fine-tuned for the same protocol during hovering and flight paths due to different data patterns.

[0062] Understandably, the protocol-link adaptation module enables the system to achieve protocol-aware, fine-grained network resource management. Traditional link optimization does not consider the characteristics of upper-layer application protocols and often adopts a uniform optimization strategy, resulting in limited effectiveness. This invention uses a mapping table to strongly correlate protocol characteristics with network configuration, enabling network resources to be "allocated on demand and precisely deployed." For example, it automatically selects a low-latency private network and configures strong error correction for high-frequency control commands, while selecting a high-bandwidth public network for image transmission data. This achieves overall service quality optimization, allowing drones using different protocols to obtain the best communication experience. This is a key manifestation of cross-protocol adaptive optimization.

[0063] Specifically, the decision-making strategy executed by the switching decision-making unit includes: First, based on the transmission parameter configuration strategy issued by the protocol-link adaptation module, the candidate links are initially screened to exclude links that do not meet the minimum bandwidth requirements, maximum latency tolerance, or specified encryption algorithm support capabilities. Secondly, the links that pass the initial screening are sorted according to the predicted stability score output by the stability prediction unit. Then, the estimated handover cost from the current active link to each candidate link is calculated, and the handover cost includes link reconstruction delay, packet reordering cost and signaling cost. Finally, a comprehensive evaluation function is constructed. The input variables of this function include the predicted stability score of the candidate link, the matching weight with the protocol preferred link attribute, and the negative weight of the handover overhead. The handover decision unit selects the candidate link with the highest value of the comprehensive evaluation function as the target link and triggers a seamless handover process one protection time interval before the potential quality degradation time point.

[0064] It should be further explained that this implementation specifies and refines the adjudication strategy, and describes its step-by-step decision-making process as follows: (1) Preliminary Screening (Hard Filtering): Based on the technical requirements specified in the transmission parameter configuration strategy, set hard filtering conditions. For example, if the strategy requires a minimum bandwidth of B_min = 2Mbps and a maximum tolerable latency of D_max = 50ms, then all candidate links with currently available bandwidth lower than B_min or average RTT higher than D_max are directly excluded. If the strategy specifies an encryption algorithm (e.g., AES-256 must be supported), links without the corresponding hardware or software acceleration capabilities are also excluded. This step ensures that candidate links meet the basic communication requirements of the protocol.

[0065] (2) Predictive score ranking: The links that pass the screening are ranked in descending order based on the predicted stability score Score_pred calculated by the stability prediction unit. The score reflects the overall quality expectation of the link in the future.

[0066] (3) Switching cost calculation: The switching cost Cost_switch needs to be quantified and calculated. This mainly includes: a) Link reconstruction delay T_setup: This includes the time required for processes such as association with a new access point / base station, authentication, and IP address acquisition. It can be estimated using historical statistics.

[0067] b) Packet reordering overhead C_reorder: During the handover, packets may be transmitted simultaneously on the old and new links, causing out-of-order packets at the receiving end. This overhead can be estimated as the product of the number of out-of-order packets and the overhead of reordering a single packet.

[0068] c) Signaling overhead C_signaling: The bandwidth and resource consumption incurred by the control signaling (such as handover request / response) generated during the handover process itself. The total overhead can be modeled as: Cost_switch = k1 T_setup+k2 C_reorder+k3 C_signaling, where k1, k2, and k3 are weighting coefficients used to unify the units.

[0069] (4) Comprehensive Evaluation and Decision-Making: Construct the evaluation function U_i. The setting of weight coefficients α, β, and γ is crucial. In a preferred embodiment, α=0.5, β=0.3, and γ=0.2. The reason for this setting is that stability (α) is the foundation, and is given the highest weight of 0.5; protocol matching degree (β) determines the business experience, and is given the second highest weight of 0.3; switching cost (γ) needs to be controlled, but should not excessively hinder necessary switching, and is given a weight of 0.2. These weights can be fine-tuned by the administrator in the system configuration interface according to the actual scenario. Calculate the U_i value of all candidate links and select the link with the largest U_i as the target link.

[0070] (5) Handover Trigger: The seamless handover process is triggered at the time point T_switch = T_degrade_current - T_protect. T_protect is the protection time interval, and its preferred value is 300ms. The reason for choosing 300ms is that it provides ample time (usually less than 200ms) for link layer switching, route updates, and session migration operations in most wireless networks (such as the switch from WiFi to 5G), while reserving a 100ms margin to cope with network jitter, ensuring that the new link is fully ready and can carry services before the T_degrade_current time point, thus achieving a smooth transition.

[0071] Understandably, this hierarchical decision-making strategy, from hard-condition filtering to soft-score ranking and comprehensive cost-benefit analysis, makes the handover decision-making process both rigorous and flexible. It avoids switching to links that don't meet basic protocol requirements or have excessively high handover costs simply to achieve high predictive scores, and also avoids remaining on deteriorating links due to excessive concern about handover costs. Through quantitative evaluation and multi-objective optimization-based decision-making, the system can make the optimal handover choice for a specific protocol session in a complex multi-link environment, achieving the best balance between link resource utilization and service quality.

[0072] Specifically, the system also includes a security adaptation module, which is integrated into the general protocol conversion gateway and works in conjunction with the protocol conversion engine and the protocol-link adaptation module. The security adaptation module is used to automatically detect the encryption method used by the UAV communication data stream. The detection method includes analyzing the payload entropy value of the data packet, identifying specific encryption protocol handshakes, and parsing encryption-related protocol fields. After identifying the encryption method, the security adaptation module dynamically loads the corresponding decryption algorithm component and performs a secure key negotiation process with the UAV to obtain or generate a decryption key, thereby decrypting the encrypted data for protocol parsing by the protocol conversion engine. At the same time, the security adaptation module synchronizes the identified encryption method strength information to the protocol-link adaptation module. When generating the transmission parameter configuration strategy, the protocol-link adaptation module considers the encryption method strength as one of the factors in selecting a communication link that supports the corresponding computing power.

[0073] It should be further explained that the security adaptation module ensures data security in cross-protocol communication and achieves adaptive adaptation of encrypted communication. The specific design is as follows: (1) Automatic detection of encryption method: The security adaptation module works in parallel with the protocol identification module to perform security feature analysis on the captured data stream.

[0074] a) Payload Entropy Analysis: Calculate the Shannon entropy H(x) = -Σp(x_i)log2p(x_i) of the packet payload (excluding the protocol header), where p(x_i) is the probability of the byte value x_i occurring. The entropy value of encrypted data is typically close to 8 (completely random). Set a threshold H_threshold = 7.5; when the calculated entropy value is higher than this threshold, there is a strong suspicion that the data is encrypted. This threshold of 7.5 is based on statistical analysis of a large amount of plaintext protocol and common encryption protocol (such as AES encryption) data, which can effectively distinguish most encrypted and unencrypted payloads.

[0075] b) Handshake header identification: Matching specific handshake header features of known encryption protocols (such as TLS / SSL, IPsec).

[0076] c) Protocol Field Parsing: Based on the protocol identification results, the encrypted fields declared in the protocol are directly parsed. For example, some proprietary protocols may have a 1-byte "encryption flag" and a 1-byte "encryption algorithm ID" in the protocol header. Combining the detection results of the above three methods, the security adaptation module can determine the encryption method (such as AES-128-CBC, ChaCha20-Poly1305) with high confidence.

[0077] (2) Dynamic decryption and key negotiation: After identifying the encryption method, the module dynamically loads the corresponding decryption algorithm component from the local or remote security component library. There are two main ways to obtain the key: a) For protocols that support key negotiation (such as some protocols that use ECDH), the security adaptation module simulates a legitimate peer and conducts a standard key negotiation process with the drone according to the protocol specifications to negotiate a session key.

[0078] (b) For protocols using a pre-shared key (PSK) or a fixed key, the system needs to import the corresponding key in the security configuration beforehand (authorization required). After obtaining the key, the module decrypts the encrypted data stream in real time and hands the plaintext data over to the protocol conversion engine for subsequent parsing.

[0079] (3) Encryption Strength Awareness and Link Adaptation: The security adaptation module notifies the protocol-link adaptation module of the encryption method and its strength (e.g., AES-256 is stronger than AES-128). When generating the transmission parameter configuration strategy, the protocol-link adaptation module considers the requirements of encryption on the link. For example, high-strength encryption (e.g., SM4 based on Chinese national cryptography) involves a large amount of computation for encryption and decryption, and its strategy will include indications such as "preferring links with encryption acceleration hardware (e.g., supporting the AES-NI instruction set)" or "requiring computing resources with higher CPU margins". When the switching adjudication unit selects a link, it will use this as a calculation factor for the link attribute matching degree MatchDegree.

[0080] Understandably, the security adaptation module enables the system to transparently handle various encrypted communications without requiring users to manually configure complex encryption parameters, greatly improving usability and security. By combining passive detection methods such as entropy analysis with protocol parsing, the accuracy and range of encryption identification are improved. More importantly, by feeding encryption strength information back to the link optimization layer, the system can select network paths with stronger computing power for high-security communication sessions, avoiding communication delays caused by excessive decryption computation load. This achieves synergistic optimization of security and performance, a crucial aspect often overlooked in existing technologies.

[0081] Specifically, the general protocol conversion gateway adopts a distributed deployment architecture, including a master gateway node and at least one slave gateway node. The master gateway node is directly connected to the UAV nest, and the slave gateway nodes are deployed at the network edge or in different geographical locations. The master gateway node is responsible for maintaining the global protocol feature library, protocol conversion rule library, and status information of each UAV session. When the movement of a UAV causes a degradation in the link quality between it and the currently serving gateway node, the switching decision unit of the link management module can trigger inter-gateway session migration while deciding to switch the network link. The master gateway node coordinates the synchronization of the current UAV's protocol identification context, protocol conversion session status, and security key materials to the target slave gateway node, which then takes over the subsequent protocol conversion and forwarding tasks, realizing the continuity of cross-geographical region protocol adaptive conversion and link optimization services.

[0082] It should be further explained that the distributed gateway architecture aims to solve the problems of limited coverage and weak mobility support under single-point deployment, and to achieve services over a wide area. The specific design is as follows: (1) Node roles and deployment: a) Primary Gateway Node: Typically deployed in a data center or near the core cluster. It acts as the control center, storing and maintaining the latest global protocol signature library, syntax / semantic rule library, and protocol-link mapping table. It also maintains a global session state table, recording which slave gateway is currently serving each online drone, along with its protocol context, transition state, and summary information of the secure session key. The primary gateway maintains heartbeat and management connections with all slave gateways.

[0083] b) Slave Gateway Nodes: Lightweight deployments at the network edge, such as near areas frequently used by drones, different cities, or base stations. Slave gateways handle specific protocol identification, conversion, and link optimization functions. They synchronize necessary protocol knowledge bases from the master gateway. Each slave gateway is responsible for the real-time services of drones within its coverage area.

[0084] (2) Inter-gateway session migration process: a) Trigger: When a drone moves, causing the link quality (assessed by A's link management module) between the current slave gateway (A) serving it to remain below a threshold, and it is predicted that the network of another slave gateway (B) is a better link for the drone, A's handover decision unit not only decides to perform network link handover, but also initiates a "session migration suggestion" to the master gateway, attaching the ID of the target slave gateway B.

[0085] b) Coordination and Synchronization: After receiving the suggestion, the main gateway first verifies the availability and load of B. If migration is permitted, it sends migration instructions to both A and B. Gateway A serializes and encrypts its local complete session state of the drone (including protocol identification results, context of the protocol conversion engine such as partially parsed syntax trees, session keys of the security adaptation module, etc.) and transmits it to gateway B through the secure channel established by the main gateway.

[0086] c) Switching and Takeover: After receiving and restoring the session state, Gateway B notifies the main gateway that it is ready. The main gateway updates the global session state table, pointing the service gateway of the drone to Gateway B. Simultaneously, it notifies the drone's network layer to direct the destination address of the data stream to Gateway B. During this process, Gateway A may briefly maintain dual transmission to ensure no packet loss. After the migration is complete, the drone establishes a new optimized link with Gateway B, and Gateway B seamlessly takes over the protocol conversion task without the drone or the drone's pod being aware of the change.

[0087] Understandably, the distributed architecture combined with the session migration mechanism expands the system's service scope from a single point to a broader area, enabling continuous communication for UAVs flying over large areas and across regions. This solves the link quality degradation problem caused by UAVs moving away from the initial service gateway in mobile scenarios. Seamless service migration is achieved through state synchronization, avoiding time-consuming operations such as protocol identification and key negotiation that UAVs need to perform when changing service nodes during flight. This ensures communication continuity and real-time control for long-endurance, large-scale operations, greatly improving the system's practicality and scalability.

[0088] Specifically, the link management module also includes a resource reservation submodule, which is used to interact with the network controller corresponding to the target link and perform bandwidth reservation based on the protocol type before the handover decision unit determines that a handover to a new communication link is required. The resource reservation submodule initiates a resource reservation request to the network infrastructure of the target link based on the transmission parameter configuration strategy provided by the protocol-link adaptation module, particularly regarding the quality of service level identifier and estimated bandwidth requirements of the protocol used by the UAV. This request carries the reserved bandwidth value, maximum latency constraint, and protocol priority identifier. After the network infrastructure confirms successful resource reservation, the handover decision unit performs the final link handover operation, thereby ensuring that after the handover is completed, the UAV's data stream can immediately obtain a transmission channel with guaranteed quality of service that conforms to its protocol characteristics, avoiding performance jitter caused by resource contention in the initial handover phase.

[0089] It should be further explained that the resource reservation submodule is the final key measure to achieve "smooth and seamless handover" and ensure Quality of Service (QoS). It is primarily designed for advanced networks that support resource reservation or network slicing technologies, such as 5G core networks, SD-WAN, or certain private networks, and its specific design is as follows: (1) Resource reservation triggering time: After the handover decision unit preliminarily selects the target link (e.g., a 5G network slice) based on the comprehensive evaluation, it will notify the resource reservation submodule before officially issuing the handover instruction.

[0090] (2) Resource reservation request generation: The resource reservation submodule generates a specific resource reservation request based on the transmission parameter configuration strategy corresponding to the current UAV session. This request must include at least: a) Reserved bandwidth value B_resv: Dynamically calculated based on the protocol type and current service mode. For example, for high-definition image transmission protocols, B_resv can be set to 1.2 times the peak bandwidth requirement defined in the strategy (e.g., 8Mbps) to allow for margin to cope with instantaneous bitrate fluctuations. The coefficient 1.2 was obtained through experiments and achieves a good balance between ensuring smoothness and avoiding excessive reservation.

[0091] b) Maximum delay constraint D_max: directly taken from the requirements in the policy, for example, the control instruction requires D_max=30ms.

[0092] c) Protocol Priority Identifier P_class: Maps the Quality of Service (QoS) level in the policy to a network-recognizable priority. For example, "EF" is mapped to a 5QI (5G QoS Identifier) ​​value of 80 in a 5G network (corresponding to ultra-low latency and high reliability services), and "AF41" is mapped to a 5QI value of 70.

[0093] (3) Interaction with the network: The resource reservation submodule initiates a resource reservation request to the management controller of the target link (such as the PCF+SMF of the 5G core network) through a standard network interface (such as the NEF (Network Capability Open Function) interface of 5G). The request format follows the relevant standards (such as 3GPP TS23.501).

[0094] (4) Handover Execution Conditions: The resource reservation submodule waits for a response from the network infrastructure. If a "resource reservation successful" confirmation is received, the handover adjudication unit is notified, and the handover adjudication unit then triggers the final handover operation. If resource reservation fails (e.g., insufficient network resources), the handover adjudication unit temporarily removes the target link from the candidate list, re-evaluates the suboptimal link, or waits for a period of time before retrying. This ensures that handover is only performed if the target link can provide the required quality of service guarantee, avoiding the awkward situation of "service quality deteriorating after handover".

[0095] Understandably, by adding a resource reservation submodule, this system elevates link optimization from a "best-effort" approach to a "quality-assured" level. Traditional handover only ensures connectivity; after handover, the data stream needs to compete for resources with other services on the network, potentially leading to performance instability in the initial stage. This invention, by pre-reserving a spot on the target link before handover, ensures that the UAV service stream immediately obtains guaranteed network resources after handover, completely eliminating potential congestion, packet loss, and latency jitter issues in the initial handover phase. This is crucial for network quality-sensitive applications such as high-precision UAV control and real-time high-definition image transmission, achieving truly seamless, high-quality communication handover.

[0096] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0097] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A cross-protocol adaptive conversion and dynamic link optimization system, characterized in that, include: A universal protocol conversion gateway is used to communicate with drones and drone nests to automatically identify the communication protocols used by the drones and perform corresponding protocol conversions. The link management module is used to connect to multiple heterogeneous communication networks, evaluate the stability parameters of each communication link in real time, and dynamically select a communication link for the UAV based on the evaluation results. The protocol-link adaptation module is connected to the general protocol conversion gateway and the link management module respectively. It is used to select the corresponding link transmission parameter configuration strategy for the link management module according to the protocol type identified by the general protocol conversion gateway, thereby realizing the coordinated control of protocol conversion and link optimization.

2. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 1, characterized in that, The general protocol conversion gateway includes a programmable protocol parsing chip, a protocol identification module, and a protocol conversion engine; The programmable protocol parsing chip is used to capture the initial communication handshake signal and subsequent business data stream between the UAV and the UAV nest. The protocol identification module is connected to the programmable protocol parsing chip and is used to perform deep packet analysis on the captured initial communication handshake signal and the service data stream, extract protocol fingerprints including frame structure features, handshake timing features and specific instruction set features, and match the protocol fingerprints with the built-in extensible protocol feature library to determine the communication protocol type and version information used by the UAV. The protocol conversion engine is connected to the protocol identification module. It has a pre-built syntax rule library and semantic mapping rules for different protocol types. Based on the output of the protocol identification module, it calls the corresponding rules to parse the private protocol data stream of the UAV into an intermediate abstract representation, and then serializes the intermediate abstract representation into a general control protocol data stream supported by the UAV's nest, thus realizing bidirectional protocol conversion.

3. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 2, characterized in that, The deep packet analysis performed by the protocol identification module includes static feature matching and dynamic behavior learning; The static feature matching is used to compare the bit pattern, data packet length distribution, and port number information of the fixed fields in the initial communication handshake signal; The dynamic behavior learning is used to continuously monitor the business data stream after communication is established. By analyzing the data packet sending interval pattern, confirmation mechanism, retransmission behavior, and statistical characteristics of payload content, a dynamic behavior profile of the UAV communication protocol is constructed. The scalable protocol feature library supports remote updates. When the protocol identification module cannot match known protocol features, the protocol fingerprint and corresponding dynamic behavior profile are marked as new protocol samples and uploaded to the cloud analysis platform. After the cloud analysis platform confirms that it is a new protocol through a clustering algorithm, a new protocol feature description file is generated and distributed to the scalable protocol feature library, realizing the online evolution of protocol identification capabilities.

4. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 2, characterized in that, The protocol conversion engine includes a protocol parsing submodule, a semantic mapping submodule, and a serialization submodule; The protocol parsing submodule performs lexical and syntactic analysis on the input private protocol data stream based on the syntax rule library, and constructs a structured syntax tree that represents its control instructions, state data and parameter information; The semantic mapping submodule maps the node elements in the structured syntax tree to intermediate operation instructions and intermediate data objects with standardized semantic definitions that are independent of specific protocols, according to the semantic mapping rules. The semantic mapping rules define the conversion functions from specific fields of the source protocol to intermediate representations and context-dependent conditional mapping logic. The serialization submodule rearranges and encapsulates the intermediate operation instructions and intermediate data objects into data packets conforming to the target protocol format according to the data format specifications of the general control protocol supported by the UAV nest, thus completing the protocol conversion. At the same time, the protocol conversion engine records the latency and resource consumption indicators during the conversion process to optimize the scheduling of subsequent conversion tasks.

5. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 1, characterized in that, The link management module includes a link monitoring unit, a stability prediction unit, and a handover decision unit; The link monitoring unit continuously collects stability parameters, including real-time signal strength, signal-to-noise ratio, data packet round-trip time, packet loss rate, and available bandwidth, from the multiple heterogeneous communication network interfaces. The stability prediction unit has a built-in time series prediction model. It receives the historical and real-time stability parameter sequences collected by the link monitoring unit, predicts the change trend of various stability parameters of each communication link in the future preset time window, and outputs the prediction results including the predicted stability score and potential quality degradation time points. The handover adjudication unit receives the prediction results from the stability prediction unit and the transmission parameter configuration strategy from the protocol-link adaptation module. Based on the preset adjudication strategy, the adjudication strategy takes into account at least the predicted stability score, the link attribute matching degree required by the transmission parameter configuration strategy, and the overhead brought by the handover itself. Before predicting the current link quality degradation, it actively triggers the link handover command to forward the UAV's data stream to the optimal target link selected by the handover adjudication unit.

6. The cross-protocol adaptive switching and dynamic link optimization system according to claims 3 and 4, characterized in that, The protocol-link adaptation module maintains a protocol feature-link configuration mapping table, which records the preferred link attribute set and transmission parameter configuration strategy associated with different protocol types and their dynamic behavior profiles. After the protocol identification module completes the protocol identification, the protocol-link adaptation module queries the mapping table according to the identified protocol type to obtain the corresponding transmission parameter configuration strategy. The transmission parameter configuration strategy includes the packet fragmentation size, forward error correction coding scheme, heartbeat packet interval, service quality level identifier, and encryption algorithm suite recommended for the protocol type. The protocol-link adaptation module distributes the transmission parameter configuration strategy to the handover decision unit of the link management module, so that when the handover decision unit performs link selection or parameter configuration, it can prioritize matching communication links that meet the preferred link attribute set and apply the transmission parameter configuration strategy to the selected link, thereby achieving deep coupling optimization of protocol characteristics and link performance.

7. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 5, characterized in that, The specific adjudication strategy executed by the switching adjudication unit includes: First, based on the transmission parameter configuration strategy issued by the protocol-link adaptation module, the candidate links are initially screened to exclude links that do not meet the minimum bandwidth requirements, maximum latency tolerance, or specified encryption algorithm support capabilities. Secondly, the links that pass the initial screening are sorted according to the predicted stability score output by the stability prediction unit. Then, the estimated handover cost from the current active link to each candidate link is calculated, and the handover cost includes link reconstruction delay, packet reordering cost and signaling cost. Finally, a comprehensive evaluation function is constructed. The input variables of this function include the predicted stability score of the candidate link, the matching weight with the protocol preferred link attribute, and the negative weight of the handover overhead. The handover decision unit selects the candidate link with the highest value of the comprehensive evaluation function as the target link and triggers a seamless handover process one protection time interval before the potential quality degradation time point.

8. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 4, characterized in that, The system also includes a security adaptation module, which is integrated into the general protocol conversion gateway and works in conjunction with the protocol conversion engine and the protocol-link adaptation module. The security adaptation module is used to automatically detect the encryption method used by the UAV communication data stream. The detection method includes analyzing the payload entropy value of the data packet, identifying specific encryption protocol handshakes, and parsing encryption-related protocol fields. After identifying the encryption method, the security adaptation module dynamically loads the corresponding decryption algorithm component and performs a secure key negotiation process with the UAV to obtain or generate a decryption key, thereby decrypting the encrypted data for protocol parsing by the protocol conversion engine. At the same time, the security adaptation module synchronizes the identified encryption method strength information to the protocol-link adaptation module. When generating the transmission parameter configuration strategy, the protocol-link adaptation module considers the encryption method strength as one of the factors in selecting a communication link that supports the corresponding computing power.

9. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 2, characterized in that, The general protocol conversion gateway adopts a distributed deployment architecture, including a master gateway node and at least one slave gateway node. The master gateway node is directly connected to the UAV nest, and the slave gateway nodes are deployed at the network edge or in different geographical locations. The primary gateway node is responsible for maintaining the global protocol feature library, protocol conversion rule library, and status information of each UAV session. When the movement of a UAV causes a deterioration in the link quality between it and the current service gateway node, the switching decision unit of the link management module can trigger inter-gateway session migration while deciding to switch network links. The primary gateway node coordinates the synchronization of the current UAV's protocol identification context, protocol conversion session status, and security key materials to the target secondary gateway node, which then takes over the subsequent protocol conversion and forwarding tasks, thereby achieving the continuity of cross-geographical region protocol adaptive conversion and link optimization services.

10. The cross-protocol adaptive conversion and dynamic link optimization system according to claim 5, characterized in that, The link management module also includes a resource reservation submodule, which is used to interact with the network controller corresponding to the target link and perform bandwidth reservation based on the protocol type before the handover decision unit determines that a handover to a new communication link is required. The resource reservation submodule initiates a resource reservation request to the network infrastructure of the target link based on the transmission parameter configuration strategy provided by the protocol-link adaptation module, particularly regarding the quality of service level identifier and estimated bandwidth requirements of the protocol used by the UAV. This request carries the reserved bandwidth value, maximum latency constraint, and protocol priority identifier. After the network infrastructure confirms successful resource reservation, the handover decision unit performs the final link handover operation, thereby ensuring that after the handover is completed, the UAV's data stream can immediately obtain a transmission channel with guaranteed quality of service that conforms to its protocol characteristics, avoiding performance jitter caused by resource contention in the initial handover phase.

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