Broadband radar data stream remote transmission method based on virtual layer

Abstract

The invention relates to the technical field of data transmission, and discloses a broadband radar data stream remote transmission method based on a virtual layer, and the method achieves the decoupling of a data stream and a physical link through the introduction of a virtual layer architecture. The problems that in a traditional scheme, the physical link resource utilization rate is low, heterogeneous network interconnection is difficult, and transmission quality guarantee is insufficient are solved. Specifically, the construction of the virtualization abstraction layer enables different transmission media to be managed in a unified mode, and interconnection obstacles caused by protocol differences are avoided; a dynamic management mechanism of the virtual channel improves the overall transmission efficiency through load balancing and resource scheduling; meanwhile, the stability and the reliability of the system are further enhanced by the self-adaptive bandwidth management and an intelligent fault switching mechanism.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, specifically to a method for remote transmission of broadband radar data streams based on a virtual layer. Background Technology

[0002] Radar systems, as an important component of modern technology, are widely used in various fields such as meteorological monitoring, traffic management, and military defense. Radar acquires information by emitting electromagnetic waves and receiving the echo signals reflected from targets. With technological advancements, modern radar systems, especially broadband radars, can generate and process massive amounts of data, enabling more accurate target identification and environmental perception. The radar data stream is the core of radar system operation, carrying data from the raw echo signal to the final target information. To meet the growing application demands, such as centralized signal processing, remote display, and data sharing, the remote extension of radar system data streams is becoming increasingly important.

[0003] However, existing radar data stream remote extension technologies face several significant challenges in application. First, current solutions are overly reliant on the physical transmission layer medium. Traditional radar data remote transmission often depends directly on specific physical links, such as fiber optics or coaxial cables, limiting system flexibility and making it difficult to adapt to different application environments and transmission media. Second, the lack of a unified standard for data stream interface protocols among radar equipment manufacturers leads to difficulties in interoperability between different systems, requiring extensive customized development for remote extension, increasing system complexity and cost. Furthermore, existing technologies are insufficient in terms of system scalability, struggling to support multi-node, long-distance synchronous data transmission, which limits the application of radar systems in wider and more complex scenarios.

[0004] Therefore, we propose a method for remote transmission of broadband radar data streams based on a virtual layer to address the aforementioned problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for remote transmission of broadband radar data streams based on a virtual layer, thereby addressing the excessive dependence of existing technologies on the physical transmission layer medium. Traditional remote transmission of radar data often relies directly on specific physical links, such as optical fibers or coaxial cables, which limits the system's flexibility and makes it difficult to adapt to different application environments and transmission media.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for remote transmission of broadband radar data streams based on a virtual layer, the specific steps of which are as follows:

[0007] S1. Data Acquisition Standardization Processing: Deploy a data acquisition unit at the radar front end to acquire the raw echo signal and perform filtering and noise reduction processing to convert heterogeneous data into a standard data frame format.

[0008] S2. Virtual Abstraction Layer Architecture Construction: A virtualization abstraction layer is built between the physical transport layer and the application layer, defining a unified virtual data flow interface specification and establishing a mapping mechanism from physical links to virtual channels;

[0009] S3. Dynamic Virtual Channel Management: Develop a virtual channel management subsystem to dynamically create virtual channels based on data flow bandwidth requirements, latency tolerance, and priority; monitor the physical link load status; and achieve load balancing scheduling.

[0010] S4. Data Encapsulation Protocol Conversion: Design the virtual layer data packet encapsulation format and develop a protocol adaptation module to realize bidirectional conversion between virtual layer protocols and physical layer protocols such as network transmission protocols and Fibre Channel protocols;

[0011] S5. Adaptive Bandwidth Quality Assurance: Integrates a bandwidth management module, uses time series prediction algorithms to estimate bandwidth requirements, implements a tiered quality of service assurance strategy, and prioritizes the transmission of high-priority data streams.

[0012] S6. Distributed clock precision synchronization: Deploy a hierarchical clock synchronization system, use a precision time protocol to achieve high-precision time synchronization between nodes, and perform timestamp calibration and data timing alignment in the virtual layer;

[0013] S7. Intelligent automatic fault switching: Deploy an intelligent fault diagnosis subsystem to monitor the health indicators of the virtual channel and quickly complete the automatic switching of the virtual channel to the backup link when a link failure is detected.

[0014] S8. End-to-end security encryption protection: Calculate cyclic redundancy check codes and hash check values ​​for virtual layer data packets, encrypt classified data using national cryptographic algorithms or high-strength encryption algorithms, and establish an access control mechanism based on digital certificates.

[0015] S9. Centralized Operation and Maintenance Intelligent Analysis: Develop a centralized operation and maintenance management platform to collect and visualize operational data from each node, integrate machine learning algorithms for performance bottleneck identification and fault risk prediction, and provide standardized interfaces to support system integration and expansion.

[0016] Preferably, in step S1, the data acquisition standardization processing is performed in the following specific manner:

[0017] S1.1 The data acquisition unit acquires the raw echo signal output by the radar antenna in real time, filters out out-of-band noise and interference components through a digital filter, calibrates and corrects the gain coefficient and bias parameters of the analog-to-digital converter, and outputs a high-quality digital echo signal.

[0018] S1.2 Identify the original data format according to the equipment model, reorganize the format according to the standard data frame specification, and construct a three-segment data frame containing a data header, a payload, and a checksum. The data header records the equipment identifier, timestamp, and type identifier. The payload is filled with the echo signal sequence. The checksum is generated by the cyclic redundancy check algorithm, and a standardized data frame with unified specifications is output.

[0019] Preferably, in step S2, the specific steps for constructing the virtual abstraction layer architecture are as follows:

[0020] S2.1 Establish a virtualization abstraction layer between the physical transmission layer and the application data processing layer, define the virtual data stream interface specification, including virtual channel identifier encoding rules, data packet frame header format and field definition, flow control signal type and triggering conditions, and design an application programming interface that includes data transmission and reception interface, channel management interface and status query interface to hide the protocol differences and rate differences of the underlying physical medium.

[0021] S2.2 Establish a physical link resource pool, register transmission media such as fiber optic links, microwave links, and satellite links as schedulable resources, record the type identifier, bandwidth capacity, transmission delay, bit error rate, and load status of each link, construct a mapping table between virtual channels and physical links, and realize the dynamic binding and switching of virtual channels and physical links through the mapping table.

[0022] Preferably, in step S3, the specific steps for dynamic management of the virtual channel are as follows:

[0023] S3.1 The virtual channel management subsystem receives data stream transmission requests, extracts characteristic parameters including transmission bandwidth, maximum transmission delay, and priority level, queries the physical link resource pool to filter candidate links that meet the bandwidth and delay requirements, selects the physical link with the lowest load rate as the bearer link, creates a virtual channel instance and generates an identifier, inserts a record containing the virtual channel identifier, physical link identifier, bandwidth quota, and priority parameters into the mapping relationship data table, and returns the virtual channel identifier to the data source.

[0024] S3.2 The virtual channel management subsystem collects physical link status data, including load rate, bit error rate, and transmission delay, according to a preset period. It compares these data with threshold parameters. When the load rate exceeds the threshold, a migration process is triggered. The system retrieves a list of virtual channels on the overloaded links from the mapping table, sorts them by priority, selects low-priority virtual channels for migration, finds target links with load rates below the threshold, completes the binding switch, updates the mapping table, and deletes the mapping record and releases bandwidth resources when the transmission is complete.

[0025] Preferably, in step S4, the specific steps for data encapsulation protocol conversion are as follows:

[0026] S4.1. Design a virtual layer data packet encapsulation format based on standard data frames. The virtual layer data packet consists of a virtual layer control header and a data payload. The control header includes a channel number field, a sequence number field, a timestamp field, a priority flag field, and a routing information field. The standard data frame is used as the data payload. A virtual layer control header is added to the front end of the data payload to form a virtual layer data packet. The values ​​of each field in the control header are filled according to the virtual channel configuration parameters and data flow attribute information.

[0027] S4.2 Develop a protocol adaptation and conversion module between the virtualization abstraction layer and the physical transport layer. The protocol identification unit queries the mapping relationship data table to determine the physical link transmission protocol type. The protocol conversion unit selects the encapsulation rule according to the protocol type and encapsulates the virtual layer data packet into the network transmission protocol message, Fibre Channel frame format or special protocol format. The conversion process only adds protocol encapsulation information to the outer layer without modifying the content of the virtual layer data packet. The receiving end protocol restoration unit strips the outer protocol encapsulation to extract the complete virtual layer data packet.

[0028] Preferably, in step S5, the adaptive bandwidth quality assurance process includes the following specific steps:

[0029] S5.1 The bandwidth management module continuously collects the bandwidth usage values ​​of each data stream using a sliding window statistical method to form a historical bandwidth usage data sequence. It also collects radar system operating mode information synchronously, establishes a correlation model between operating mode and bandwidth demand, inputs the historical data sequence into a time series prediction algorithm for calculation, obtains the predicted bandwidth demand value for future time periods, initiates a bandwidth reservation request to the physical link resource pool based on the prediction results, marks the reserved bandwidth range on the physical link, and updates the bandwidth quota parameters of the virtual channel.

[0030] S5.2. Based on the business type and importance, data flows are divided into three levels: high priority, medium priority, and low priority. Differentiated service quality assurance parameters are defined for each level, including minimum guaranteed bandwidth, maximum allowable transmission delay, and upper limit of packet loss rate. Priority information is written into the virtual layer data packet control header. The bandwidth management module monitors the total available bandwidth and the sum of actual demand in real time. When the total available bandwidth is less than the actual demand, a dynamic adjustment mechanism is triggered to allocate bandwidth resources in descending order of priority, giving priority to the demand of high-priority data flows, and restricting or suspending low-priority data flows when necessary.

[0031] Preferably, in step S6, the specific steps for precise synchronization of the distributed clock are as follows:

[0032] S6.1 Deploy a hierarchical clock synchronization architecture. Configure a high-precision reference clock source at the top layer as the time reference for the entire network. Deploy clock server nodes in the middle layer. The clock server periodically synchronizes with the reference clock source through a precision time protocol. After receiving the time synchronization message, it calculates the clock deviation and adjusts the local clock. Deploy radar nodes at the bottom layer as slave clock devices. The radar node selects the clock server with the least communication latency as the reference clock source, sends a synchronization request and receives a response message, calculates the round-trip transmission delay and adjusts the local clock according to the correction time.

[0033] S6.2 The timestamp processing module receives virtual layer data packets sent by each radar node, extracts the timestamp value of the data packet generation time from the timestamp field of the control header, obtains the network transmission delay measurement value of the data packet from the source node to the virtualization abstraction layer, calculates the actual generation time of the data based on the timestamp value minus the transmission delay, establishes a multi-node data buffer, sorts the data packets of each node in time order according to the actual generation time and writes them into the buffer, and establishes a mapping index table between the data packet storage address and the actual generation time.

[0034] Preferably, in step S7, the specific steps for intelligent fault automatic switching are as follows:

[0035] S7.1 The intelligent fault diagnosis subsystem establishes a data flow health assessment model, collects real-time operating status indicators of the virtual channel, including packet loss rate, out-of-order rate, transmission delay, and delay jitter, compares the indicator values ​​with preset normal threshold ranges, and marks an abnormal state when the indicator exceeds the threshold. It then sends test probe messages to the bound physical link and determines the fault type based on the probe message response, response delay, and physical layer signal status analysis. The fault types are divided into three categories: complete physical link interruption, link performance degradation, and transient interference.

[0036] S7.2 The fault handling module selects the corresponding handling strategy according to the fault type identifier. When the physical link is completely interrupted, it selects a backup link from the resource pool, establishes a new connection on the backup link and configures the transmission parameters, notifies the data source to switch the data stream to the new connection and disconnects the old connection, updates the mapping relationship table and records the switching time. When the link performance degrades, it adjusts the transmission control parameters, including reducing the sending rate, enabling forward error correction coding, and increasing the retransmission timeout. When there is transient interference, it starts the data retransmission mechanism. After the receiving end discovers that the sequence number is missing, it sends a retransmission request. The sending end retransmits the corresponding data packet and stores the fault event information in the fault knowledge base.

[0037] Preferably, in step S8, the specific steps for end-to-end security encryption protection are as follows:

[0038] S8.1. Perform Cyclic Redundancy Check (CRC) algorithm on virtual layer data packets to generate a checksum, and use a secure hash algorithm to generate a hash digest value. Write the checksum and hash digest value into the checksum field of the control header. The receiving end recalculates the checksum value and compares it with the original checksum value. If the values ​​match, the data packet is accepted. If the values ​​differ, the data packet is discarded and a retransmission request is sent. Select the appropriate encryption algorithm according to the data stream security level attribute to encrypt the data payload. Obtain the data encryption key from the key management system to complete the encryption operation. The receiving end uses the decryption key to restore the plaintext data.

[0039] S8.2 The Certificate Authority issues digital certificates to the data sender and receiver. Before establishing a connection, the remote receiver sends the digital certificate. The sender verifies the certificate validity period, digital signature, and certificate revocation list. After successful verification, the connection is allowed. An access permission configuration record is created for the receiver, including the allowed data stream types, virtual channel identifiers, and operation types. When the receiver initiates an access request, it queries the permission configuration to determine whether access is allowed. An intrusion detection module is deployed at key nodes to collect access logs and compare them with the normal behavior model. When abnormal behavior is detected, an alarm message is generated and sent to the security management platform.

[0040] Preferably, in step S9, the specific steps of centralized operation and maintenance intelligent analysis are as follows:

[0041] S9.1 Build a centralized operation and maintenance management platform. Establish communication connections with the virtualization abstraction layer module, physical link management module, and equipment monitoring module of each physical node through a standardized data acquisition interface. Send data acquisition requests to each monitoring node according to a preset acquisition cycle to collect operational data such as virtual channel operation status, data traffic statistics, physical link quality parameters, and equipment hardware status. After classifying the collected data by timestamp and data source, store it in a time-series database. Develop a visual monitoring interface including a system topology diagram display module, a real-time indicator dashboard module, and a historical data chart analysis module, supporting multi-dimensional data queries.

[0042] S9.2 integrates an intelligent analysis tool module, extracting historical operational data from a time-series database as training samples. It performs data preprocessing, including integrity checks, missing value imputation, outlier removal, and data normalization. Clustering algorithms are applied to identify operational modes, association rule algorithms to identify performance bottlenecks, and time-series prediction algorithms to predict potential faults and label risk levels. A system analysis report is generated, including performance bottleneck descriptions, fault risk warnings, capacity planning suggestions, and configuration optimization suggestions. An application programming interface module is developed, providing open data query interfaces, status monitoring interfaces, configuration management interfaces, and alarm subscription interfaces. A reserved functional extension plugin interface supports dynamically loading customized functional modules.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1. This method decouples data flow from physical links by introducing a virtual layer architecture, solving the problems of low physical link resource utilization, difficulty in interconnecting heterogeneous networks, and insufficient transmission quality assurance in traditional solutions. Specifically, the construction of the virtualization abstraction layer enables unified management of different transmission media, avoiding interconnection barriers caused by protocol differences; the dynamic management mechanism of the virtual channel improves overall transmission efficiency through load balancing and resource scheduling; at the same time, adaptive bandwidth management and intelligent fault switching mechanisms further enhance the stability and reliability of the system.

[0045] 2. A complete closed-loop dynamic scheduling mechanism has been established through the virtual channel management subsystem. By collecting and analyzing physical link status data in real time, the system can promptly detect potential congestion risks and take preventive measures. When dealing with overloaded links, the system adopts a priority sorting strategy to ensure that the transmission continuity of high-priority data streams is not affected. Simultaneously, through dynamically updating the mapping table and optimizing the resource reclamation mechanism, the system achieves seamless migration of virtual channels and efficient utilization of bandwidth resources. This solution effectively solves the problem of insufficient real-time adaptability in the mapping process between virtual channels and physical links, significantly improving the system's reliability and scalability. Attached Figure Description

[0046] Figure 1 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1: Please refer to Figure 1 A method for remote transmission of broadband radar data streams based on a virtual layer, the specific steps of which are as follows:

[0049] S1. Data Acquisition Standardization Processing: Deploy a data acquisition unit at the radar front end to acquire the raw echo signal and perform filtering and noise reduction processing to convert heterogeneous data into a standard data frame format.

[0050] S2. Virtual Abstraction Layer Architecture Construction: A virtualization abstraction layer is built between the physical transport layer and the application layer, defining a unified virtual data flow interface specification and establishing a mapping mechanism from physical links to virtual channels;

[0051] S3. Dynamic Virtual Channel Management: Develop a virtual channel management subsystem to dynamically create virtual channels based on data flow bandwidth requirements, latency tolerance, and priority; monitor the physical link load status; and achieve load balancing scheduling.

[0052] S4. Data Encapsulation Protocol Conversion: Design the virtual layer data packet encapsulation format and develop a protocol adaptation module to realize bidirectional conversion between virtual layer protocols and physical layer protocols such as network transmission protocols and Fibre Channel protocols;

[0053] S5. Adaptive Bandwidth Quality Assurance: Integrates a bandwidth management module, uses time series prediction algorithms to estimate bandwidth requirements, implements a tiered quality of service assurance strategy, and prioritizes the transmission of high-priority data streams.

[0054] S6. Distributed clock precision synchronization: Deploy a hierarchical clock synchronization system, use a precision time protocol to achieve high-precision time synchronization between nodes, and perform timestamp calibration and data timing alignment in the virtual layer;

[0055] S7. Intelligent automatic fault switching: Deploy an intelligent fault diagnosis subsystem to monitor the health indicators of the virtual channel and quickly complete the automatic switching of the virtual channel to the backup link when a link failure is detected.

[0056] S8. End-to-end security encryption protection: Calculate cyclic redundancy check codes and hash check values ​​for virtual layer data packets, encrypt classified data using national cryptographic algorithms or high-strength encryption algorithms, and establish an access control mechanism based on digital certificates.

[0057] S9. Centralized Operation and Maintenance Intelligent Analysis: Develop a centralized operation and maintenance management platform to collect and visualize the operation data of each node, integrate machine learning algorithms to identify performance bottlenecks and predict fault risks, and provide standardized interfaces to support system integration and expansion.

[0058] In this embodiment, the method decouples data flow from physical links by introducing a virtual layer architecture, solving the problems of low physical link resource utilization, difficulty in interconnecting heterogeneous networks, and insufficient transmission quality assurance in traditional solutions. Specifically, the construction of the virtualization abstraction layer enables unified management of different transmission media, avoiding interconnection barriers caused by protocol differences; the dynamic management mechanism of the virtual channel improves overall transmission efficiency through load balancing and resource scheduling; and the adaptive bandwidth management and intelligent fault switching mechanism further enhance the stability and reliability of the system.

[0059] A data acquisition unit is deployed at the radar front end to capture raw echo signals in real time, perform filtering and noise reduction, extract effective echo components, and convert heterogeneous data into a unified standard data frame format, thereby eliminating device dependencies and laying the foundation for subsequent virtual layer processing. Furthermore, a virtualization abstraction layer is built between the physical transport layer and the application layer, defining a unified virtual data stream interface specification and establishing a mapping mechanism from physical links to virtual channels. This masks underlying physical differences and supports dynamic resource scheduling and flexible switching. Specifically, a virtual channel management subsystem is developed to dynamically create virtual channels based on data stream bandwidth requirements, latency tolerance, and priority. It monitors the physical link load status and implements load balancing scheduling to avoid single-link overload and improve overall resource utilization.

[0060] A smart fault diagnosis subsystem is deployed to monitor virtual channel health indicators. When a link failure is detected, the virtual channel is automatically switched to a backup link to avoid transmission interruption and improve system resilience. Cyclic redundancy check codes and hash check values ​​are calculated for virtual layer data packets. Confidential data is encrypted using national cryptographic algorithms or high-strength encryption algorithms, and an access control mechanism based on digital certificates is established to ensure data integrity and security. Finally, a centralized operation and maintenance management platform is developed to collect and visualize operational data from each node. Machine learning algorithms are integrated to identify performance bottlenecks and predict fault risks. Standardized interfaces are provided to support system integration and expansion, enhancing the adaptability of the solution.

[0061] Through the synergistic operation of the above-mentioned technical features, the problems of low utilization of physical link resources, difficulty in interconnecting heterogeneous networks, and insufficient transmission quality assurance in the remote transmission of broadband radar data streams are fundamentally solved, realizing efficient remote transmission of radar data across regions and media.

[0062] Example 2: Please refer to Figure 1 In step S1, the specific method for data acquisition standardization processing is as follows:

[0063] S1.1 The data acquisition unit acquires the raw echo signal output by the radar antenna in real time, filters out out-of-band noise and interference components through a digital filter, calibrates and corrects the gain coefficient and bias parameters of the analog-to-digital converter, and outputs a high-quality digital echo signal.

[0064] S1.2 Identify the original data format according to the equipment model, reorganize the format according to the standard data frame specification, and construct a three-segment data frame containing a data header, a payload, and a checksum. The data header records the equipment identifier, timestamp, and type identifier. The payload is filled with the echo signal sequence. The checksum is generated by the cyclic redundancy check algorithm, and a standardized data frame with unified specifications is output.

[0065] In this embodiment: During data acquisition, the data acquisition unit first acquires the raw echo signal output by the radar antenna in real time. This real-time design effectively avoids signal delay and attenuation, meeting the high-resolution requirements of broadband radar. Subsequently, a digital filter performs precise filtering for specific frequency bands, and its parameters can be dynamically adjusted according to the actual interference environment, thereby significantly improving signal purity. In the analog-to-digital conversion stage, hardware nonlinearity errors are effectively compensated by calibrating and correcting the gain coefficient and bias parameters, ensuring that the digital signal accurately reflects the original physical quantities. In terms of data format processing, the system can automatically identify the raw data formats of different device models and uniformly convert them into a three-segment standard data frame containing a data header, payload, and checksum. The data header design supports data traceability and distributed system synchronization, the payload fully preserves the core signal content, and the checksum can promptly detect bit errors during transmission or storage. Through the above processing flow, not only are the problems of unstable signal quality and inconsistent formats solved, but a reliable basic input is also provided for subsequent virtual layer processing.

[0066] The above technical solution enables high-quality radar data acquisition in a unified format, effectively solving problems such as insufficient noise suppression of the original echo signal, uncorrected analog-to-digital conversion errors, and conversion errors caused by differences in data formats of different devices, thus significantly improving signal quality and system compatibility.

[0067] Example 3: Please refer to Figure 1 In step S2, the specific steps for constructing the virtual abstraction layer architecture are as follows:

[0068] S2.1 Establish a virtualization abstraction layer between the physical transmission layer and the application data processing layer, define the virtual data stream interface specification, including virtual channel identifier encoding rules, data packet frame header format and field definition, flow control signal type and triggering conditions, and design an application programming interface that includes data transmission and reception interface, channel management interface and status query interface to hide the protocol differences and rate differences of the underlying physical medium.

[0069] S2.2 Establish a physical link resource pool, register transmission media such as fiber optic links, microwave links, and satellite links as schedulable resources, record the type identifier, bandwidth capacity, transmission delay, bit error rate, and load status of each link, construct a mapping table between virtual channels and physical links, and realize the dynamic binding and switching of virtual channels and physical links through the mapping table.

[0070] In this embodiment, the introduction of a virtualization abstraction layer effectively solves the problems of difficult interconnection of heterogeneous networks and low resource utilization. First, the establishment of the virtualization abstraction layer decouples the system architecture, allowing the application layer to be unaware of the differences in the underlying physical media, laying the foundation for subsequent unified interface design. Second, the detailed definition of the virtual data flow interface specification, including virtual channel identifier encoding rules, packet frame header format and field definitions, flow control signal types and triggering conditions, provides a guarantee for accurate data flow management in complex network environments. Furthermore, the construction of the physical link resource pool and the application of the mapping table not only achieve unified management of heterogeneous links but also overcome the shortcomings of low resource utilization and lack of self-healing capabilities in traditional solutions through dynamic binding and switching mechanisms.

[0071] This solution, by improving the construction process of the virtual abstraction layer, successfully addresses the flexible scheduling requirements of multiple types of transmission media, providing reliable technical support for efficient remote transmission of radar data across regions and media.

[0072] Example 4: Please refer to Figure 1 In step S3, the specific steps for dynamic management of virtual channels are as follows:

[0073] S3.1 The virtual channel management subsystem receives data stream transmission requests, extracts characteristic parameters including transmission bandwidth, maximum transmission delay, and priority level, queries the physical link resource pool to filter candidate links that meet the bandwidth and delay requirements, selects the physical link with the lowest load rate as the bearer link, creates a virtual channel instance and generates an identifier, inserts a record containing the virtual channel identifier, physical link identifier, bandwidth quota, and priority parameters into the mapping relationship data table, and returns the virtual channel identifier to the data source.

[0074] S3.2 The virtual channel management subsystem collects physical link status data, including load rate, bit error rate, and transmission delay, according to a preset period. It compares these data with threshold parameters. When the load rate exceeds the threshold, a migration process is triggered. The system retrieves a list of virtual channels on the overloaded links from the mapping table, sorts them by priority, selects low-priority virtual channels for migration, finds target links with load rates below the threshold, completes the binding switch, updates the mapping table, and deletes the mapping record and releases bandwidth resources when the transmission is complete.

[0075] In this embodiment, the virtual channel management subsystem operates as follows: First, upon receiving a data stream transmission request, the system parses key parameters in the request, including transmission bandwidth, maximum transmission delay, and priority level. Based on these parameters, the system queries the physical link resource pool to filter out a set of candidate links that meet the requirements and selects the optimal bearer link based on the real-time load status. This process ensures a precise match between link selection and data stream demands, avoiding the risk of transmission congestion due to insufficient link capacity. After establishing the initial mapping relationship, the system continuously monitors the operating status of each physical link, including key indicators such as load rate, bit error rate, and transmission delay. When the load rate of a link exceeds a preset threshold, the system automatically triggers a migration process, reallocating the affected virtual channels according to a priority strategy. This dynamic adjustment mechanism not only achieves load balancing but also significantly improves the overall utilization rate of physical link resources.

[0076] A complete closed-loop dynamic scheduling mechanism has been established through the virtual channel management subsystem. By collecting and analyzing physical link status data in real time, the system can promptly identify potential congestion risks and take preventative measures. When handling overloaded links, the system employs a priority sorting strategy to ensure that the transmission continuity of high-priority data streams is not affected. Simultaneously, through dynamically updating the mapping table and optimizing the resource reclamation mechanism, the system achieves seamless migration of virtual channels and efficient utilization of bandwidth resources. This solution effectively solves the problem of insufficient real-time adaptability in the mapping process between virtual channels and physical links, significantly improving the system's reliability and scalability.

[0077] Example 5: Please refer to Figure 1 In step S4, the specific steps for data encapsulation protocol conversion are as follows:

[0078] S4.1. Design a virtual layer data packet encapsulation format based on standard data frames. The virtual layer data packet consists of a virtual layer control header and a data payload. The control header includes a channel number field, a sequence number field, a timestamp field, a priority flag field, and a routing information field. The standard data frame is used as the data payload. A virtual layer control header is added to the front end of the data payload to form a virtual layer data packet. The values ​​of each field in the control header are filled according to the virtual channel configuration parameters and data flow attribute information.

[0079] S4.2 Develop a protocol adaptation and conversion module between the virtualization abstraction layer and the physical transport layer. The protocol identification unit queries the mapping relationship data table to determine the physical link transmission protocol type. The protocol conversion unit selects the encapsulation rule according to the protocol type and encapsulates the virtual layer data packet into the network transmission protocol message, Fibre Channel frame format or special protocol format. The conversion process only adds protocol encapsulation information to the outer layer without modifying the content of the virtual layer data packet. The receiving end protocol restoration unit strips the outer protocol encapsulation to extract the complete virtual layer data packet.

[0080] In this embodiment, a complete adaptive bandwidth quality assurance mechanism is constructed to address the challenges of dynamic bandwidth demand fluctuations and quality of service (QoS) assurance in radar data stream transmission. First, the bandwidth management module continuously monitors the bandwidth usage of each data stream using a sliding window statistical method. Simultaneously, it establishes a correlation model based on radar system operating mode information. This design allows the system to identify bandwidth demand trends in advance, avoiding resource shortages due to sudden data stream increases. Second, a time series prediction algorithm is used to calculate the predicted bandwidth demand for future time periods. Based on this, a bandwidth reservation request is initiated to the physical link resource pool, marking the reserved bandwidth interval on the physical link and updating the bandwidth quota parameters of the virtual channel. This process ensures resource reservation for critical data transmission. Furthermore, by prioritizing data streams and defining differentiated QoS assurance parameters, priority information is embedded in the virtual layer data packet control header, enabling each node in the transmission process to identify the importance of the data stream. When the total available bandwidth is less than the actual demand, the system automatically triggers a dynamic adjustment mechanism, allocating bandwidth resources according to priority order and prioritizing the transmission of high-priority data streams. This hierarchical guarantee strategy effectively solves the QoS assurance problem in bandwidth-constrained scenarios.

[0081] Example 6: Please refer to Figure 1 In step S5, the specific steps for adaptive bandwidth quality assurance are as follows:

[0082] S5.1 The bandwidth management module continuously collects the bandwidth usage values ​​of each data stream using a sliding window statistical method to form a historical bandwidth usage data sequence. It also collects radar system operating mode information synchronously, establishes a correlation model between operating mode and bandwidth demand, inputs the historical data sequence into a time series prediction algorithm for calculation, obtains the predicted bandwidth demand value for future time periods, initiates a bandwidth reservation request to the physical link resource pool based on the prediction results, marks the reserved bandwidth range on the physical link, and updates the bandwidth quota parameters of the virtual channel.

[0083] S5.2. Based on the business type and importance, data flows are divided into three levels: high priority, medium priority, and low priority. Differentiated service quality assurance parameters are defined for each level, including minimum guaranteed bandwidth, maximum allowable transmission delay, and upper limit of packet loss rate. Priority information is written into the virtual layer data packet control header. The bandwidth management module monitors the total available bandwidth and the sum of actual demand in real time. When the total available bandwidth is less than the actual demand, a dynamic adjustment mechanism is triggered to allocate bandwidth resources in descending order of priority, giving priority to the demand of high-priority data flows, and restricting or suspending low-priority data flows when necessary.

[0084] In this embodiment: The bandwidth management module first collects the bandwidth usage of each data stream using a sliding window statistical method, and constructs a correlation model by combining it with the radar system's operating mode information, thereby generating a historical bandwidth usage data sequence. This data is input into a time series prediction algorithm to calculate the bandwidth demand for future time periods. Based on the prediction results, the bandwidth management module initiates a bandwidth reservation request to the physical link resource pool, marks the reserved bandwidth interval on the physical link, and updates the bandwidth quota parameters of the virtual channel. This process realizes the advance planning and dynamic allocation of bandwidth resources, avoiding resource contention issues. In addition, the bandwidth management module divides data streams into high, medium, and low priorities according to service type and importance, and defines differentiated quality of service (QoS) assurance parameters for each priority. These parameters include minimum guaranteed bandwidth, maximum allowable transmission delay, and upper limit of packet loss rate, ensuring that different critical data can receive tailored transmission guarantees. Priority information is written into the virtual layer packet control header, enabling each node to identify data stream priorities during transmission and supporting end-to-end priority processing. When the total available bandwidth is less than the actual demand, the dynamic adjustment mechanism is triggered, allocating bandwidth resources in descending order of priority, prioritizing the needs of high-priority data streams, and restricting or suspending low-priority data streams when necessary.

[0085] Through the above technical solutions, the problems of inaccurate resource allocation and insufficient quality of service in broadband radar data stream transmission are solved, ensuring that high-priority data streams can still be reliably transmitted even when resources are scarce. The dynamic prediction mechanism of the bandwidth management module, combined with a refined priority management strategy, not only improves the pertinence and accuracy of bandwidth prediction, but also optimizes resource utilization through real-time monitoring and dynamic adjustment mechanisms, maintaining the core functions of the system.

[0086] Example 7: Please refer to Figure 1 In step S6, the specific steps for precise synchronization of the distributed clock are as follows:

[0087] S6.1 Deploy a hierarchical clock synchronization architecture. Configure a high-precision reference clock source at the top layer as the time reference for the entire network. Deploy clock server nodes in the middle layer. The clock server periodically synchronizes with the reference clock source through a precision time protocol. After receiving the time synchronization message, it calculates the clock deviation and adjusts the local clock. Deploy radar nodes at the bottom layer as slave clock devices. The radar node selects the clock server with the least communication latency as the reference clock source, sends a synchronization request and receives a response message, calculates the round-trip transmission delay and adjusts the local clock according to the correction time.

[0088] S6.2 The timestamp processing module receives virtual layer data packets sent by each radar node, extracts the timestamp value of the data packet generation time from the timestamp field of the control header, obtains the network transmission delay measurement value of the data packet from the source node to the virtualization abstraction layer, calculates the actual generation time of the data based on the timestamp value minus the transmission delay, establishes a multi-node data buffer, sorts the data packets of each node in time order according to the actual generation time and writes them into the buffer, and establishes a mapping index table between the data packet storage address and the actual generation time.

[0089] In this embodiment, a hierarchical clock synchronization architecture is deployed to achieve high-precision unification of clocks across all nodes in the distributed radar system. A top-level high-precision reference clock source provides a unified time reference for the entire network. Mid-level clock server nodes maintain nanosecond-level synchronization with the reference source through a precise time protocol. Bottom-level radar nodes synchronize with the nearest clock server through a request-response mechanism, forming a three-tiered time distribution system. This architecture solves the problem of the number of synchronization links increasing dramatically with the number of nodes in traditional point-to-point synchronization methods, reducing network load and synchronization overhead. The hierarchical structure provides excellent scalability; newly added radar nodes only need to establish a synchronization connection with the nearest clock server to join the system, without needing to reconfigure the network-wide synchronization relationship. Through periodic clock deviation calculation and continuous adjustment mechanisms, the local clocks of each node can continuously track and converge to a unified time reference, ensuring that radar nodes distributed in different geographical locations remain consistent in the time dimension, providing a reliable time reference for time correlation analysis of multi-node data.

[0090] The timestamp processing module accurately calculates the actual generation time of the raw radar data by extracting the timestamp of the data packet's generation time and subtracting the network transmission delay, eliminating the problem of data arrival time disorder caused by transmission path differences and network latency fluctuations. A multi-node data buffer is established to reorder data packets according to their actual generation time, ensuring that subsequent processing stages receive a data stream strictly organized in chronological order. The establishment of a mapping index table allows the application layer to quickly locate and retrieve data within a specific time range, supporting efficient time window queries and data backtracking analysis. This mechanism solves the problem of time sequence alignment of multi-source data in distributed radar systems, providing accurate time reference data for signal processing algorithms requiring strict time synchronization, such as coherent accumulation, multi-station positioning, and target track association. Through software-level time sequence calibration compensation, the uncertainty of network transmission delay is effectively overcome, ensuring precise time alignment of data across nodes in the distributed system.

[0091] By combining a hierarchical clock synchronization system and a timestamp processing mechanism, this solution systematically addresses the time synchronization problem in the long-distance transmission of distributed radar data. Traditional distributed radar systems typically employ satellite timing or dedicated clock synchronization networks, requiring each node to be equipped with an independent high-precision clock device, resulting in high construction costs and complex maintenance. This solution achieves hierarchical time distribution through a layered architecture, requiring only a small number of reference clock sources at the top layer. The intermediate and bottom layers can achieve the required synchronization accuracy using lower-cost network synchronization clocks, reducing system construction and maintenance costs. Traditional solutions often rely on GPS timestamps or local clock markers for data timing alignment. When transmission delays are significant or network conditions are complex, timing misalignments can easily occur, affecting the data fusion effect across multiple nodes. This solution compensates for transmission delays at the software level by implementing delay measurement and timing calibration at the virtualization abstraction layer, ensuring accurate data timing alignment even in long-distance transmission and heterogeneous network environments. By establishing a mapping index table and buffer management mechanism, efficient data storage and rapid retrieval are achieved, supporting real-time data stream processing and historical data backtracking analysis.

[0092] Example 8: Please refer to Figure 1 In step S7, the specific steps for intelligent fault automatic switching are as follows:

[0093] S7.1 The intelligent fault diagnosis subsystem establishes a data flow health assessment model, collects real-time operating status indicators of the virtual channel, including packet loss rate, out-of-order rate, transmission delay, and delay jitter, compares the indicator values ​​with preset normal threshold ranges, and marks an abnormal state when the indicator exceeds the threshold. It then sends test probe messages to the bound physical link and determines the fault type based on the probe message response, response delay, and physical layer signal status analysis. The fault types are divided into three categories: complete physical link interruption, link performance degradation, and transient interference.

[0094] S7.2 The fault handling module selects the corresponding handling strategy according to the fault type identifier. When the physical link is completely interrupted, it selects a backup link from the resource pool, establishes a new connection on the backup link and configures the transmission parameters, notifies the data source to switch the data stream to the new connection and disconnects the old connection, updates the mapping relationship table and records the switching time. When the link performance degrades, it adjusts the transmission control parameters, including reducing the sending rate, enabling forward error correction coding, and increasing the retransmission timeout. When there is transient interference, it starts the data retransmission mechanism. After the receiving end discovers that the sequence number is missing, it sends a retransmission request. The sending end retransmits the corresponding data packet and stores the fault event information in the fault knowledge base.

[0095] In this embodiment, a multi-layered fault diagnosis and handling mechanism is constructed to achieve intelligent response to link failure scenarios. First, the intelligent fault diagnosis subsystem, based on a data flow health assessment model, monitors key operational indicators of the virtual channel in real time and quickly identifies abnormal states by dynamically comparing them against preset threshold ranges. When an anomaly is detected, the system sends test probe messages to deeply analyze the link status, combining response conditions, latency performance, and physical layer signal characteristics to accurately determine the fault type. This process ensures the scientific accuracy of fault classification, laying the foundation for subsequent differentiated processing.

[0096] The fault handling module adopts targeted strategies based on the fault type. In the event of a complete physical link outage, the system selects a backup link from the resource pool, quickly establishes a new connection and configures its parameters, and achieves seamless switching by updating the mapping table, thus ensuring the continuity of data transmission. For scenarios involving link performance degradation, the system dynamically adjusts transmission control parameters, such as reducing the transmission rate, enabling forward error correction coding, or extending the retransmission timeout, to effectively maintain transmission quality while avoiding unnecessary link switching and reducing resource waste. For transient interference, a data retransmission mechanism is activated, triggering retransmission requests based on missing sequence numbers to ensure data integrity. Furthermore, all fault event information is stored in a fault knowledge base, providing data support for continuous system optimization.

[0097] A comprehensive data flow health assessment model is established through an intelligent fault diagnosis subsystem to monitor key operational indicators of the virtual channel in real time. When an abnormal state is detected, the system proactively sends test probe messages to confirm and classify the fault. This proactive detection mechanism based on multi-dimensional indicators can accurately distinguish different types of link faults. Subsequently, the fault handling module adopts corresponding handling strategies according to the specific fault type: for complete interruption, it quickly switches to a backup link to ensure uninterrupted data transmission; for performance degradation scenarios, it adjusts transmission parameters to adapt to link changes; for transient interference, it initiates a sequence number-based retransmission mechanism to ensure data integrity. These handling measures are organically combined with the previously established virtualization abstraction layer architecture and virtual channel management mechanism to form a complete fault self-healing system, effectively solving the reliability problem in the transmission of broadband radar data streams. At the same time, fault handling experience is accumulated in the fault knowledge base, providing an important foundation for continuous system optimization.

[0098] Through the above technical solution, this method not only solves the shortcomings of traditional solutions in terms of general fault diagnosis mechanisms and lack of differentiated processing strategies, but also significantly improves the reliability of radar data stream transmission and resource utilization efficiency. Combined with the aforementioned mechanisms such as virtual channel dynamic management and adaptive bandwidth quality assurance, this solution further enhances the overall performance of the system, ensuring efficient and stable remote data transmission even in complex network environments.

[0099] Example 9: Please refer to Figure 1 In step S8, the specific steps for end-to-end security encryption protection are as follows:

[0100] S8.1. Perform Cyclic Redundancy Check (CRC) algorithm on virtual layer data packets to generate a checksum, and use a secure hash algorithm to generate a hash digest value. Write the checksum and hash digest value into the checksum field of the control header. The receiving end recalculates the checksum value and compares it with the original checksum value. If the values ​​match, the data packet is accepted. If the values ​​differ, the data packet is discarded and a retransmission request is sent. Select the appropriate encryption algorithm according to the data stream security level attribute to encrypt the data payload. Obtain the data encryption key from the key management system to complete the encryption operation. The receiving end uses the decryption key to restore the plaintext data.

[0101] S8.2 The Certificate Authority issues digital certificates to the data sender and receiver. Before establishing a connection, the remote receiver sends the digital certificate. The sender verifies the certificate validity period, digital signature, and certificate revocation list. After successful verification, the connection is allowed. An access permission configuration record is created for the receiver, including the allowed data stream types, virtual channel identifiers, and operation types. When the receiver initiates an access request, it queries the permission configuration to determine whether access is allowed. An intrusion detection module is deployed at key nodes to collect access logs and compare them with the normal behavior model. When abnormal behavior is detected, an alarm message is generated and sent to the security management platform.

[0102] In this embodiment, by implementing an end-to-end secure encryption protection mechanism, the issues of data confidentiality, integrity, and access control faced by broadband radar data streams during remote transmission are effectively resolved. Specifically, by generating a checksum using a cyclic redundancy check (CRC) algorithm on the virtual layer data packets, transmission errors can be detected based on the data packet content, thereby ensuring data integrity during transmission. A secure hash algorithm is used to generate a hash digest value, creating a unique identifier based on the data packet content to verify that the data has not been tampered with, further enhancing data reliability. The checksum and hash digest value are written into the checksum field of the control header, tightly integrating this security information with the data packet for efficient verification by the receiving end. The receiving end recalculates the checksum and compares it with the original checksum. This comparison mechanism ensures data consistency; when the values ​​match, the data packet is accepted; when the values ​​differ, the data packet is discarded and a retransmission request is sent, thus promptly handling erroneous data and ensuring transmission quality.

[0103] The appropriate encryption algorithm is selected based on the data stream's security level to encrypt the data payload. The encryption strength is dynamically adjusted according to the data's sensitivity, satisfying high security requirements while avoiding performance overhead caused by over-encryption of low-security data. Encryption operations are performed using encryption keys obtained from the key management system. A centralized key management system enhances key security and prevents key leakage. The receiving end uses the decryption key to restore the plaintext data, ensuring that only authorized recipients can access the original data and maintaining data confidentiality. Certificate Authorities (CAs) issue digital certificates to both the sending and receiving ends, establishing a trusted identity authentication framework to prevent unauthorized devices from accessing the network. The remote receiving end sends its digital certificate before establishing a connection, requiring identity verification during the connection initialization phase. The sending end verifies the certificate's validity, digital signature, and certificate revocation list, ensuring the certificate's validity and trustworthiness through multi-dimensional checks and avoiding the use of expired or revoked certificates. Connection establishment is allowed only after successful verification, with strict verification controls enhancing connection security.

[0104] This method defines fine-grained access policies by creating access permission configuration records for the receiving end, including allowed data stream types, virtual channel identifiers, and operation types, thus specifying the scope of permissions and achieving precise control. When the receiving end initiates an access request, it queries the permission configuration to determine whether access is allowed and executes access control in real time to prevent unauthorized operations. An intrusion detection module is deployed at key nodes to proactively monitor the system's security status; access logs are collected and compared with normal behavior models to identify abnormal activities through behavioral analysis; when abnormal behavior is detected, alarm messages are generated and sent to the security management platform to respond promptly to potential threats and improve overall security protection capabilities. Through the above technical solutions, not only is the lack of data transmission security during intelligent automatic fault switching resolved, but the system's anti-attack capabilities and operational stability are also significantly enhanced.

[0105] Example 10: Please refer to Figure 1 In step S9, the specific steps of centralized operation and maintenance intelligent analysis are as follows:

[0106] S9.1 Build a centralized operation and maintenance management platform. Establish communication connections with the virtualization abstraction layer module, physical link management module, and equipment monitoring module of each physical node through a standardized data acquisition interface. Send data acquisition requests to each monitoring node according to a preset acquisition cycle to collect operational data such as virtual channel operation status, data traffic statistics, physical link quality parameters, and equipment hardware status. After classifying the collected data by timestamp and data source, store it in a time-series database. Develop a visual monitoring interface including a system topology diagram display module, a real-time indicator dashboard module, and a historical data chart analysis module, supporting multi-dimensional data queries.

[0107] S9.2 integrates an intelligent analysis tool module, extracting historical operational data from a time-series database as training samples. It performs data preprocessing, including integrity checks, missing value imputation, outlier removal, and data normalization. Clustering algorithms are applied to identify operational modes, association rule algorithms to identify performance bottlenecks, and time-series prediction algorithms to predict potential faults and label risk levels. A system analysis report is generated, including performance bottleneck descriptions, fault risk warnings, capacity planning suggestions, and configuration optimization suggestions. An application programming interface module is developed, providing open data query interfaces, status monitoring interfaces, configuration management interfaces, and alarm subscription interfaces. A reserved functional extension plugin interface supports dynamically loading customized functional modules.

[0108] In this embodiment, a centralized operation and maintenance management platform was constructed to achieve comprehensive monitoring and intelligent analysis of the radar data flow system. During the data acquisition phase, the platform establishes communication connections with the virtualization abstraction layer module, physical link management module, and equipment monitoring module of each physical node through a standardized data acquisition interface, ensuring the diversity and uniformity of data sources. Sending data acquisition requests to each monitoring node according to a preset acquisition cycle effectively avoids the data silos caused by traditional decentralized monitoring methods, while ensuring the continuity and timeliness of data capture. The acquired operational data, including virtual channel operating status, data traffic statistics, physical link quality parameters, and equipment hardware status, covers multi-dimensional indicators of the system from data flow transmission to hardware operation, providing complete basic data for subsequent analysis. The acquired data is categorized by timestamp and data source and stored in a time-series database. Organizing the data using time-series characteristics facilitates tracing historical status changes and correlation analysis, significantly improving the efficiency and queryability of data management.

[0109] By integrating intelligent analysis tools, the system's intelligence level was further enhanced. Historical operational data was extracted from a time-series database as training samples, and model training was conducted based on the time-series patterns of historical data, improving the time-series relevance and predictive reliability of the analysis results. Data preprocessing, including integrity checks, missing value imputation, outlier removal, and data normalization, systematically cleansed and standardized the raw data, eliminating noise interference and data bias, providing high-quality input for intelligent algorithms. Clustering algorithms were applied to identify operating modes, automatically classifying system behavior categories based on data characteristics, revealing implicit patterns under different working states, and aiding in understanding system dynamic changes. Association rule algorithms were applied to identify performance bottleneck factors, accurately locating key factors affecting system performance by mining the correlations between indicators, avoiding the blindness of experience-based troubleshooting. Time series prediction algorithms were applied to predict potential faults and label risk levels, combining historical trends to extrapolate future states, achieving early warning and risk quantification of faults, shifting maintenance work from passive response to proactive prevention.

[0110] This method not only solves the problems of a lack of systematic data acquisition processes and insufficient analysis mechanisms, but also significantly improves system maintenance efficiency and the initiative in resource scheduling. The combination of a centralized operation and maintenance management platform and intelligent analysis tool modules forms a complete operation and maintenance system that not only meets the current system's monitoring needs but also has good scalability and adaptability, providing a strong guarantee for the long-term stable operation of the radar data stream system.

[0111] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0112] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for remote transmission of broadband radar data streams based on a virtual layer, characterized in that: The specific steps are as follows: S1. Data Acquisition Standardization Processing: Deploy a data acquisition unit at the radar front end to acquire the raw echo signal and perform filtering and noise reduction processing to convert heterogeneous data into a standard data frame format. S2. Virtual Abstraction Layer Architecture Construction: A virtualization abstraction layer is built between the physical transport layer and the application layer, defining a unified virtual data flow interface specification and establishing a mapping mechanism from physical links to virtual channels; S3. Dynamic Virtual Channel Management: Develop a virtual channel management subsystem to dynamically create virtual channels based on data flow bandwidth requirements, latency tolerance, and priority; monitor the physical link load status; and achieve load balancing scheduling. S4. Data Encapsulation Protocol Conversion: Design the virtual layer data packet encapsulation format and develop a protocol adaptation module to realize bidirectional conversion between virtual layer protocols and physical layer protocols such as network transmission protocols and Fibre Channel protocols; S5. Adaptive Bandwidth Quality Assurance: Integrates a bandwidth management module, uses time series prediction algorithms to estimate bandwidth requirements, implements a tiered quality of service assurance strategy, and prioritizes the transmission of high-priority data streams. S6. Distributed clock precision synchronization: Deploy a hierarchical clock synchronization system, use a precision time protocol to achieve high-precision time synchronization between nodes, and perform timestamp calibration and data timing alignment in the virtual layer; S7. Intelligent automatic fault switching: Deploy an intelligent fault diagnosis subsystem to monitor the health indicators of the virtual channel and quickly complete the automatic switching of the virtual channel to the backup link when a link failure is detected. S8. End-to-end security encryption protection: Calculate cyclic redundancy check codes and hash check values ​​for virtual layer data packets, encrypt classified data using national cryptographic algorithms or high-strength encryption algorithms, and establish an access control mechanism based on digital certificates. S9. Centralized Operation and Maintenance Intelligent Analysis: Develop a centralized operation and maintenance management platform to collect and visualize the operation data of each node, integrate machine learning algorithms to identify performance bottlenecks and predict fault risks, and provide standardized interfaces to support system integration and expansion.

2. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 1, characterized in that: In step S1, the data acquisition standardization process is specifically implemented as follows: S1.1 The data acquisition unit acquires the raw echo signal output by the radar antenna in real time, filters out out-of-band noise and interference components through a digital filter, calibrates and corrects the gain coefficient and bias parameters of the analog-to-digital converter, and outputs a high-quality digital echo signal. S1.2 Identify the original data format according to the equipment model, reorganize the format according to the standard data frame specification, and construct a three-segment data frame containing a data header, a payload, and a checksum. The data header records the equipment identifier, timestamp, and type identifier. The payload is filled with the echo signal sequence. The checksum is generated by the cyclic redundancy check algorithm, and a standardized data frame with unified specifications is output.

3. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 2, characterized in that: In step S2, the specific steps for constructing the virtual abstraction layer architecture are as follows: S2.1 Establish a virtualization abstraction layer between the physical transmission layer and the application data processing layer, define the virtual data stream interface specification, including virtual channel identifier encoding rules, data packet frame header format and field definition, flow control signal type and triggering conditions, and design an application programming interface that includes data transmission and reception interface, channel management interface and status query interface to hide the protocol differences and rate differences of the underlying physical medium. S2.2 Establish a physical link resource pool, register transmission media such as fiber optic links, microwave links, and satellite links as schedulable resources, record the type identifier, bandwidth capacity, transmission delay, bit error rate, and load status of each link, construct a mapping table between virtual channels and physical links, and realize the dynamic binding and switching of virtual channels and physical links through the mapping table.

4. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 3, characterized in that: In step S3, the specific steps for dynamic management of the virtual channel are as follows: S3.1 The virtual channel management subsystem receives data stream transmission requests, extracts characteristic parameters including transmission bandwidth, maximum transmission delay, and priority level, queries the physical link resource pool to filter candidate links that meet the bandwidth and delay requirements, selects the physical link with the lowest load rate as the bearer link, creates a virtual channel instance and generates an identifier, inserts a record containing the virtual channel identifier, physical link identifier, bandwidth quota, and priority parameters into the mapping relationship data table, and returns the virtual channel identifier to the data source. S3.2 The virtual channel management subsystem collects physical link status data, including load rate, bit error rate, and transmission delay, according to a preset period. It compares these data with threshold parameters. When the load rate exceeds the threshold, a migration process is triggered. The system retrieves a list of virtual channels on the overloaded links from the mapping table, sorts them by priority, selects low-priority virtual channels for migration, finds target links with load rates below the threshold, completes the binding switch, updates the mapping table, and deletes the mapping record and releases bandwidth resources when the transmission is complete.

5. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 4, characterized in that: In step S4, the specific steps for data encapsulation protocol conversion are as follows: S4.

1. Design a virtual layer data packet encapsulation format based on standard data frames. The virtual layer data packet consists of a virtual layer control header and a data payload. The control header includes a channel number field, a sequence number field, a timestamp field, a priority flag field, and a routing information field. The standard data frame is used as the data payload. A virtual layer control header is added to the front end of the data payload to form a virtual layer data packet. The values ​​of each field in the control header are filled according to the virtual channel configuration parameters and data flow attribute information. S4.2 Develop a protocol adaptation and conversion module between the virtualization abstraction layer and the physical transport layer. The protocol identification unit queries the mapping relationship data table to determine the physical link transmission protocol type. The protocol conversion unit selects the encapsulation rule according to the protocol type and encapsulates the virtual layer data packet into the network transmission protocol message, Fibre Channel frame format or special protocol format. The conversion process only adds protocol encapsulation information to the outer layer without modifying the content of the virtual layer data packet. The receiving end protocol restoration unit strips the outer protocol encapsulation to extract the complete virtual layer data packet.

6. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 5, characterized in that: In step S5, the specific steps for adaptive bandwidth quality assurance are as follows: S5.1 The bandwidth management module continuously collects the bandwidth usage values ​​of each data stream using a sliding window statistical method to form a historical bandwidth usage data sequence. It also collects radar system operating mode information synchronously, establishes a correlation model between operating mode and bandwidth demand, inputs the historical data sequence into a time series prediction algorithm for calculation, obtains the predicted bandwidth demand value for future time periods, initiates a bandwidth reservation request to the physical link resource pool based on the prediction results, marks the reserved bandwidth range on the physical link, and updates the bandwidth quota parameters of the virtual channel. S5.

2. Based on the business type and importance, data flows are divided into three levels: high priority, medium priority, and low priority. Differentiated service quality assurance parameters are defined for each level, including minimum guaranteed bandwidth, maximum allowable transmission delay, and upper limit of packet loss rate. Priority information is written into the virtual layer data packet control header. The bandwidth management module monitors the total available bandwidth and the sum of actual demand in real time. When the total available bandwidth is less than the actual demand, a dynamic adjustment mechanism is triggered to allocate bandwidth resources in descending order of priority, giving priority to the demand of high-priority data flows, and restricting or suspending low-priority data flows when necessary.

7. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 6, characterized in that: In step S6, the specific steps for precise synchronization of the distributed clock are as follows: S6.1 Deploy a hierarchical clock synchronization architecture. Configure a high-precision reference clock source at the top layer as the time reference for the entire network. Deploy clock server nodes in the middle layer. The clock server periodically synchronizes with the reference clock source through a precision time protocol. After receiving the time synchronization message, it calculates the clock deviation and adjusts the local clock. Deploy radar nodes at the bottom layer as slave clock devices. The radar node selects the clock server with the least communication latency as the reference clock source, sends a synchronization request and receives a response message, calculates the round-trip transmission delay and adjusts the local clock according to the correction time. S6.2 The timestamp processing module receives virtual layer data packets sent by each radar node, extracts the timestamp value of the data packet generation time from the timestamp field of the control header, obtains the network transmission delay measurement value of the data packet from the source node to the virtualization abstraction layer, calculates the actual generation time of the data based on the timestamp value minus the transmission delay, establishes a multi-node data buffer, sorts the data packets of each node in time order according to the actual generation time and writes them into the buffer, and establishes a mapping index table between the data packet storage address and the actual generation time.

8. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 7, characterized in that: In step S7, the specific steps for intelligent fault automatic switching are as follows: S7.1 The intelligent fault diagnosis subsystem establishes a data flow health assessment model, collects real-time operating status indicators of the virtual channel, including packet loss rate, out-of-order rate, transmission delay, and delay jitter, compares the indicator values ​​with preset normal threshold ranges, and marks an abnormal state when the indicator exceeds the threshold. It then sends test probe messages to the bound physical link and determines the fault type based on the probe message response, response delay, and physical layer signal status analysis. The fault types are divided into three categories: complete physical link interruption, link performance degradation, and transient interference. S7.2 The fault handling module selects the corresponding handling strategy according to the fault type identifier. When the physical link is completely interrupted, it selects a backup link from the resource pool, establishes a new connection on the backup link and configures the transmission parameters, notifies the data source to switch the data stream to the new connection and disconnects the old connection, updates the mapping relationship table and records the switching time. When the link performance degrades, it adjusts the transmission control parameters, including reducing the sending rate, enabling forward error correction coding, and increasing the retransmission timeout. When there is transient interference, it starts the data retransmission mechanism. After the receiving end discovers that the sequence number is missing, it sends a retransmission request. The sending end retransmits the corresponding data packet and stores the fault event information in the fault knowledge base.

9. The method for remote transmission of broadband radar data stream based on a virtual layer according to claim 8, characterized in that: In step S8, the specific steps for end-to-end security encryption protection are as follows: S8.

1. Perform Cyclic Redundancy Check (CRC) algorithm on virtual layer data packets to generate a checksum, and use a secure hash algorithm to generate a hash digest value. Write the checksum and hash digest value into the checksum field of the control header. The receiving end recalculates the checksum value and compares it with the original checksum value. If the values ​​match, the data packet is accepted. If the values ​​differ, the data packet is discarded and a retransmission request is sent. Select the appropriate encryption algorithm according to the data stream security level attribute to encrypt the data payload. Obtain the data encryption key from the key management system to complete the encryption operation. The receiving end uses the decryption key to restore the plaintext data. S8.2 The Certificate Authority issues digital certificates to the data sender and receiver. Before establishing a connection, the remote receiver sends the digital certificate. The sender verifies the certificate validity period, digital signature, and certificate revocation list. After successful verification, the connection is allowed. An access permission configuration record is created for the receiver, including the allowed data stream types, virtual channel identifiers, and operation types. When the receiver initiates an access request, it queries the permission configuration to determine whether access is allowed. An intrusion detection module is deployed at key nodes to collect access logs and compare them with the normal behavior model. When abnormal behavior is detected, an alarm message is generated and sent to the security management platform.

10. A method for remote transmission of broadband radar data stream based on a virtual layer according to claim 9, characterized in that: In step S9, the specific steps of centralized operation and maintenance intelligent analysis are as follows: S9.1 Build a centralized operation and maintenance management platform. Establish communication connections with the virtualization abstraction layer module, physical link management module, and equipment monitoring module of each physical node through a standardized data acquisition interface. Send data acquisition requests to each monitoring node according to a preset acquisition cycle to collect operational data such as virtual channel operation status, data traffic statistics, physical link quality parameters, and equipment hardware status. After classifying the collected data by timestamp and data source, store it in a time-series database. Develop a visual monitoring interface including a system topology diagram display module, a real-time indicator dashboard module, and a historical data chart analysis module, supporting multi-dimensional data queries. S9.2 integrates an intelligent analysis tool module, extracting historical operational data from a time-series database as training samples. It performs data preprocessing, including integrity checks, missing value imputation, outlier removal, and data normalization. Clustering algorithms are applied to identify operational modes, association rule algorithms to identify performance bottlenecks, and time-series prediction algorithms to predict potential faults and label risk levels. A system analysis report is generated, including performance bottleneck descriptions, fault risk warnings, capacity planning suggestions, and configuration optimization suggestions. An application programming interface module is developed, providing open data query interfaces, status monitoring interfaces, configuration management interfaces, and alarm subscription interfaces. A reserved functional extension plugin interface supports dynamically loading customized functional modules.

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