A communication signal processing method and a communication receiver

Abstract

This invention relates to the field of communication transmission technology, and more particularly to a communication signal processing method and a communication receiver. The method includes the following steps: acquiring location information data of the communication receiver; constructing a network domain from the location information data to generate a communication connection network domain; acquiring multi-source heterogeneous communication signals from the communication connection network domain to obtain standard multi-source heterogeneous communication signals; classifying the communication connection network domain by network domain compatibility level to generate network domain compatibility level data; adjusting the optimal mapping path of the communication connection network domain using the network domain compatibility level data to generate dynamic topology mapping rules; and performing seamless multi-protocol adaptation and conversion of the communication connection network domain based on standard multi-source heterogeneous communication signals using the dynamic topology mapping rules to generate a protocol adaptive conversion strategy. This invention improves the efficiency and security of cross-domain communication through intelligent scheduling, protocol adaptation, dynamic topology adjustment, and signal steganography techniques.

Description

Technical Field

[0001] This invention relates to the field of communication transmission technology, and in particular to a communication signal processing method and a communication receiver. Background Technology

[0002] Initially, communication signal processing primarily relied on analog signal amplification, modulation, and demodulation techniques. With the development of information theory, signal processing technology gradually shifted towards digitalization. In the 1960s, with the advent of digital computers and high-speed digital signal processors (DSPs), digital signal processing (DSP) became one of the core technologies in the field of communications. With the maturity of large-scale integrated circuit (VLSI) technology, the performance of DSP hardware was significantly improved, and the processing power of communication systems increased dramatically, especially in wireless communication, marking a new stage in communication signal processing technology. With the rise of mobile communication technologies such as 4G and 5G, communication signal processing technology entered a highly complex and intelligent development stage. Multi-carrier technologies, represented by OFDM (Orthogonal Frequency Division Multiplexing), MIMO (Multiple-Input Multiple-Output) technology, and adaptive filtering technology have been widely used in high-speed mobile communications. However, in the current environment where multiple networks such as 5G / 6G, satellite Internet, Wi-Fi, and optical communication coexist, existing technologies are unable to efficiently achieve seamless switching and resource coordination across networks. At the same time, traditional routing and protocol stack optimization schemes are difficult to adapt to large-scale dynamic topology changes, such as drone swarms and intelligent transportation networks, which leads to poor signal compatibility when transmitting signals across heterogeneous networks. Summary of the Invention

[0003] Therefore, it is necessary to provide a communication signal processing method and a communication receiver to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objective, a communication signal processing method is provided, the method comprising the following steps:

[0005] Step S1: Acquire communication receiver location information data; construct a network domain from the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals from the communication connection network domain to obtain standard multi-source heterogeneous communication signals;

[0006] Step S2: Classify the communication connection network domain by network domain compatibility level to generate network domain compatibility level data; use the network domain compatibility level data to adjust the optimal mapping path of the communication connection network domain to generate dynamic topology mapping rules; use the dynamic topology mapping rules to perform seamless multi-protocol adaptation and conversion of the communication connection network domain based on standard multi-source heterogeneous communication signals to generate protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform QoS intelligent scheduling of the communication connection network domain to generate network transmission intelligent scheduling data.

[0007] Step S3: Based on the intelligent scheduling data of network transmission, construct a virtual cross-domain network view for the communication connection network domain and generate a virtual cross-domain network view; dynamically migrate computing tasks on the virtual cross-domain network view to generate virtual node dynamic migration data; and perform distributed collaborative scheduling of standard multi-source heterogeneous communication signals through the virtual node dynamic migration data to generate heterogeneous signal collaborative scheduling data.

[0008] Step S4: Steganographically write the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data. When the security authentication result is true, decrypt the heterogeneous signal transmission steganographic data to generate decrypted heterogeneous transmission signals to perform cross-domain signal secure transmission operations.

[0009] This invention provides fundamental data support for subsequent multi-source heterogeneous communication signal acquisition by acquiring the location information of the communication receiver and constructing a communication connection network domain. This lays a solid foundation for accurate network domain management and signal acquisition, better addressing the heterogeneity of different communication technologies and equipment, and improving the comprehensiveness and accuracy of signal acquisition. Through compatibility level classification and dynamic topology mapping of network domains, communication paths can be adjusted according to the characteristics of different networks, thereby achieving seamless adaptation and optimization of cross-domain networks. Adaptive protocol conversion and intelligent QoS scheduling optimize resource allocation based on actual network conditions, ensuring the efficiency and quality of data transmission, especially in large-scale dynamic network environments, improving network resource utilization and service quality. Virtualizing the cross-domain network view and dynamically migrating computation tasks enables more flexible and efficient network resource scheduling. Through distributed collaborative scheduling, signals can be optimized for transmission based on network status and load, avoiding network bottlenecks and resource waste, and improving overall communication efficiency and network responsiveness. Steganography and security authentication of signals ensure data security during cross-domain signal transmission, preventing data tampering or theft. The data decryption process ensures that only authenticated signals can be successfully decrypted and transmitted, greatly enhancing the security of cross-domain communication, especially in open, heterogeneous network environments, ensuring the confidentiality and integrity of information. Therefore, this invention improves the efficiency and security of cross-domain communication through intelligent scheduling, protocol adaptation, dynamic topology adjustment, and signal steganography techniques.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the location information data of the communication receiver;

[0012] Step S12: Perform topology analysis on the communication receiver location information data to generate communication topology data; construct a network domain for the communication receiver location information data based on the communication topology data to generate a communication connection network domain;

[0013] Step S13: Deploy a network feature acquisition agent for the communication connection network domain to obtain network feature acquisition agent deployment data; use the network feature acquisition agent deployment data to acquire multi-source heterogeneous communication signals in the communication connection network domain to obtain multi-source heterogeneous communication signals;

[0014] Step S14: Perform signal preprocessing on the multi-source heterogeneous communication signal to generate a standard multi-source heterogeneous communication signal. The signal preprocessing includes signal denoising, signal filtering, and signal normalization.

[0015] This invention provides precise foundational data for subsequent network analysis and deployment by acquiring the location information of communication receivers, which helps ensure the coverage and reliability of the communication network. Topology analysis reveals the connections between receivers and the network structure, helping to identify potential bottlenecks, optimize network layout, and guarantee communication quality. Deploying a feature acquisition agent allows for real-time acquisition of various communication signals from the network, enabling analysis of network performance. This facilitates dynamic monitoring of network conditions and the identification and resolution of potential problems such as signal interference and network congestion. Signal preprocessing, including denoising, filtering, and normalization, effectively improves signal quality, eliminates external interference, and enhances signal stability and accuracy. This is crucial for subsequent data analysis and processing, improving the reliability and precision of the communication network.

[0016] Preferably, constructing a network domain from communication receiver location information data based on communication topology data includes:

[0017] The communication topology data is processed by collecting communication node information to obtain communication topology node data; the coordinate format of the communication receiver location information data is converted to generate communication receiver coordinate data; and the signal coverage range is analyzed based on the communication topology node data and the communication receiver coordinate data to generate communication area range data.

[0018] Topological connectivity data is obtained by calculating the topological connectivity of communication topology data using communication area range data; based on the topological connectivity data, network domains are divided into communication received coordinate data to generate communication connection network domains.

[0019] This invention, by collecting node information from communication topology data, can accurately understand the status and function of each communication node, providing crucial data for subsequent network planning and optimization. This helps to comprehensively grasp the distribution of each node in the network, thereby rationally planning network resources. The coordinate format conversion of communication receiver location information data unifies data from different sources into a standard format, helping to eliminate problems caused by inconsistent data formats. By generating communication receiver coordinate data, consistent and accurate location information can be ensured for subsequent analysis and calculation. Signal coverage analysis of communication topology node data and receiver coordinate data can accurately calculate the effective coverage area of ​​the communication system, helping to identify network blind spots, weak signal areas, and potential optimization points, thereby improving overall communication quality and reliability. Topology connectivity calculation can assess the connectivity between network nodes, helping to identify isolated areas, overloaded nodes, or unstable links in the network, providing guidance for subsequent network optimization and ensuring the stability and continuity of communication links. Based on topology connectivity data, network domain partitioning of communication receiver coordinates helps to divide the communication network into multiple subnetworks or regions for more refined management and control. Each network domain can allocate resources, enhance signals, and balance loads differently according to needs, thereby improving the overall efficiency of the communication system.

[0020] Preferably, step S2 includes the following steps:

[0021] Step S21: Perform network-level feature analysis on the communication connection network domain to generate network domain-level feature data; use the network domain-level feature data to classify the communication connection network domain into network domain compatibility level data to generate network domain compatibility level data.

[0022] Step S22: Perform cooperative topology mapping on the communication connection network domain using network domain compatibility level data to generate a cooperative topology mapping matrix; adjust the optimal mapping path of the communication connection network domain according to the cooperative topology mapping matrix to generate dynamic topology mapping rules;

[0023] Step S23: Analyze the communication layer usage of the communication connection network domain based on standard multi-source heterogeneous communication signals using dynamic topology mapping rules to generate communication network layer usage data; perform seamless multi-protocol adaptation and conversion on the communication network layer usage data to generate a protocol adaptive conversion strategy;

[0024] Step S24: Use the protocol adaptive conversion strategy to perform QoS intelligent scheduling on the communication connection network domain and generate intelligent scheduling data for network transmission.

[0025] This invention analyzes the network hierarchy characteristics of communication network domains to gain a comprehensive understanding of the topology and functional features of each network layer. This helps identify bottlenecks, critical nodes, and potential optimization areas, providing a theoretical basis for network optimization and resource allocation. Classifying compatibility levels based on the hierarchical characteristics of network domains effectively assesses compatibility between different domains, providing strong support for subsequent collaborative work and multi-network integration. This ensures smooth collaboration between different network layers, avoiding performance degradation or resource waste due to incompatibility. Through collaborative topology mapping, the collaborative relationships between different network domains can be identified, and a topology mapping matrix reflecting these relationships can be constructed. This provides a precise framework for collaboration between network domains, facilitating effective sharing of network resources. Optimal path adjustment based on the collaborative topology mapping matrix optimizes data flow paths between multiple network domains, reducing network latency and resource conflicts, and improving overall network efficiency. Dynamically adjusting path rules allows for flexible responses to changes in network load and node failures, ensuring communication reliability and flexibility. By analyzing network domain communication layers based on standard multi-source heterogeneous communication signals, the load and resource usage of different network layers in actual operation can be assessed. This helps identify potential resource bottlenecks or overload issues, supporting more precise network resource management. Seamless multi-protocol adaptation and conversion based on communication network layer usage data provides flexibility for the network in heterogeneous environments, dynamically selecting suitable protocols to ensure smooth network communication across different protocols and technology stacks. This strategy ensures the network can adapt to different technical requirements and network environments, thereby improving overall interoperability. Through protocol adaptive conversion strategies, QoS intelligent scheduling of the network helps optimize network resource allocation, prioritize critical services, and improve network service quality. Intelligent scheduling of network transmission can reduce congestion, increase transmission rates, and reduce latency, ensuring efficient transmission of various services within the network. In scenarios with diverse service demands, intelligent scheduling data provides efficient decision support for practical applications. This scheduling data can automatically adjust transmission strategies based on real-time network conditions, improving the network's adaptability and optimization level.

[0026] Preferably, seamless multi-protocol adaptation and conversion of data used at the communication network layer includes:

[0027] The system collects signal transmission traffic packets from data used at the communication network layer to obtain signal transmission traffic packets; it then classifies the signal transmission traffic packets according to transmission protocols to generate transmission traffic packet protocol classification data; finally, it extracts traffic features from the transmission traffic packet protocol classification data to obtain protocol traffic feature data.

[0028] Based on protocol traffic characteristic data, the protocol adaptation rules for the data used at the communication network layer are verified, and protocol adaptation rules are generated. These protocol adaptation rules include TCP-UDP adaptation rules, QUIC-TCP adaptation rules, and CCN-TCP adaptation rules.

[0029] The signal transmission traffic packets are converted to the transmission protocol using TCP-UDP adaptation rules to generate transmission protocol conversion data; the signal transmission traffic packets are converted to the application protocol using QUIC-TCP adaptation rules to generate application protocol conversion data; and the signal transmission traffic packets are converted to the network protocol using CCN-TCP adaptation rules to generate network protocol conversion data.

[0030] Network performance analysis is performed on the communication connection network domain to generate network performance data. Based on the network performance data, dynamic protocol selection is performed on the transmission protocol conversion data, application protocol conversion data, and network protocol conversion data to generate an adaptive protocol conversion strategy.

[0031] This invention, by collecting signal transmission traffic packets, can accurately monitor network traffic and data transmission status, thus providing raw data support for subsequent protocol adaptation. This helps to understand network traffic patterns and data transmission in real time, providing a basis for protocol selection and optimization. Classifying the collected signal traffic packets by protocol helps to group different types of traffic, enabling more targeted optimization and adjustment based on protocol type, which improves the efficiency and accuracy of the protocol adaptation process. By extracting traffic features from the protocol classification data of the transmission traffic packets, the transmission characteristics of different protocols (such as bandwidth, latency, packet size, throughput, etc.) can be identified. These features provide data support for protocol adaptation, helping to ensure the efficiency and accuracy of the adaptation process. Based on the traffic feature data, protocol adaptation rules (such as TCP-UDP, QUIC-TCP, CCN-TCP, etc.) are determined, providing clear operational standards for smooth conversion between different protocols in the network. These rules ensure that the conversion and adaptation between different protocols will not cause data loss, increased latency, or compatibility issues, thereby optimizing network performance. By converting transport protocols according to TCP-UDP adaptation rules, it is possible to effectively switch between TCP and UDP based on network conditions and traffic characteristics. TCP is characterized by high reliability and accurate transmission, making it suitable for applications with high reliability requirements, while UDP is suitable for applications with higher real-time requirements (such as video streaming and voice communication). This conversion helps to optimize traffic based on actual conditions. Through QUIC-TCP protocol conversion rules, optimization can be performed at the application layer. The QUIC protocol has lower latency and higher concurrency performance than TCP, making it particularly suitable for high-concurrency, low-latency application scenarios (such as video streaming and web page loading). This adaptation can improve application response speed and user experience while ensuring transmission quality. The conversion of CCN (Content-Centric Networking) to TCP protocol at the network layer provides flexible protocol adaptation options. CCN can more effectively optimize information sharing and storage, making it particularly suitable for large-scale content delivery networks. Through CCN-TCP conversion, content caching efficiency and transmission performance can be improved, and network congestion can be reduced. By performing network performance analysis on the communication connection network domain and generating network performance data, various network indicators such as latency, bandwidth, packet loss rate, and congestion can be comprehensively evaluated, providing real-time feedback for protocol selection and adjustment.

[0032] Preferably, step S24 includes the following steps:

[0033] Step S241: Analyze the real-time status of the communication link in the communication connection network domain using the protocol adaptive conversion strategy to obtain real-time status data of the communication link; extract key indicators from the real-time status data of the communication link to obtain QoS key indicator data, including network latency extraction, jitter extraction, packet loss rate extraction and throughput extraction.

[0034] Step S242: Calculate the current load of network nodes in the communication connection network domain using QoS key indicator data to obtain network node load data; use the network node load data to perform cross-domain traffic balancing analysis on the communication connection network domain to generate full network load assessment data;

[0035] Step S243: Classify traffic priorities in the communication connection network domain based on the overall network load assessment data to generate traffic priority data, which includes high-priority traffic and low-priority traffic; allocate communication signal bandwidth in the communication connection network domain based on the traffic priority data to generate communication signal resource allocation data.

[0036] Step S244: Reserve bandwidth for high-priority traffic based on communication signal resource allocation data to generate high-priority bandwidth allocation data; reschedule low-priority traffic based on high-priority bandwidth allocation data to generate low-priority traffic scheduling data.

[0037] Step S245: Use high-priority bandwidth allocation data and low-priority traffic scheduling data to perform end-to-end QoS intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data.

[0038] This invention provides a comprehensive understanding of the current network environment's health by analyzing the status of communication links in real time. This includes key parameters such as transmission link reliability, latency, and bandwidth usage, helping to promptly identify potential network problems, such as link bottlenecks or performance degradation, and enabling corrective measures. By extracting key QoS indicators such as network latency, jitter, packet loss rate, and throughput, detailed insights into various aspects of network quality can be gained. Especially under high traffic or load fluctuations, monitoring changes in these indicators allows for early warnings and optimization of network performance, facilitating timely detection and handling of network issues and ensuring optimal communication quality. Calculating the current load of network nodes provides real-time information on each node's processing capacity and current load, helping to identify load imbalances and pinpoint performance bottlenecks, providing data support for subsequent optimization. Cross-domain traffic balancing analysis allows for the rational allocation of load across different regions and network domains, preventing latency and packet loss issues caused by traffic overload in certain nodes or regions. The generation of comprehensive network load assessment data facilitates optimized global traffic distribution, improving resource utilization efficiency and reducing network congestion. By prioritizing network traffic based on its different requirements, critical business traffic (such as real-time video and voice calls) is separated from less urgent traffic (such as file downloads and data transmission with low real-time requirements). This classification ensures that important applications receive priority access to network resources, thereby improving user experience, especially for latency-sensitive applications. Based on traffic priority data, network bandwidth is allocated rationally to ensure that high-priority traffic receives sufficient bandwidth to avoid latency and stuttering. Low-priority traffic is appropriately restricted or adjusted when bandwidth resources are limited, ensuring overall network stability and efficiency. By reserving bandwidth for high-priority traffic, critical applications (such as real-time video and VoIP) can still operate stably under high network load, which is crucial for ensuring the transmission quality of critical services and preventing high-priority traffic from being affected by network congestion. By rescheduling low-priority traffic, the bandwidth requirements of non-critical applications can be effectively adjusted under high traffic conditions, preventing them from interfering with high-priority traffic. This rescheduling strategy dynamically optimizes network resources based on real-time traffic demand, ensuring that priority traffic is guaranteed while maximizing network bandwidth utilization. By implementing end-to-end intelligent QoS scheduling based on high-priority bandwidth allocation and low-priority traffic scheduling data, more granular traffic management and resource allocation can be achieved. This intelligent scheduling can automatically adjust traffic strategies according to real-time network conditions, ensuring the stability and timeliness of data transmission and avoiding waste of network resources or the formation of bottlenecks. Through intelligent scheduling, the system can dynamically optimize network transmission paths and resource allocation, improving data transmission efficiency and network performance. The resulting intelligent scheduling data provides precise operational basis for network management, ensuring the efficient flow of different types of traffic within the network.

[0039] Preferably, step S3 includes the following steps:

[0040] Step S31: Based on the intelligent scheduling data of network transmission, construct a virtual cross-domain network view for the communication connection network domain and generate a virtual cross-domain network view;

[0041] Step S32: Divide the virtual cross-domain network view into control domains and generate distributed control node mapping data; deploy distributed network control nodes in the virtual cross-domain network view according to the distributed control node mapping data to obtain distributed virtual network control nodes;

[0042] Step S33: Utilize the distributed virtual network control node to dynamically migrate computation tasks in the virtual cross-domain network view, generating virtual node dynamic migration data;

[0043] Step S34: By dynamically migrating data through virtual nodes, distributed collaborative scheduling of standard multi-source heterogeneous communication signals is performed to generate heterogeneous signal collaborative scheduling data.

[0044] This invention constructs a virtual cross-domain network view based on intelligent scheduling data from network transmission, enabling unified management across different network domains. This virtual view facilitates centralized monitoring and scheduling of cross-domain network resources, overcoming the limitations of traditional physical network architectures and improving the flexibility and efficiency of network management. Constructing a virtual cross-domain network view provides network administrators with a clear view of the global network status, making the interactions and load distribution between different network domains readily apparent, facilitating the identification and resolution of potential performance bottlenecks or conflicts. By dividing the virtual cross-domain network view into control domains and deploying nodes based on distributed control node mapping data, distributed control of network domains is achieved. This distributed management approach reduces the risk of single points of failure, improving the system's fault tolerance and scalability. The deployment of distributed control nodes helps distribute computational and data flow tasks across multiple nodes, thereby achieving load balancing, avoiding resource bottlenecks on single nodes, and improving the overall system's processing capacity and response speed. By utilizing distributed virtual network control nodes to dynamically migrate computational tasks across a virtual cross-domain network view, flexible scheduling of tasks across different network nodes can be achieved. This dynamic migration capability allows the system to adjust the deployment location of tasks in real time based on changes in network load, latency, or bandwidth, ensuring that computational tasks always run on the most suitable node. Dynamic migration of computational tasks leads to more efficient use of network resources, enabling elastic allocation of node resources based on demand fluctuations, thereby optimizing network resource utilization, reducing waste, and improving overall network performance. Distributed collaborative scheduling of signal transmission for dynamically migrated data from virtual nodes enables efficient scheduling of multi-source heterogeneous communication signals. This helps handle different types of signal streams and network traffic, reducing latency and packet loss rates through rational signal transmission scheduling, and improving network service quality and throughput. Collaborative scheduling of heterogeneous signals, through the rational integration and synchronization of different signal sources, reduces resource contention and maximizes signal transmission utilization. Cross-domain collaborative scheduling enhances the coordination between network nodes, enabling the system to maintain efficient signal flow even under various protocol and network conditions.

[0045] Preferably, step S31 includes the following steps:

[0046] Step S311: Based on the intelligent scheduling data of network transmission, perform physical network resource modeling on the communication connection network domain to generate physical network domain resource modeling data; perform network status analysis on the physical network domain resource modeling data to generate network status data;

[0047] Step S312: Logically divide the physical network domain resource modeling data according to the network status data to generate physical network domain logical division data; use the physical network domain logical division data to perform virtualization mapping on the communication connection network domain to generate virtual network elements;

[0048] Step S313: Perform virtual network slicing on the physical network domain resource modeling data using virtual network elements to generate virtual network domain partitioning data; establish virtual connections on the virtual network domain partitioning data using virtual tunneling technology to generate virtual cross-domain connection data;

[0049] Step S314: Use the preset virtual network management platform to construct a network view of the virtual cross-domain connection data, thereby obtaining a virtual cross-domain network view.

[0050] This invention models physical network resources based on intelligent network transmission scheduling data, providing a comprehensive understanding of key indicators such as resource distribution, bandwidth, latency, and node capacity. This modeling approach offers fundamental data support for subsequent network optimization and scheduling decisions, enabling more precise management of physical resources. Network status analysis of the physical network resource modeling data allows for real-time monitoring of network operation (e.g., bandwidth utilization, network health, node load). Network administrators can then adjust network configurations and resource allocation based on real-time data, optimizing network performance and proactively identifying and resolving potential network bottlenecks. Logical partitioning of physical network resources based on network status data allows for partitioning according to different needs (e.g., traffic load, latency requirements), ensuring each logical region best suits its business requirements. This partitioning effectively avoids excessive resource concentration or waste, improving network resource utilization efficiency. Virtualization mapping through logical partitioning data enables flexible mapping from physical to virtual resources. The generation of virtual network elements allows for more flexible allocation and scheduling of network resources, freeing them from physical limitations. This mapping enhances network resilience and scalability, enabling rapid adaptation and optimization of resources under different needs and environments. Virtual network slicing, achieved through virtual network elements, enables on-demand allocation of network resources. Each virtual network slice can operate independently, ensuring that different services (such as high-bandwidth video transmission and low-latency voice calls) receive varying levels of network resource support. Slicing technology significantly improves the flexibility and efficiency of network resources while avoiding resource conflicts and over-allocation. Establishing virtual connections using virtual tunneling technology enables cross-domain communication between different physical domains. The establishment of virtual connections allows different virtual networks to seamlessly interconnect, providing cross-domain network services. This reduces the complexity between physical networks, improving connection flexibility, scalability, and management efficiency. Building a virtual cross-domain network view through a pre-defined virtual network management platform allows for unified management and monitoring of all parts of the cross-domain network. This allows network administrators to schedule and manage cross-domain resources through a single platform, simplifying network maintenance and control and improving management efficiency. The construction of a virtual cross-domain network view makes cross-domain resource scheduling more intuitive and efficient. Administrators can view the resource usage of each domain in a unified view, adjust resource allocation in a timely manner, and achieve optimal resource scheduling.

[0051] Preferably, step S4 includes the following steps:

[0052] Step S41: Perform spectrum conversion on the heterogeneous signal collaborative scheduling data to generate a heterogeneous signal scheduling spectrum diagram; perform frequency hopping on the heterogeneous signal scheduling spectrum diagram to generate heterogeneous signal transmission steganography data.

[0053] Step S42: Perform security authentication on the heterogeneous signal transmission steganography data. If the security authentication result is false, the transmission is interrupted. If the security authentication result is true, the heterogeneous signal transmission steganography data is decrypted to generate a decrypted heterogeneous transmission signal for cross-domain secure signal transmission.

[0054] This invention utilizes spectrum conversion and frequency hopping to make heterogeneous signals more covert during transmission, reducing the risk of external interference or eavesdropping. This technology makes the signal transmission process more difficult to capture and analyze, effectively enhancing signal privacy. The frequency hopping process continuously changes the signal transmission frequency, effectively avoiding potential interference or being targeted by attackers due to fixed frequencies. This frequency hopping technology improves the stability and anti-interference capabilities of signal transmission in complex signal environments. By distributing signals across different frequencies, the risk of congestion in a specific frequency band can be reduced, bandwidth utilization can be improved, and the overall efficiency of cross-domain communication can be enhanced. Secure authentication of steganographic data transmitted in heterogeneous signals ensures the integrity and reliability of the signal during transmission. The security authentication mechanism guarantees the legitimacy of the signal before transmission, preventing malicious tampering and forgery. This process effectively prevents potential man-in-the-middle attacks, signal forgery, and other security threats. The system determines whether to interrupt transmission by judging the result of security authentication. If the authentication is false, the system can immediately interrupt signal transmission, thereby preventing insecure signals from being transmitted to the target network. This immediate security response mechanism effectively reduces potential security risks and ensures signal reliability. Once security authentication is successful, the heterogeneous signal is decrypted to ensure that it has not been interfered with by any third party or tampered with during transmission. The decrypted signal ensures the originality and accuracy of the data, enabling cross-domain signal transmission operations to proceed according to the predetermined objectives, guaranteeing data integrity and transmission quality during communication.

[0055] This specification provides a communication receiver for performing the above-described communication signal processing method, the communication receiver comprising:

[0056] The signal acquisition module is used to acquire location information data of the communication receiver; construct a network domain from the location information data of the communication receiver to generate a communication connection network domain; and acquire multi-source heterogeneous communication signals from the communication connection network domain to obtain standard multi-source heterogeneous communication signals.

[0057] The transmission adaptation module is used to classify the communication connection network domain into network domain compatibility levels and generate network domain compatibility level data; it uses the network domain compatibility level data to adjust the optimal mapping path of the communication connection network domain, thereby generating dynamic topology mapping rules; it uses the dynamic topology mapping rules to perform seamless multi-protocol adaptation and conversion of the communication connection network domain based on standard multi-source heterogeneous communication signals, generating a protocol adaptive conversion strategy; and it uses the protocol adaptive conversion strategy to perform QoS intelligent scheduling of the communication connection network domain, generating intelligent network transmission scheduling data.

[0058] The collaborative scheduling module is used to construct a virtual cross-domain network view of the communication connection network domain based on intelligent scheduling data of network transmission, and generate a virtual cross-domain network view; to dynamically migrate computing tasks in the virtual cross-domain network view, and generate virtual node dynamic migration data; and to perform distributed collaborative scheduling of signal transmission of standard multi-source heterogeneous communication signals through virtual node dynamic migration data, thereby generating heterogeneous signal collaborative scheduling data.

[0059] The security authentication module is used to perform steganography on the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganography data; to perform security authentication on the heterogeneous signal transmission steganography data; and when the security authentication result is true, to decrypt the heterogeneous signal transmission steganography data to generate decrypted heterogeneous transmission signals for performing cross-domain signal secure transmission operations.

[0060] The beneficial effects of this invention lie in its ability to provide a precise network structure foundation for subsequent signal acquisition by acquiring the location information of the communication receiver and constructing a network domain. Acquiring multi-source heterogeneous communication signals ensures broad signal coverage, adapts to the needs of different communication standards, and achieves compatibility between different networks, laying the foundation for subsequent protocol adaptation and optimization. By classifying network domain compatibility and adjusting the optimal mapping path, this module can optimize the connection efficiency between network domains, achieving seamless adaptation of cross-domain networks. This dynamic topology mapping and protocol adaptive conversion not only improves network flexibility but also optimizes resource scheduling based on real-time network conditions, ensuring efficient signal transmission and QoS guarantees, and improving service quality. By constructing a virtual cross-domain network view and performing dynamic computational task migration, flexible scheduling of computational tasks and network resources can be achieved, avoiding network congestion and resource waste. Through distributed collaborative scheduling, signal transmission can be dynamically adjusted according to the load and network status of each node, optimizing the signal transmission efficiency of the entire system and improving the overall performance and responsiveness of the cross-domain network. By steganography and security authentication of signals, data security during cross-domain signal transmission is ensured. Steganography ensures the confidentiality of data during transmission, preventing signal tampering or theft. Through security authentication and data decryption, it ensures that only legitimately authorized signals can be successfully decrypted and transmitted, thereby greatly improving system security and ensuring data integrity and confidentiality during signal transmission. Therefore, this invention improves the efficiency and security of cross-domain communication through intelligent scheduling, protocol adaptation, dynamic topology adjustment, and signal steganography. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating the steps of a communication signal processing method.

[0062] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0063] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0064] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0065] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0066] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0067] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0068] To achieve the above objectives, please refer to Figures 1 to 3 A communication signal processing method, the method comprising the following steps:

[0069] Step S1: Acquire communication receiver location information data; construct a network domain from the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals from the communication connection network domain to obtain standard multi-source heterogeneous communication signals;

[0070] Step S2: Classify the communication connection network domain by network domain compatibility level to generate network domain compatibility level data; use the network domain compatibility level data to adjust the optimal mapping path of the communication connection network domain to generate dynamic topology mapping rules; use the dynamic topology mapping rules to perform seamless multi-protocol adaptation and conversion of the communication connection network domain based on standard multi-source heterogeneous communication signals to generate protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform QoS intelligent scheduling of the communication connection network domain to generate network transmission intelligent scheduling data.

[0071] Step S3: Based on the intelligent scheduling data of network transmission, construct a virtual cross-domain network view for the communication connection network domain and generate a virtual cross-domain network view; dynamically migrate computing tasks on the virtual cross-domain network view to generate virtual node dynamic migration data; and perform distributed collaborative scheduling of standard multi-source heterogeneous communication signals through the virtual node dynamic migration data to generate heterogeneous signal collaborative scheduling data.

[0072] Step S4: Steganographically write the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data. When the security authentication result is true, decrypt the heterogeneous signal transmission steganographic data to generate decrypted heterogeneous transmission signals to perform cross-domain signal secure transmission operations.

[0073] This invention provides fundamental data support for subsequent multi-source heterogeneous communication signal acquisition by acquiring the location information of the communication receiver and constructing a communication connection network domain. This lays a solid foundation for accurate network domain management and signal acquisition, better addressing the heterogeneity of different communication technologies and equipment, and improving the comprehensiveness and accuracy of signal acquisition. Through compatibility level classification and dynamic topology mapping of network domains, communication paths can be adjusted according to the characteristics of different networks, thereby achieving seamless adaptation and optimization of cross-domain networks. Adaptive protocol conversion and intelligent QoS scheduling optimize resource allocation based on actual network conditions, ensuring the efficiency and quality of data transmission, especially in large-scale dynamic network environments, improving network resource utilization and service quality. Virtualizing the cross-domain network view and dynamically migrating computation tasks enables more flexible and efficient network resource scheduling. Through distributed collaborative scheduling, signals can be optimized for transmission based on network status and load, avoiding network bottlenecks and resource waste, and improving overall communication efficiency and network responsiveness. Steganography and security authentication of signals ensure data security during cross-domain signal transmission, preventing data tampering or theft. The data decryption process ensures that only authenticated signals can be successfully decrypted and transmitted, greatly enhancing the security of cross-domain communication, especially in open, heterogeneous network environments, ensuring the confidentiality and integrity of information. Therefore, this invention improves the efficiency and security of cross-domain communication through intelligent scheduling, protocol adaptation, dynamic topology adjustment, and signal steganography techniques.

[0074] In this embodiment of the invention, reference Figure 1 The above is a flowchart illustrating the steps of a communication signal processing method according to the present invention. In this example, the communication signal processing method includes the following steps:

[0075] Step S1: Acquire communication receiver location information data; construct a network domain from the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals from the communication connection network domain to obtain standard multi-source heterogeneous communication signals;

[0076] In this embodiment of the invention, the real-time geographic location information of the communication receiver is obtained through the Global Positioning System (GPS), base station positioning, or other positioning technologies. Location information typically includes data such as longitude, latitude, altitude, and timestamps, and dynamic location can be obtained through periodic sampling. Based on the receiver's location information, the receiver is spatially divided using a Geographic Information System (GIS) or other positioning algorithms to construct a network domain. The network domain can be implemented by dividing the receiver's location into regions, gridding, or using partitioning algorithms to ensure that each region or grid has similar signal characteristics. The establishment of the network domain involves the distribution of physical infrastructure (such as base stations, transmission towers, etc.) and can also consider the impact of environmental factors (such as terrain, buildings, etc.) on signal transmission. Based on the existing network domain division, a communication connection network based on the receiver's location and the network domain is constructed. The connection network domain can be based on the wireless signal transmission paths between receivers, constructing a connection matrix to indicate whether the communication link between receivers is valid. The network domain can also be represented as a topology diagram according to the communication protocol, recording the link information (such as signal strength, transmission rate, etc.) between each node (communication receiver). Multi-source heterogeneous signals refer to signals from different types of communication technologies (such as Wi-Fi, LTE, 5G, satellite communication, etc.). Signal acquisition is performed within the network domain of the receiver, and the types of signals involved include, but are not limited to: radio frequency bands (e.g., Wi-Fi signals in the 2.4GHz and 5GHz bands); mobile communication frequency bands (e.g., signals in the 4G, 5G, and LTE bands); and satellite communication signals. During signal acquisition, factors such as sampling frequency, signal-to-noise ratio, and signal strength are considered to ensure data integrity and accuracy. The acquired signals are processed and standardized according to unified standards (e.g., time synchronization, frequency alignment, etc.). The standardization process includes: signal synchronization in the time and frequency domains; preprocessing operations such as signal denoising and filtering; and fusion of multi-source signals, enabling unified analysis of signals from different sources on the same platform. The result is a standardized, multi-source heterogeneous communication signal set, which can be used for subsequent analysis and optimization. Ultimately, a standardized multi-source heterogeneous communication signal dataset is formed, containing signals from different communication technologies. This dataset can be used for subsequent tasks such as communication network optimization, resource allocation, and interference suppression.

[0077] Step S2: Classify the communication connection network domain by network domain compatibility level to generate network domain compatibility level data; use the network domain compatibility level data to adjust the optimal mapping path of the communication connection network domain to generate dynamic topology mapping rules; use the dynamic topology mapping rules to perform seamless multi-protocol adaptation and conversion of the communication connection network domain based on standard multi-source heterogeneous communication signals to generate protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform QoS intelligent scheduling of the communication connection network domain to generate network transmission intelligent scheduling data.

[0078] In this embodiment of the invention, compatibility level refers to the degree of compatibility between different network domains in terms of communication protocols, technical standards, frequency band usage, latency, etc. For classification, several key compatibility indicators need to be defined first, such as: compatibility between different communication protocols (e.g., compatibility between Wi-Fi and LTE / 5G); whether different frequency bands are compatible or shareable (e.g., compatibility between 2.4GHz and 5GHz signals); for example, whether 4G and 5G can be seamlessly switched through a specific protocol; and the compatibility of latency and bandwidth requirements of different networks is evaluated. Communication network domains are classified using algorithms (e.g., cluster analysis, decision trees, etc.). Based on the compatibility indicators of each domain, they are divided into multiple levels (e.g., high, medium, low), generating network domain compatibility level data. Each network domain can be assigned a compatibility level identifier, forming a compatibility level dataset that records the specific compatibility degree of each network domain. Based on the compatibility level of each network domain, the mapping path between them is optimized. Here, "mapping path" refers to the transmission route of communication signals between different network domains. For network domains with high compatibility, the direct and shortest mapping path is selected; for those with low compatibility, path selection is optimized through intermediary gateways and protocol conversion to ensure smooth signal transmission between different network domains. Dynamic topology mapping rules are generated using the optimized mapping paths, recording the connection and signal forwarding rules between each network domain. These dynamic topology mapping rules specifically include: forwarding paths between each communication network domain; conversion protocols between network domains (e.g., IP, Wi-Fi, LTE); and quality parameters such as latency and bandwidth during signal conversion. Using these dynamic topology mapping rules, protocol adaptation is performed based on standard multi-source heterogeneous communication signals. By adapting to different protocol layers, seamless conversion between multiple network protocols is ensured. Low-level physical and link layer signals are converted to formats suitable for upper-layer protocols (e.g., IP, TCP). The most suitable protocol is selected for communication between different network domains, such as dynamically selecting the appropriate protocol between Wi-Fi and 4G. The protocol conversion process is kept imperceptible to users or devices, ensuring communication stability. Based on the above steps, a "protocol adaptive switching strategy" is generated, including: selection logic and forwarding rules for different protocols, strategies for dynamically adjusting protocol adaptation methods (such as adjusting protocol switching strategies based on network load and signal quality), and specific strategies for seamless switching between different network domains. Based on the protocol adaptive switching strategy, resources in each communication connection network domain are intelligently scheduled to ensure optimal network service quality (QoS). QoS scheduling includes: dynamically allocating bandwidth based on network domain compatibility and demand to guarantee the transmission quality of high-priority tasks and traffic. Through network path optimization and protocol switching strategies, the transmission latency of data packets in the network is reduced. Traffic in the network is prioritized to ensure that important communication traffic (such as real-time voice and video) is transmitted first.Based on network load, the system rationally schedules communication traffic between different network domains to avoid overloading a single node or network domain. Based on QoS scheduling results, it generates intelligent network transmission scheduling data.

[0079] Step S3: Based on the intelligent scheduling data of network transmission, construct a virtual cross-domain network view for the communication connection network domain and generate a virtual cross-domain network view; dynamically migrate computing tasks on the virtual cross-domain network view to generate virtual node dynamic migration data; and perform distributed collaborative scheduling of standard multi-source heterogeneous communication signals through the virtual node dynamic migration data to generate heterogeneous signal collaborative scheduling data.

[0080] In this embodiment of the invention, network transmission intelligent scheduling data (such as bandwidth, latency, traffic, QoS policies, etc.) is used to virtualize the physical infrastructure, protocol adaptation layer, and network topology of each communication connection network domain. Virtualization technologies (such as SDN, NFV, etc.) are used to abstract the network domains, generating a virtualized network view spanning multiple network domains. Each network domain is mapped to different nodes in the virtual network based on its state (such as load, connection quality, bandwidth, etc.). Using a virtual network topology, the connection relationships between various physical network domains are mapped to virtual connections, forming a cross-domain network view. The network view should be dynamically adaptable, updating in real time as network states change (such as bandwidth changes, device failures, etc.). Based on the intelligent scheduling information of the network domains, a virtual network view containing multiple physical network domains, communication protocols, and data flow paths is constructed. This view not only includes physical layer and link layer information but also displays the virtualized resource allocation and connection relationships between network domains. Virtual view data includes: virtual nodes (corresponding to physical access points, servers, base stations, etc.) and their attributes. Virtual connections (communication links, protocol adaptation paths, etc.) and their bandwidth, latency, traffic, and other statuses are monitored. Dynamic migration of computing tasks is performed based on the current virtual cross-domain network view and real-time network status. Migration decisions are based on the following factors: selecting low-load virtual nodes to host computing tasks, ensuring low-latency tasks are executed on low-latency network nodes, and dynamically adjusting task allocation based on the resource requirements of the computing tasks (such as computing power, storage capacity, etc.). Computing tasks are divided into multiple sub-tasks according to their resource requirements. The processing capacity, current load, and network status of each virtual node are evaluated. Based on the evaluation results, the most suitable virtual node or network domain is selected to execute the task. The task or computing sub-task is migrated to the target virtual node. Information such as the source node, target node, migration duration, and resource requirements for each task migration is recorded. Migration data can be used for subsequent performance monitoring, load balancing adjustments, and fault tolerance. Based on the dynamic migration data of virtual nodes, transmission scheduling is performed on standard multi-source heterogeneous communication signals, which include different types of wireless signals (such as Wi-Fi, LTE, 5G, etc.) and different frequency bands. In heterogeneous network environments, a distributed collaborative scheduling mechanism enables the effective fusion of various communication signals. Based on scheduling data from virtual nodes, cross-domain scheduling of different signal streams can be performed, avoiding signal interference and optimizing resource utilization. Through collaborative operations across network protocols, different signal types (such as Wi-Fi, LTE, and satellite communication) can work together, maximizing signal transmission efficiency. The scheduling results record information such as the transmission path, protocol compatibility, and bandwidth allocation for each signal stream.

[0081] Step S4: Steganographically write the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data. When the security authentication result is true, decrypt the heterogeneous signal transmission steganographic data to generate decrypted heterogeneous transmission signals to perform cross-domain signal secure transmission operations.

[0082] In this invention, signal steganography is a technique that hides data within communication signals, preventing the steganographic data from being easily identified or leaked during transmission. It is commonly used to enhance communication security and prevent malicious eavesdropping or tampering. In heterogeneous signal collaborative scheduling data, steganography is applied to the signal transmission process to hide key information of the transmitted data (such as protocol parameters, identification, and transmission content). Depending on the heterogeneous signal type (such as Wi-Fi, LTE, 5G, etc.), a suitable signal carrier is selected as the steganography carrier. This can be the signal's frequency, amplitude, phase, time window, etc. The sensitive data to be steganized (such as signal scheduling information, user data, identifiers, etc.) is converted into a binary encoded stream or a specific encoding format. Depending on the steganography method, LSB (least significant bit) technology, pseudo-noise sequences, redundant bit stuffing, etc., can be used. The converted data is then embedded into the carrier of the transmitted signal. For example, frequency modulation technology can be used to encode binary data into the signal frequency, or phase encoding can be used to embed data into the signal's phase waveform. Steganized data is combined with the original signal to generate a heterogeneous signal for transmitting the steganized data. This steganized data contains a data stream that appears ordinary in the original signal but has been steganized. The steganized data can be seamlessly integrated with other content in the signal (such as ordinary communication data). It is difficult to detect, preventing data from being directly stolen by hackers or third-party attackers. During the transmission of steganized data, it is necessary to ensure the integrity of the data and the trustworthiness of its source. Therefore, security authentication must be performed during data transmission to verify its legitimacy and integrity. The goal of authentication is to ensure that the data has not been tampered with, forged, or leaked during transmission, and that only authorized recipients can decrypt the data and access sensitive information. The sender can digitally sign the steganized data to generate signed data. The signing process uses the sender's private key for encryption, generating an authentication identifier (such as a hash value combined with the encrypted data). The receiver uses the sender's public key to verify the digital signature, ensuring the reliability of the data source and that the data has not been tampered with during transmission. A Message Authentication Code (MAC) is added to the steganized data. By performing cryptographic hashing on the data, a hash value is generated as verification information. The receiver performs the same MAC calculation on the received data to verify its integrity. Security authentication requires a key exchange protocol (such as the Diffie-Hellman protocol) to ensure both parties share the same key for subsequent encryption and decryption operations. If the authentication result is true (i.e., the data source is authentic and has not been tampered with), the data can be further decrypted and processed. If the authentication result is false, the data is discarded or an alarm mechanism is triggered to prevent the leakage of sensitive information. A suitable encryption method is selected to encrypt the steganographic data (such as symmetric encryption, asymmetric encryption, etc.). Common encryption algorithms include AES (Advanced Encryption Standard), RSA encryption, and ECC (Elliptic Curve Cryptography). After successful authentication, the receiver uses the corresponding key to perform the decryption operation.The steganographic data during transmission is restored to obtain the original signal data and the valid steganographic data within it. The sender and receiver use a shared key for encryption and decryption. Encryption is performed using the sender's private key, and decryption is performed using the sender's public key. The decrypted signal contains the original communication content and also recovers the valid information of the steganographic data. At this point, the signal is ready for secure cross-domain transmission, ensuring the confidentiality, integrity, and authenticity of the information. The decrypted signal can be securely transmitted between multiple network domains. During transmission, the signal will rely on the previous security authentication and steganography mechanisms to ensure that the data is not tampered with or leaked. The transmission involves different physical and logical network domains (such as different communication protocols, transmission media, etc.). Through the combination of signal steganography and encryption, the security of both the data at the signal's transport layer and application layer is guaranteed during cross-domain transmission.

[0083] Preferably, step S1 includes the following steps:

[0084] Step S11: Obtain the location information data of the communication receiver;

[0085] Step S12: Perform topology analysis on the communication receiver location information data to generate communication topology data; construct a network domain for the communication receiver location information data based on the communication topology data to generate a communication connection network domain;

[0086] Step S13: Deploy a network feature acquisition agent for the communication connection network domain to obtain network feature acquisition agent deployment data; use the network feature acquisition agent deployment data to acquire multi-source heterogeneous communication signals in the communication connection network domain to obtain multi-source heterogeneous communication signals;

[0087] Step S14: Perform signal preprocessing on the multi-source heterogeneous communication signal to generate a standard multi-source heterogeneous communication signal. The signal preprocessing includes signal denoising, signal filtering, and signal normalization.

[0088] In this embodiment of the invention, location information data is acquired from multiple communication receivers or sensor systems. Data sources specifically include GPS, Wi-Fi signals, base station positioning data, etc., and precise receiver locations are obtained through geolocation technology. The acquired location information data is converted into a suitable format (such as longitude, latitude, altitude, etc.) to ensure data consistency and availability. Graph theory and other mathematical methods are used to analyze the receiver location information and establish the topology of the communication network. Communication links can be identified by calculating the relative distances between receivers, signal strength, and other communication parameters. Based on the topology analysis results, the communication relationships between receivers are determined, and a communication connection network domain is constructed. At this point, it can be determined which receivers have direct connections and which require communication through relay nodes, thus preparing for subsequent communication signal acquisition. Network feature acquisition agents are deployed at key locations in the communication connection network domain. These agents are responsible for collecting network performance data in real time, such as bandwidth, latency, and packet loss rate. During deployment, nodes with relatively concentrated network traffic are selected to ensure comprehensive monitoring of the communication environment. The agents collect network feature data periodically or on demand based on changes in the communication environment. This data helps identify network bottlenecks, signal strength changes, and other issues, providing a basis for subsequent signal processing. Noise components in signals are removed using noise suppression algorithms (such as wavelet transform and Kalman filtering). The noise removal process should consider the signal's properties to avoid destroying useful information. Appropriate filters (such as low-pass, high-pass, and band-pass filters) are used to remove unwanted frequency components. For example, high-frequency interference signals can be filtered out, making the signal smoother. Amplitude normalization is performed on multi-source heterogeneous communication signals to ensure that the signal amplitudes are within the same range. This step helps eliminate amplitude differences caused by different signal sources, facilitating subsequent analysis.

[0089] Preferably, constructing a network domain from communication receiver location information data based on communication topology data includes:

[0090] The communication topology data is processed by collecting communication node information to obtain communication topology node data; the coordinate format of the communication receiver location information data is converted to generate communication receiver coordinate data; and the signal coverage range is analyzed based on the communication topology node data and the communication receiver coordinate data to generate communication area range data.

[0091] Topological connectivity data is obtained by calculating the topological connectivity of communication topology data using communication area range data; based on the topological connectivity data, network domains are divided into communication received coordinate data to generate communication connection network domains.

[0092] In this embodiment of the invention, information about all communication nodes in the network, such as base stations, routers, and access points, is extracted based on communication topology data. The location information, communication capabilities, and connection status of these nodes need to be collected. Real-time collection can be performed using a network management system or automated tools. The collected communication node data should be stored in a structured database to ensure rapid access for subsequent analysis. This data will include the node type (base station, terminal, etc.), geographical location (longitude, latitude), and current communication status. The location information of communication receivers is represented in different coordinate systems, such as geographic coordinates (latitude and longitude) or projected coordinates. To unify subsequent calculations and analysis, it needs to be converted to a standard coordinate format, such as UTM (Universal Transverse Mercator) or a local coordinate system. The converted receiver coordinate data is standardized to ensure the consistency and comparability of all data, avoiding analytical errors caused by differences in coordinate formats. Based on the coordinate information, signal strength, and other parameters of the communication receivers, the signal coverage range of each receiver is analyzed using propagation models (such as free space propagation models, urban environment propagation models, etc.). The signal coverage area of ​​each receiver is represented as a circular or polygonal region. The size of the coverage area can be dynamically adjusted based on actual signal strength, environmental factors, and network configuration. This method yields the signal coverage area of ​​the entire network. Based on communication topology node data and communication area range data, topology connectivity analysis is performed. This analyzes which nodes have direct communication links and which nodes require data transmission through relay nodes. Typically, connectivity calculations utilize graph theory methods such as shortest path algorithms and depth-first search (DFS) to evaluate the reachability between different nodes. The calculation results are transformed into a connectivity matrix or topology graph, representing the direct communication links between communication nodes and their connection status, thereby identifying which nodes are communication bottlenecks or faulty nodes. Based on the topology connectivity data, clustering algorithms (such as K-means clustering, DBSCAN, etc.) are used to partition the network, determining the network domain to which each receiver belongs. The partitioning of network domains should consider the communication quality and distance between nodes to ensure good communication performance between nodes within each domain. Based on the algorithm results, a network domain label is generated for each receiver, forming a communication connection network domain. This network domain optimizes routing and resource allocation, improving overall communication performance. Through refined processing of the network domain, a usable communication connection network domain is ultimately generated. Receivers, nodes, and communication links within each network domain are effectively organized to facilitate subsequent network optimization and fault recovery. The final communication connection network domain is optimized and verified to ensure that the network domain division meets communication quality requirements and that no communication interruptions or bottlenecks occur. Verification can be performed through simulation testing, real-world scenario testing, and other methods.

[0093] As an example of the present invention, reference is made to... Figure 2 As shown, step S2 in this example includes:

[0094] Step S21: Perform network-level feature analysis on the communication connection network domain to generate network domain-level feature data; use the network domain-level feature data to classify the communication connection network domain into network domain compatibility level data to generate network domain compatibility level data.

[0095] Step S22: Perform cooperative topology mapping on the communication connection network domain using network domain compatibility level data to generate a cooperative topology mapping matrix; adjust the optimal mapping path of the communication connection network domain according to the cooperative topology mapping matrix to generate dynamic topology mapping rules;

[0096] Step S23: Analyze the communication layer usage of the communication connection network domain based on standard multi-source heterogeneous communication signals using dynamic topology mapping rules to generate communication network layer usage data; perform seamless multi-protocol adaptation and conversion on the communication network layer usage data to generate a protocol adaptive conversion strategy;

[0097] Step S24: Use the protocol adaptive conversion strategy to perform QoS intelligent scheduling on the communication connection network domain and generate intelligent scheduling data for network transmission.

[0098] In this embodiment of the invention, hierarchical features of each node in the network are extracted based on the topology of the communication connection network domain. These hierarchical features specifically include the role of each network node (e.g., core node, access node, edge node), the connection method between nodes (e.g., star, ring, mesh), and performance parameters such as communication capacity, bandwidth, and latency. Based on the results of the hierarchical feature extraction, network domain hierarchical feature data is generated. This data typically includes the attributes, connection type, topology, and communication capabilities of each node's hierarchy. The network domain hierarchical feature data is used to analyze the compatibility between different communication domains, evaluating their communication capabilities and technical compatibility (e.g., whether they support the same communication protocol, data transmission rate, etc.). Algorithms (e.g., clustering algorithms or compatibility index calculations) can be used to classify network domains by compatibility level. Based on the compatibility analysis results, network domains are divided into different compatibility levels, such as high compatibility, medium compatibility, and low compatibility, generating network domain compatibility level data. Using the network domain compatibility level data, multiple network domains are used for collaborative topology mapping. The purpose of collaborative topology mapping is to establish efficient communication paths between different network domains, ensuring the lowest latency and maximum bandwidth utilization during data transmission. The mapping result is represented as a matrix, with elements indicating the connectivity and performance parameters between different network domains. This matrix aids in subsequent path optimization and adjustment. Based on the cooperative topology mapping matrix, path optimization algorithms (such as Dijkstra's algorithm or the shortest path algorithm) are used to calculate the optimal path. The optimal transmission path of data flows in the communication network is adjusted to improve the overall network performance. Based on the path optimization results, dynamic topology mapping rules are formed. These rules dynamically adjust the connectivity and paths between communication network domains, ensuring that the network can adaptively optimize according to real-time conditions. Through dynamic topology mapping rules and standard multi-source heterogeneous communication signals, the communication requirements and resource utilization of different layers are analyzed. Usage can be evaluated for each layer in the communication connection network domain based on indicators such as traffic monitoring, bandwidth usage, and latency measurement. The usage of each communication layer is recorded, including load, bandwidth, latency, and transmission success rate. By collecting and analyzing this data, network administrators can understand the current load status and potential bottlenecks of the network. In a multi-protocol environment, by analyzing the usage data of communication layers, the adaptation requirements between different protocols (such as TCP / IP, UDP, HTTP, etc.) are determined. The adaptation and conversion strategy will include conversion rules, priorities, and policies between protocols to ensure seamless connectivity between different network layers. Based on layer-specific data usage and protocol adaptation requirements, adaptive protocol conversion strategies will be generated. These strategies can optimize protocol stack selection, data format conversion, and bandwidth utilization adjustments to ensure efficient compatibility across all layers. Based on the adaptive protocol conversion strategy, quality control and Quality of Service (QoS) assessments will be performed on the communication connection network domain.Evaluation metrics include bandwidth, latency, jitter, and packet loss. Based on these metrics, the intelligent scheduling system optimizes data flow scheduling. Utilizing AI or machine learning algorithms, it predicts network traffic and latency changes and automatically adjusts traffic allocation based on the real-time network status. By dynamically adjusting the priorities of different traffic flows, it ensures that high-priority traffic (such as real-time video and voice) receives sufficient bandwidth. Based on the QoS intelligent scheduling results, it generates intelligent network transmission scheduling data. This scheduling data includes the priority, allocated bandwidth, and latency requirements for each data flow, ensuring the communication network can efficiently execute different tasks.

[0099] Preferably, seamless multi-protocol adaptation and conversion of data used at the communication network layer includes:

[0100] The system collects signal transmission traffic packets from data used at the communication network layer to obtain signal transmission traffic packets; it then classifies the signal transmission traffic packets according to transmission protocols to generate transmission traffic packet protocol classification data; finally, it extracts traffic features from the transmission traffic packet protocol classification data to obtain protocol traffic feature data.

[0101] Based on protocol traffic characteristic data, the protocol adaptation rules for the data used at the communication network layer are verified, and protocol adaptation rules are generated. These protocol adaptation rules include TCP-UDP adaptation rules, QUIC-TCP adaptation rules, and CCN-TCP adaptation rules.

[0102] The signal transmission traffic packets are converted to the transmission protocol using TCP-UDP adaptation rules to generate transmission protocol conversion data; the signal transmission traffic packets are converted to the application protocol using QUIC-TCP adaptation rules to generate application protocol conversion data; and the signal transmission traffic packets are converted to the network protocol using CCN-TCP adaptation rules to generate network protocol conversion data.

[0103] Network performance analysis is performed on the communication connection network domain to generate network performance data. Based on the network performance data, dynamic protocol selection is performed on the transmission protocol conversion data, application protocol conversion data, and network protocol conversion data to generate an adaptive protocol conversion strategy.

[0104] In this embodiment of the invention, based on communication nodes and traffic paths in the network, key information such as the transmission content, transmission time, and destination of each data packet is obtained by collecting traffic information contained in network layer usage data. Data packets are collected to generate signal transmission traffic packets, which contain traffic requirements and protocol types at various layers. Network probe devices or traffic collection tools are used for monitoring, collecting data packets from the physical layer to the application layer and storing them as traffic packets. The collected signal transmission traffic packets are classified according to protocols to identify various transmission protocols (such as TCP, UDP, QUIC, CCN, etc.). This step can be automatically classified using protocol identifiers, header information, and transmission methods. Through protocol analysis and classification, transmission traffic packet protocol classification data is generated. This data includes the specific protocol type used by each data packet and its traffic proportion. Traffic features are extracted from the transmission traffic packet protocol classification data. The traffic characteristics of each protocol are analyzed, such as bandwidth usage, latency, packet loss rate, jitter, peak traffic, etc. These features can be obtained through statistical analysis, traffic modeling, and other methods. Traffic characteristics of each protocol are extracted from traffic packets, including protocol type frequency, traffic distribution, latency fluctuations, etc., generating protocol traffic characteristic data. Based on the protocol traffic characteristic data, a series of adaptation rules are designed to ensure seamless conversion between different protocols at different network layers. For example, for applications requiring real-time transmission (such as video streaming or games), UDP is more efficient, while TCP is suitable for scenarios requiring reliable transmission. Adaptation rules between TCP and UDP are designed to ensure that applications can switch between the two according to real-time needs. QUIC is an efficient UDP-based protocol suitable for low-latency applications. Adaptation rules between QUIC and TCP are designed to ensure automatic switching to TCP under high load to guarantee reliability. Content-centric networks (CCN) use content-addressed transmission, while TCP is suitable for traditional end-to-end transmission. Adaptation rules between CCN and TCP are formulated based on traffic characteristics and network environment. The above adaptation strategies are organized into protocol adaptation rules such as TCP-UDP adaptation rules, QUIC-TCP adaptation rules, and CCN-TCP adaptation rules, and detailed conversion conditions are defined for each rule. Using TCP-UDP adaptation rules, protocol conversion is performed on signal transmission traffic packets. Low-latency packets (such as real-time data) are converted to UDP to ensure smooth data transmission. Transmission protocol conversion data is generated after the conversion. Using QUIC-TCP adaptation rules, application protocol conversion is performed on traffic with high real-time requirements (such as video and games), switching to QUIC for efficient transmission. Application protocol conversion data is generated after the conversion. According to CCN-TCP adaptation rules, network protocol conversion is performed on content-addressed traffic (such as content delivery network traffic), switching to TCP to ensure data transmission stability and reliability. Network protocol conversion data is generated after the conversion.Network performance data, including bandwidth utilization, network latency, packet loss rate, and latency fluctuations, is acquired in real time through network monitoring tools. A comprehensive analysis of the performance of each node in the network is performed. Based on the analysis results, network performance data is generated, reflecting the performance of different protocols under different network conditions and evaluating the effectiveness of protocol switching. Combining network performance data, protocol traffic characteristic data, and protocol adaptation rules, the appropriate protocol is intelligently selected. For example, QUIC is prioritized in low-latency network environments, while TCP is prioritized under high reliability requirements. Based on the dynamic protocol selection results, an adaptive protocol switching strategy is generated. This strategy automatically adjusts the transmission protocol according to network conditions and traffic demands to ensure optimal network transmission performance and minimum latency. The protocol adaptation strategy is dynamically adjusted through a feedback mechanism. The network management system optimizes protocol selection in real time based on changes in network conditions (such as bandwidth fluctuations and latency changes) to ensure communication efficiency and quality.

[0105] Preferably, step S24 includes the following steps:

[0106] Step S241: Analyze the real-time status of the communication link in the communication connection network domain using the protocol adaptive conversion strategy to obtain real-time status data of the communication link; extract key indicators from the real-time status data of the communication link to obtain QoS key indicator data, including network latency extraction, jitter extraction, packet loss rate extraction and throughput extraction.

[0107] Step S242: Calculate the current load of network nodes in the communication connection network domain using QoS key indicator data to obtain network node load data; use the network node load data to perform cross-domain traffic balancing analysis on the communication connection network domain to generate full network load assessment data;

[0108] Step S243: Classify traffic priorities in the communication connection network domain based on the overall network load assessment data to generate traffic priority data, which includes high-priority traffic and low-priority traffic; allocate communication signal bandwidth in the communication connection network domain based on the traffic priority data to generate communication signal resource allocation data.

[0109] Step S244: Reserve bandwidth for high-priority traffic based on communication signal resource allocation data to generate high-priority bandwidth allocation data; reschedule low-priority traffic based on high-priority bandwidth allocation data to generate low-priority traffic scheduling data.

[0110] Step S245: Use high-priority bandwidth allocation data and low-priority traffic scheduling data to perform end-to-end QoS intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data.

[0111] In this embodiment of the invention, the status of each communication link in the communication connection network domain is monitored in real time using a protocol adaptive switching strategy. Real-time data of the links is collected using network detectors and link status monitoring tools (such as SNMP, NetFlow, etc.), and the performance of each link, such as latency, bandwidth utilization, and packet loss rate, is analyzed. Real-time link status data is collected through a network management system or monitoring platform, forming real-time status data including parameters such as latency, packet loss rate, and latency fluctuation for each link. Latency data for each link is extracted by monitoring the latency. Latency refers to the transmission time of data from the source node to the destination node and is usually an important indicator of link quality. Jitter (latency variation) data for each link is extracted, reflecting the degree of fluctuation in packet transmission time. The ratio of lost packets to total packets in a link reflects the stability of the link. The actual transmission rate of each link is calculated to evaluate bandwidth utilization and transmission capacity. The extracted latency, jitter, packet loss rate, and throughput data are integrated into QoS key indicator data, providing a basis for subsequent network scheduling. Based on the QoS key indicator data, the current load of each network node is analyzed. The system monitors node CPU utilization, memory usage, traffic load, and network interface input / output bandwidth to assess node load pressure. Based on node load monitoring results, it generates load data for each node, reflecting the current network node's carrying capacity. Analysis of node load data assesses traffic distribution across communication domains, identifying any traffic imbalances. The system aggregates node load data for a comprehensive network load assessment, evaluating for node overload or idleness, and optimizing network traffic to balance node load. Priorities for different traffic types are defined based on network requirements. High-priority traffic typically represents latency-sensitive applications (e.g., video streaming, voice calls, real-time gaming), while low-priority traffic represents less latency-sensitive applications (e.g., file downloads, large file transfers). Traffic in the communication connection network domain is prioritized based on traffic type, quality of service requirements, and protocol characteristics, creating high-priority and low-priority traffic datasets. Bandwidth resources are allocated rationally based on the comprehensive network load assessment data and traffic priority data, ensuring high-priority traffic receives sufficient bandwidth to guarantee real-time transmission quality. Based on bandwidth allocation rules, resource allocation data for communication signals is generated, including bandwidth allocation for each traffic category. Based on traffic priority data, bandwidth resources are reserved for high-priority traffic to ensure optimal transmission quality under any network conditions. This can be achieved by dynamically adjusting bandwidth reservations based on traffic priority and protocol rules. According to the bandwidth allocation rules, the required bandwidth is allocated to each high-priority traffic, generating bandwidth reservation data. For low-priority traffic, after bandwidth allocation for high-priority traffic, the remaining bandwidth resources are rescheduled to optimize the transmission of low-priority traffic.Adjusting bandwidth allocation for low-priority traffic ensures that transmission needs are met without impacting high-priority traffic. Based on high-priority bandwidth allocation data and low-priority traffic scheduling data, end-to-end QoS intelligent scheduling is performed through a network scheduler. The intelligent scheduling system dynamically selects appropriate scheduling strategies based on real-time network status, bandwidth utilization, and traffic priority. End-to-end scheduling is executed through the intelligent scheduling system, generating intelligent network transmission scheduling data, including real-time bandwidth allocation and traffic path optimization information.

[0112] As an example of the present invention, reference is made to... Figure 3 As shown, step S3 in this example includes:

[0113] Step S31: Based on the intelligent scheduling data of network transmission, construct a virtual cross-domain network view for the communication connection network domain and generate a virtual cross-domain network view;

[0114] Step S32: Divide the virtual cross-domain network view into control domains and generate distributed control node mapping data; deploy distributed network control nodes in the virtual cross-domain network view according to the distributed control node mapping data to obtain distributed virtual network control nodes;

[0115] Step S33: Utilize the distributed virtual network control node to dynamically migrate computation tasks in the virtual cross-domain network view, generating virtual node dynamic migration data;

[0116] Step S34: By dynamically migrating data through virtual nodes, distributed collaborative scheduling of standard multi-source heterogeneous communication signals is performed to generate heterogeneous signal collaborative scheduling data.

[0117] In this embodiment of the invention, comprehensive network status data is generated by collecting and analyzing transmission conditions, traffic characteristics, and network topology information of various communication domains based on intelligent network transmission scheduling data. A virtual cross-domain network view is generated based on the collected intelligent scheduling data. This view simulates communication relationships, bandwidth allocation, traffic paths, latency constraints, and other elements between multiple domains, presenting the virtualized topology of the entire network graphically. The virtual cross-domain network view includes the comprehensive performance of multiple domains and nodes, such as link quality, traffic load, and QoS requirements, helping to achieve virtualization processing at the network layer and supporting cross-domain scheduling decisions. Based on the virtual cross-domain network view, network control domains are divided. Each control domain is divided into areas based on the network's geographical distribution, bandwidth requirements, latency limits, and control strategies to ensure efficient network control within each area. Virtual control nodes are set up within each control domain according to the divided control domains, generating control domain-node mapping data. This data records the location of each control node, the control domain it is responsible for, and its scheduling functions. Using the distributed control node mapping data, control nodes are deployed in the virtual cross-domain network view. Based on the control domain partitioning, distributed control nodes are responsible for managing traffic scheduling, bandwidth allocation, and QoS guarantees for their respective control domains. The location and function of control nodes are dynamically adjusted according to the load, network requirements, and QoS requirements of each control domain, ensuring that distributed control can automatically optimize based on network conditions. Distributed virtual network control nodes monitor resource usage and network performance (such as bandwidth, latency, and traffic load) in the virtual cross-domain network view. When the computing resources of some nodes are saturated or the load is too high, a dynamic task migration mechanism is triggered. Based on network performance data and node load data, a decision is made on whether to migrate the computing tasks of virtual nodes to other nodes. Dynamic migration decisions need to consider factors such as network topology, bandwidth availability between nodes, and computing resources. The migration process involves data transmission and rescheduling of computing tasks, generating virtual node dynamic migration data, recording the source node, target node, migration data volume, and latency. Based on the virtual node dynamic migration data, distributed control nodes coordinate the scheduling of communication signals in the network. The signals involved can be multi-source heterogeneous signals from different communication domains; the scheduling goal is to achieve efficient signal transmission in the network, ensuring a balance between bandwidth allocation, latency control, and priority traffic. By employing strategies at the network control layer, signal traffic is dynamically adjusted to the most suitable transmission path, ensuring smooth transmission of heterogeneous signals (such as signals from protocols like TCP, UDP, and QUIC) under different network conditions. This process generates heterogeneous signal collaborative scheduling data, including information on signal sources, target nodes, selected transmission paths, bandwidth allocation, and protocol conversion, ultimately forming a comprehensive scheduling decision scheme.

[0118] Preferably, step S31 includes the following steps:

[0119] Step S311: Based on the intelligent scheduling data of network transmission, perform physical network resource modeling on the communication connection network domain to generate physical network domain resource modeling data; perform network status analysis on the physical network domain resource modeling data to generate network status data;

[0120] Step S312: Logically divide the physical network domain resource modeling data according to the network status data to generate physical network domain logical division data; use the physical network domain logical division data to perform virtualization mapping on the communication connection network domain to generate virtual network elements;

[0121] Step S313: Perform virtual network slicing on the physical network domain resource modeling data using virtual network elements to generate virtual network domain partitioning data; establish virtual connections on the virtual network domain partitioning data using virtual tunneling technology to generate virtual cross-domain connection data;

[0122] Step S314: Use the preset virtual network management platform to construct a network view of the virtual cross-domain connection data, thereby obtaining a virtual cross-domain network view.

[0123] In this embodiment of the invention, by analyzing the communication connection network domain, various network performance data, including bandwidth, latency, throughput, and packet loss rate, are collected. This data provides a foundation for modeling physical network resources. Using the collected network performance data, physical network resources are modeled to generate physical network domain resource modeling data. This data specifically includes elements such as computing resources, storage resources, transmission bandwidth, and latency for each physical node, and constructs a relationship diagram of network topology, physical links, switches, and other resources. Based on the physical network domain resource modeling data, network status is analyzed, including but not limited to the health status, load, and fault areas of network links. Through real-time monitoring and evaluation, network status data is generated, which is used to further optimize the configuration and scheduling of network resources. Based on the network status data, the physical network domain is logically divided into different parts. The purpose of logical division is to optimize the use of network resources based on network topology and status, such as dividing according to criteria like traffic density, latency requirements, or load distribution. Through logical division, physical network domain logical division data is obtained. This data describes the resource requirements, priorities, and network topology relationships of each logically divided region. By logically partitioning data within physical network domains and employing virtualization technology, physical network resources are mapped into multiple virtual network elements. This virtualization mapping process abstracts physical resources into virtual resources, enabling flexible allocation and management. These virtual network elements represent abstract network resources, such as virtual routers, switches, and virtual links, laying the foundation for subsequent virtual network slicing and cross-domain connections. Physical network domain resources are then sliced ​​using these virtual network elements. Virtual network slicing divides physical network resources into multiple logical virtual networks, each with its own independent network resources and quality of service (QoS) guarantees. This process generates virtual network domain partitioning data, including resource allocation data such as bandwidth, latency, and traffic characteristics for each virtual network domain, as well as how these slices are mapped to physical network resources. Based on the virtual network domain partitioning data, virtual tunneling technologies (such as GRE and VXLAN) are used to establish virtual connections between different virtual network domains. These virtual connections ensure seamless transmission of cross-domain communication traffic between virtual network slices. This step generates virtual cross-domain connection data, describing the parameters, connection paths, bandwidth allocation, and other information of the virtual tunnels. The system utilizes a pre-defined virtual network management platform to process virtual cross-domain connection data. This platform analyzes and displays the virtual network's topology, connection status, and traffic load from a global perspective. Based on this data, a virtual network view is constructed on the platform. This view displays the connection relationships between different virtual network domains, network performance status, and resource utilization. Through this network view, network administrators can achieve efficient management, monitoring, and optimization of cross-domain networks.

[0124] Preferably, step S4 includes the following steps:

[0125] Step S41: Perform spectrum conversion on the heterogeneous signal collaborative scheduling data to generate a heterogeneous signal scheduling spectrum diagram; perform frequency hopping on the heterogeneous signal scheduling spectrum diagram to generate heterogeneous signal transmission steganography data.

[0126] Step S42: Perform security authentication on the heterogeneous signal transmission steganography data. If the security authentication result is false, the transmission is interrupted. If the security authentication result is true, the heterogeneous signal transmission steganography data is decrypted to generate a decrypted heterogeneous transmission signal for cross-domain secure signal transmission.

[0127] In this embodiment of the invention, heterogeneous signal collaborative scheduling data is collected based on data from different signal sources. This data includes signal frequency, bandwidth requirements, and latency requirements. Based on the collected heterogeneous signal scheduling data, a spectrum conversion operation is performed to map the signal spectrum from one frequency band to another. The purpose of spectrum conversion is to avoid frequency band conflicts, optimize spectrum utilization, or make the signal more adaptable to transmission in different network environments. After spectrum conversion, a heterogeneous signal scheduling spectrum diagram is generated. This diagram shows the frequency distribution, bandwidth allocation, and positions of different signals in the spectrum, providing basic data for subsequent frequency hopping and signal transmission steganography. Based on the generated heterogeneous signal scheduling spectrum diagram, a frequency hopping operation is performed. Frequency hopping is a technique that dynamically changes the signal frequency during transmission to improve anti-interference capabilities, increase security, or adapt to different network transmission conditions. Through frequency hopping, the signal jumps to different frequencies, avoiding the influence of interfering signals. After frequency hopping, the generated heterogeneous signal transmission steganography data contains hidden information, including both the transmitted content of the signal itself and hidden encoded information. This data features steganography during transmission, effectively hiding the data transmission content and increasing communication anonymity. The generated heterogeneous signal transmission steganography data undergoes security authentication, a process that verifies the signal's integrity, origin, and legitimacy. Security authentication can employ encryption mechanisms, such as digital signatures or MAC authentication codes, to verify that the data originates from a legitimate sender and has not been tampered with during transmission. If the security authentication result is false, indicating tampering, forgery, or security risks, the system will interrupt transmission. The purpose of transmission interruption is to prevent tampered or insecure data from entering the network and protect data security. If security authentication passes, the next step is data decryption, ensuring secure signal data transmission. Decryption is performed on the data that has passed security authentication. Data decryption uses the same key used for encryption to recover the original heterogeneous transmission signal. This step ensures that the data has not been leaked or modified during transmission, and only legitimately authenticated users can access the original signal. The decrypted signal is the clear heterogeneous transmission signal, containing the original signal content, and can be used for cross-domain signal transmission. Ultimately, the decrypted heterogeneous signals will be securely transmitted across domains. Cross-domain transmission ensures the smooth transfer of signals between different networks or regions, and the signal content is effectively protected during transmission, avoiding the risks of information leakage and tampering.

[0128] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0129] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A communication signal processing method, characterized in that, Includes the following steps: Step S1: Acquire communication receiver location information data; construct a network domain from the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals from the communication connection network domain to obtain standard multi-source heterogeneous communication signals; Step S2: Classify the communication connection network domain by network domain compatibility level to generate network domain compatibility level data; use the network domain compatibility level data to adjust the optimal mapping path of the communication connection network domain to generate dynamic topology mapping rules. By using dynamic topology mapping rules to perform seamless multi-protocol adaptation and conversion of the communication connection network domain based on standard multi-source heterogeneous communication signals, a protocol adaptive conversion strategy is generated. The protocol adaptive conversion strategy is then used to perform QoS intelligent scheduling of the communication connection network domain, generating intelligent network transmission scheduling data. Step S3: Based on the intelligent scheduling data of network transmission, construct a virtual cross-domain network view for the communication connection network domain and generate a virtual cross-domain network view; Dynamically migrate computational tasks on the virtual cross-domain network view to generate dynamic migration data for virtual nodes; By dynamically migrating data through virtual nodes, distributed collaborative scheduling of signal transmission for standard multi-source heterogeneous communication signals is performed, thereby generating heterogeneous signal collaborative scheduling data. Step S4: Steganographically write the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganographic data; Security authentication is performed on the steganographic data transmitted in heterogeneous signals. When the security authentication result is true, the steganographic data transmitted in heterogeneous signals is decrypted to generate a decrypted heterogeneous transmission signal, so as to perform cross-domain signal secure transmission operation.

2. The communication signal processing method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the location information data of the communication receiver; Step S12: Perform topology analysis on the communication receiver location information data to generate communication topology data; construct a network domain for the communication receiver location information data based on the communication topology data to generate a communication connection network domain; Step S13: Deploy a network feature acquisition agent for the communication connection network domain to obtain network feature acquisition agent deployment data; use the network feature acquisition agent deployment data to acquire multi-source heterogeneous communication signals in the communication connection network domain to obtain multi-source heterogeneous communication signals; Step S14: Perform signal preprocessing on the multi-source heterogeneous communication signal to generate a standard multi-source heterogeneous communication signal. The signal preprocessing includes signal denoising, signal filtering, and signal normalization.

3. The communication signal processing method according to claim 2, characterized in that, The network domain construction based on communication receiver location information data according to communication topology data includes: The communication topology data is processed by collecting communication node information to obtain communication topology node data; the coordinate format of the communication receiver location information data is converted to generate communication receiver coordinate data; and the signal coverage range is analyzed based on the communication topology node data and the communication receiver coordinate data to generate communication area range data. Topological connectivity data is obtained by calculating the topological connectivity of communication topology data using communication area range data; based on the topological connectivity data, network domains are divided into communication received coordinate data to generate communication connection network domains.

4. The communication signal processing method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform network-level feature analysis on the communication connection network domain to generate network domain-level feature data; use the network domain-level feature data to classify the communication connection network domain into network domain compatibility level data to generate network domain compatibility level data. Step S22: Perform cooperative topology mapping on the communication connection network domain using network domain compatibility level data to generate a cooperative topology mapping matrix; adjust the optimal mapping path of the communication connection network domain according to the cooperative topology mapping matrix to generate dynamic topology mapping rules; Step S23: Analyze the communication layer usage of the communication connection network domain based on standard multi-source heterogeneous communication signals using dynamic topology mapping rules to generate communication network layer usage data; perform seamless multi-protocol adaptation and conversion on the communication network layer usage data to generate a protocol adaptive conversion strategy; Step S24: Use the protocol adaptive conversion strategy to perform QoS intelligent scheduling on the communication connection network domain and generate intelligent scheduling data for network transmission.

5. The communication signal processing method according to claim 4, characterized in that, Seamless multi-protocol adaptation and conversion of data used at the communication network layer includes: The system collects signal transmission traffic packets from data used at the communication network layer to obtain signal transmission traffic packets; it then classifies the signal transmission traffic packets according to transmission protocols to generate transmission traffic packet protocol classification data; finally, it extracts traffic features from the transmission traffic packet protocol classification data to obtain protocol traffic feature data. Based on protocol traffic characteristic data, the protocol adaptation rules for the data used at the communication network layer are verified, and protocol adaptation rules are generated. These protocol adaptation rules include TCP-UDP adaptation rules, QUIC-TCP adaptation rules, and CCN-TCP adaptation rules. The signal transmission traffic packets are converted to the transmission protocol using TCP-UDP adaptation rules to generate transmission protocol conversion data; the signal transmission traffic packets are converted to the application protocol using QUIC-TCP adaptation rules to generate application protocol conversion data; and the signal transmission traffic packets are converted to the network protocol using CCN-TCP adaptation rules to generate network protocol conversion data. Network performance analysis is performed on the communication connection network domain to generate network performance data. Based on the network performance data, dynamic protocol selection is performed on the transmission protocol conversion data, application protocol conversion data, and network protocol conversion data to generate an adaptive protocol conversion strategy.

6. The communication signal processing method according to claim 4, characterized in that, Step S24 includes the following steps: Step S241: Analyze the real-time status of the communication link in the communication connection network domain using the protocol adaptive conversion strategy to obtain real-time status data of the communication link; extract key indicators from the real-time status data of the communication link to obtain QoS key indicator data, including network latency extraction, jitter extraction, packet loss rate extraction and throughput extraction. Step S242: Calculate the current load of network nodes in the communication connection network domain using QoS key indicator data to obtain network node load data; use the network node load data to perform cross-domain traffic balancing analysis on the communication connection network domain to generate full network load assessment data; Step S243: Classify traffic priorities in the communication connection network domain based on the overall network load assessment data to generate traffic priority data, which includes high-priority traffic and low-priority traffic; allocate communication signal bandwidth in the communication connection network domain using the traffic priority data to generate communication signal resource allocation data; Step S244: Based on the communication signal resource allocation data, bandwidth is reserved for high-priority traffic to generate high-priority bandwidth allocation data; based on the high-priority bandwidth allocation data, traffic is rescheduled for low-priority traffic to generate low-priority traffic scheduling data. Step S245: Use high-priority bandwidth allocation data and low-priority traffic scheduling data to perform end-to-end QoS intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data.

7. The communication signal processing method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Based on the intelligent scheduling data of network transmission, construct a virtual cross-domain network view for the communication connection network domain and generate a virtual cross-domain network view; Step S32: Divide the virtual cross-domain network view into control domains and generate distributed control node mapping data; deploy distributed network control nodes in the virtual cross-domain network view according to the distributed control node mapping data to obtain distributed virtual network control nodes; Step S33: Utilize the distributed virtual network control node to dynamically migrate computation tasks in the virtual cross-domain network view, generating virtual node dynamic migration data; Step S34: By dynamically migrating data through virtual nodes, distributed collaborative scheduling of standard multi-source heterogeneous communication signals is performed to generate heterogeneous signal collaborative scheduling data.

8. The communication signal processing method according to claim 7, characterized in that, Step S31 includes the following steps: Step S311: Based on the intelligent scheduling data of network transmission, perform physical network resource modeling on the communication connection network domain to generate physical network domain resource modeling data; perform network status analysis on the physical network domain resource modeling data to generate network status data; Step S312: Logically divide the physical network domain resource modeling data according to the network status data to generate physical network domain logical division data; use the physical network domain logical division data to virtualize and map the communication connection network domain to generate virtual network elements; Step S313: Perform virtual network slicing on the physical network domain resource modeling data using virtual network elements to generate virtual network domain partitioning data; establish virtual connections on the virtual network domain partitioning data using virtual tunneling technology to generate virtual cross-domain connection data; Step S314: Use the preset virtual network management platform to construct a network view of the virtual cross-domain connection data, thereby obtaining a virtual cross-domain network view.

9. The communication signal processing method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform spectrum conversion on the heterogeneous signal collaborative scheduling data to generate a heterogeneous signal scheduling spectrum diagram; perform frequency hopping on the heterogeneous signal scheduling spectrum diagram to generate heterogeneous signal transmission steganography data. Step S42: Perform security authentication on the heterogeneous signal transmission steganography data. If the security authentication result is false, the transmission is interrupted. If the security authentication result is true, the heterogeneous signal transmission steganography data is decrypted to generate a decrypted heterogeneous transmission signal for cross-domain secure signal transmission.

10. A communication receiver, characterized in that, The communication receiver is used to perform the communication signal processing method as described in claim 1, and includes: The signal acquisition module is used to acquire location information data of the communication receiver; construct a network domain from the location information data of the communication receiver to generate a communication connection network domain; and acquire multi-source heterogeneous communication signals from the communication connection network domain to obtain standard multi-source heterogeneous communication signals. The transmission adaptation module is used to classify the communication connection network domain into network domain compatibility levels and generate network domain compatibility level data; it uses the network domain compatibility level data to adjust the optimal mapping path of the communication connection network domain, thereby generating dynamic topology mapping rules; it uses the dynamic topology mapping rules to perform seamless multi-protocol adaptation and conversion of the communication connection network domain based on standard multi-source heterogeneous communication signals, generating a protocol adaptive conversion strategy; and it uses the protocol adaptive conversion strategy to perform QoS intelligent scheduling of the communication connection network domain, generating intelligent network transmission scheduling data. The collaborative scheduling module is used to construct a virtual cross-domain network view of the communication connection network domain based on intelligent scheduling data of network transmission, and generate a virtual cross-domain network view; to dynamically migrate computing tasks in the virtual cross-domain network view, and generate virtual node dynamic migration data; and to perform distributed collaborative scheduling of signal transmission of standard multi-source heterogeneous communication signals through virtual node dynamic migration data, thereby generating heterogeneous signal collaborative scheduling data. The security authentication module is used to perform steganography on the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganography data; to perform security authentication on the heterogeneous signal transmission steganography data; and when the security authentication result is true, to decrypt the heterogeneous signal transmission steganography data to generate decrypted heterogeneous transmission signals for performing cross-domain signal secure transmission operations.

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