Communication signal processing method and communication receiver

Abstract

The invention relates to the technical field of communication transmission, in particular to a communication signal processing method and a communication receiver. The method comprises the following steps: acquiring position information data of a communication receiver; performing network domain construction on the communication receiver position information data to generate a communication connection network domain; performing multi-source heterogeneous communication signal acquisition on the communication connection network domain to obtain a standard multi-source heterogeneous communication signal; performing network domain compatibility level classification on the communication connection network domain to generate network domain compatibility level data; performing optimal mapping path adjustment on the communication connection network domain by using the network domain compatibility level data so as to generate a dynamic topology mapping rule; and performing multi-protocol seamless adaptation conversion on the communication connection network domain based on the standard multi-source heterogeneous communication signal through a dynamic topology mapping rule to generate a protocol adaptive conversion strategy. According to the invention, through intelligent scheduling, protocol adaptation, dynamic topology adjustment and signal steganography technologies, the efficiency and security of cross-domain communication are improved.

Description

Technical Field

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

[0002] Initially, communication signal processing mainly relied on the amplification, modulation and demodulation technology of analog signals. With the development of information theory, signal processing technology gradually turned to digitalization. In the 1960s, with the emergence 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 has been significantly improved, and the processing capacity of communication systems has increased significantly, especially in wireless communications, marking that communication signal processing technology has entered a new stage. With the rise of mobile communication technologies such as 4G and 5G, communication signal processing technology has entered a highly complex and intelligent development stage. Multi-carrier technology 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 an environment where multiple networks such as 5G / 6G, satellite Internet, Wi-Fi, and optical communications coexist, existing technologies are difficult to efficiently achieve seamless switching across networks and coordinated resource scheduling. At the same time, traditional routing and protocol stack optimization solutions are difficult to adapt to large-scale dynamic topology changes, such as drone swarms and intelligent transportation networks, which leads to poor compatibility of signals when transmitted across heterogeneous networks. Summary of the invention

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

[0004] To achieve the above object, 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 for the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals for the communication connection network domain to obtain a standard multi-source heterogeneous communication signal;

[0006] Step S2: classify the network domain compatibility level of the communication connection network domain and 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, thereby generating a dynamic topology mapping rule; perform multi-protocol seamless adaptation conversion on the communication connection network domain based on standard multi-source heterogeneous communication signals through the dynamic topology mapping rule, and generate a protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform Qos intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data;

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

[0008] Step S4: Perform transmission signal steganography on the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data, and when the result of the security authentication is true, perform data decryption on the heterogeneous signal transmission steganographic data to generate a decrypted heterogeneous transmission signal to perform cross-domain signal security transmission operations.

[0009] The present invention provides basic 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, which lays a solid foundation for realizing accurate management of the network domain and signal acquisition, can better cope with the heterogeneity of different communication technologies and equipment, and improve the comprehensiveness and accuracy of signal acquisition. By classifying the compatibility level of the network domain and dynamically mapping the topology, the communication path can be adjusted according to the characteristics of different networks, thereby realizing seamless adaptation and optimization of the cross-domain network. Protocol adaptive conversion and Qos intelligent scheduling can optimize resource allocation according to actual network conditions, ensure the efficiency and quality of data transmission, especially in large-scale dynamic network environments, improve the utilization rate of network resources and service quality. By virtualizing the cross-domain network view and dynamically migrating computing tasks, more flexible and efficient network resource scheduling can be achieved. Through distributed collaborative scheduling, the signal can be optimized and transmitted according to the network status and load conditions, avoiding network bottlenecks and resource waste, and improving the overall communication efficiency and network responsiveness. By performing steganographic processing and security authentication on the signal, the data security in the cross-domain signal transmission process is guaranteed to prevent the data from being tampered with or stolen. The data decryption process ensures that only authenticated signals can be successfully decrypted and transmitted, greatly improving the security of cross-domain communications, especially in an open heterogeneous network environment, ensuring the confidentiality and integrity of information. Therefore, the present invention improves the efficiency and security of cross-domain communications through intelligent scheduling, protocol adaptation, dynamic topology adjustment and signal steganography technology.

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

[0011] Step S11: Acquire communication receiver location information data;

[0012] Step S12: performing topological analysis on the communication receiver location information data to generate communication topology data; performing network domain construction on the communication receiver location information data according to the communication topology data to generate a communication connection network domain;

[0013] Step S13: performing network feature collection agent deployment on the communication connection network domain to obtain network feature collection agent deployment data; performing multi-source heterogeneous communication signal collection on the communication connection network domain through the network feature collection agent deployment data to obtain multi-source heterogeneous communication signals;

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

[0015] The present invention provides accurate basic data for subsequent network analysis and deployment by acquiring the location information of the communication receiver, which helps to ensure the coverage and reliability of the communication network. Through topological analysis, the connection relationship and network structure between receivers can be revealed, which helps to identify potential bottleneck areas, optimize the layout of the network, and ensure the quality of communication. By deploying feature acquisition agents, various communication signals can be collected from the network in real time and the network performance can be analyzed, which helps to dynamically monitor the network status and discover and solve potential problems such as signal interference and network congestion. Signal preprocessing includes steps such as denoising, filtering and normalization, which can effectively improve the quality of the signal, eliminate external interference, and enhance the stability and accuracy of the signal, which is crucial for subsequent data analysis and processing, and can improve the reliability and accuracy of the communication network.

[0016] Preferably, constructing a network domain for the communication receiver location information data according to the communication topology data includes:

[0017] Collect communication node information on the communication topology data to obtain communication topology node data; convert the communication receiver location information data into coordinate format to generate communication reception coordinate data; analyze the signal coverage range based on the communication topology node data and the communication reception coordinate data to generate communication area range data;

[0018] The communication area range data is used to calculate the topological connectivity of the communication topology data to obtain the topological connectivity data; based on the topological connectivity data, the communication receiving coordinate data is divided into network domains to generate a communication connection network domain.

[0019] The present invention can accurately understand the state and function of each communication node by collecting node information in communication topology data, and provide key data for subsequent network planning and optimization, which helps to fully grasp the distribution of each node in the network, and then reasonably plan network resources. The coordinate format conversion of the communication receiver position information data unifies the data from different sources into a standard format, which helps to eliminate the problems caused by the inconsistent data format. By generating communication reception coordinate data, it can be ensured that consistent and accurate location information is used in subsequent analysis and calculation. By analyzing the signal coverage range of communication topology node data and receiver coordinate data, the effective coverage area of ​​the communication system can be accurately calculated, which can help identify the blind spots, weak signal areas and potential optimization points of the network, thereby improving the overall communication quality and reliability. Through topological connectivity calculation, the connection between each node of the network can be evaluated, which can help identify isolated areas, overloaded nodes or unstable links in the network, provide guidance for subsequent network optimization, and ensure the stability and continuity of the communication link. Based on the topological connectivity data, the communication reception coordinates are divided into network domains, which helps to divide the communication network into multiple sub-networks or regions for more refined management and control. Each network domain can perform different resource allocation, signal enhancement and load balancing according to needs, thereby improving the overall efficiency of the communication system.

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

[0021] Step S21: Performing network-level characterization on the communication connection network domain to generate network-domain-level characterization data; using the network-domain-level characterization data to perform network-domain compatibility level classification on the communication connection network domain to generate network-domain-compatibility level data;

[0022] Step S22: using the network domain compatibility level data to perform collaborative topology mapping on the communication connection network domain to generate a collaborative topology mapping matrix; adjusting the optimal mapping path of the communication connection network domain according to the collaborative topology mapping matrix, thereby generating a dynamic topology mapping rule;

[0023] Step S23: Perform communication layer usage analysis on the communication connection network domain based on standard multi-source heterogeneous communication signals through dynamic topology mapping rules to generate communication network layer usage data; perform multi-protocol seamless adaptation 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 to generate network transmission intelligent scheduling data.

[0025] The present invention can fully understand the topological structure and functional characteristics of each level of the network by analyzing the network hierarchical characteristics of the communication connection network domain, which helps to identify bottlenecks, important nodes and potential optimization areas in the network, and provides a theoretical basis for network optimization and resource allocation. According to the hierarchical characteristics of the network domain, the compatibility level classification can effectively evaluate the compatibility between each network domain, thereby providing strong support for subsequent collaborative work and multi-network integration. Ensure that networks at different levels can collaborate smoothly to avoid performance degradation or resource waste caused by incompatibility. Through collaborative topology mapping, the collaborative relationship between different network domains can be identified, and a topology mapping matrix reflecting these relationships can be constructed, which provides an accurate framework for collaboration between network domains and helps to achieve effective sharing of network resources. According to the collaborative topology mapping matrix, the optimal path adjustment can optimize the data flow path between multiple network domains, reduce network delays and resource conflicts, and improve overall network efficiency. Dynamically adjust the path rules to flexibly respond to network load changes and node failures, and ensure the reliability and flexibility of communication. By analyzing the use of the communication layer of the network domain based on standard multi-source heterogeneous communication signals, the load and resource usage of different network layers in actual work can be evaluated, which helps to identify potential resource bottlenecks or overload problems and supports more accurate network resource management. Based on the use data of the communication network layer, seamless adaptation and conversion of multiple protocols are performed, which provides flexibility for the network in a heterogeneous environment, and can dynamically select suitable protocols to ensure smooth network communication across different protocols and technology stacks. This strategy ensures that the network can adapt to different technical requirements and network environments, thereby improving overall interoperability. Through the protocol adaptive conversion strategy, QoS intelligent scheduling of the network helps to optimize the allocation of network resources, ensure priority processing of key services, and improve the service quality of the network. By intelligently scheduling network transmission, congestion can be reduced, transmission rate can be increased, and latency can be reduced, ensuring efficient transmission of various services in the network. In scenarios with multiple service requirements, intelligent scheduling data provides efficient decision support for practical applications. These scheduling data can automatically adjust the transmission strategy according to the real-time network status to improve the network's adaptability and optimization level.

[0026] Preferably, performing multi-protocol seamless adaptation conversion on communication network layer usage data includes:

[0027] The communication network layer usage data is used to collect signal transmission flow packets to obtain signal transmission flow packets; the signal transmission flow packets are classified by transmission protocol to generate transmission flow packet protocol classification data; the transmission flow packet protocol classification data is extracted by flow feature to obtain protocol flow feature data;

[0028] According to the protocol traffic characteristic data, the protocol adaptation rules of the communication network layer usage data are verified to generate the protocol adaptation rules, wherein the protocol adaptation rules include TCP-UDP adaptation rules, QUIC-TCP adaptation rules and CCN-TCP adaptation rules;

[0029] Perform transmission protocol conversion on signal transmission traffic packets using TCP-UDP adaptation rules to generate transmission protocol conversion data; perform application protocol conversion on signal transmission traffic packets using QUIC-TCP adaptation rules to generate application protocol conversion data; perform network protocol conversion on signal transmission traffic packets according to CCN-TCP adaptation rules to generate network protocol conversion data;

[0030] Perform network performance analysis on the communication connection network domain to generate network performance data; perform dynamic protocol selection on the transmission protocol conversion data, application protocol conversion data and network protocol conversion data based on the network performance data, thereby generating a protocol adaptive conversion strategy.

[0031] The present invention can accurately monitor network traffic and data transmission status by collecting signal transmission traffic packets, and then provide raw data support for subsequent protocol adaptation, which helps to understand the traffic mode and data transmission situation in the network in real time, and provides a basis for protocol selection and optimization. The collected signal traffic packets are classified by protocol, which helps to group different types of traffic, so that more targeted optimization and adjustment can be performed according to the protocol type, which can improve the efficiency and accuracy of the protocol adaptation process. By extracting traffic features from the transmission traffic packet protocol classification data, the transmission features of different protocols (such as bandwidth, delay, data packet size, throughput, etc.) can be identified. These features provide data support for protocol adaptation and help ensure efficiency and accuracy in the adaptation process. According to the traffic feature data, the protocol adaptation rules (such as TCP-UDP, QUIC-TCP, CCN-TCP, etc.) are determined, which will provide clear operating standards for the smooth conversion between different protocols in the network. Through these rules, it can be ensured that the conversion and adaptation between different protocols will not cause data loss, delay increase or compatibility problems, thereby optimizing the performance of the network. By converting the transport protocol according to the TCP-UDP adaptation rules, it is possible to effectively switch between TCP and UDP according to network conditions and traffic characteristics. TCP has the characteristics of high reliability and accurate transmission, and is 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 according to actual conditions. Through the QUIC-TCP protocol conversion rules, optimization can be performed at the application layer. Compared with TCP, the QUIC protocol has lower latency and higher concurrency performance, and is particularly suitable for high-concurrency, low-latency application scenarios (such as video streaming, web page loading, etc.). This adaptation can improve the response speed and experience of the application while ensuring transmission quality. The conversion of CCN (Content-Centric Networking) and TCP protocols brings flexible protocol adaptation options to the network layer. CCN can optimize information sharing and storage more effectively, and is particularly suitable for large-scale content distribution networks. Through CCN-TCP conversion, the cache efficiency and transmission performance of content 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, congestion, etc. can be comprehensively evaluated, which provides 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 by using the protocol adaptive conversion strategy to obtain the 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, wherein the key indicator extraction includes network delay 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 the Qos key indicator data to obtain network node load data; perform cross-domain traffic balancing analysis on the communication connection network domain using the network node load data to generate network-wide load evaluation data;

[0035] Step S243: classifying the traffic priority of the communication connection network domain according to the network-wide load evaluation data to generate traffic priority data, wherein the traffic priority data includes high priority traffic and low priority traffic; allocating communication signal bandwidth to the communication connection network domain according to the traffic priority data to generate communication signal resource allocation data;

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

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

[0038] The present invention can fully understand the health status of the current network environment by analyzing the state of the communication link in real time, including key parameters such as reliability, delay, and bandwidth usage of the transmission link, which helps to timely discover potential network problems, such as link bottlenecks or performance degradation, so as to take measures to adjust. By extracting key QoS indicators such as network delay, jitter, packet loss rate, and throughput, various aspects of network quality can be understood in detail, especially in the case of high traffic or load fluctuations, the changes of these indicators can be monitored, early warning and network performance can be optimized, which helps to timely discover and deal with network problems and ensure that the communication quality remains at the best level. By calculating the current load of the network node, the processing capacity and current load of each node can be understood in real time, which helps to discover the load imbalance problem in the network, identify those nodes that become performance bottlenecks, and provide data support for subsequent optimization. Through cross-domain traffic balancing analysis, the load can be reasonably distributed between different regions and network domains to avoid delays, packet loss, and other problems in certain nodes or regions due to traffic overload. The generation of network-wide load evaluation data helps to optimize the allocation of network traffic on a global scale, improve resource utilization efficiency, and reduce network congestion. By classifying the priorities of network traffic according to different requirements, important business traffic (such as real-time video, voice calls, etc.) is distinguished from less urgent traffic (such as file downloads, data transmission with low real-time requirements). This classification ensures that important applications can get network resources first, thereby improving the user experience, especially for delay-sensitive applications. Based on traffic priority data, the network bandwidth is reasonably allocated to ensure that high-priority traffic can get enough bandwidth to avoid problems such as delays and freezes. Low-priority traffic is moderately restricted or adjusted when bandwidth resources are limited to ensure the overall stability and efficiency of the network. By reserving bandwidth for high-priority traffic, it ensures that key applications (such as real-time video, VoIP, etc.) can still run stably when the network load is high. This is crucial to ensure the transmission quality of key services and avoid high-priority traffic 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 to avoid them interfering with high-priority traffic. This rescheduling strategy can dynamically optimize network resources according to real-time traffic requirements, ensuring that priority traffic is guaranteed while maximizing the use of network bandwidth. By performing end-to-end intelligent QoS scheduling based on high-priority bandwidth allocation and low-priority traffic scheduling data, more refined traffic management and resource allocation can be achieved. This intelligent scheduling can automatically adjust traffic strategies according to the real-time network status to ensure the stability and timeliness of data transmission and avoid the waste of network resources or the generation of bottlenecks. Through intelligent scheduling, the system can dynamically optimize network transmission paths and resource allocation, improve data transmission efficiency and network performance. The resulting intelligent scheduling data provides an accurate operational basis for network management, ensuring the efficient flow of different types of traffic in the network.

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

[0040] Step S31: constructing a virtual cross-domain network view for the communication connection network domain based on the network transmission intelligent scheduling data to generate a virtual cross-domain network view;

[0041] Step S32: divide the virtual cross-domain network view into control domains to 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: dynamically migrate computing tasks on the virtual cross-domain network view using the distributed virtual network control node to generate virtual node dynamic migration data;

[0043] Step S34: Perform distributed collaborative scheduling of signal transmission for standard multi-source heterogeneous communication signals through dynamic migration data of virtual nodes, thereby generating heterogeneous signal collaborative scheduling data.

[0044] The present invention can realize unified management between different network domains by constructing a virtual cross-domain network view based on network transmission intelligent scheduling data. This virtual view helps to centrally monitor and schedule cross-domain network resources, breaks through the limitations of traditional physical network architecture, and improves the flexibility and efficiency of network management. Constructing a virtual cross-domain network view provides network managers with a clear view of the global network status, so that the interaction and load distribution between different network domains can be seen at a glance, which is convenient for identifying and solving potential performance bottlenecks or conflicts. By dividing the control domain of the virtual cross-domain network view and deploying nodes based on distributed control node mapping data, distributed control of the network domain is realized. This distributed management method reduces the risk of single point failure and improves the fault tolerance and scalability of the system. The deployment of distributed control nodes helps to disperse computing and data flow tasks to multiple nodes, thereby achieving load balancing, avoiding resource bottlenecks of a single node, and improving the processing power and response speed of the entire system. By using distributed virtual network control nodes to dynamically migrate computing tasks in virtual cross-domain network views, flexible scheduling of tasks between different network nodes can be achieved. This dynamic migration capability enables the system to adjust the deployment location of tasks in real time according to changes in network load, delay or bandwidth, ensuring that computing tasks always run on the most suitable nodes. The dynamic migration of computing tasks enables more efficient use of network resources, and node resources can be flexibly allocated according to demand fluctuations, thereby optimizing the utilization of network resources, reducing resource waste, and improving the overall performance of the network. By performing distributed collaborative scheduling of signal transmission for the dynamic migration data of virtual nodes, efficient scheduling of multi-source heterogeneous communication signals can be achieved, which helps to process different types of signal flows and network traffic, and reduces delays and packet loss rates by reasonably scheduling signal transmission, thereby improving the network's service quality and throughput. The collaborative scheduling of heterogeneous signals can reduce resource competition and maximize the utilization of signal transmission by reasonably integrating and synchronizing different signal sources. Cross-domain collaborative scheduling enhances the coordination between network nodes, allowing the system to maintain efficient signal flow even under multiple protocols and network conditions.

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

[0046] Step S311: Performing physical network resource modeling on the communication connection network domain based on the network transmission intelligent scheduling data to generate physical network domain resource modeling data; performing 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; virtualize and map the communication connection network domain using the physical network domain logical division data to generate virtual network elements;

[0048] Step S313: virtual network slicing is performed on the physical network domain resource modeling data through virtual network elements to generate virtual network domain partitioning data; virtual connections are established on the virtual network domain partitioning data according to virtual tunnel technology to generate virtual cross-domain connection data;

[0049] Step S314: constructing a network view for the virtual cross-domain connection data using a preset virtual network management platform, thereby obtaining a virtual cross-domain network view.

[0050] The present invention can fully understand the key indicators of the physical network resource distribution, bandwidth, delay, node capacity, etc. by modeling the physical network resources based on the network transmission intelligent scheduling data. This modeling method provides basic data support for subsequent network optimization and scheduling decisions, so that physical resources can be managed more accurately. The network status analysis of the physical network resource modeling data can monitor the operation status of the network in real time (such as bandwidth utilization, network health, node load, etc.). In this way, the network administrator can adjust the network configuration and resource allocation according to the real-time data, optimize the network performance, and identify and solve the potential network bottleneck in advance. The physical network resources can be logically divided according to the network status data, and the network resources can be partitioned according to different requirements (such as traffic load, delay requirements, etc.), so that each logical area can best meet its business needs. This division can effectively avoid excessive concentration or waste of resources and improve the utilization efficiency of network resources. Virtualization mapping is performed through logical division data to achieve flexible mapping of physical resources to virtual resources. The generation of virtual network elements enables network resources to be more flexibly allocated and scheduled, no longer subject to physical restrictions. This mapping enhances the elasticity and scalability of the network, and can quickly adapt and optimize resources under different requirements and environments. Virtual network slicing through virtual network elements can achieve on-demand allocation of network resources. Each virtual network slice can run independently to ensure that different services (such as high-bandwidth video transmission and low-latency voice calls) can be supported by different network resources. Slicing technology greatly improves the flexibility and efficiency of network resources, while avoiding resource conflicts and over-allocation. Using virtual tunnel technology to establish virtual connections, cross-domain communication between different physical domains can be achieved. The establishment of virtual connections enables different virtual networks to be seamlessly interconnected, thereby providing cross-domain network services. It improves the flexibility, scalability and management efficiency of connections by reducing the complexity between physical networks. By building a virtual cross-domain network view through a preset virtual network management platform, various parts of the cross-domain network can be managed and monitored in a unified manner. In this way, network administrators can 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 under a unified view, adjust resource allocation in a timely manner, and achieve optimal resource scheduling.

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

[0052] Step S41: performing spectrum conversion on the heterogeneous signal cooperative scheduling data to generate a heterogeneous signal scheduling spectrum diagram; performing frequency hopping on the heterogeneous signal scheduling spectrum diagram to generate heterogeneous signal transmission steganographic data;

[0053] Step S42: Perform security authentication on the steganographic data transmitted by the heterogeneous signal. When the result of the security authentication is false, the transmission interruption processing is performed; when the result of the security authentication is true, the steganographic data transmitted by the heterogeneous signal is decrypted to generate a decrypted heterogeneous transmission signal to perform a cross-domain signal security transmission operation.

[0054] The present invention can make the signal more concealed during transmission by performing spectrum conversion and frequency hopping on heterogeneous signals, reducing the risk of external interference or signal eavesdropping. This technology makes the signal transmission process more difficult to capture and analyze, and effectively improves the privacy of the signal. The frequency hopping process makes the transmission frequency of the signal constantly change, which can effectively avoid potential interference or being locked by attackers due to fixed frequency. This frequency hopping technology can improve the stability and anti-interference of signal transmission when facing a complex signal environment. By distributing the signal on different frequencies for transmission, the congestion risk of a certain frequency band can be reduced, the bandwidth utilization rate can be improved, and the overall efficiency of cross-domain communication can be enhanced. Security authentication of the steganographic data transmitted by heterogeneous signals can ensure the integrity and reliability of the signal during transmission. The security authentication mechanism ensures the legitimacy of the signal before transmission, prevents malicious tampering and forgery of the signal, and this process effectively prevents potential man-in-the-middle attacks, signal forgery and other security threats. The system determines whether to interrupt the transmission by judging the result of security authentication. If the authentication is false, the system can immediately interrupt the signal transmission to prevent the unsafe signal from being transmitted to the target network. This immediate security response mechanism can effectively reduce potential security risks and ensure the credibility of the signal. When the security authentication is passed, the heterogeneous signal is decrypted to ensure that there is no third-party interference or data tampering during the transmission process. The decrypted signal ensures the originality and accuracy of the data, so that the cross-domain signal transmission operation can be carried out according to the predetermined goal, ensuring the data integrity and transmission quality during the communication process.

[0055] In this specification, a communication receiver is provided, which is used to execute the above communication signal processing method, and the communication receiver includes:

[0056] The signal acquisition module is used to obtain the communication receiver location information data; construct a network domain for the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals in the communication connection network domain to obtain a standard multi-source heterogeneous communication signal;

[0057] The transmission adaptation module is used to classify the network domain compatibility level of the communication connection network domain and 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, thereby generating a dynamic topology mapping rule; perform multi-protocol seamless adaptation conversion on the communication connection network domain based on standard multi-source heterogeneous communication signals through the dynamic topology mapping rule, and generate a protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform Qos intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data;

[0058] The collaborative scheduling module is used to construct a virtual cross-domain network view for the communication connection network domain based on the network transmission intelligent scheduling data, and generate a virtual cross-domain network view; dynamically migrate computing tasks for the virtual cross-domain network view, and generate virtual node dynamic migration data; perform signal transmission distributed collaborative scheduling for 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 transmission signal steganography on heterogeneous signal collaborative scheduling data and generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data, and when the result of the security authentication is true, decrypt the heterogeneous signal transmission steganographic data to generate a decrypted heterogeneous transmission signal to perform cross-domain signal security transmission operations.

[0060] The beneficial effect of the present invention is that by obtaining the location information of the communication receiver and constructing the network domain, an accurate network structure foundation can be provided for subsequent signal acquisition. The acquisition of multi-source heterogeneous communication signals can ensure wide signal coverage and adapt to the needs of different communication standards, thereby achieving compatibility between different networks and laying the foundation for subsequent protocol adaptation and optimization. By classifying the compatibility of network domains and adjusting the optimal mapping path, the module can optimize the connection efficiency between network domains and achieve seamless adaptation of cross-domain networks. This dynamic topology mapping and protocol adaptive conversion not only improves the flexibility of the network, but also optimizes resource scheduling according to real-time network conditions, ensures efficient transmission of signals and Qos guarantee, and improves the quality of service. By constructing a virtual cross-domain network view and performing dynamic computing task migration, flexible scheduling of computing tasks and network resources can be achieved to avoid network congestion and waste of resources. Through distributed collaborative scheduling, signal transmission can be dynamically adjusted according to the load and network status of each node, the signal transmission efficiency of the entire system can be optimized, and the overall performance and responsiveness of the cross-domain network can be improved. By performing steganography and security authentication on the signal, data security during cross-domain signal transmission is ensured. Steganography technology ensures the confidentiality of data during transmission and prevents signals from being tampered with or stolen. Through security authentication and data decryption, it is ensured that only legally authorized signals can be successfully decrypted and transmitted, thereby greatly improving the security of the system and ensuring the data integrity and confidentiality during signal transmission. Therefore, the present invention improves the efficiency and security of cross-domain communication through intelligent scheduling, protocol adaptation, dynamic topology adjustment and signal steganography technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of a process flow of a communication signal processing method;

[0062] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0063] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

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

[0065] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0066] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks 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 only 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, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0068] To achieve this, 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 for the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals for the communication connection network domain to obtain a standard multi-source heterogeneous communication signal;

[0070] Step S2: classify the network domain compatibility level of the communication connection network domain and 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, thereby generating a dynamic topology mapping rule; perform multi-protocol seamless adaptation conversion on the communication connection network domain based on standard multi-source heterogeneous communication signals through the dynamic topology mapping rule, and generate a protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform Qos intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data;

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

[0072] Step S4: Perform transmission signal steganography on the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data, and when the result of the security authentication is true, perform data decryption on the heterogeneous signal transmission steganographic data to generate a decrypted heterogeneous transmission signal to perform cross-domain signal security transmission operations.

[0073] The present invention provides basic 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, which lays a solid foundation for realizing accurate management of the network domain and signal acquisition, can better cope with the heterogeneity of different communication technologies and equipment, and improve the comprehensiveness and accuracy of signal acquisition. By classifying the compatibility level of the network domain and dynamically mapping the topology, the communication path can be adjusted according to the characteristics of different networks, thereby realizing seamless adaptation and optimization of the cross-domain network. Protocol adaptive conversion and Qos intelligent scheduling can optimize resource allocation according to actual network conditions, ensure the efficiency and quality of data transmission, especially in large-scale dynamic network environments, improve the utilization rate of network resources and service quality. By virtualizing the cross-domain network view and dynamically migrating computing tasks, more flexible and efficient network resource scheduling can be achieved. Through distributed collaborative scheduling, the signal can be optimized and transmitted according to the network status and load conditions, avoiding network bottlenecks and resource waste, and improving the overall communication efficiency and network responsiveness. By performing steganographic processing and security authentication on the signal, the data security in the cross-domain signal transmission process is guaranteed to prevent the data from being tampered with or stolen. The data decryption process ensures that only authenticated signals can be successfully decrypted and transmitted, greatly improving the security of cross-domain communications, especially in an open heterogeneous network environment, ensuring the confidentiality and integrity of information. Therefore, the present invention improves the efficiency and security of cross-domain communications through intelligent scheduling, protocol adaptation, dynamic topology adjustment and signal steganography technology.

[0074] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a communication signal processing method of 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 for the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals for the communication connection network domain to obtain a standard multi-source heterogeneous communication signal;

[0076] In an embodiment of the present 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. The location information generally includes data such as longitude, latitude, altitude, timestamp, etc., and the dynamic location can be obtained by regular sampling. According to the location information of the receiver, the receiver is spatially divided through a geographic information system (GIS) or other positioning algorithms to construct a network domain. The network domain can be realized by performing regional division, grid processing or partitioning algorithm on the receiver position to ensure that each area 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 the influence of environmental factors (such as terrain, buildings, etc.) on signal transmission can also be considered. On the basis of the existing network domain division, a communication connection network based on the receiver position and the network domain is constructed. The connection network domain can be based on the wireless signal transmission path between the receivers, and a connection matrix is ​​constructed to indicate whether the communication link between the receivers is valid. The network domain can also be represented by a topological 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 communications, etc.). The signal acquisition is performed on the network domain where the receiver is located. The types of signals involved include but are not limited to: radio frequency bands: such as Wi-Fi signals in the 2.4GHz and 5GHz frequency bands; mobile communication frequency bands: such as signals in the 4G, 5G, and LTE frequency bands; satellite communication signals, etc. When acquiring signals, factors such as the sampling frequency, signal-to-noise ratio, and signal strength of the signal are considered to ensure the integrity and accuracy of the data. The various signals collected are processed and standardized according to unified standards (such as time synchronization, frequency alignment, etc.). The standardization process includes: signal synchronization in the time domain and frequency domain; preprocessing operations such as signal denoising and filtering; and fusion of multi-source signals, so that signals from different sources can be analyzed uniformly on the same platform. The result is a standardized, multi-source heterogeneous communication signal set that can be used for subsequent analysis and optimization. Finally, a standardized multi-source heterogeneous communication signal data set is formed, which contains signals from different communication technologies. This data set can be used for subsequent tasks such as communication network optimization, resource allocation, and interference suppression.

[0077] Step S2: classify the network domain compatibility level of the communication connection network domain and 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, thereby generating a dynamic topology mapping rule; perform multi-protocol seamless adaptation conversion on the communication connection network domain based on standard multi-source heterogeneous communication signals through the dynamic topology mapping rule, and generate a protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform Qos intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data;

[0078] In the embodiment of the present invention, the compatibility level refers to the degree of compatibility of different network domains in terms of communication protocols, technical standards, frequency band usage, delay, etc. In order to classify, it is necessary to first define several key compatibility indicators, such as: compatibility between different communication protocols (such as compatibility between Wi-Fi and LTE, 5G). Whether different frequency bands can be compatible or shared with each other (such as compatibility between 2.4GHz and 5GHz signals). For example, whether 4G and 5G can be seamlessly switched through a specific protocol. The compatibility of delays and bandwidth requirements of different networks is evaluated. The communication network domains are classified by algorithms (such as cluster analysis, decision trees, etc.), and are divided into multiple levels (such as high, medium, low, etc.) based on the compatibility indicators of each domain, and network domain compatibility level data is generated. A compatibility level identifier can be assigned to each network domain to form a compatibility level data set to record the specific compatibility level of each network domain. According to the compatibility level of each network domain, the mapping path between them is optimized, and the "mapping path" here refers to the transmission route of the communication signal between different network domains. For network domains with high compatibility, a direct and shortest mapping path is selected; for network domains with low compatibility, path selection is optimized through intermediary gateways, protocol conversion, etc. to ensure smooth transmission of signals between different network domains. Dynamic topology mapping rules are generated through optimized mapping paths to record the connection and signal forwarding rules between each network domain. Dynamic topology mapping rules specifically include: forwarding paths between each communication network domain. Conversion protocols between network domains (such as IP protocol, Wi-Fi protocol, LTE protocol, etc.). Quality parameters such as delay and bandwidth during signal conversion. Using dynamic topology mapping rules, protocol adaptation is performed based on standard multi-source heterogeneous communication signals. By adapting different protocol layers, communication signals can be seamlessly converted between multiple network protocols. Convert low-level physical and link layer signals into formats suitable for upper-layer protocols (such as IP, TCP). Select the most appropriate protocol for communication between different network domains, such as dynamically selecting the adapted protocol between Wi-Fi and 4G. Ensure that users or devices are not aware of the protocol conversion process to ensure the stability of communication. According to the above steps, a "protocol adaptive conversion strategy" is generated, which includes: selection logic and forwarding rules for different protocols, strategies for dynamically adjusting protocol adaptation methods (such as adjusting protocol switching strategies according to network load and signal quality), and specific strategies for seamless switching between different network domains. Based on the protocol adaptive conversion strategy, the resources of each communication connection network domain are intelligently scheduled to ensure the optimization of network quality of service (QoS). QoS scheduling includes: dynamically allocating bandwidth according to the compatibility and requirements of the network domain to ensure the transmission quality of high-priority tasks and traffic. Through the optimization of network paths and protocol conversion strategies, the transmission delay of data packets in the network is reduced. Prioritize the traffic in the network to ensure that important communication traffic (such as real-time voice, video, etc.) is transmitted first.According to the network load, the communication traffic between each network domain is reasonably scheduled to avoid overloading a single node or network domain. According to the QoS scheduling results, the network transmission intelligent scheduling data is generated.

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

[0080] In an embodiment of the present invention, by using network transmission intelligent scheduling data (such as bandwidth, delay, traffic, QoS strategy, etc.), the physical infrastructure, protocol adaptation layer and network topology of each communication connection network domain are virtualized and modeled. Through virtualization technology (such as SDN, NFV, etc.), the network domain is abstracted to generate a virtualized network view spanning multiple network domains. Each network domain is mapped to different nodes in the virtual network according to its status (such as load, connection quality, bandwidth, etc.). Using the virtual network topology, the connection relationship between each physical network domain is mapped to a virtual connection to form a cross-domain network view. The network view should have dynamic adaptability and can be updated in real time as the network status changes (such as bandwidth changes, equipment failures, etc.). According to the intelligent scheduling information of the network domain, a virtual network view containing multiple physical network domains, communication protocols and data flow paths is constructed. This view not only contains information on the physical layer and link layer, but also can display the resource allocation after virtualization and the connection relationship 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 states. Dynamically migrate computing tasks 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 carry computing tasks, ensuring that low-latency tasks are executed on low-latency network nodes, and dynamically adjusting task allocation based on the resource requirements of computing tasks (such as computing power, storage capacity, etc.). Divide computing tasks into multiple subtasks based on their resource requirements. Evaluate the processing power, current load, network status, etc. of each virtual node. Based on the evaluation results, select the most suitable virtual node or network domain to perform the task. Migrate tasks or computing subtasks to the target virtual node. Record the source node, target node, migration duration, migration resource requirements, and other information of each task migration. Migration data can be used for subsequent performance monitoring, load balancing adjustment, and fault tolerance. Based on the dynamic migration data of virtual nodes, standard multi-source heterogeneous communication signals are transmitted and scheduled. The multi-source heterogeneous communication signals here include different types of wireless signals (such as Wi-Fi, LTE, 5G, etc.) and different frequency bands. In a heterogeneous network environment, the distributed collaborative scheduling mechanism is used to achieve effective integration of multiple communication signals. Based on the scheduling data of virtual nodes, different signal streams can be scheduled across domains to avoid signal interference and optimize resource utilization. Through the collaborative operation of cross-network protocols, different signal types (such as Wi-Fi, LTE, satellite communications, etc.) can work together to maximize signal transmission efficiency. The scheduling results record information such as the transmission path, protocol adaptation, and bandwidth allocation of each signal stream.

[0081] Step S4: Perform transmission signal steganography on the heterogeneous signal collaborative scheduling data to generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data, and when the result of the security authentication is true, perform data decryption on the heterogeneous signal transmission steganographic data to generate a decrypted heterogeneous transmission signal to perform cross-domain signal security transmission operations.

[0082] In an embodiment of the present invention, signal steganography is a technology that hides data in a communication signal so that the steganographic data will not be easily identified or leaked during transmission. It is often used to enhance the security of communications and prevent information from being maliciously monitored or tampered with. In heterogeneous signal collaborative scheduling data, steganography technology is applied to the process of transmitting signals to hide key information of the transmitted data (such as protocol parameters, identity identification, transmission content, etc.). According to the type of heterogeneous signals (such as Wi-Fi, LTE, 5G, etc.), a suitable signal carrier is selected as a steganographic carrier. It can be the frequency, amplitude, phase, time window, etc. of the signal. The sensitive data to be steganographic (such as signal scheduling information, user data, identification, etc.) is converted into a binary code stream or a specific coding format. According to different steganographic methods, LSB (least significant bit) technology, pseudo-noise sequence, redundant bit filling and other methods can be used. The converted data is embedded in the carrier of the transmission signal. For example, frequency modulation technology is used to encode binary data into the frequency of the signal, or data is embedded in the phase waveform of the signal through phase encoding. The steganographic data is combined with the original signal to generate the steganographic data for heterogeneous signal transmission. This steganographic data contains a data stream that looks ordinary in the original signal but has been steganographically processed. The steganographic data can be seamlessly integrated with other contents of the signal (such as ordinary communication data). It is not easy to detect, so as to prevent the data from being directly stolen by hackers or third-party attackers. When the steganographic data is transmitted, it is necessary to ensure the integrity of the data and the credibility of the source. Therefore, during the data transmission process, security authentication must be performed 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 steganographic data to generate signature data. The signing process uses the sender's private key to encrypt and generate 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 to ensure that the source of the data is reliable and that the data has not been tampered with during transmission. Add a message authentication code (MAC) to the steganographic data. By encrypting and hashing the data, a hash value is generated as verification information. The receiver performs the same MAC calculation on the received data to verify the integrity of the data. In security authentication, a key exchange protocol (such as the Diffie-Hellman protocol) is required to ensure that both parties share the same key for subsequent encryption and decryption operations. If the authentication result is true (that is, 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. Select a suitable encryption method to encrypt the steganographic data (such as symmetric encryption, asymmetric encryption, etc.). Common encryption algorithms include AES (Advanced Encryption Standard), RSA encryption, ECC (Elliptic Curve Cryptography), etc. After the receiver verifies that the authentication is passed, it uses the corresponding key to perform the decryption operation.The steganographic data in transmission is restored to obtain the original signal data and the effective data steganographic therein. The sender and the receiver use a shared key for encryption and decryption. Encryption is performed using the sender's private key, and the receiver uses the sender's public key for decryption. The signal obtained after decryption contains the original communication content, and the effective information of the steganographic data is also restored. At this point, the signal is ready for cross-domain secure transmission to ensure the confidentiality, integrity and authenticity of the information. The decrypted signal can be safely transmitted between multiple network domains. During the transmission process, the signal will be based on the previous security authentication and steganography mechanism 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, it is ensured that during the cross-domain transmission process, both the signal transmission layer and the application layer data can remain secure.

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

[0084] Step S11: Acquire communication receiver location information data;

[0085] Step S12: performing topological analysis on the communication receiver location information data to generate communication topology data; performing network domain construction on the communication receiver location information data according to the communication topology data to generate a communication connection network domain;

[0086] Step S13: performing network feature collection agent deployment on the communication connection network domain to obtain network feature collection agent deployment data; performing multi-source heterogeneous communication signal collection on the communication connection network domain through the network feature collection agent deployment data to obtain multi-source heterogeneous communication signals;

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

[0088] In an embodiment of the present invention, location information data is obtained from multiple communication receivers or sensor systems. The data source specifically includes GPS, Wi-Fi signals, base station positioning data, etc., and the precise receiver location is obtained through geographic positioning technology. The acquired location information data is converted into an applicable format (such as longitude, latitude, altitude, etc.) to ensure the consistency and availability of the data. The location information of the receiver is analyzed by mathematical methods such as graph theory to establish the topological structure of the communication network. The communication link can be identified by calculating the relative distance, signal strength and other communication parameters between the receivers. According to the topological analysis results, the communication relationship between the receivers is determined, and a communication connection network domain is constructed. At this time, it can be determined which receivers have direct connections and which need to communicate through relay nodes, thereby preparing for subsequent communication signal collection. Network feature collection 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, delay, packet loss rate, etc. When deploying, select nodes with relatively concentrated network traffic to ensure that the communication environment can be fully monitored. The agent collects network feature data regularly or on demand according to changes in the communication environment. These data will help identify network bottlenecks, signal strength changes and other problems, and provide a basis for subsequent signal processing. Remove the noise components in the signal through noise suppression algorithms (such as wavelet transform, Kalman filter, etc.). The noise removal process should take into account the nature of the signal to avoid destroying useful information. Use appropriate filters (such as low-pass, high-pass, and band-pass filters) to remove unnecessary frequency components. For example, high-frequency interference signals can be filtered out to make the signal smoother. Amplitude normalization is performed on multi-source heterogeneous communication signals to ensure that the signal amplitudes are in the same range. This step helps to eliminate amplitude differences caused by different signal sources and facilitates subsequent analysis.

[0089] Preferably, constructing a network domain for the communication receiver location information data according to the communication topology data includes:

[0090] Collect communication node information on the communication topology data to obtain communication topology node data; convert the communication receiver location information data into coordinate format to generate communication reception coordinate data; analyze the signal coverage range based on the communication topology node data and the communication reception coordinate data to generate communication area range data;

[0091] The communication area range data is used to calculate the topological connectivity of the communication topology data to obtain the topological connectivity data; based on the topological connectivity data, the communication receiving coordinate data is divided into network domains to generate a communication connection network domain.

[0092] In an embodiment of the present invention, based on the communication topology data, the information of all communication nodes in the network, such as base stations, routers, access points, etc., is extracted. The location information, communication capabilities, connection status, etc. of these nodes need to be collected. Real-time collection can be performed through a network management system or an automated tool. The collected communication node data should be stored in a structured database to ensure that subsequent analysis can be quickly accessed. These data will include the type of node (base station, terminal, etc.), geographical location (longitude, latitude) and current communication status. The location information of the communication receiver is represented by different coordinate systems, such as geographic coordinates (latitude and longitude) or projection coordinates. In order to unify subsequent calculations and analysis, it needs to be converted into a standard coordinate format, such as UTM (Universal Transverse Mercator) or a local coordinate system. The converted received coordinate data is standardized to ensure the consistency and comparability of all data and avoid analysis errors caused by differences in coordinate formats. According to the coordinate information, signal strength and other parameters of the communication receiver, a propagation model (such as a free space propagation model, an urban environment propagation model, etc.) is used to analyze the signal coverage of each receiver. The signal coverage of each receiver is represented as a circular area or a polygonal area. The size of the coverage area can be dynamically adjusted according to the actual signal strength, environmental factors and network configuration. In this way, the signal coverage area of ​​the entire network can be obtained. Based on the communication topology node data and the communication area range data, topological connectivity analysis is performed. Analyze which nodes have direct communication links and which nodes need to transmit data through relay nodes. Usually, connectivity calculations use methods such as the shortest path algorithm and depth-first search (DFS) in graph theory to evaluate the reachability between different nodes. The calculation results are converted into a connectivity matrix or topological graph to represent the direct communication links and their connection status between each communication node, and then identify which nodes are communication bottlenecks or faulty nodes. According to the topological connectivity data, a clustering algorithm (such as K-means clustering algorithm, DBSCAN, etc.) is used to divide the network and determine the network domain to which each receiver belongs. The division of network domains should take into account the communication quality and distance between nodes to ensure that the nodes in each domain have good communication performance. According to the algorithm results, the network domain label to which each receiver belongs is generated to form a communication connection network domain. This network domain can optimize routing and resource allocation and improve overall communication performance. By refining the network domain, an operational communication connection network domain is finally generated. The receivers, nodes and communication links in each network domain are effectively organized together to facilitate subsequent network optimization and fault recovery. The final communication connection network domain is optimized and verified to ensure that the division of the network domain meets the communication quality requirements and there is no communication interruption or bottleneck. This can be verified through simulation tests, actual scenario tests and other methods.

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

[0094] Step S21: Performing network-level characterization on the communication connection network domain to generate network-domain-level characterization data; using the network-domain-level characterization data to perform network-domain compatibility level classification on the communication connection network domain to generate network-domain-compatibility level data;

[0095] Step S22: using the network domain compatibility level data to perform collaborative topology mapping on the communication connection network domain to generate a collaborative topology mapping matrix; adjusting the optimal mapping path of the communication connection network domain according to the collaborative topology mapping matrix, thereby generating a dynamic topology mapping rule;

[0096] Step S23: Perform communication layer usage analysis on the communication connection network domain based on standard multi-source heterogeneous communication signals through dynamic topology mapping rules to generate communication network layer usage data; perform multi-protocol seamless adaptation 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 to generate network transmission intelligent scheduling data.

[0098] In an embodiment of the present invention, hierarchical features of each node in the network are extracted according to the topological structure of the communication connection network domain. The hierarchical features specifically include the roles of network nodes (such as core nodes, access nodes, edge nodes, etc.), the connection modes between nodes (such as star, ring, mesh, etc.), and the communication capacity, bandwidth, delay and other performance parameters of the nodes. Based on the results of hierarchical feature extraction, network domain hierarchical feature data is generated. The data generally includes the attributes of the hierarchy of each node, the connection type, the topological relationship, the communication capability, etc. The network domain hierarchical feature data is used to analyze the compatibility between different communication domains, and to evaluate the communication capability and technical compatibility between them (for example, whether the same communication protocol, data transmission rate, etc. are supported). Algorithms (such as clustering algorithms or compatibility index calculations) can be used to classify the compatibility levels of network domains. According to the results of the compatibility analysis, the network domains are divided into different compatibility levels, such as high compatibility, medium compatibility, low compatibility, etc., and network domain compatibility level data is generated. Using the network domain compatibility level data, multiple network domains are subjected to collaborative topological mapping. The purpose of collaborative topological mapping is to establish efficient communication paths between different network domains to ensure the lowest delay and maximum bandwidth utilization during data transmission. The mapping result is represented as a matrix, and the elements of the matrix represent the connection relationship and performance parameters between different network domains. This matrix is ​​helpful for subsequent path optimization and adjustment. Based on the collaborative topology mapping matrix, the path optimization algorithm (such as Dijkstra algorithm or shortest path algorithm) is used to calculate the optimal path. The optimal transmission path of the data flow in the communication network is adjusted to improve the overall performance of the network. According to the results of path optimization, dynamic topology mapping rules are formed. These rules will dynamically adjust the connection relationship and path between the communication network domains to ensure that the network can be adaptively optimized according to the real-time situation. Through dynamic topology mapping rules and standard multi-source heterogeneous communication signals, the communication requirements and resource utilization of different layers are analyzed. The usage of each layer in the communication connection network domain can be evaluated based on indicators such as traffic monitoring, bandwidth occupancy, and delay measurement. The usage of each communication layer is recorded, including load, bandwidth, delay, transmission success rate, etc. The collection and analysis of these data can help network managers understand the current load status and potential bottlenecks of the network. In a multi-protocol environment, the adaptation requirements between different protocols (such as TCP / IP, UDP, HTTP, etc.) are determined by analyzing the usage data of the communication layer. The adaptation conversion strategy will include conversion rules, priorities and strategies between protocols to ensure that each protocol can be seamlessly connected between different network layers. According to the layer usage data and protocol adaptation requirements, the protocol adaptive conversion strategy is generated. These strategies can optimize the selection of protocol stacks, data format conversion and bandwidth utilization adjustment to ensure that protocols at all levels are efficient and compatible. According to the protocol adaptive conversion strategy, the quality control and service quality (QoS) evaluation of the communication connection network domain are carried out.Evaluation indicators include bandwidth, latency, jitter, packet loss, etc. Based on these indicators, the intelligent scheduling system will optimize the scheduling of data flows. Using AI or machine learning algorithms, network traffic and latency changes are predicted, and traffic distribution is automatically adjusted according to the real-time status of the network. By dynamically adjusting the priority of different traffic flows, high-priority traffic (such as real-time video, voice, etc.) is ensured to get sufficient bandwidth first. Based on the QoS intelligent scheduling results, network transmission intelligent scheduling data is generated. The scheduling data contains the priority of each data flow, the allocated bandwidth, the latency requirements, etc., to ensure that the communication network can efficiently perform different tasks.

[0099] Preferably, performing multi-protocol seamless adaptation conversion on communication network layer usage data includes:

[0100] The communication network layer usage data is used to collect signal transmission flow packets to obtain signal transmission flow packets; the signal transmission flow packets are classified by transmission protocol to generate transmission flow packet protocol classification data; the transmission flow packet protocol classification data is extracted by flow feature to obtain protocol flow feature data;

[0101] According to the protocol traffic characteristic data, the protocol adaptation rules of the communication network layer usage data are verified to generate the protocol adaptation rules, wherein the protocol adaptation rules include TCP-UDP adaptation rules, QUIC-TCP adaptation rules and CCN-TCP adaptation rules;

[0102] Perform transmission protocol conversion on signal transmission traffic packets using TCP-UDP adaptation rules to generate transmission protocol conversion data; perform application protocol conversion on signal transmission traffic packets using QUIC-TCP adaptation rules to generate application protocol conversion data; perform network protocol conversion on signal transmission traffic packets according to CCN-TCP adaptation rules to generate network protocol conversion data;

[0103] Perform network performance analysis on the communication connection network domain to generate network performance data; perform dynamic protocol selection on the transmission protocol conversion data, application protocol conversion data and network protocol conversion data based on the network performance data, thereby generating a protocol adaptive conversion strategy.

[0104] In an embodiment of the present invention, by collecting the traffic information contained in the network layer usage data based on the 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. Data packets are collected to generate signal transmission traffic packets, which contain traffic requirements and protocol types at each level. Use a network probe device or a traffic collection tool for monitoring, collect data packets from the physical layer to the application layer, and store them as traffic packets. Perform protocol classification on the collected signal transmission traffic packets to identify various types of transmission protocols (such as TCP, UDP, QUIC, CCN, etc.). This step can be automatically classified by protocol identifier, header information, and transmission mode. Through protocol analysis and classification, transmission traffic packet protocol classification data is generated. This data contains the specific protocol type used by each data packet and its traffic proportion. Traffic feature extraction is performed on the transmission traffic packet protocol classification data. Analyze the traffic characteristics of each protocol, such as bandwidth occupancy, delay, packet loss rate, jitter, peak traffic, etc. These features can be obtained by statistical analysis, traffic modeling, and other methods. Extract the traffic characteristics of each protocol from the traffic packets, including the frequency of the protocol type, traffic distribution, latency fluctuation, etc., and generate protocol traffic characteristic data. Based on the protocol traffic characteristic data, design a series of adaptation rules to ensure seamless conversion of different protocols between different network layers. For example, for applications that require real-time transmission (such as video streaming or games), it is more efficient to use the UDP protocol, while TCP is suitable for scenarios that require reliable transmission. Design adaptation rules between TCP and UDP to ensure that applications can switch between the two according to real-time needs. QUIC is an efficient protocol based on UDP and is suitable for low-latency applications. Design adaptation rules between QUIC and TCP protocols to ensure automatic switching to TCP protocol under high load to ensure reliability. Content-centric networking (CCN) uses a content-addressed transmission method, while TCP is suitable for traditional end-to-end transmission. According to traffic characteristics and network environment, formulate adaptation rules between CCN and TCP. 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 define detailed conversion conditions for each rule. Using the TCP-UDP adaptation rules, perform protocol conversion on signal transmission traffic packets, and convert traffic packets that require low latency (such as real-time data) into UDP protocol to ensure smooth data transmission. Generate transmission protocol conversion data after conversion. Through the QUIC-TCP adaptation rules, perform application protocol conversion on traffic with high real-time requirements (such as videos and games), and switch to the QUIC protocol for efficient transmission. Generate application protocol conversion data after conversion. According to the CCN-TCP adaptation rules, perform network protocol conversion on content-addressed traffic (such as content distribution network traffic) and switch to TCP protocol to ensure the stability and reliability of data transmission. Generate network protocol conversion data after conversion.Through network monitoring tools, network performance data is obtained in real time, including bandwidth utilization, network delay, packet loss rate, delay fluctuation, etc. A comprehensive analysis is performed on the performance of each node in the network. Network performance data is generated based on the analysis results to reflect the performance of different protocols under different network conditions and evaluate the effect of protocol switching. The appropriate protocol is intelligently selected by combining network performance data, protocol traffic characteristic data, and protocol adaptation rules. For example, in a low-latency network environment, the QUIC protocol is preferred, while under high reliability requirements, the TCP protocol is preferred. Based on the dynamic protocol selection results, a protocol adaptive conversion strategy is generated. This strategy automatically adjusts the transmission protocol according to the network status and traffic demand to ensure the best performance and minimum delay of network transmission. The protocol adaptation strategy is dynamically adjusted through the feedback mechanism. The network management system optimizes the protocol selection in real time according to changes in network status (such as bandwidth fluctuations, delay changes, etc.) 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 by using the protocol adaptive conversion strategy to obtain the 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, wherein the key indicator extraction includes network delay 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 the Qos key indicator data to obtain network node load data; perform cross-domain traffic balancing analysis on the communication connection network domain using the network node load data to generate network-wide load evaluation data;

[0108] Step S243: classifying the traffic priority of the communication connection network domain according to the network-wide load evaluation data to generate traffic priority data, wherein the traffic priority data includes high priority traffic and low priority traffic; allocating communication signal bandwidth to the communication connection network domain according to the traffic priority data to generate communication signal resource allocation data;

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

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

[0111] In the embodiment of the present invention, the status of each communication link in the communication connection network domain is monitored in real time by using a protocol adaptive conversion strategy. Real-time data of the link is collected through a network detector and a link status monitoring tool (such as SNMP, NetFlow, etc.), and the performance of each link is analyzed, such as delay, bandwidth utilization, packet loss rate, etc. Real-time link status data is collected through a network management system or a monitoring platform to form real-time status data containing parameters such as delay, packet loss rate, and delay fluctuation of each link. By monitoring the delay of the link, the delay data of each link is extracted. Delay refers to the transmission time of data from the source node to the target node, which is usually an important indicator for measuring link quality. The jitter (delay variation) data of each link is extracted to reflect the degree of fluctuation of the data packet transmission time. The ratio of the number of data packets lost in the link to the total number of data packets is counted to reflect the stability of the link. The actual transmission rate of each link is calculated to evaluate the bandwidth utilization and transmission capacity. The above-extracted delay, jitter, packet loss rate and throughput data are integrated into Qos key indicator data to provide a basis for subsequent network scheduling. Based on the Qos key indicator data, the current load of each network node is analyzed. Monitor the CPU usage, memory occupancy, traffic load, input / output bandwidth of the network interface and other indicators of the node to evaluate the load pressure of the node. Generate the load data of each node based on the node load monitoring results to reflect the carrying capacity of the current network node. By analyzing the node load data, evaluate the traffic distribution in each communication domain and determine whether there is traffic imbalance. Summarize the node load data and conduct a network-wide load assessment to assess whether there are any nodes that are overloaded or idle, and optimize and adjust the network-wide traffic to balance the load of each node. Define the priority of different traffic based on network requirements. High-priority traffic is usually latency-sensitive applications (such as video streaming, voice calls, real-time games, etc.), while low-priority traffic is less sensitive applications (such as file downloads, large file transfers, etc.). Based on factors such as traffic type, service quality requirements, and protocol characteristics, prioritize the traffic in the communication connection network domain to form a data set of high-priority and low-priority traffic. Rationally allocate bandwidth resources based on the network-wide load assessment data and traffic priority data. Ensure that high-priority traffic obtains sufficient bandwidth in the network to ensure the quality of real-time transmission. According to the bandwidth allocation rules, the resource allocation data of the communication signal is generated, including the bandwidth allocation of each traffic category. According to the traffic priority data, bandwidth resources are reserved for high-priority traffic to ensure that it can obtain the best transmission quality under any network conditions. The bandwidth reservation can be dynamically adjusted through traffic priority, protocol rules, etc. According to the bandwidth allocation rules, the required bandwidth is allocated to each high-priority traffic, and bandwidth reservation data is generated. For low-priority traffic, after the bandwidth of high-priority traffic is allocated, the remaining bandwidth resources are rescheduled to optimize the transmission of low-priority traffic.Adjust the bandwidth allocation of low-priority traffic to ensure that the transmission needs of low-priority traffic are still met without affecting 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 the network scheduler. The intelligent scheduling system will dynamically select the appropriate scheduling strategy based on factors such as real-time network status, bandwidth utilization, and traffic priority. End-to-end scheduling is performed through the intelligent scheduling system to generate network transmission intelligent scheduling data, including real-time transmission bandwidth allocation, traffic path optimization and other information.

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

[0113] Step S31: constructing a virtual cross-domain network view for the communication connection network domain based on the network transmission intelligent scheduling data to generate a virtual cross-domain network view;

[0114] Step S32: divide the virtual cross-domain network view into control domains to 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: dynamically migrate computing tasks on the virtual cross-domain network view using the distributed virtual network control node to generate virtual node dynamic migration data;

[0116] Step S34: Perform distributed collaborative scheduling of signal transmission for standard multi-source heterogeneous communication signals through dynamic migration data of virtual nodes, thereby generating heterogeneous signal collaborative scheduling data.

[0117] In an embodiment of the present invention, by collecting and analyzing the transmission conditions, traffic characteristics, network topology information, etc. of each communication domain based on network transmission intelligent scheduling data, comprehensive network status data is formed. Based on the collected intelligent scheduling data, a virtual cross-domain network view is generated. This view simulates the communication relationship, bandwidth allocation, traffic path, delay constraint and other elements between multiple domains, and presents the virtualized topology of the entire network in a graphical manner. The virtual cross-domain network view contains the comprehensive performance of multiple domains and nodes, such as link quality, traffic load, QoS requirements, etc., to help realize virtualization processing at the network level and support cross-domain scheduling decisions. Based on the virtual cross-domain network view, the network control domain is divided. Each control domain is divided into regions according to the geographical distribution, bandwidth requirements, delay constraints and control strategies of the network to ensure efficient control of the network in each region. According to the divided control domains, virtual control nodes are set in each control domain to generate control domain and node mapping data. This data records the location of each control node, the control domain it is responsible for and its scheduling function. Using the distributed control node mapping data, the control node is deployed in the virtual cross-domain network view. Based on the control domain division, the distributed control node will be responsible for managing the traffic scheduling, bandwidth allocation and QoS guarantee of the corresponding control domain. According to the load, network demand and service quality requirements of each control domain, the location and function of the control node are dynamically adjusted to ensure that the distributed control can be automatically optimized according to the network status. Through the distributed virtual network control node, the resource usage and network performance (such as bandwidth, delay, traffic load, etc.) in the virtual cross-domain network view are monitored. When the computing resources of some nodes are saturated or the load is too high, the dynamic task migration mechanism is triggered. According to the network performance data and node load data, the decision is made whether the computing task of the virtual node is migrated to other nodes. The dynamic migration decision needs 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 delay of the migration, etc. According to the dynamic migration data of the virtual node, the communication signals in the network are coordinated and scheduled through the distributed control node. The signals involved can be multi-source heterogeneous signals from different communication domains. The scheduling goal is to achieve efficient transmission of signals in the network and ensure the balance of bandwidth allocation, delay control and priority traffic. Through the strategy of the network control layer, the signal flow is dynamically adjusted to the most appropriate transmission path to ensure the smooth transmission of heterogeneous signals (such as signals of TCP, UDP, QUIC and other protocols) under different network conditions. This process generates heterogeneous signal collaborative scheduling data, including information such as signal source, target node, selected transmission path, bandwidth allocation, protocol conversion, etc., and finally forms a comprehensive scheduling decision plan.

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

[0119] Step S311: Performing physical network resource modeling on the communication connection network domain based on the network transmission intelligent scheduling data to generate physical network domain resource modeling data; performing 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; virtualize and map the communication connection network domain using the physical network domain logical division data to generate virtual network elements;

[0121] Step S313: virtual network slicing is performed on the physical network domain resource modeling data through virtual network elements to generate virtual network domain partitioning data; virtual connections are established on the virtual network domain partitioning data according to virtual tunnel technology to generate virtual cross-domain connection data;

[0122] Step S314: constructing a network view for the virtual cross-domain connection data using a preset virtual network management platform, thereby obtaining a virtual cross-domain network view.

[0123] In an embodiment of the present invention, by analyzing the communication connection network domain, various network performance data such as bandwidth, delay, throughput, packet loss rate, etc. are collected, and these data provide a basis for modeling physical network resources. The collected network performance data is used to model the physical network resources and generate physical network domain resource modeling data. This data specifically includes the computing resources, storage resources, transmission bandwidth, delay and other elements of 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, the network status is analyzed, including but not limited to the health status, load status, fault area and the like of the network link. Through real-time monitoring and evaluation, network status data is generated, and these data are used to further optimize the configuration and scheduling of network resources. According to the network status data, logical division is performed for each part of the physical network domain. The purpose of logical division is to optimize the use of network resources according to the network topology and status, such as division according to standards such as traffic density, delay requirements or load distribution. Through logical division, physical network domain logical division data is obtained. This data describes the resource requirements, priorities, network topology relationships and the like of each logically divided area. Using the physical network domain logical partition data, the physical network resources are mapped into multiple virtual network elements through virtualization technology. The virtualization mapping process abstracts physical resources into virtual resources, so that resources can be flexibly allocated and managed. These virtual network elements represent abstract network resources, such as virtual routers, switches, virtual links, etc., laying the foundation for subsequent virtual network slicing and cross-domain connections. Physical network domain resources are sliced ​​through virtual network elements. Virtual network slicing is to divide physical network resources into multiple logical virtual networks, each slice has its own independent network resources and service quality assurance. This process generates virtual network domain partition data, including resource allocation data such as bandwidth, latency, and traffic characteristics of each virtual network domain, and how these slices are mapped to physical network resources. According to the virtual network domain partition data, virtual tunnel technology (such as GRE, VXLAN, etc.) is used to establish virtual connections between different virtual network domains. These virtual connections ensure that cross-domain communication traffic can be seamlessly transmitted between virtual network slices. This step will generate virtual cross-domain connection data, describing the parameters of the virtual tunnel, connection path, bandwidth allocation and other information. The virtual cross-domain connection data is processed using the preset virtual network management platform. The network management platform can analyze and display the topology, connection status, traffic load, etc. of the virtual network from a global perspective. On the virtual network management platform, a virtual network view is constructed based on the virtual cross-domain connection data. This view shows the connection relationship, network performance status, and resource utilization between different virtual network domains. Through this network view, network administrators can achieve efficient management, monitoring, and optimization of cross-domain networks.

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

[0125] Step S41: performing spectrum conversion on the heterogeneous signal cooperative scheduling data to generate a heterogeneous signal scheduling spectrum diagram; performing frequency hopping on the heterogeneous signal scheduling spectrum diagram to generate heterogeneous signal transmission steganographic data;

[0126] Step S42: Perform security authentication on the steganographic data transmitted by the heterogeneous signal. When the result of the security authentication is false, the transmission interruption processing is performed; when the result of the security authentication is true, the steganographic data transmitted by the heterogeneous signal is decrypted to generate a decrypted heterogeneous transmission signal to perform a cross-domain signal security transmission operation.

[0127] In an embodiment of the present invention, heterogeneous signal collaborative scheduling data is collected based on data from different signal sources, and these data include signal frequency, bandwidth requirements, delay requirements, etc. According to the collected heterogeneous signal scheduling data, a spectrum conversion operation is performed to map the spectrum of the signal 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. The diagram shows the frequency distribution, bandwidth allocation and position of different signals in the spectrum, which provides 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 technology that dynamically changes the signal frequency during transmission, which is used to improve anti-interference ability, increase security or adapt to different network transmission conditions. Through frequency hopping, the signal will jump to different frequencies to avoid the influence of interference signals. After frequency hopping, the generated heterogeneous signal transmission steganographic data contains secret information, including both the transmission content of the signal itself and the hidden coding information. This data has a steganographic function during the transmission process, which can effectively hide the content of data transmission and increase the concealment of communication. The generated heterogeneous signal transmission steganographic data is securely authenticated. This process includes verification of the integrity, source and legitimacy of the signal. Security authentication can adopt an encryption authentication mechanism, such as using digital signatures, authentication codes (MACs), etc. to verify whether the data comes from a legitimate sender and has not been tampered with during transmission. If the security authentication result is false, that is, when tampering, forgery or security risks are detected in the signal, the system will perform transmission interruption processing. The purpose of transmission interruption is to prevent tampered or unsafe data from entering the network and protect data security. If the security authentication passes, the next step of data decryption processing is performed to ensure the secure transmission of signal data. The data that passes the security authentication is decrypted. Data decryption uses the key used for encryption to restore the original heterogeneous transmission signal. This step ensures that the data has not been leaked or modified during transmission, and only users who have been legally authenticated can access the original signal. The decrypted signal is a clear heterogeneous transmission signal, which contains the original signal content and can be used for cross-domain signal transmission. Finally, the decrypted heterogeneous signals will be securely transmitted across domains. Cross-domain transmission ensures the smooth transmission of signals between different networks or regions, and the signal content during the transmission process is effectively protected, avoiding the risk of information leakage and tampering.

[0128] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0129] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A communication signal processing method, characterized in that: The following steps are involved: Step S1: Acquire communication receiver location information data; construct a network domain for the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals for the communication connection network domain to obtain a standard multi-source heterogeneous communication signal; Step S2: classifying the communication connection network domain by network domain compatibility level to generate network domain compatibility level data; using the network domain compatibility level data to adjust the optimal mapping path of the communication connection network domain, thereby generating a dynamic topology mapping rule; Through dynamic topology mapping rules, the communication connection network domain is seamlessly adapted to multiple protocols based on standard multi-source heterogeneous communication signals to generate a protocol adaptive conversion strategy; the communication connection network domain is intelligently scheduled for QoS using the protocol adaptive conversion strategy to generate network transmission intelligent scheduling data; Step S3: constructing a virtual cross-domain network view for the communication connection network domain based on the network transmission intelligent scheduling data to generate a virtual cross-domain network view; Dynamically migrate computing tasks on the virtual cross-domain network view and generate dynamic migration data of virtual nodes; Through the dynamic migration of virtual node data, standard multi-source heterogeneous communication signals are distributedly coordinated to generate heterogeneous signal coordinated scheduling data; Step S4: performing transmission signal steganography on the heterogeneous signal cooperative scheduling data to generate heterogeneous signal transmission steganographic data; The steganographic data transmitted by the heterogeneous signal is securely authenticated. When the result of the security authentication is true, the steganographic data transmitted by the heterogeneous signal is decrypted to generate a decrypted heterogeneous transmission signal to perform a cross-domain signal security transmission operation.

2. The communication signal processing method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire communication receiver location information data; Step S12: performing topological analysis on the communication receiver location information data to generate communication topology data; performing network domain construction on the communication receiver location information data according to the communication topology data to generate a communication connection network domain; Step S13: performing network feature collection agent deployment on the communication connection network domain to obtain network feature collection agent deployment data; performing multi-source heterogeneous communication signal collection on the communication connection network domain through the network feature collection agent deployment data to obtain multi-source heterogeneous communication signals; Step S14: performing signal preprocessing on the multi-source heterogeneous communication signal to generate a standard multi-source heterogeneous communication signal, wherein 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 of the communication receiver location information data based on the communication topology data includes: Collect communication node information on the communication topology data to obtain communication topology node data; convert the communication receiver location information data into coordinate format to generate communication reception coordinate data; analyze the signal coverage range based on the communication topology node data and the communication reception coordinate data to generate communication area range data; The communication area range data is used to calculate the topological connectivity of the communication topology data to obtain the topological connectivity data; based on the topological connectivity data, the communication receiving coordinate data is divided into network domains to generate a communication connection network domain.

4. The communication signal processing method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Performing network-level characterization on the communication connection network domain to generate network-domain-level characterization data; using the network-domain-level characterization data to perform network-domain compatibility level classification on the communication connection network domain to generate network-domain-compatibility level data; Step S22: using the network domain compatibility level data to perform collaborative topology mapping on the communication connection network domain to generate a collaborative topology mapping matrix; adjusting the optimal mapping path of the communication connection network domain according to the collaborative topology mapping matrix, thereby generating a dynamic topology mapping rule; Step S23: Perform communication layer usage analysis on the communication connection network domain based on standard multi-source heterogeneous communication signals through dynamic topology mapping rules to generate communication network layer usage data; perform multi-protocol seamless adaptation 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 to generate network transmission intelligent scheduling data.

5. The communication signal processing method according to claim 4, characterized in that: The seamless multi-protocol adaptation and conversion of communication network layer usage data includes: The communication network layer usage data is used to collect signal transmission flow packets to obtain signal transmission flow packets; the signal transmission flow packets are classified by transmission protocol to generate transmission flow packet protocol classification data; the transmission flow packet protocol classification data is extracted by flow feature to obtain protocol flow feature data; According to the protocol traffic characteristic data, the protocol adaptation rules of the communication network layer usage data are verified to generate the protocol adaptation rules, wherein the protocol adaptation rules include TCP-UDP adaptation rules, QUIC-TCP adaptation rules and CCN-TCP adaptation rules; Perform transmission protocol conversion on signal transmission traffic packets using TCP-UDP adaptation rules to generate transmission protocol conversion data; perform application protocol conversion on signal transmission traffic packets using QUIC-TCP adaptation rules to generate application protocol conversion data; perform network protocol conversion on signal transmission traffic packets according to CCN-TCP adaptation rules to generate network protocol conversion data; Perform network performance analysis on the communication connection network domain to generate network performance data; perform dynamic protocol selection on the transmission protocol conversion data, application protocol conversion data and network protocol conversion data based on the network performance data, thereby generating a protocol adaptive 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 by using the protocol adaptive conversion strategy to obtain the 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, wherein the key indicator extraction includes network delay 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 the Qos key indicator data to obtain network node load data; perform cross-domain traffic balancing analysis on the communication connection network domain using the network node load data to generate network-wide load evaluation data; Step S243: classifying the traffic priority of the communication connection network domain according to the network-wide load evaluation data to generate traffic priority data, wherein the traffic priority data includes high priority traffic and low priority traffic; allocating communication signal bandwidth to the communication connection network domain according to the traffic priority data to generate communication signal resource allocation data; Step S244: reserving bandwidth for high-priority traffic based on the communication signal resource allocation data to generate high-priority bandwidth allocation data; rescheduling low-priority traffic based on the high-priority bandwidth allocation data to generate low-priority traffic scheduling data; Step S245: Use the high-priority bandwidth allocation data and the low-priority traffic scheduling data to perform end-to-end QoS intelligent scheduling on the communication connection network domain to 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: constructing a virtual cross-domain network view for the communication connection network domain based on the network transmission intelligent scheduling data to generate a virtual cross-domain network view; Step S32: divide the virtual cross-domain network view into control domains to 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: dynamically migrate computing tasks on the virtual cross-domain network view using the distributed virtual network control node to generate virtual node dynamic migration data; Step S34: Perform distributed collaborative scheduling of signal transmission for standard multi-source heterogeneous communication signals through dynamic migration data of virtual nodes, thereby generating 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: Performing physical network resource modeling on the communication connection network domain based on the network transmission intelligent scheduling data to generate physical network domain resource modeling data; performing 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; virtualize and map the communication connection network domain using the physical network domain logical division data to generate virtual network elements; Step S313: virtual network slicing is performed on the physical network domain resource modeling data through virtual network elements to generate virtual network domain partitioning data; virtual connections are established on the virtual network domain partitioning data according to virtual tunnel technology to generate virtual cross-domain connection data; Step S314: constructing a network view for the virtual cross-domain connection data using a preset virtual network management platform, 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: performing spectrum conversion on the heterogeneous signal cooperative scheduling data to generate a heterogeneous signal scheduling spectrum diagram; performing frequency hopping on the heterogeneous signal scheduling spectrum diagram to generate heterogeneous signal transmission steganographic data; Step S42: Perform security authentication on the steganographic data transmitted by the heterogeneous signal. When the result of the security authentication is false, the transmission interruption processing is performed; when the result of the security authentication is true, the steganographic data transmitted by the heterogeneous signal is decrypted to generate a decrypted heterogeneous transmission signal to perform a cross-domain signal security transmission operation.

10. A communication receiver, characterized in that: For executing the communication signal processing method according to claim 1, the communication receiver comprises: The signal acquisition module is used to obtain the communication receiver location information data; construct a network domain for the communication receiver location information data to generate a communication connection network domain; collect multi-source heterogeneous communication signals in the communication connection network domain to obtain a standard multi-source heterogeneous communication signal; The transmission adaptation module is used to classify the network domain compatibility level of the communication connection network domain and 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, thereby generating a dynamic topology mapping rule; perform multi-protocol seamless adaptation conversion on the communication connection network domain based on standard multi-source heterogeneous communication signals through the dynamic topology mapping rule, and generate a protocol adaptive conversion strategy; use the protocol adaptive conversion strategy to perform Qos intelligent scheduling on the communication connection network domain, and generate network transmission intelligent scheduling data; The collaborative scheduling module is used to construct a virtual cross-domain network view for the communication connection network domain based on the network transmission intelligent scheduling data, and generate a virtual cross-domain network view; dynamically migrate computing tasks for the virtual cross-domain network view, and generate virtual node dynamic migration data; perform signal transmission distributed collaborative scheduling for 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 transmission signal steganography on heterogeneous signal collaborative scheduling data and generate heterogeneous signal transmission steganographic data; perform security authentication on the heterogeneous signal transmission steganographic data, and when the result of the security authentication is true, decrypt the heterogeneous signal transmission steganographic data to generate a decrypted heterogeneous transmission signal to perform cross-domain signal security transmission operations.

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