Edge coordination and link anti-interference management method for communication automation

Abstract

The application belongs to the technical field of communication automation, and discloses an edge coordination and link anti-interference management and control method for communication automation.Through the construction of three core modules of edge node intelligent coordination scheduling, communication link anti-interference dynamic regulation and control, and service priority dynamic management and control, six unique innovations are proposed, covering the whole process of communication automation from edge coordination, link anti-interference to service priority management and control. By using edge node load balancing coordination, link interference source accurate identification, service priority dynamic sorting and other cores, the industry bottlenecks of traditional communication automation, such as low efficiency of edge node coordination, weak link anti-interference ability and rigid management and control of service priority, are broken through, realizing efficient coordination scheduling of edge nodes, dynamic protection of communication links and precise adaptive management and control of service priority, significantly improving the edge coordination efficiency, link transmission stability and service scheduling rationality of communication automation, adapting to the needs of multiple scenarios such as industrial edge communication, intelligent terminal edge access and complex interference scene communication, and especially solving the technical problems of edge node load imbalance, link interference prevention and control lag and mismatch between service priority and transmission demand.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a method for edge collaboration and link anti-interference control in communication automation. Background Technology

[0002] In the current field of communication automation, with the rapid development of edge computing and 5G-A technology, edge node deployment is becoming increasingly widespread, and communication scenarios are gradually developing towards "edgeization, high interference, and multiple services." However, existing technologies still face specific and unresolved practical problems in the three sub-scenarios of "edge node collaboration, link anti-interference, and service priority management." These are all scenario-specific problems, not macro-level challenges, and have no overlap with the technical direction of the document being opened. Specifically: 1. Inefficient edge node collaboration, lack of load balancing and dynamic networking: Existing edge node collaboration mostly adopts the "fixed networking + static allocation" mode, lacking edge node load balancing collaboration and dynamic edge node networking collaboration mechanism; resulting in edge node load imbalance (some nodes are overloaded and stuck, while some nodes are idle and wasted), rigid network structure, inability to dynamically adjust according to service access volume and load changes, low edge collaboration response efficiency, and inability to adapt to the dynamic access requirements of multiple services.

[0003] 2. Weak anti-interference capability of links, lack of accurate identification and dynamic adaptation: Existing link anti-interference mostly adopts the "fixed anti-interference strategy + passive defense" mode, lacking accurate identification of link interference sources and no dynamic adaptation mechanism for link anti-interference; resulting in inaccurate identification of interference sources, incorrect type judgment, and the inability of anti-interference strategies to dynamically adjust according to interference type and intensity, poor interference suppression effect, and link transmission is easily affected by interference, resulting in packet loss and interruption, and insufficient transmission stability.

[0004] 3. Rigid business priority management, lacking dynamic sorting and adaptation calculation: Existing business priority management mostly adopts the "fixed priority + static resource allocation" model, lacking dynamic sorting of business priorities and no business priority-resource adaptation calculation mechanism; resulting in a mismatch between business priorities and actual transmission needs (highly important businesses do not receive sufficient resources, while low-priority businesses consume too many resources), blind priority adjustment, inability to dynamically optimize based on link status and edge collaboration, and poor rationality of business transmission.

[0005] Existing communication automation methods have not achieved core innovations in "intelligent collaborative scheduling of edge nodes, dynamic control of communication link anti-interference, and dynamic management of service priorities." In particular, there are significant gaps in the modeling and solution of edge node load balancing, accurate identification of link interference sources, and service priority-resource adaptation calculation, which cannot solve the aforementioned specific problems. Summary of the Invention

[0006] Addressing the three specific problems raised in the background technology, the present invention aims to provide a method for edge collaboration and link anti-interference control in communication automation. This method achieves efficient collaborative scheduling of edge nodes, dynamic anti-interference protection of communication links, and precise adaptation and control of service priorities. It solves the problems of inefficient edge node collaboration, weak link anti-interference capability, and rigid service priority control. The entire process emphasizes innovation and modeling solution without involving intellectual activity rules. It improves the efficiency of edge collaboration in communication automation, the stability of link transmission, and the rationality of service scheduling. It further improves the communication automation technology system in edge and high-interference scenarios, and conforms to the development direction of strategic emerging industries and the requirements of invention priority examination policy.

[0007] The present invention is implemented through the following specific technical solution: (I) Intelligent Cooperative Scheduling Module for Edge Nodes This module is designed to achieve load balancing, dynamic networking, and efficient collaboration of edge nodes. It constructs an intelligent collaborative modeling system for edge nodes, solves the problems of inefficient collaboration and load imbalance of edge nodes, improves the collaborative response efficiency and resource utilization of edge nodes, and provides a basic guarantee for automated edge transmission of communication.

[0008] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed networking and static allocation", we construct an integrated modeling logic of "edge node data collection - load characteristic modeling - load balancing modeling - dynamic networking modeling - collaborative verification modeling". Combining edge node characteristics (CPU utilization / memory usage / service access volume), service access requirements, and link transmission characteristics, we establish edge node load models, load balancing models, and dynamic networking models. We design edge node load balancing collaboration and edge node dynamic networking collaboration to achieve efficient collaborative scheduling of edge nodes.

[0009] First, deploy multi-source data acquisition components to collect load data (CPU utilization, memory usage, service access volume), resource data (available computing power, storage capacity), and service access data (service type, access quantity, transmission requirements) of communication edge nodes, and construct an edge node collaborative data resource pool. Then, design edge node load balancing collaboration, extract multi-dimensional load characteristics of edge nodes, construct an edge node load quantification model, quantify the load pressure of each node, and adopt improved particle swarm optimization to dynamically allocate service access tasks with the goal of "load balancing and efficient response," achieving edge node load balancing. Next, design edge node dynamic networking collaboration, based on edge node load status, service access requirements, and link transmission quality, construct a dynamic networking model, and adjust the edge node network structure in real time (adding / removing nodes, adjusting node connection relationships) to achieve flexible networking and collaborative linkage of edge nodes. Finally, construct a collaborative verification model to quantify edge node load balancing rate, collaborative response latency, and resource utilization, dynamically optimize parameters, and ensure the effectiveness of intelligent collaborative scheduling of edge nodes.

[0010] 1: Edge node load balancing collaboration To address the issues of "unbalanced load and inefficient collaboration at edge nodes" in existing technologies, an integrated model for edge node load quantification, balanced allocation, and parameter optimization is constructed. This model achieves precise load balancing and efficient collaboration at edge nodes, resolves the coexistence of overload and idleness, and fills the technological gap in edge node load balancing and collaboration for communication automation.

[0011] 2: Dynamic networking and collaboration of edge nodes To address the issues of "rigid edge networking and slow collaborative response" in existing technologies, an integrated model for edge node state modeling, dynamic networking, and collaborative linkage is constructed. This model enables flexible adjustment and efficient collaboration of the edge node networking structure, solves the problems of rigid networking and slow response, and fills the technological gap in dynamic networking and collaboration of edge nodes in communication automation.

[0012] (II) Communication Link Anti-interference Dynamic Control Module This module is designed to accurately identify interference sources in communication links, dynamically adapt anti-interference strategies, and suppress interference. It constructs a dynamic control modeling system for link anti-interference, solves the problems of weak link anti-interference capability and poor interference suppression effect, improves link transmission stability, and ensures normal service transmission.

[0013] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed anti-interference and passive defense," we construct an integrated modeling logic of "link data acquisition - interference feature modeling - interference identification modeling - anti-interference adaptation modeling - interference suppression modeling." Combining link interference characteristics (interference frequency / interference intensity / interference type), link transmission parameters, and service transmission requirements, we establish an interference feature library, an interference identification model, and an anti-interference adaptation model. We design accurate identification of link interference sources and dynamic adaptation of link anti-interference to achieve dynamic control and stability assurance of link anti-interference.

[0014] First, a real-time monitoring component is deployed to collect interference data (interference frequency, interference intensity, interference signal waveform), transmission parameter data (transmission frequency, power, encoding method), and link status data (packet loss rate, latency, signal strength) of the communication link, thus constructing a data resource pool for link anti-interference management. Then, a precise identification system for link interference sources is designed, constructing a link interference feature library, extracting multi-dimensional core features of interference signals, and employing an improved support vector machine. Through interference feature matching and clustering analysis, precise identification, type judgment (human interference / natural interference), and location positioning of interference sources are achieved. Next, a dynamic adaptation system for link anti-interference is designed. Based on the type of interference source, interference intensity, and link transmission requirements, an anti-interference strategy adaptation model is constructed, dynamically adjusting anti-interference strategies (frequency switching, power adjustment, encoding optimization) to achieve precise interference suppression. Finally, an anti-interference verification model is constructed to quantify the accuracy of interference identification, the effect of interference suppression, and the link transmission compliance rate, dynamically optimizing parameters to ensure the effectiveness of dynamic control of link anti-interference.

[0015] 3: Accurate identification of link interference sources To address the problems of inaccurate interference source identification and incorrect type judgment in existing technologies, an integrated model for interference feature modeling, matching and identification, and location positioning is constructed to achieve accurate identification and type judgment of interference sources, solve the problems of large interference identification errors and ambiguous positioning, and fill the technical gap in accurate identification of interference sources in communication automation links.

[0016] 4: Dynamic adaptation for link anti-interference To address the problem of "fixed anti-interference strategies and poor suppression effects" in existing technologies, an integrated model of interference type adaptation, dynamic strategy adjustment and interference suppression is constructed to achieve accurate adaptation and dynamic optimization of anti-interference strategies, solve the problems of passive anti-interference and poor suppression effects, and fill the technical gap of dynamic adaptation of anti-interference in communication automation links.

[0017] (III) Dynamic Management Module for Business Priority This module's core functionality includes dynamic sorting of business priorities, precise allocation of resources, and dynamic adjustment of priorities. It constructs a dynamic management and control modeling system for business priorities, resolving issues of rigid business priority management and resource mismatch, improving the rationality of business transmission and user experience, and ensuring priority transmission of highly important services.

[0018] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed priority and static allocation," we construct an integrated modeling logic of "business data collection - priority feature modeling - dynamic sorting modeling - resource adaptation modeling - adaptation accounting modeling." Combining business characteristics (data importance / latency requirements / bandwidth requirements), link status characteristics, and edge collaboration characteristics, we establish a business priority model, a dynamic sorting model, and a resource adaptation model. We design dynamic sorting of business priorities and business priority-resource adaptation accounting to achieve precise control of business priorities.

[0019] First, integrate business transmission demand data (business type, data importance, latency requirements, bandwidth requirements), link status data (transmission quality, interference), and edge collaboration data (edge ​​node load, resource status) to construct a business priority management data resource pool. Then, design a dynamic business priority ranking system, extract multi-dimensional priority features of businesses, construct a business priority ranking model, and use an improved analytic hierarchy process (AHP) to quantify the priority weight of each business, achieving dynamic updates and accurate ranking of business priorities (prioritizing high-importance, low-latency-requirement businesses). Design a business priority-resource adaptation calculation system, quantifying the compatibility between business priorities and resource allocation through the formula. Based on the ranking results and adaptation calculation values, dynamically allocate edge node resources and link resources to ensure high-priority businesses receive sufficient resources. Finally, construct a management verification model to quantify the accuracy of business priority ranking, resource adaptation, and high-priority business transmission compliance rate, dynamically optimizing parameters to ensure the effectiveness of dynamic business priority management.

[0020] 5: Dynamic prioritization of business functions To address the issues of "fixed business priorities and inaccurate sorting" in existing technologies, an integrated model for business priority feature modeling, dynamic sorting, and weight optimization is constructed to achieve accurate sorting and dynamic updating of business priorities, solve the problems of rigid sorting and mismatch with requirements, and fill the technical gap in dynamic sorting of business priorities in communication automation.

[0021] 6: Business Priority - Resource Adaptation Calculation To address the issues of "priority and resource mismatch and blind adjustment" in existing technologies, an integrated model of quantitative adaptation calculation formula and resource allocation optimization is constructed to achieve accurate adaptation of business priorities and resource allocation, solve the problems of unreasonable adaptation and directionless adjustment, and fill the technical gap in communication automation business priority-resource adaptation calculation.

[0022] Beneficial effects 1. Edge Node Load Balancing and Collaboration: Abandoning the crude approach of static allocation, a modeling system for load quantification, balanced allocation, and parameter optimization is constructed, significantly improving the load balancing rate and resource utilization of edge nodes, completely solving the problem of coexistence of overload and idleness, focusing on efficient collaborative innovation of edge nodes, and meeting the development needs of strategic emerging industries in the industrial internet. 2. Dynamic networking and collaboration of edge nodes: Construct a modeling system for node status modeling, dynamic networking and collaborative linkage, which significantly improves the flexibility of edge node networking and the efficiency of collaborative response, completely solves the problems of network rigidity and response lag, and fills the technical gap in dynamic networking and collaboration of edge nodes. 3. Precise identification of link interference sources: Construct a modeling system for interference feature modeling, matching identification and localization, which significantly improves the accuracy of interference source identification and type judgment, completely solving the problems of large interference identification errors and ambiguous localization, and focusing on the innovation of precise anti-interference prevention and control of links; 4. Dynamic Adaptation of Link Interference Resistance: Construct a modeling system for interference adaptation, strategy adjustment and suppression, which significantly improves the link's anti-interference capability and transmission stability, completely solves the problems of passive anti-interference and poor suppression effect, and fills the technical gap in dynamic adaptation of link interference resistance. 5. Dynamic Business Priority Ranking: Construct a modeling system for priority feature modeling, dynamic ranking, and weight optimization. This significantly improves the accuracy and adaptability of business priority ranking, completely solving the problems of rigid ranking and mismatch with requirements, and focusing on precise business control innovation. 6. Business Priority-Resource Adaptation Accounting: A modeling system for adaptation accounting and resource optimization is constructed, which significantly improves the rationality of business priority and resource allocation, greatly enhances the accuracy of adjustment, fills the technical gap in business priority-resource adaptation accounting, and meets the requirements of the invention priority examination policy. Attached Figure Description

[0023] Figure 1 : Workflow diagram of the edge node intelligent collaborative scheduling module Detailed Implementation

[0024] The following four specific embodiments illustrate the implementation steps of the present invention in detail.

[0025] Example 1: Industrial Edge Communication Collaborative Anti-interference Scenario Implementation steps Step 1: Data Acquisition and Parameter Setting: Collect business requirement data for industrial edge communication (industrial control business - high importance and low latency, environmental monitoring business - medium importance, log transmission business - low importance), link status data (link interference, transmission quality), and edge collaboration data (edge ​​node load, resource status). Set the priority of industrial edge communication services and the resource compatibility threshold. .

[0026] Step 2: Dynamic Priority Ranking of Business Services: Dynamic priority ranking of business services is adopted. Priority characteristics (data importance, latency requirements) of each service are extracted, a priority ranking model is constructed, and the priority weight of each service is quantified by improving the analytic hierarchy process to achieve dynamic priority ranking of business services (industrial control services > environmental monitoring services > log transmission services).

[0027] Step 3: Priority-Resource Adaptation Calculation and Allocation: Business priority-resource adaptation calculation is adopted, using the adaptation calculation formula... Quantify the fit between business priorities and resource allocation, and calculate the priority weight of each business. Resource allocation satisfaction rate Transmission delay With the proportion of resource consumption Based on the sorting results and fit values, edge node resources and link resources are dynamically allocated (sufficient computing power and low-interference links are allocated to industrial control services).

[0028] Step 4: Adaptation Optimization and Effect Verification: If the adaptation value... Iteratively optimize resource allocation strategies (adjust resource allocation ratios for each business to improve the resource fulfillment rate for high-priority businesses); verify the accuracy of business priority ranking, resource adaptability (≥0.90), and transmission compliance rate of high-priority businesses (no lag or interruption in industrial control services).

[0029] Step 5: Continuous optimization: Collect data on changes in business requirements, link status, and edge collaboration in industrial edge communication, dynamically adjust priority weights and adaptability thresholds, and improve adaptability to sudden business disruptions and interference fluctuations in industrial production scenarios.

[0030] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed priority and static allocation," this paper constructs an integrated closed-loop model of "data acquisition - priority modeling - dynamic sorting - adaptation calculation," which addresses the high-reliability transmission requirements of industrial edge communication. Using business priority characteristics, edge collaboration characteristics, and link status characteristics as core inputs, this approach breaks through the limitations of rigid business priority control and resource mismatch. Business priority characteristic modeling accurately depicts the importance of business and transmission requirements; dynamic sorting modeling enables real-time priority updates; adaptation calculation formula modeling achieves quantitative evaluation of the priority and resource adaptation effect; and parameter optimization modeling enables precise adjustment of resource allocation, filling the gap in dynamic management modeling of business priorities in industrial edge communication. The modeling process focuses on the high reliability and high priority business assurance requirements of industrial scenarios, which is completely different from the fixed priority and non-calculation modeling ideas and technical directions of existing technologies. It also has no overlap with the modeling logic of the currently opened document, representing a completely new modeling direction that meets the development needs of the strategic emerging industries of the industrial internet and the requirements of the invention priority examination policy.

[0031] Dynamic business priority ranking, through multi-dimensional feature extraction, weight quantification, and dynamic sorting, significantly improves the accuracy and adaptability of business priority ranking compared to the traditional fixed priority mode, completely solving the problems of rigid ranking and mismatch with actual needs, and ensuring priority transmission of high-importance, low-latency services. Business priority-resource adaptation accounting, through adaptation accounting formula quantification and parameter optimization, significantly improves the rationality of the adaptation between business priority and resource allocation compared to the resource allocation mode without energy accounting, greatly improving resource utilization and avoiding insufficient resources for high-priority services and waste of resources for low-priority services. The synergistic effect of these two types enables industrial edge communication to achieve "precise priority ranking, precise resource adaptation, and priority guarantee for high-priority services." Compared with existing technologies, the rationality and reliability of service transmission are qualitatively improved, fully meeting the high requirements of industrial edge communication and conforming to the requirement of "enhancing the core competitiveness of the industry" in the invention priority examination policy.

[0032] Existing technologies employ a "fixed priority + static resource allocation" business priority management model, lacking dynamic sorting and adaptation calculations. This leads to a mismatch between business priorities and actual transmission needs, haphazard priority adjustments, and an inability to guarantee priority transmission for highly important services. Furthermore, resource utilization is low, failing to meet the high reliability requirements of industrial edge communication. This embodiment, through innovation and modeling optimization, achieves dynamic management and precise resource adaptation of industrial edge communication business priorities, completely resolving the pain points of existing technologies. Both sorting accuracy and resource adaptation meet industrial edge communication standards, and there is no overlap with existing technologies, the technical direction of the currently opened document, or the modeling approach. Its innovation is prominent, its practicality is strong, and it aligns with the development needs of strategic emerging industries and the requirements of invention priority examination policies.

[0033] Example 2: Smart Terminal Edge Access Collaboration Scenario (corresponding to the edge node intelligent collaborative scheduling module) Implementation steps Step 1: Edge Node and Business Data Acquisition: Deploy multi-source data acquisition components to collect edge node data (CPU utilization, memory usage, available computing power), business access data (number of mobile phones / tablets / smart devices connected, business type - video / voice / file transfer), and resource data (storage capacity, link bandwidth) for smart terminal edge access scenarios.

[0034] Step 2: Edge Node Load Balancing Collaboration: Edge node load balancing collaboration is adopted to extract multi-dimensional load characteristics of edge nodes, construct a load quantification model, quantify the load pressure of each node, and dynamically allocate service access tasks by improving particle swarm optimization (distributing video services with high computing power requirements to nodes with low load and sufficient computing power) to achieve edge node load balancing.

[0035] Step 3: Dynamic Networking Adjustment of Edge Nodes: Dynamic networking collaboration of edge nodes is adopted. Based on the load status of edge nodes, service access volume and link transmission quality, a dynamic networking model is constructed, and the network structure of edge nodes is adjusted in real time (adding access nodes to share high loads and deleting idle nodes to save resources) to achieve collaborative linkage of edge nodes.

[0036] Step 4: Verification and optimization of collaborative effects: Verify the load balancing rate of edge nodes (no overload, no idle), collaborative response latency (meeting the transmission requirements of terminal services), and resource utilization; collect feedback data from terminal services, optimize load quantification parameters and network adjustment thresholds, and improve collaborative response efficiency.

[0037] Step 5: Continuous optimization: Collect data on changes in smart terminal access (number of accesses, service types) and edge node operation data, dynamically update the edge node load model, optimize parameters, and improve the ability to adapt to sudden changes in terminal access and service types.

[0038] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed networking and static allocation," this paper constructs an integrated closed-loop model of "data acquisition, load modeling, load balancing and coordination, and dynamic networking." It uses the diverse service requirements of smart terminal edge access, edge node characteristics, and link transmission characteristics as core inputs, overcoming the limitations of inefficient edge node coordination and load imbalance. Edge node load modeling achieves precise quantification of load pressure, load balancing modeling achieves reasonable allocation of service tasks, and dynamic networking modeling achieves flexible adjustment of the network structure, filling the gap in edge node coordination modeling for smart terminal edge access. The modeling process focuses on the flexibility and efficiency requirements of smart terminal access, completely different from the fixed networking and static allocation modeling approaches and technical directions of existing technologies. It also has no overlap with the current modeling logic, representing a completely new modeling direction that aligns with the development needs of the strategic emerging industry of new mobile communication networks.

[0039] Edge node load balancing collaboration, through load quantization, balanced allocation, and parameter optimization, significantly improves the load balancing rate and resource utilization of edge nodes compared to the traditional static allocation mode, completely solving the problem of coexistence of overload and idleness, and avoiding node lag and resource waste. Edge node dynamic networking collaboration, through dynamic networking modeling and structural adjustment, significantly improves the networking flexibility and collaborative response efficiency of edge nodes compared to the traditional fixed networking mode, completely solving the problems of network rigidity and response lag, and adapting to the dynamic changes in the number of terminal accesses and service types. These two types of collaboration enable smart terminal edge access to achieve "load balancing, flexible networking, and efficient collaboration." Compared with existing technologies, edge collaboration efficiency and terminal service transmission experience are qualitatively improved, fully meeting the diverse needs of smart terminal edge access.

[0040] Existing technologies employ a "fixed networking + static allocation" edge node collaboration mode, lacking load balancing and dynamic networking. This leads to edge node load imbalance, a rigid network structure, and an inability to dynamically adjust based on terminal access volume and service changes. Consequently, collaborative response efficiency is low, and terminal services are prone to stuttering and latency. This embodiment, through innovation and modeling optimization, achieves highly efficient collaborative scheduling of edge nodes for smart terminal edge access, completely resolving the pain points of existing technologies. Both collaborative efficiency and resource utilization meet the standards for smart terminal edge access. Furthermore, it does not overlap with existing technologies, current technical directions, or implementation scenarios, highlighting its innovation, strong practicality, and alignment with the development needs of strategic emerging industries.

[0041] Example 3: Communication link anti-interference management scenario in complex interference situations (corresponding to the communication link anti-interference dynamic control module) Implementation steps Step 1: Link-related data collection: Collect communication link data in complex interference scenarios (such as industrial plants and densely populated urban areas), including interference data (interference frequency, interference intensity, interference signal waveform), transmission parameter data (transmission frequency, power, encoding method), and link status data (packet loss rate, delay, signal strength).

[0042] Step 2: Precise identification of link interference sources: By employing precise identification of link interference sources, a link interference feature library is constructed, multi-dimensional core features of interference signals are extracted, and interference feature matching and improved support vector machine clustering analysis are used to accurately identify the type (human interference / natural interference) and location of interference sources, and to distinguish the impact range of different interferences.

[0043] Step 3: Link Anti-interference Dynamic Adaptation and Suppression: Link anti-interference dynamic adaptation is adopted. Based on the type of interference source, the intensity of interference and the link transmission requirements, an anti-interference strategy adaptation model is constructed, and the anti-interference strategy is dynamically adjusted (frequency switching is used for human interference, and power adjustment and coding optimization are used for natural interference) to achieve precise suppression of interference.

[0044] Step 4: Verification and optimization of anti-interference effect: Verify the accuracy of interference identification, the effect of interference suppression, and the link transmission compliance rate (no obvious packet loss or interruption); collect link operation data, optimize the interference feature extraction parameters and the anti-interference strategy adaptation threshold, and improve the anti-interference capability.

[0045] Step 5: Continuous optimization: Collect interference change data (interference type, intensity fluctuation) and link status data in complex interference scenarios, dynamically update the interference feature library, optimize parameters, and improve the adaptability and anti-interference capability for complex interference scenarios.

[0046] Modeling Innovation Principles Abandoning the traditional, crude modeling approach of "fixed anti-interference and passive defense," this paper constructs an integrated closed-loop model encompassing "data acquisition, interference modeling, identification and location, and anti-interference adaptation." It uses the interference characteristics, link transmission requirements, and transmission parameter characteristics of complex interference scenarios as core inputs, overcoming the limitations of weak link anti-interference capabilities and poor interference suppression. Interference feature modeling achieves precise characterization of interference signals, interference identification modeling achieves precise identification and location of interference sources, and anti-interference adaptation modeling enables dynamic adjustment of anti-interference strategies, filling the gap in anti-interference modeling for communication links in complex interference scenarios. The modeling process focuses on the anti-interference stability requirements of complex interference scenarios, completely differing from the fixed anti-interference and passive defense modeling approaches and technical directions of existing technologies. It also has no overlap with the current modeling logic, representing a completely new modeling direction that aligns with the communication development needs of strategic emerging industries in complex scenarios.

[0047] Accurate identification of link interference sources: Through multi-dimensional interference feature extraction, matching, and clustering analysis, the accuracy of interference source identification and type judgment is significantly improved compared to the traditional single-feature identification mode, completely solving the problems of large interference identification errors and ambiguous positioning, and providing a precise basis for adjusting anti-interference strategies. Dynamic adaptation of link anti-interference: Through interference adaptation, dynamic adjustment and suppression of strategies, the anti-interference capability and transmission stability of the link are significantly improved compared to the traditional fixed anti-interference strategy, completely solving the problems of passive anti-interference and poor suppression effect, and can achieve precise suppression according to different interference types. The synergistic effect of these two types enables communication in complex interference scenarios to achieve "accurate interference identification, dynamic strategy adaptation, and stable and reliable transmission". Compared with existing technologies, the link anti-interference capability and transmission stability have been qualitatively improved, fully meeting the communication needs of complex interference scenarios.

[0048] Existing technologies employ a "fixed anti-interference strategy + passive defense" link anti-interference mode, lacking precise identification and dynamic adaptation. Interference source identification is inaccurate, type judgment is incorrect, and the anti-interference strategy cannot be adjusted according to interference changes, resulting in poor interference suppression and susceptibility to packet loss and interruption in link transmission. This embodiment, through innovation and modeling optimization, achieves dynamic anti-interference management of communication links in complex interference scenarios, completely resolving the pain points of existing technologies. Both interference identification accuracy and anti-interference effectiveness meet communication standards for complex interference scenarios. Furthermore, it has no overlap with existing technologies, current technical directions, or implementation scenarios, highlighting its innovation, strong practicality, and alignment with the development needs of strategic emerging industries.

[0049] Example 4: Multi-scenario converged edge communication platform (industrial + smart terminal + complex interference) collaborative management scenario (integrating three core modules) Implementation steps Step 1: Intelligent Collaborative Scheduling of Edge Nodes: Collect edge node data, service access data, and resource data from the multi-scenario fusion platform. Using two components of the intelligent collaborative scheduling module for edge nodes, extract the load characteristics of edge nodes, achieve node load balancing through load balancing collaboration, adjust the network structure through dynamic networking collaboration, and output the collaborative scheduling scheme for edge nodes.

[0050] Step 2: Dynamic control of link anti-interference: Collect link interference data, transmission parameter data, and link status data from the platform. Use two functions of the communication link anti-interference dynamic control module to extract interference signal characteristics. Accurately identify the type and location of interference by identifying the interference source. Adjust the anti-interference strategy through dynamic adaptation to achieve interference suppression and link stability.

[0051] Step 3: Dynamic Management of Business Priorities: Collect platform business demand data, link status data, and edge collaboration data. Use two items from the dynamic management module of business priorities to extract business priority features, determine business priorities through dynamic sorting, quantify the degree of adaptation through the adaptation calculation formula, and dynamically allocate resources to achieve precise management of business priorities.

[0052] Step 4: Multi-module collaborative management and control: The three core modules realize real-time data interaction through a high-speed communication bus, integrate the results of edge collaboration, link anti-interference, and service priority management, and output the full-domain collaborative anti-interference management and control instructions of the multi-scenario fusion platform to achieve precise collaborative communication among multiple scenarios, services, and edge nodes, ensuring the stable and efficient operation of the platform.

[0053] Step 5: Full-process verification and optimization: Verify the platform's edge node load balancing rate, interference identification accuracy, and business priority adaptability; collect business feedback from various scenarios; optimize the parameters of the three major modules; realize continuous optimization and scenario expansion of platform communication; and meet the needs of multi-scenario integrated applications in strategic emerging industries and the requirements of the invention priority examination policy.

[0054] Modeling Innovation Principles Abandoning the traditional, crude modeling approach of "independent modules and single-level control," this paper constructs an integrated, full-domain modeling logic encompassing "edge collaboration, link anti-interference, business control, and multi-module fusion." It uses the diverse business needs, edge node characteristics, complex interference features, and transmission requirements of multi-scenario fusion platforms as core inputs, overcoming the limitations of traditional communication automation modules being independent and lacking coordination. The deep integration of these three core modules achieves a closed-loop process across edge collaboration, link anti-interference, and business control. Edge collaboration modeling enables efficient node collaboration, link anti-interference modeling ensures stable transmission, and business priority modeling achieves precise control, filling the gap in full-domain collaborative anti-interference modeling for multi-scenario fusion edge communication platforms. The modeling process focuses on the multi-scenario fusion and efficient anti-interference needs of strategic emerging industries, completely differing from the single-module, single-scenario modeling approaches and technical directions of existing technologies. It also has no overlap with the current modeling logic, representing a completely new modeling direction that complies with the policy requirements for priority examination of inventions.

[0055] The six core aspects of this invention achieve synergistic efficiency in a multi-scenario converged edge communication platform: Two aspects of edge node collaboration enable load balancing and dynamic networking of edge nodes across multiple scenarios, significantly improving edge collaboration efficiency and resource utilization compared to traditional independent management modes, and greatly accelerating the response speed of multi-scenario service access; two aspects of link anti-interference enable accurate identification and dynamic adaptation of link interference across multiple scenarios, significantly improving link anti-interference capability and transmission stability compared to traditional passive anti-interference modes, and significantly reducing the platform's transmission failure rate; two aspects of service priority management enable accurate sorting and resource adaptation of services across multiple scenarios, significantly improving the rationality of service transmission and the guarantee capability of high-priority services compared to traditional fixed priority modes, and greatly improving the multi-scenario service experience; these six aspects, working in conjunction with the three main modules, achieve comprehensive optimization of the multi-scenario converged edge communication platform in terms of "efficient edge collaboration, stable link anti-interference, and precise service management." Compared to existing technologies, this represents a qualitative leap in communication automation, fully meeting the multi-scenario converged communication needs of strategic emerging industries and complying with the requirement of "promoting industrial transformation and upgrading" in the invention priority examination policy.

[0056] Existing communication automation methods suffer from problems such as independent and singular modules, lack of collaborative anti-interference control, and absence of edge node load balancing and dynamic networking, accurate identification and dynamic adaptation of link interference sources, and dynamic prioritization and adaptation calculation of services. This results in inefficient multi-scenario edge communication collaboration, weak link anti-interference capabilities, and rigid service priority control, making it difficult to meet the high requirements of multi-scenario integrated edge communication platforms. This embodiment, through three core modules and six core integrated innovations, achieves edge collaboration and link anti-interference control in communication automation, completely resolving the pain points of existing technologies. It significantly improves the edge collaboration efficiency, link transmission stability, and service scheduling rationality in multi-scenario communication. Furthermore, it has no overlap with existing technologies, current technical directions, or implementation scenarios, highlighting its innovation, strong practicality, and wide applicability to various communication automation scenarios such as industry, smart terminals, and complex interference.

[0057] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for automated edge collaboration and link anti-interference control in communication, characterized in that, Includes the following steps: S1: Intelligent collaborative scheduling and processing of edge nodes. It collects load data, resource data and service access data of communication edge nodes, and realizes load balancing, dynamic networking and efficient collaboration of edge nodes through edge node load balancing collaboration and edge node dynamic networking collaboration, and outputs edge node collaborative scheduling scheme. S2: Dynamic control and processing of communication link anti-interference, real-time collection of interference data, transmission parameter data and link status data of communication link, through accurate identification of link interference source and dynamic adaptation of link anti-interference, to achieve accurate identification of interference source, dynamic adaptation of link anti-interference strategy and interference suppression, and ensure stable link transmission. S3: Dynamic management and control of service priorities. It integrates service transmission demand data, link status data and edge collaboration data. Through dynamic sorting of service priorities and service priority-resource adaptation calculation, it realizes dynamic sorting of service priorities, precise allocation of resources and dynamic adjustment of priorities, forming a closed loop of communication automation full-process collaborative anti-interference management and control. In step S3, the business priority-resource adaptation calculation includes an adaptation calculation formula, which is as follows: The constraints are , This is the business priority-resource fit value. The priority weight for the i-th type of business. The resource allocation satisfaction rate for the i-th type of business. For the transmission delay of the i-th type of service, This represents the resource consumption percentage for the i-th type of business. The appropriate threshold for compatibility is set based on the service type and transmission requirements.

2. The method according to claim 1, characterized in that, The edge node load balancing collaboration in step S1 includes the following sub-steps: extracting the load characteristics of edge nodes, constructing an edge node load balancing model, and achieving balanced load distribution and efficient collaboration of edge nodes through load quantization calculation and collaborative scheduling optimization.

3. The method according to claim 1, characterized in that, The dynamic networking and collaboration of edge nodes in step S1 dynamically adjusts the edge node networking structure based on the edge node load status, service access requirements, and link transmission quality, thereby achieving flexible networking and collaborative linkage of edge nodes and improving edge collaborative response efficiency.

4. The method according to claim 1, characterized in that, In step S2, the link interference source is accurately identified, a link interference feature library is constructed, multi-dimensional features of the interference signal are extracted, and the interference source is accurately identified, its type is determined, and its location is determined through interference feature matching and cluster analysis.

5. The method according to claim 1, characterized in that, The link anti-interference dynamic adaptation in step S2 dynamically adjusts the anti-interference strategy based on the type of interference source, the intensity of interference, and the link transmission requirements, so as to achieve dynamic improvement of the link anti-interference capability and ensure transmission stability.

6. The method according to claim 1, characterized in that, The dynamic sorting of service priorities in step S3 integrates service transmission latency requirements, data importance, and transmission bandwidth requirements to construct a service priority sorting model, thereby achieving dynamic updates and accurate sorting of service priorities.

7. The method according to claim 1, characterized in that, The service priority-resource adaptation calculation in step S3 quantifies the compatibility between service priority and resource allocation through the adaptation calculation formula. If the compatibility value is lower than the threshold, the resource allocation strategy is iteratively optimized to improve the service transmission adaptability.

8. The method according to claim 1, characterized in that, The business priority-resource compatibility threshold It can be flexibly adjusted according to the scenario, industrial edge communication Smart terminal edge access Communication in complex interference scenarios .

9. The method according to any one of claims 1-8, characterized in that, The method can be applied to communication automation scenarios such as industrial edge communication, smart terminal edge access, communication in complex interference scenarios, and vehicle-to-everything (V2X) edge communication, to achieve edge collaboration, link anti-interference, and service priority management.

10. A communication automation edge collaboration and link anti-interference control system, characterized in that, include: Edge node intelligent collaborative scheduling module, communication link anti-interference dynamic control module, service priority dynamic management and control module, multi-source data acquisition module, and collaborative anti-interference engine module; The edge collaboration module performs the functions of claims 1-2, the link anti-interference module performs the functions of claims 4-5, and the service priority management module performs the functions of claims 6-7. Each module achieves real-time data interaction through a high-speed communication bus, thereby completing automated edge collaboration and link anti-interference management.

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