An edge collaboration and latency optimization method for communication automation
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANJIN JINWEIZE COMMUNICATION ENGINEERING CO LTD
- Filing Date
- 2026-03-26
- Publication Date
- 2026-06-09
Smart Images
Figure CN122179436A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of information technology, specifically relating to a method for edge collaboration and latency optimization in communication automation. Background Technology
[0002] In the current field of communication automation, with the rapid development of edge computing, industrial internet, and smart terminals, communication scenarios exhibit the core characteristics of "low latency, high collaboration, and high fault tolerance." The requirements for edge node collaboration, communication latency control, and multi-link redundancy fault tolerance are becoming increasingly stringent. However, existing technologies still face specific and unresolved practical problems in the three sub-scenarios of "edge node collaboration, communication latency optimization, and multi-link redundancy fault tolerance." These are all scenario-specific problems, not macro-level challenges, and have no overlap with the current technical direction outlined in this document. Specifically: 1. Inefficient edge node collaboration, lack of intelligent load balancing and collaborative scheduling: Existing edge node management mostly adopts the "fixed allocation + manual scheduling" mode, which lacks edge node load balancing and intelligent node collaborative scheduling mechanism; resulting in uneven load distribution and low collaboration efficiency of edge nodes, with some nodes overloaded and stuck, and some nodes idle, which seriously affects the response speed of communication automation, and is especially unsuitable for low latency and high load edge communication scenarios.
[0003] 2. Latency control is crude and fluctuates greatly, lacking precise feature extraction and dynamic optimization: Existing communication latency management mostly adopts the "fixed threshold + passive adjustment" mode, lacking latency feature extraction and dynamic latency optimization mechanism; this leads to inaccurate communication latency feature identification, rigid optimization methods, and large latency fluctuations, which cannot meet the needs of low-latency communication and affect the service quality and stability of communication automation.
[0004] 3. Insufficient redundancy and fault tolerance, weak fault self-healing capability, and lack of intelligent redundancy configuration and self-healing: Existing multi-link management and control mostly adopts the "fixed redundancy + manual switching" mode, which lacks link redundancy configuration and fault self-healing scheduling mechanism; resulting in unreasonable allocation of multi-link redundancy resources, delayed fault identification, slow self-healing switching, and inability to achieve rapid self-healing after a fault, which easily causes communication interruption and affects the fault resistance capability of communication automation.
[0005] Existing methods for communication automation lack core innovations in "intelligent edge node collaboration, dynamic optimization of communication latency, and multi-link redundancy and fault tolerance," particularly in edge collaboration modeling, latency optimization solutions, and redundancy and fault tolerance modeling solutions, thus failing to address the aforementioned specific problems. This invention focuses on the development needs of strategic emerging industries (industrial internet, edge computing, and information technology services), proposing an innovation-driven edge collaboration and latency optimization method to fill existing technological gaps, facilitating the upgrade of communication automation towards "low latency, high collaboration, and high fault tolerance," and meeting the requirements of the invention priority examination policy. Summary of the Invention
[0006] Addressing the three specific problems raised in the background technology, the present invention aims to provide an edge collaboration and latency optimization method for communication automation. This method achieves efficient collaborative scheduling of edge nodes, precise optimization and dynamic control of communication latency, and intelligent redundancy, fault tolerance, and self-healing of multi-links. It solves the problems of inefficient edge collaboration, coarse latency control, and insufficient redundancy and fault tolerance. The entire process emphasizes innovation and modeling and solving, without involving rules of intellectual activity. It improves the response speed, latency stability, and fault resistance of communication automation, further perfecting the communication automation technology system in low-latency, high-collaboration, and high-fault-tolerance scenarios. This method aligns with the development direction of strategic emerging industries and the requirements of invention priority examination policies.
[0007] The present invention is implemented through the following specific technical solution: (I) Edge Node Intelligent Collaboration Module This module is designed to achieve load balancing, intelligent collaboration, and efficient scheduling of edge nodes. It constructs an intelligent collaborative modeling system for edge nodes, solving problems such as inefficient edge node collaboration, uneven load distribution, and idle resources. It improves the response speed and edge collaboration efficiency of communication automation, provides technical support for edge management and control of communication automation, and meets the development needs of strategic emerging industries such as the Industrial Internet and edge computing.
[0008] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed allocation and manual scheduling," we construct an integrated modeling logic of "node data acquisition - load characteristic modeling - collaborative characteristic modeling - load balancing modeling - scheduling modeling - verification and optimization modeling." Combining the operational status characteristics of edge nodes (load rate, response speed), communication load characteristics (data throughput, task priority), and link status characteristics (transmission rate, latency), we establish load characteristic models, collaborative characteristic models, load balancing models, scheduling models, and verification and optimization models. We design edge node load balancing and node collaborative scheduling to achieve intelligent collaboration of edge nodes.
[0009] First, deploy edge node monitoring and data acquisition components to collect edge node operating status data (load rate, response speed), communication load data (data throughput, task priority), and link status data (transmission rate, latency), constructing an edge node collaborative data resource pool. Then, design edge node load balancing, extracting core features of edge node load, communication load, and link status to build a load balancing model, achieving accurate identification, quantification, and balanced scheduling of edge node load, avoiding node overload or idleness. Next, design node collaborative scheduling, building a collaborative scheduling model based on node load data, communication load data, and link status data to achieve intelligent collaboration, task allocation, and efficient scheduling of edge nodes, outputting node collaboration commands. Finally, construct a verification and optimization model to quantify node load balancing, collaborative efficiency, and response speed, dynamically optimizing parameters and scheduling strategies to ensure effective edge node collaboration.
[0010] 1: Edge node load balancing To address the issues of uneven load distribution at edge nodes and the lack of a system load balancing method in existing technologies, an integrated model for edge node load, communication load, and link status modeling is constructed. This model enables accurate identification, quantification, and balanced scheduling of edge node load, solves the core data support problem of inefficient edge collaboration, and fills the technological gap in intelligent load balancing of edge nodes in communication automation.
[0011] 2: Node Cooperative Scheduling To address the issues of inefficient edge node collaboration and lack of intelligent scheduling mechanisms in existing technologies, an integrated model is constructed to model edge node load, communication load, and link status. This model enables intelligent collaboration, task allocation, and efficient scheduling of edge nodes, resolving the core pain point of inefficient edge collaboration and filling the technological gap in intelligent collaborative scheduling of edge nodes for communication automation.
[0012] (ii) Communication delay dynamic optimization module This module is designed to identify, precisely optimize, and dynamically manage communication latency features. It constructs a dynamic optimization modeling system for communication latency, which solves the problems of coarse latency control, large fluctuations, and inability to meet low latency requirements. It improves the latency stability and service quality of automated communication, provides technical support for latency management in automated communication, and meets the development needs of strategic emerging industries such as edge computing and information technology services.
[0013] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed threshold and passive adjustment," we construct an integrated modeling logic of "latency data acquisition - latency feature modeling - load correlation modeling - optimization modeling - control and management modeling - verification and optimization modeling." Combining communication latency characteristics (transmission latency, processing latency), node load characteristics (load rate, workload), and link transmission characteristics (transmission rate, packet loss rate), we establish latency feature models, load correlation models, optimization models, control and management models, and verification and optimization models. We design latency feature extraction and dynamic latency optimization to achieve dynamic optimization of communication latency.
[0014] First, deploy latency monitoring and data acquisition components to collect communication latency data (transmission latency, processing latency), node load data (load rate, workload), and link transmission data (transmission rate, packet loss rate), constructing a communication latency optimization data resource pool. Then, design latency feature extraction to extract core features of communication latency, node load, and link transmission, constructing a latency feature model to achieve accurate identification, classification, and quantification of communication latency, providing data support for latency optimization. Next, design dynamic latency optimization based on latency feature data, node load data, and link transmission data, combined with latency optimization calculation formulas, constructing a latency optimization model to achieve accurate optimization and dynamic control of communication latency, outputting latency optimization commands. Finally, construct and verify the optimization model to quantify latency optimization accuracy, latency stability, and low latency compliance rate, dynamically optimizing parameters and strategies to ensure the effectiveness of latency optimization.
[0015] 3: Delay Feature Extraction To address the problems of inaccurate latency feature identification and lack of latency feature extraction methods in existing technologies, an integrated model for communication latency, node load, and link transmission modeling is constructed to achieve accurate identification, classification, and quantification of communication latency, solve the core data support problem for latency optimization, and fill the technical gap in intelligent feature extraction of communication latency in communication automation.
[0016] 4: Dynamic latency optimization To address the problems of "coarse latency control, large fluctuations, and lack of dynamic optimization mechanisms" in existing technologies, an integrated model is constructed that models latency characteristics, node load, and link transmission. Combined with latency optimization calculation formulas, this model enables precise optimization and dynamic control of communication latency, solving the core pain point of large latency fluctuations and filling the technological gap in intelligent dynamic optimization of communication latency in communication automation.
[0017] (III) Multi-link Redundancy Fault-Tolerant Module The core of this module is to realize intelligent redundancy configuration, fault identification and self-healing scheduling of multi-links, and to build a multi-link redundancy fault-tolerant modeling system. It solves the problems of insufficient redundancy fault tolerance, weak fault self-healing ability and easy communication interruption, improves the fault resistance and reliability of communication automation, and provides technical support for communication automation redundancy management and control, which meets the development needs of strategic emerging industries such as industrial Internet and edge computing.
[0018] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed redundancy and manual switching," we construct an integrated modeling logic of "link data acquisition - link status modeling - redundancy feature modeling - configuration modeling - self-healing modeling - verification and optimization modeling." Combining the characteristics of multi-link operating status (transmission rate, failure frequency), latency optimization characteristics (optimized latency, stability), and node collaboration characteristics (collaboration efficiency, load status), we establish link status model, redundancy feature model, configuration model, self-healing model, and verification and optimization model. We design link redundancy configuration and fault self-healing scheduling to achieve multi-link redundancy fault tolerance.
[0019] First, deploy multi-link monitoring and data acquisition components to collect multi-link operational status data (transmission rate, failure frequency), latency optimization data (optimized latency, stability), and node collaboration data (collaboration efficiency, load status), constructing a multi-link redundancy and fault-tolerant data resource pool. Then, design link redundancy configurations, extracting core features of multi-link operational status, latency optimization, and node collaboration to build a redundancy configuration model, achieving intelligent redundancy configuration and redundant resource optimization for multiple links, avoiding waste of redundant resources. Next, design fault self-healing scheduling, based on multi-link operational status data, fault characteristic data, and latency optimization data, constructing a self-healing model to achieve fault identification, self-healing scheduling, and redundancy switching for multiple links, ensuring uninterrupted communication. Finally, construct a verification and optimization model to quantify the rationality of redundancy configuration, fault self-healing speed, and communication reliability, dynamically optimizing parameters and fault-tolerant strategies to ensure the effectiveness of multi-link redundancy and fault tolerance.
[0020] 5: Link redundancy configuration To address the issues of "unreasonable allocation of redundant resources and lack of intelligent redundancy configuration methods" in existing technologies, an integrated model for multi-link operation status, latency optimization, and node collaborative modeling is constructed. This model enables intelligent redundancy configuration and optimization of redundant resources across multiple links, solves the core prerequisite problem of insufficient redundancy tolerance, and fills the technological gap in intelligent redundancy configuration for multi-link communication automation.
[0021] 6: Fault self-healing scheduling To address the issues of "lagging fault identification, slow self-healing switching, and lack of intelligent self-healing mechanism" in existing technologies, an integrated model is constructed to optimize the multi-link operating status, fault characteristics, and latency. This model enables fault identification, self-healing scheduling, and redundancy switching across multiple links, resolving the core pain point of weak fault self-healing capability and filling the technological gap in intelligent fault self-healing scheduling for multi-link communication automation.
[0022] Beneficial effects 1. Edge Node Load Balancing: Abandoning the crude approach of no system load balancing, we construct an integrated system for modeling edge node load, communication load, and link status. This significantly improves node load balancing and resource utilization, completely solving the problems of uneven load distribution and idle resources on edge nodes. It provides reliable data support for edge collaboration, focuses on intelligent collaborative innovation of edge nodes, and meets the development needs of strategic emerging industries such as the Industrial Internet and edge computing. 2. Node Cooperative Scheduling: Construct an integrated system for modeling edge node load, communication load, and link status. This significantly improves the efficiency and response speed of edge node collaboration, completely solves the problems of inefficient edge node collaboration and lack of intelligent scheduling mechanisms, fills the technical gap in intelligent collaborative scheduling of edge nodes, and improves the speed of automated communication response. 3. Delay Feature Extraction: Construct an integrated system for modeling communication delay, node load, and link transmission. The accuracy of delay feature identification and quantification is significantly improved, completely solving the problems of inaccurate delay feature identification and lack of delay feature extraction methods. This provides reliable data support for delay optimization, focuses on dynamic optimization and innovation of communication delay, and meets the development needs of strategic emerging industries in information technology services. 4. Dynamic delay optimization: Construct an integrated system for modeling delay characteristics, node load, and link transmission. Combined with delay optimization calculation formulas, the accuracy and stability of delay optimization are significantly improved. This completely solves the problems of coarse delay control, large fluctuations, and lack of dynamic optimization mechanisms, filling the technical gap in intelligent dynamic optimization of communication delay and meeting the needs of low-latency communication. 5. Link Redundancy Configuration: Construct an integrated system for multi-link operation status, latency optimization, and node collaborative modeling. The rationality of redundancy configuration and resource utilization are significantly improved, completely solving the problems of unreasonable allocation of redundant resources and lack of intelligent redundancy configuration methods. Focusing on multi-link redundancy fault tolerance innovation, it meets the development needs of strategic emerging industries in the industrial internet. 6. Fault self-healing scheduling: Construct an integrated system for multi-link operation status, fault characteristics and latency optimization modeling, which significantly improves fault identification speed and self-healing switching efficiency, completely solves the problems of lagging fault identification, slow self-healing switching and lack of intelligent self-healing mechanism, fills the technical gap of multi-link intelligent fault self-healing scheduling, and meets the requirements of the invention priority examination policy. Attached Figure Description
[0023] Figure 1 Detailed Implementation of the Edge Node Intelligent Collaboration Module Workflow Diagram The following four specific embodiments illustrate the implementation steps of the present invention in detail.
[0024] Example 1: Industrial Internet Edge Control Scenario Implementation steps Step 1: Data Acquisition and Parameter Setting: Collect edge node operation status data, communication load data, link status data, communication latency data, and multi-link operation status data for the industrial internet edge management scenario, and set the basic communication latency. Delay optimization coefficient Link adaptation coefficient Minimum allowable communication latency in industrial control edge scenarios Node maximum load rate Maximum transmission rate of the link Configured according to the link hardware configuration.
[0025] Step 2: Intelligent Collaboration of Edge Nodes: By adopting edge node load balancing and node collaborative scheduling, the core features of edge node load, communication load and link status are extracted to construct a load balancing and collaborative scheduling model, realize load balancing, intelligent collaboration and efficient scheduling of industrial Internet edge nodes, output node collaboration instructions, and avoid node overload or idleness.
[0026] Step 3: Dynamic Optimization of Communication Latency: Using latency feature extraction and dynamic latency optimization, core features of communication latency, node load, and link transmission are extracted. A latency feature and optimization model is constructed, and a latency optimization calculation formula is used for dynamic latency optimization. It enables precise optimization and dynamic control of edge communication latency in the industrial internet, outputs latency optimization commands, and meets low latency control requirements.
[0027] Step 4: Multi-link redundancy fault tolerance: By adopting link redundancy configuration and fault self-healing scheduling, the core features of multi-link operation status, latency optimization and node collaboration are extracted, and a redundancy configuration and self-healing model is constructed to realize intelligent redundancy configuration, fault identification and self-healing scheduling of multi-links, ensuring uninterrupted edge communication of the industrial Internet.
[0028] Step 5: Full-process verification and optimization: Verify node load balancing, collaboration efficiency, latency optimization accuracy, latency stability, redundancy configuration rationality, and fault self-healing speed to ensure that the requirements of "low latency, high collaboration, and high fault tolerance" for industrial internet edge management are met; collect feedback data from each module, optimize parameters and collaboration, optimization, and fault tolerance strategies to improve adaptability to industrial internet edge management scenarios.
[0029] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed allocation, passive adjustment, and manual switching," this paper constructs an integrated closed-loop model of "edge collaboration, latency optimization, and redundancy tolerance." It uses the low latency, high collaboration, and high fault tolerance requirements of industrial internet edge management, along with node characteristics, latency characteristics, link characteristics, and redundancy characteristics, as core inputs. This overcomes the limitations of inefficient edge collaboration, extensive latency control, and insufficient redundancy tolerance. Edge collaboration modeling achieves efficient collaboration and load balancing of edge nodes; latency optimization modeling achieves precise optimization and dynamic control of communication latency; and redundancy tolerance modeling achieves intelligent redundancy and fault self-healing of multiple links, filling the gap in integrated edge-latency-redundancy collaboration modeling for industrial internet edge management. The modeling process focuses on the scenario requirements of low latency, high collaboration, and high fault tolerance for industrial internet edge management. It is completely different from the single-module, extensive modeling approach and technical direction of existing technologies, and has no overlap with the modeling logic of current open documents. It represents a completely new modeling direction, conforming to the development needs of the industrial internet and edge computing strategic emerging industries, as well as the requirements of the invention priority examination policy.
[0030] Edge node load balancing, through modeling edge node load, communication load, and link status, significantly improves node load balancing and resource utilization compared to traditional load balancing methods without system load balancing, completely solving the problems of uneven load distribution and resource idleness at edge nodes, and providing reliable data support for edge collaboration. Node collaborative scheduling, through modeling edge node load, communication load, and link status and collaborative scheduling strategies, significantly improves edge node collaboration efficiency and response speed compared to traditional manual scheduling methods, completely solving the problem of inefficient edge node collaboration and enhancing the responsiveness of industrial internet edge management. Latency feature extraction, through modeling communication latency, node load, and link transmission, significantly improves latency feature recognition accuracy and quantification accuracy compared to traditional methods without latency feature extraction, completely solving the problem of inaccurate latency feature recognition and providing reliable data support for latency optimization. Dynamic latency optimization, through modeling latency features, node load, and link transmission and latency optimization calculation formulas, significantly improves latency optimization accuracy and stability compared to traditional passive adjustment methods. The qualitative improvements are significant, completely resolving the issues of crude and highly volatile latency control, and meeting the low latency requirements of industrial internet edge management. Link redundancy configuration, through multi-link operational status, latency optimization, and node collaborative modeling, significantly improves the rationality of redundancy configuration and resource utilization compared to traditional fixed redundancy modes, completely resolving the problem of unreasonable redundancy resource allocation and providing a scientific basis for redundancy fault tolerance. Fault self-healing scheduling, through multi-link operational status, fault characteristics, and latency optimization modeling, and self-healing scheduling strategies, significantly improves fault identification speed and self-healing switching efficiency compared to traditional manual switching modes, completely resolving the problem of weak fault self-healing capabilities and ensuring uninterrupted communication. Six synergistic effects enable industrial internet edge management to achieve "low latency, high collaboration, and high fault tolerance." Compared to existing technologies, edge collaboration efficiency, latency stability, and fault resistance capabilities are qualitatively improved, fully meeting the high requirements of industrial internet edge management and conforming to the requirements of the invention priority examination policy of "enhancing core industrial competitiveness, optimizing communication latency, and strengthening communication fault tolerance."
[0031] Existing technologies employ a "fixed allocation + passive adjustment + manual switching" model, lacking edge node load balancing, node collaborative scheduling, latency feature extraction, dynamic latency optimization, link redundancy configuration, and fault self-healing scheduling. This results in inefficient edge collaboration, large latency fluctuations, and insufficient redundancy tolerance, failing to meet the low latency, high collaboration, and high fault tolerance requirements of industrial internet edge management. It is prone to problems such as node overload and lag, excessive communication latency, and communication interruptions after failures. This embodiment, through innovation and modeling optimization, achieves edge collaboration, latency optimization, and redundancy tolerance for industrial internet edge management, completely resolving the pain points of existing technologies. Node load balancing, collaboration efficiency, latency stability, and fault self-healing speed all meet industrial internet edge management standards. Furthermore, it does not 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 meets the development needs of strategic emerging industries and the requirements of the invention priority examination policy.
[0032] Example 2: Low-latency communication scenario for smart terminals (corresponding to edge node intelligent collaboration and dynamic communication latency optimization module) Implementation steps Step 1: Relevant Data Collection: Deploy monitoring and data collection components to collect edge node operation status data, communication load data, link status data, and communication latency data in low-latency communication scenarios for smart terminals. Construct an edge collaboration and latency optimization data resource pool to meet the "ultra-low latency, high response, and high stability" requirements of low-latency communication for smart terminals.
[0033] Step 2: Intelligent Collaboration of Edge Nodes: By employing edge node load balancing and node collaborative scheduling, the core features of edge node load, communication load, and link status are extracted to construct a load balancing and collaborative scheduling model. This model enables load balancing, intelligent collaboration, and efficient scheduling of intelligent terminal edge nodes, outputs node collaboration commands, improves edge node response speed, and avoids node overload affecting latency.
[0034] Step 3: Dynamic Optimization of Communication Latency: By using latency feature extraction and dynamic latency optimization, the core features of communication latency, node load and link transmission are extracted, a latency feature and optimization model is constructed, and a latency optimization calculation formula is combined to achieve accurate optimization and dynamic control of low-latency communication of smart terminals. Latency optimization instructions are output to control the communication latency within the minimum allowable range and meet the ultra-low latency requirements.
[0035] Step 4: Full-process collaborative management and control: Integrate the results of intelligent collaboration of edge nodes and dynamic optimization of communication latency, output full-domain collaborative management and control instructions for low-latency communication scenarios of smart terminals, realize the linkage between edge collaboration and latency optimization, and ensure ultra-low latency, high response and high stability of low-latency communication of smart terminals.
[0036] Step 5: Continuous optimization: Verify node load balancing, collaboration efficiency, latency optimization accuracy, latency stability, and low latency compliance rate; collect feedback data on low latency communication scenarios of smart terminals, optimize parameters and collaboration, optimize strategies, improve adaptability to low latency communication scenarios, and ensure low latency and high stability of smart terminal communication.
[0037] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed allocation and passive adjustment," this paper constructs an integrated closed-loop model of "edge collaboration, latency optimization, and low-latency adaptation." It takes the ultra-low latency, high response, and high stability requirements of smart terminal low-latency communication, along with node characteristics, latency characteristics, and link characteristics, as core inputs. This overcomes the limitations of inefficient edge collaboration, extensive latency control, and insufficient low-latency adaptation. Edge collaboration modeling achieves efficient collaboration and load balancing of edge nodes; latency optimization modeling achieves precise optimization and dynamic management of communication latency; and low-latency adaptation modeling achieves precise adaptation of edge collaboration and latency optimization to low-latency scenarios, filling the gap in integrated edge-latency collaboration modeling for smart terminal low-latency communication. The modeling process focuses on the scenario requirements of ultra-low latency and high response in smart terminal low-latency communication, which is completely different from the single-module, extensive modeling approach and technical direction of existing technologies. It also has no overlap with the modeling logic of current open documents, representing a completely new modeling direction that meets the development needs of strategic emerging industries such as edge computing and new mobile communication networks.
[0038] Edge node load balancing, through modeling edge node load, communication load, and link status, significantly improves node load balancing and resource utilization compared to traditional load balancing methods without system balancing, completely solving the problems of uneven load distribution and resource idleness among edge nodes, and providing reliable node support for low-latency communication. Node collaborative scheduling, through modeling edge node load, communication load, and link status, and collaborative scheduling strategies, significantly improves edge node collaboration efficiency and response speed compared to traditional manual scheduling methods, completely solving the problem of inefficient edge node collaboration and enhancing the responsiveness of smart terminal communication. Latency feature extraction, through modeling communication latency, node load, and link transmission, significantly improves latency feature extraction compared to traditional methods without latency feature extraction. Significantly improved recognition accuracy and quantification accuracy completely solve the problem of inaccurate latency feature recognition, providing reliable data support for latency optimization; dynamic latency optimization, through latency features, node load and link transmission modeling, and latency optimization calculation formulas, significantly improves latency optimization accuracy and latency stability compared to the traditional passive adjustment mode, completely solving the problem of coarse and fluctuating latency control, keeping communication latency within the minimum allowable range, and meeting the ultra-low latency requirements of smart terminals; four synergistic effects enable smart terminals to achieve "ultra-low latency, high response, and high stability" in low-latency communication. Compared with existing technologies, edge collaboration efficiency, latency stability, and low latency compliance rate are qualitatively improved, fully meeting the needs of low-latency communication for smart terminals.
[0039] Existing technologies employ a "fixed allocation + passive adjustment" model, lacking edge node load balancing, node collaborative scheduling, latency feature extraction, and dynamic latency optimization. This results in inefficient edge collaboration, large latency fluctuations, and insufficient low-latency adaptation, failing to meet the ultra-low latency, high response, and high stability requirements of smart terminals for low-latency communication. It is prone to issues such as excessive communication latency, slow response, and stuttering, impacting user experience. This embodiment, through innovation and modeling optimization, achieves edge collaboration and latency optimization for low-latency communication in smart terminals, completely resolving the pain points of existing technologies. Node load balancing, collaboration efficiency, latency stability, and low-latency compliance all meet the standards for low-latency communication in smart terminals. 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.
[0040] Example 3: Enterprise-level multi-link backup communication scenario (corresponding to multi-link redundancy fault tolerance and intelligent edge node collaboration module) Implementation steps Step 1: Data Acquisition and Scenario Adaptation: Collect edge node operation status data, communication load data, link status data, multi-link operation status data, and fault history data for enterprise-level multi-link backup communication scenarios. Adapt to the "high fault tolerance, high reliability, and high stability" requirements of enterprise-level multi-link backup and build an edge collaboration and redundant fault-tolerant data resource pool.
[0041] Step 2: Intelligent Collaboration of Edge Nodes: By employing edge node load balancing and node collaborative scheduling, the core features of edge node load, communication load, and link status are extracted to construct a load balancing and collaborative scheduling model. This model enables enterprise-level edge node load balancing, intelligent collaboration, and efficient scheduling, outputs node collaboration commands, ensures stable operation of edge nodes, and provides support for multi-link backup.
[0042] Step 3: Multi-link Redundancy Fault Tolerance: By adopting link redundancy configuration and fault self-healing scheduling, the core features of multi-link operation status, latency optimization and node collaboration are extracted to build a redundancy configuration and self-healing model. This enables intelligent redundancy configuration, fault identification and self-healing scheduling of enterprise-level multi-links, and outputs fault-tolerant management instructions to ensure rapid self-healing and switching after any link failure, ensuring uninterrupted communication.
[0043] Step 4: Collaboration and Fault Tolerance Optimization: Integrate the results of intelligent edge node collaboration and multi-link redundancy fault tolerance to achieve collaborative optimization of edge collaboration and redundancy fault tolerance. Adjust link redundancy configuration according to the load status of edge nodes and optimize node collaboration strategy according to link failure status. Balance edge collaboration efficiency and redundancy fault tolerance capability to ensure high fault tolerance, high reliability and high stability of enterprise-level multi-link backup communication.
[0044] Step 5: Continuous optimization: Verify node load balancing, collaboration efficiency, redundancy configuration rationality, fault self-healing speed, and communication reliability; collect data on changes in enterprise-level multi-link backup communication requirements, optimize parameters and collaboration and fault tolerance strategies, improve adaptability to enterprise-level multi-link backup scenarios, and ensure high reliability and stability of enterprise-level communication.
[0045] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed allocation and manual switching," this paper constructs an integrated closed-loop model of "edge collaboration, redundancy fault tolerance, and high reliability adaptation." It uses the high fault tolerance, high reliability, and high stability requirements, node characteristics, link characteristics, and redundancy characteristics of enterprise-level multi-link backup communication as core inputs, overcoming the limitations of inefficient edge collaboration, insufficient redundancy fault tolerance, and inadequate high reliability adaptation. Edge collaboration modeling achieves efficient collaboration and load balancing of edge nodes; redundancy fault tolerance modeling achieves intelligent redundancy and fault self-healing of multiple links; and high reliability adaptation modeling achieves precise adaptation of edge collaboration and redundancy fault tolerance to enterprise-level high reliability requirements, filling the gap in integrated edge-redundancy collaboration modeling for enterprise-level multi-link backup communication. The modeling process focuses on the scenario requirements of high fault tolerance, high reliability, and high stability in enterprise-level multi-link backup communication, completely different from the single-module, extensive modeling approach and technical direction of existing technologies. It has no overlap with the modeling logic of current open documents, representing a completely new modeling direction that aligns with the development needs of strategic emerging industries such as the Industrial Internet and information technology services.
[0046] Edge node load balancing, through modeling edge node load, communication load, and link status, significantly improves node load balancing and resource utilization compared to traditional load balancing-less modes, completely resolving issues of uneven load distribution and resource idleness at edge nodes, and providing stable node support for enterprise-level multi-link backup. Node collaborative scheduling, through modeling edge node load, communication load, and link status, and collaborative scheduling strategies, significantly improves edge node collaboration efficiency and response speed compared to traditional manual scheduling, completely resolving inefficient edge node collaboration and ensuring stable edge node operation. Link redundancy configuration, through multi-link operating status, latency optimization, and node collaborative modeling, improves the rationality and resource utilization of redundancy configuration compared to traditional fixed redundancy modes. The system significantly improves resource utilization, completely resolving the problem of unreasonable allocation of redundant resources and providing a scientific basis for redundancy fault tolerance. Fault self-healing scheduling, through multi-link operating status, fault characteristics, and latency optimization modeling and self-healing scheduling strategies, significantly improves fault identification speed and self-healing switching efficiency compared to traditional manual switching modes, completely resolving the problem of weak fault self-healing capabilities. This ensures rapid self-healing switching after any link failure, guaranteeing uninterrupted communication. These four synergistic effects achieve edge collaboration and redundancy fault tolerance for enterprise-level multi-link backup communication. Compared to existing technologies, edge collaboration efficiency, redundancy configuration rationality, fault self-healing speed, and communication reliability are qualitatively improved, fully meeting the needs of enterprise-level multi-link backup communication and complying with the invention priority examination policy.
[0047] Existing technologies employ a "fixed allocation + manual switching" model, lacking edge node load balancing, node collaborative scheduling, link redundancy configuration, and fault self-healing scheduling. This results in inefficient edge collaboration, insufficient redundancy tolerance, and inadequate high-reliability adaptation, failing to meet the high fault tolerance, high reliability, and high stability requirements of enterprise-level multi-link backup communication. It is prone to problems such as node overload, wasted redundant resources, and communication interruptions after failures, threatening enterprise communication security. This embodiment, through innovation and model optimization, achieves edge collaboration and redundancy tolerance for enterprise-level multi-link backup communication, completely resolving the pain points of existing technologies. Node load balancing, collaboration efficiency, redundancy configuration rationality, and fault self-healing speed all meet enterprise-level multi-link backup communication standards. Furthermore, it has no overlap with existing technologies, current technical directions, or implementation scenarios, highlighting its innovation, strong practicality, and aligning with the development needs of strategic emerging industries.
[0048] Example 4: Multi-scenario integrated low-latency, high-fault-tolerant communication automation platform (edge + low latency + multi-link) scenario (integrating three core modules) Implementation steps Step 1: Intelligent Collaboration of Edge Nodes: Collect edge node operation status data, communication load data, and link status data from the multi-scenario fusion platform. Utilize two components of the intelligent collaboration module to achieve load balancing, intelligent collaboration, and efficient scheduling of edge nodes across multiple scenarios. Output node collaboration commands to ensure low overload and high-response operation of edge nodes in multiple scenarios.
[0049] Step 2: Dynamic Optimization of Communication Latency: Collect platform communication latency data, node load data, and link transmission data. Utilize two components of the dynamic communication latency optimization module, combined with the latency optimization calculation formula, to achieve precise optimization and dynamic control of communication latency in multiple scenarios. Output latency optimization commands to meet low latency requirements in multiple scenarios.
[0050] Step 3: Multi-link redundancy and fault tolerance: Collect multi-link operation status data, latency optimization data, and node collaboration data from the platform. Employ two components of the multi-link redundancy and fault tolerance module to achieve intelligent redundancy configuration, fault identification, and self-healing scheduling for multiple scenarios and multiple links. Output fault tolerance control commands to ensure high fault tolerance and high reliability in multi-scenario communication.
[0051] Step 4: Multi-module collaborative management and control: The three core modules achieve real-time data interaction through high-speed communication links, integrate the results of edge collaboration, latency optimization, and redundancy fault tolerance, and output full-domain collaborative management and control instructions for the multi-scenario fusion platform. This enables edge-latency-redundancy full-domain management and control across multiple scenarios, nodes, and links, ensuring stable, efficient, low-latency, and high-fault-tolerant operation of the platform.
[0052] Step 5: Full-process verification and optimization: Verify node load balancing, collaboration efficiency, latency optimization accuracy, latency stability, redundancy configuration rationality, and fault self-healing speed. Collect business feedback from various scenarios, optimize the parameters and collaboration, optimization, and fault tolerance strategies of the three major modules, and achieve continuous optimization and scenario expansion of automated communication management and control of the platform. This meets the development needs of strategic emerging industries for multi-scenario integration, low latency, high collaboration, and high fault tolerance, as well as the requirements of the invention priority examination policy.
[0053] Modeling Innovation Principles Abandoning the traditional, crude modeling approach of "independent modules and single control," this paper constructs an integrated, full-domain modeling logic encompassing "edge collaboration, latency optimization, redundancy and fault tolerance, and multi-scenario fusion." It uses the multi-service requirements, node characteristics, latency characteristics, link characteristics, and low-latency, high-fault-tolerance requirements of multi-scenario fusion platforms as core inputs. This overcomes the limitations of traditional communication automation modules, which are often independent, lack collaboration, have insufficient scenario adaptability, and are deficient in adapting to low-latency, high-fault-tolerance scenarios. The deep fusion modeling of these three core modules achieves closed-loop control of the entire process from edge to latency to redundancy, while edge collaborative modeling enables efficient collaboration of edge nodes across multiple scenarios. With load balancing, latency optimization modeling enables precise optimization and dynamic control of communication latency in multiple scenarios, while redundancy and fault tolerance modeling enables intelligent redundancy and fault self-healing in multiple scenarios and links. This fills the gap in multi-scenario integrated low-latency, high-fault-tolerance communication automation edge-latency-redundancy collaborative full-domain modeling. The modeling process focuses on the development needs of strategic emerging industries for multi-scenario integration, low latency, high collaboration, and high fault tolerance. It is completely different from the single-module, single-scenario modeling ideas and technical directions of existing technologies, and has no overlap with the modeling logic of the currently opened document. It is a brand-new modeling direction and meets the requirements of the invention priority examination policy.
[0054] The six core aspects of this invention achieve synergistic efficiency in a multi-scenario integrated low-latency, high-fault-tolerant automated communication platform: Two aspects of edge collaboration enable efficient collaboration and load balancing of edge nodes across multiple scenarios. Compared to traditional fixed allocation modes, node load balancing, collaboration efficiency, and response speed are significantly improved, completely resolving the problems of inefficient edge node collaboration and uneven load distribution, ensuring stable operation of edge nodes across multiple scenarios; Two aspects of latency optimization enable precise optimization and dynamic control of communication latency across multiple scenarios. Compared to traditional passive adjustment modes, latency optimization accuracy, latency stability, and low-latency compliance rates are significantly improved, completely resolving the problems of coarse latency control and large fluctuations, meeting the low-latency requirements of multiple scenarios; Two aspects of redundancy and fault tolerance enable intelligent multi-scenario multi-link communication. Redundancy and fault self-healing significantly improve the rationality of redundancy configuration, fault identification speed, and self-healing switching efficiency compared to traditional manual switching modes. This completely solves the problems of insufficient redundancy tolerance and weak fault self-healing capability, ensuring high reliability of communication in multiple scenarios. The six features work synergistically with the three major modules to achieve full-domain optimization of "low latency, high collaboration, and high fault tolerance" in the multi-scenario integrated low-latency and high-fault-tolerant communication automation platform. Compared with existing technologies, the level of communication automation management and control has achieved a qualitative leap, fully meeting the high efficiency, stability, low latency, and high reliability requirements of multi-scenario integrated low-latency and high-fault-tolerant communication in strategic emerging industries. This aligns with the requirements of the invention priority examination policy to "promote industrial transformation and upgrading, enhance core industrial competitiveness, optimize communication latency, and enhance communication fault tolerance."
[0055] Existing communication automation methods suffer from problems such as independent and singular modules, lack of collaborative management and control, poor scenario adaptability, and insufficient low-latency and high-fault-tolerance adaptation. They lack edge node load balancing, node collaborative scheduling, latency feature extraction, dynamic latency optimization, link redundancy configuration, and fault self-healing scheduling. Multi-scenario integrated communication edge collaboration is inefficient, suffers from large latency fluctuations, and insufficient redundancy and fault tolerance, making it difficult to meet the low-latency, high-collaboration, and high-fault-tolerance requirements of a multi-scenario integrated low-latency and high-fault-tolerance communication automation platform. This embodiment, through three core modules and six core integrated innovations, achieves edge collaboration and latency optimization in communication automation, completely solving the pain points of existing technologies. It significantly improves the edge collaboration efficiency, latency stability, and fault resistance of multi-scenario communication, and has no overlap with existing technologies, current technical directions, or implementation scenarios. Its innovations are prominent, its practicality is strong, and it conforms to the relevant scope of the "Guidance Catalogue of Key Products and Services of Strategic Emerging Industries (2021 Edition)" and the requirements of the invention priority examination policy. It can be widely applied to various communication automation scenarios such as industrial internet edge management and control, low-latency communication for smart terminals, and enterprise-level multi-link backup.
[0056] 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 edge collaboration and latency optimization in communication automation, characterized in that, Includes the following steps: S1: Intelligent collaborative processing of edge nodes, collecting edge node operating status data, communication load data and link status data, and realizing load balancing, intelligent collaboration and efficient scheduling of edge nodes through edge node load balancing and node collaborative scheduling, and outputting node collaborative instructions; S2: Dynamic optimization of communication latency. It collects communication latency data, node load data and link transmission data. Through latency feature extraction and dynamic latency optimization, it realizes feature identification, precise optimization and dynamic control of communication latency, and outputs latency optimization instructions. S3: Multi-link redundancy fault tolerance processing, collects multi-link operation status data, latency optimization data and node collaboration data, and realizes intelligent redundancy configuration, fault identification and self-healing scheduling of multi-links through link redundancy configuration and fault self-healing scheduling, forming a closed loop of edge-latency-redundancy collaborative management and control for the entire communication automation process; The time delay dynamic optimization in step S2 includes a time delay optimization calculation formula, which is as follows: The constraints are , For optimal communication latency, Based on basic communication latency, For the delay optimization coefficient (0.2≤ ≤0.8), This represents the current edge node load rate. This represents the node's maximum load rate (value 1). Link adaptation coefficient (0.3≤ ≤0.6), For link transmission rate, This represents the maximum transmission rate of the link. The minimum allowable communication latency is set according to the requirements of the communication scenario.
2. The method according to claim 1, characterized in that, The edge node load balancing in step S1 includes the following sub-steps: extracting edge node load characteristics, communication load characteristics and link status characteristics, constructing an edge node load balancing modeling system, and realizing accurate identification, quantification and balanced scheduling of edge node load.
3. The method according to claim 1, characterized in that, The node collaborative scheduling in step S1 is based on edge node load data, communication load data and link status data to build a node collaborative modeling system, so as to realize intelligent collaboration, task allocation and efficient scheduling of edge nodes.
4. The method according to claim 1, characterized in that, The delay feature extraction in step S2 involves extracting communication delay features, node load features, and link transmission features to construct a delay feature modeling system, thereby achieving accurate identification, classification, and feature quantification of communication delay.
5. The method according to claim 1, characterized in that, The dynamic delay optimization in step S2 is based on delay characteristic data, node load data, and link transmission data, combined with delay optimization calculation formula, to construct a delay optimization modeling system and achieve accurate optimization and dynamic control of communication delay.
6. The method according to claim 1, characterized in that, The link redundancy configuration in step S3 involves extracting multi-link operating status features, latency optimization features, and node collaboration features to construct a link redundancy modeling system, thereby realizing intelligent redundancy configuration and redundancy resource optimization for multiple links.
7. The method according to claim 1, characterized in that, The fault self-healing scheduling in step S3 is based on multi-link operating status data, fault characteristic data, and latency optimization data to construct a fault self-healing modeling system, thereby realizing fault identification, self-healing scheduling, and redundancy switching of multiple links.
8. The method according to claim 1, characterized in that, The minimum allowable communication delay It can be flexibly adjusted according to the communication scenario, such as industrial control edge scenarios. Low latency scenarios for smart terminals Typical edge communication scenarios Delay optimization coefficient It can be dynamically adjusted according to load changes.
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 internet edge control, low-latency communication of smart terminals, enterprise-level multi-link backup, and edge communication in remote areas, to achieve edge node collaboration, communication latency optimization, and multi-link redundancy fault tolerance.
10. A communication automation edge collaboration and latency optimization system, characterized in that, include: Edge node intelligent collaboration module, communication latency dynamic optimization module, multi-link redundancy fault tolerance module, multi-source data acquisition module, and collaborative management and control engine module; The edge collaboration module performs the functions of claims 1-2, the latency optimization module performs the functions of claims 4-5, and the redundancy fault tolerance module performs the functions of claims 6-7. Each module achieves real-time data interaction through a high-speed communication link, thereby completing automated edge collaboration and latency optimization.