A communication automation traffic scheduling and quality monitoring multi-protocol adaptation system and method

Abstract

This invention belongs to the field of communication automation technology and discloses a multi-protocol adaptation system and method for traffic scheduling and quality monitoring in communication automation. The invention proposes six unique innovations by constructing three core modules: intelligent traffic scheduling, dynamic communication quality monitoring, and multi-protocol adaptive compatibility, covering the entire process of communication automation from traffic scheduling and quality monitoring to multi-protocol adaptation. Employing core technologies such as dynamic traffic allocation, accurate quality assessment, and adaptive protocol parsing, it overcomes the industry bottlenecks of traditional communication automation, including rigid traffic scheduling, lagging quality monitoring, and difficulties in multi-protocol compatibility. It achieves accurate traffic scheduling, real-time communication quality monitoring, and adaptive compatibility with multiple protocols, significantly improving the traffic utilization efficiency, quality controllability, and multi-scenario adaptability of communication automation. It adapts to the needs of various scenarios such as industrial heterogeneous communication, multi-terminal collaborative communication, and cross-domain converged communication, and particularly solves the technical problems of uneven traffic distribution, difficulty in timely detection of quality anomalies, and lack of seamless multi-protocol compatibility.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a multi-protocol adaptation system and method for automated traffic scheduling and quality monitoring in communication. Background Technology

[0002] In the current field of communication automation, with the deep application of the Industrial Internet and multi-terminal collaboration technologies, communication scenarios are characterized by "diversified services, heterogeneous terminals, and diversified protocols." The requirements for traffic scheduling efficiency, controllable communication quality, and multi-protocol compatibility are increasing. However, existing technologies still face specific and unresolved practical problems in the three sub-scenarios of "service traffic scheduling, communication quality monitoring, and multi-protocol adaptation." 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. Rigid traffic scheduling, lacking dynamic allocation and load balancing: Existing traffic scheduling mostly adopts the "fixed allocation + static control" mode, lacking dynamic allocation of business traffic and traffic load balancing control mechanism; resulting in a mismatch between traffic allocation and business needs and link load, with some links being overloaded and congested, and some links having idle resources, resulting in low traffic utilization efficiency and failing to meet the traffic scheduling needs of multi-service and multi-terminal collaborative communication.

[0003] 2. Lagging quality monitoring, lack of accurate assessment and anomaly warning: Existing communication quality monitoring mostly adopts the "periodic sampling + manual judgment" mode, which lacks accurate assessment of communication quality and has no quality anomaly warning mechanism; this results in the inability to quantify and assess communication quality in real time, and the difficulty in timely detection and warning of quality anomalies, which can easily cause business transmission interruption, data loss, and affect the stable operation of communication automation systems, failing to meet the high reliability communication requirements of the Industrial Internet.

[0004] 3. Difficulty in multi-protocol compatibility, lack of adaptive parsing and adaptation: Existing multi-protocol processing mostly adopts the "fixed protocol adaptation + manual configuration" mode, lacking multi-protocol feature extraction and parsing, and also lacking protocol adaptive adaptation mechanism; resulting in different protocols being unable to be seamlessly compatible, requiring manual configuration of protocol parameters when the terminal accesses, resulting in low adaptation efficiency, failing to meet the multi-protocol adaptation needs of industrial heterogeneous communication and cross-domain converged communication, and restricting the expansion of communication automation scenarios.

[0005] Existing communication automation methods lack core innovations in "intelligent scheduling of service traffic, dynamic monitoring of communication quality, and adaptive compatibility of multiple protocols," particularly in the modeling and solving of dynamic traffic allocation, accurate quality assessment, and adaptive multi-protocol adaptation, thus failing to address the aforementioned specific problems. This invention focuses on the development needs of strategic emerging industries (industrial internet, new mobile communication networks, and industrial software), proposing an innovation-driven method for traffic scheduling and quality monitoring with multi-protocol adaptation. This fills a gap in existing technologies, helping communication automation upgrade towards "efficient scheduling, accurate monitoring, and multi-protocol compatibility," and complies with the requirements of the invention priority examination policy. Summary of the Invention

[0006] In response to the three specific problems raised in the background art, the purpose of this invention is to provide a multi-protocol adaptation system and method for automated traffic scheduling and quality monitoring in communication, so as to achieve accurate scheduling of service traffic, real-time monitoring of communication quality, and adaptive compatibility of multiple protocols, and solve the problems of rigid traffic scheduling, lagging quality monitoring, and difficulty in multi-protocol compatibility.

[0007] The present invention is implemented through the following specific technical solution: (I) Intelligent scheduling module for business traffic This module is designed to accurately allocate, load balance, and dynamically schedule business traffic. It constructs an intelligent business traffic scheduling modeling system to solve the problems of rigid traffic scheduling and low traffic utilization efficiency, thereby improving the flexibility and efficiency of traffic scheduling and providing a fundamental guarantee for automated and efficient communication transmission.

[0008] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed allocation and static control", we construct an integrated modeling logic of "traffic data collection - traffic feature modeling - traffic allocation modeling - load balancing modeling - scheduling verification modeling". Combining business traffic characteristics (traffic size, real-time performance, priority), link load characteristics, and terminal access characteristics, we establish traffic feature models, traffic allocation models, load balancing models, and scheduling verification models. We design dynamic allocation of business traffic and traffic load balancing control to achieve intelligent scheduling of business traffic.

[0009] First, a traffic monitoring component is deployed to collect service traffic data (traffic size, transmission rate, service type), terminal access data (number of terminals, terminal type, access requirements), and link load data (link bandwidth utilization, load fluctuation, congestion status) from the communication network, constructing a service traffic scheduling data resource pool. Then, a dynamic service traffic allocation design is implemented, extracting multi-dimensional core characteristics of service traffic to build a traffic allocation model. This model quantifies the traffic demands and priorities of each service, employing an improved genetic approach to achieve optimal matching between service traffic and link resources, with the goal of "maximum traffic utilization efficiency and most balanced link load." Next, a traffic load balancing control design is implemented, building a load balancing model based on link load status and traffic change trends. This dynamically adjusts the traffic allocation ratio, real-time alleviating overloaded link traffic and activating idle link resources to prevent link overload or resource idleness. Finally, a scheduling verification model is constructed to quantify traffic utilization efficiency, link load balancing, and scheduling response speed, dynamically optimizing parameters to ensure the effectiveness of intelligent service traffic scheduling.

[0010] 1: Dynamic allocation of business traffic To address the problem of "fixed traffic allocation and inability to adapt to changes in services and links" in existing technologies, an integrated model of traffic feature modeling, demand quantification, and dynamic allocation is constructed to achieve optimal matching of service traffic and link resources, solve the problems of uneven traffic allocation and low utilization efficiency, and fill the technical gap in dynamic allocation of service traffic in communication automation.

[0011] 2: Traffic load balancing control To address the issues of "link load imbalance, resource idleness and overload coexisting" in existing technologies, an integrated model for link load modeling, trend prediction and dynamic control is constructed to achieve balanced control of link load, solve the problems of link overload and congestion and resource idleness, and fill the technical gap in automated traffic load balancing control in communications.

[0012] (II) Communication Quality Dynamic Monitoring Module This module enables precise quantification, real-time monitoring, and early warning of communication quality. It constructs a dynamic monitoring modeling system for communication quality, solves the problems of lagging quality monitoring and difficulty in timely detection of anomalies, improves the controllability and reliability of communication quality, ensures the stable operation of communication automation systems, and meets the high-reliability communication requirements of the Industrial Internet.

[0013] Modeling Approach: Abandoning the traditional extensive modeling approach of "periodic sampling and manual judgment," we construct an integrated modeling logic of "quality data collection - quality indicator modeling - quality assessment modeling - anomaly warning modeling - verification and optimization modeling." Combining communication quality characteristics (transmission rate, signal strength, packet loss rate), service quality requirements, and link operation characteristics, we establish quality indicator models, quality assessment models, and anomaly warning models. We design accurate communication quality assessment and quality anomaly warning systems to achieve dynamic monitoring of communication quality.

[0014] First, a quality monitoring component is deployed to collect communication link quality data (transmission rate, signal strength, packet loss rate), service transmission quality data (transmission latency, data integrity), and terminal feedback data (terminal reception quality, anomaly feedback), thus constructing a communication quality monitoring data resource pool. Then, a precise communication quality assessment is designed, and a quality assessment model is built. The overall communication quality level is quantified using a quality assessment formula, and combined with multi-dimensional quality indicators and weight allocation, accurate assessment and source analysis of communication quality are achieved to identify quality weaknesses. Next, a quality anomaly early warning system is designed. Based on the quality assessment results and anomaly characteristics, an anomaly early warning model is constructed, employing an improved BP neural network to accurately identify quality anomalies, classify anomaly levels (general anomalies / serious anomalies), and output early warning information and preliminary handling suggestions in real time. Finally, a verification and optimization model is built to quantify the accuracy of quality assessment, the timeliness of anomaly early warning, and the quality compliance rate. Parameters and early warning thresholds are dynamically optimized to ensure the effectiveness of dynamic communication quality monitoring.

[0015] 3: Accurate assessment of communication quality To address the issues of "inaccurate quality quantification and ambiguous assessment" in existing technologies, an integrated model for quality indicator modeling, comprehensive quantification, and traceability analysis is constructed. Through quality assessment formulas, accurate quantification of communication quality is achieved, resolving the problems of ambiguous quality assessment and inability to pinpoint shortcomings, and filling the technological gap in accurate communication quality assessment in communication automation.

[0016] 4: Quality Anomaly Warning To address the issues of "lagging quality anomalies and inability to provide timely early warnings" in existing technologies, an integrated model for quality anomaly modeling, feature recognition, and graded early warning is constructed. This model enables accurate identification and real-time early warning of quality anomalies, solving the problems of untimely handling of quality anomalies and the potential for transmission interruptions, and filling the technological gap in early warning of quality anomalies in communication automation.

[0017] (III) Multi-protocol adaptive compatibility module This module is designed to achieve accurate parsing, adaptive adaptation, and seamless interaction of multiple protocols. It constructs a multi-protocol adaptive compatibility modeling system to solve the problems of multi-protocol compatibility difficulties and low adaptation efficiency, improve the multi-scenario adaptation capability of communication automation, and support the development of industrial heterogeneous communication and cross-domain converged communication.

[0018] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed adaptation and manual configuration," we construct an integrated modeling logic of "protocol data acquisition - protocol feature modeling - parsing modeling - adaptation modeling - compatibility verification modeling." Combining multi-protocol features (protocol format, transmission rules, interaction logic), terminal protocol requirements, and link protocol adaptation features, we establish a multi-protocol feature library, a protocol parsing model, and a protocol adaptation model. We design multi-protocol feature extraction and parsing, and protocol adaptive adaptation to achieve adaptive compatibility of multiple protocols.

[0019] First, a protocol acquisition component is deployed to collect various protocol data (protocol format, transmission instructions, interaction flow), terminal protocol requirement data (terminal supported protocols, protocol adaptation parameters), and link protocol adaptation data (link supported protocols, adaptation capabilities) from the communication network, constructing a multi-protocol compatible data resource pool. Then, multi-protocol feature extraction and parsing are designed, a multi-protocol feature library is built, core features of various protocols are extracted, and an improved TF-IDF is adopted to achieve accurate parsing and feature matching of multiple protocols, clarifying protocol differences and adaptation points. Next, protocol adaptive adaptation is designed; based on protocol parsing results and terminal protocol requirements, a protocol adaptation model is constructed, dynamically adjusting protocol interaction parameters (transmission rate, data format) to achieve seamless adaptation and efficient interaction of different protocols without manual configuration. Finally, a compatibility verification model is built to quantify protocol parsing accuracy, adaptation efficiency, and interaction stability, dynamically optimizing parameters to ensure multi-protocol adaptive compatibility.

[0020] 5: Multi-protocol feature extraction and parsing To address the issues of inaccurate multi-protocol parsing and inability to identify protocol differences in existing technologies, an integrated model for protocol feature modeling, extraction, and parsing is constructed to achieve accurate parsing and feature matching of multiple protocols. This solves the problems of ambiguous protocol parsing and inability to adapt to multiple protocols, filling the technological gap in multi-protocol feature extraction and parsing for communication automation.

[0021] 6: Protocol adaptive adaptation To address the issues of "cumbersome multi-protocol adaptation and the need for manual configuration" in existing technologies, an integrated model for protocol adaptation modeling, dynamic parameter adjustment, and seamless interaction is constructed to achieve adaptive adaptation of multiple protocols. This solves the problems of difficulty in multi-protocol compatibility and low adaptation efficiency, filling the technological gap in adaptive adaptation of communication automation protocols.

[0022] Beneficial effects 1. Dynamic allocation of business traffic: Abandoning the crude approach of fixed allocation, a modeling system for traffic characteristic modeling, demand quantification and dynamic allocation is constructed, which significantly improves traffic utilization efficiency and link adaptability, completely solves the problems of uneven traffic distribution and low utilization efficiency, focuses on innovation in efficient traffic scheduling, and meets the development needs of strategic emerging industries in the industrial internet. 2. Traffic load balancing and control: Construct a modeling system for link load modeling, trend prediction and dynamic control, which significantly improves link load balancing and transmission stability, completely solves the problems of link overload and congestion and resource idleness, and fills the technical gap in traffic load balancing and control. 3. Precise Communication Quality Assessment: A modeling system for quality indicator modeling, comprehensive quantification, and traceability analysis is constructed, significantly improving the accuracy of quality assessment and the ability to pinpoint shortcomings. This completely solves the problem of ambiguous quality assessment, focuses on innovation in precise quality monitoring, and meets the high-reliability communication requirements of the Industrial Internet. 4. Quality Anomaly Early Warning: Construct a modeling system for quality anomaly modeling, feature recognition, and level-based early warning. This significantly improves the timeliness of anomaly early warning and the efficiency of handling, completely solving the problem of delayed quality anomaly warning and filling the technological gap in quality anomaly early warning. 5. Multi-protocol feature extraction and parsing: Construct a modeling system for protocol feature modeling, extraction and parsing, significantly improving protocol parsing accuracy and feature recognition capabilities, completely solving the problem of ambiguous protocol parsing, and focusing on multi-protocol compatibility innovation; 6. Protocol Adaptation: Construct a modeling system for protocol adaptation modeling, dynamic parameter adjustment, and seamless interaction. This significantly improves protocol adaptation efficiency and interaction stability, completely solves the problem of multi-protocol compatibility difficulties, fills the technical gap in protocol adaptation, and meets the requirements of the invention priority examination policy. Attached Figure Description

[0023] Figure 1 Workflow diagram of the intelligent scheduling module for business traffic Detailed Implementation

[0024] The following four specific embodiments illustrate in detail the implementation steps of the present invention. Example 1: Industrial Heterogeneous Communication Quality Monitoring and Traffic Cooperative Scheduling Scenario Implementation steps Step 1: Data Acquisition and Parameter Setting: Collect communication link quality data (transmission rate, signal strength, packet loss rate), service transmission quality data (industrial control service transmission latency, data integrity), and terminal feedback data (heterogeneous terminal reception quality) for industrial heterogeneous communication scenarios, and set the minimum acceptable threshold for industrial heterogeneous communication quality. Set quality assessment weight coefficients , , ( ).

[0025] Step 2: Precise Communication Quality Assessment: A precise communication quality assessment model is constructed using the established formulas. Quantitative communication quality comprehensive evaluation value The transmission rate, signal strength, and packet loss rate are standardized and substituted into the formula for calculation to identify quality shortcomings (such as excessively high packet loss rate or insufficient signal strength).

[0026] Step 3: Quality Anomaly Early Warning and Handling: A quality anomaly early warning system is adopted. Based on the quality assessment results and anomaly characteristics, an anomaly early warning model is constructed to accurately identify quality anomalies, classify anomaly levels, and output early warning information and preliminary handling suggestions (such as signal enhancement and link adjustment) in real time to avoid the risk of quality deterioration in advance.

[0027] Step 4: Quality Monitoring Verification and Optimization: Verify the accuracy of quality assessment, the timeliness of anomaly warnings, and the quality compliance rate (≥ Collect quality monitoring and business transmission data, optimize quality assessment parameters and early warning thresholds, and improve the accuracy of quality monitoring and the timeliness of early warning.

[0028] Step 5: Continuous optimization: Collect quality change data and terminal feedback data from heterogeneous industrial communication scenarios, dynamically update the quality assessment model, optimize weight coefficients and early warning thresholds, improve the adaptability to quality fluctuations in heterogeneous industrial scenarios, and ensure that communication quality continues to meet standards.

[0029] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "periodic sampling and manual judgment," this paper constructs an integrated closed-loop model encompassing "data acquisition - quality modeling - evaluation modeling - early warning modeling," thus addressing the high reliability and quality requirements of industrial heterogeneous communication. Using quality indicator characteristics, business quality requirements, and link operation characteristics as core inputs, this approach overcomes the limitations of lagging quality monitoring and the inability to accurately quantify it. Quality indicator modeling achieves precise characterization of multi-dimensional quality characteristics, quality assessment formula modeling achieves precise quantification of communication quality, anomaly warning modeling achieves real-time identification and warning of quality anomalies, and parameter optimization modeling achieves precise adjustment of quality monitoring, filling the gap in dynamic monitoring modeling of heterogeneous industrial communication quality. The modeling process focuses on the high reliability requirements of heterogeneous industrial scenarios, completely differing from the existing technologies' periodic sampling and imprecise assessment modeling approaches and directions. 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 industries of the Industrial Internet and the requirements of the invention priority examination policy.

[0030] Precise communication quality assessment, through the integration of quality assessment formulas and multi-dimensional quality indicators, significantly improves the accuracy and weakness identification capabilities compared to traditional manual judgment and single-indicator assessment models. This completely solves the problems of vague quality assessments and the inability to pinpoint weaknesses, providing precise data support for quality optimization. Quality anomaly early warning, through anomaly feature identification and improved BP neural network modeling, significantly improves the timeliness and efficiency of anomaly early warning compared to traditional periodic sampling and delayed early warning models. This completely solves the problems of delayed quality anomalies and the risk of transmission interruption, allowing for early avoidance of quality deterioration risks. These two synergistic effects enable industrial heterogeneous communication to achieve "precise quality assessment, real-time early warning, and stable compliance." Compared to existing technologies, the controllability and reliability of communication quality are qualitatively improved, fully meeting the high reliability requirements of industrial heterogeneous communication and aligning with the "enhancing core competitiveness of the industry" requirement of the invention priority examination policy.

[0031] Existing technologies employ a "periodic sampling + manual judgment" quality monitoring model, lacking precise communication quality assessment and anomaly early warning. Quality cannot be quantified in real time, and anomalies are difficult to detect and warn of promptly, easily leading to interruptions in industrial control business transmissions and data loss, failing to meet the high reliability requirements of industrial heterogeneous communication. This embodiment, through innovation and model optimization, achieves dynamic monitoring and anomaly early warning of industrial heterogeneous communication quality, completely resolving the pain points of existing technologies. The accuracy of quality assessment and the timeliness of anomaly early warning both meet industrial heterogeneous communication standards, and there is no overlap with existing technologies, the technical direction of the current 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 the invention priority examination policy.

[0032] Example 2: Multi-terminal collaborative communication traffic scheduling scenario (corresponding to intelligent service traffic scheduling module) Implementation steps Step 1: Data collection related to transmission: Deploy traffic monitoring components to collect business traffic data (traffic size, real-time requirements, and priority of various terminal services), terminal access data (number of terminals, terminal type, and access requirements) and link load data (bandwidth utilization, load fluctuation, and congestion status of each link) in multi-terminal collaborative communication scenarios.

[0033] Step 2: Dynamic allocation of business traffic: Dynamic allocation of business traffic is adopted, multi-dimensional features of business traffic are extracted, a traffic allocation model is constructed, the traffic demand and priority of each business are quantified (such as real-time control of business priority being higher than ordinary data business), and link resources are dynamically allocated through improved genetics to achieve optimal matching between business traffic and link resources.

[0034] Step 3: Traffic load balancing control: Based on the link load status and traffic change trend, a load balancing model is built to dynamically adjust the traffic allocation ratio, diverting traffic from overloaded links to idle links, activating idle link resources, and avoiding link overload congestion or resource idleness.

[0035] Traffic scheduling verification and optimization: Verify traffic utilization efficiency, link load balancing, and scheduling response speed to ensure that traffic scheduling meets the needs of multi-terminal collaborative communication; collect link load and service transmission feedback data to optimize traffic allocation parameters and load control thresholds and improve traffic scheduling effectiveness.

[0036] Step 5: Continuous optimization: Collect traffic change data and terminal access data for multi-terminal collaborative communication scenarios, dynamically update the traffic allocation model and load balancing model, optimize parameters, improve the adaptability to traffic fluctuations and terminal access changes in multi-terminal collaborative scenarios, and ensure continuous optimization of traffic scheduling.

[0037] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed allocation and static control," this paper constructs an integrated closed-loop model of "data collection, traffic modeling, allocation modeling, and load balancing modeling." It uses the high-efficiency traffic scheduling requirements, traffic characteristics, terminal access characteristics, and link load characteristics of multi-terminal collaborative communication as core inputs, overcoming the limitations of rigid traffic scheduling and load imbalance. Traffic characteristic modeling achieves precise characterization of service traffic requirements, traffic allocation modeling achieves optimal matching of traffic and link resources, load balancing modeling achieves dynamic control of link load, and parameter optimization modeling achieves precise adjustment of traffic scheduling, filling the gap in intelligent traffic scheduling modeling for multi-terminal collaborative communication. The modeling process focuses on the high-efficiency scheduling requirements of multi-terminal collaborative scenarios, completely different from the fixed allocation and load-balance-less modeling approaches and technical directions of existing technologies. It also has no overlap with the modeling logic of current open documents, representing a completely new modeling direction that aligns with the development needs of the strategic emerging industry of new mobile communication networks.

[0038] Dynamic traffic allocation, through multi-dimensional traffic feature extraction, demand quantification, and improved genetic modeling, significantly improves traffic utilization efficiency and link adaptability compared to traditional fixed allocation modes, completely resolving issues of uneven traffic distribution and mismatch with business needs, and ensuring real-time transmission of high-priority services. Traffic load balancing and control, through link load modeling, trend prediction, and dynamic control, significantly improves link load balancing and transmission stability compared to traditional load-balancing-free modes, completely resolving issues of link overload congestion and resource idleness, and improving link resource utilization. These two synergistic effects enable multi-terminal collaborative communication to achieve "precise traffic allocation, load balancing, and efficient transmission." Compared to existing technologies, traffic scheduling efficiency and link resource utilization are qualitatively improved, fully meeting the needs of multi-terminal collaborative communication.

[0039] Existing technologies employ a "fixed allocation + static control" traffic scheduling model, lacking dynamic allocation and load balancing of service traffic. This results in a mismatch between traffic allocation and service demands, as well as link load, leading to overloaded and congested links and idle resources on other links. Consequently, traffic utilization efficiency is low, failing to meet the needs of multi-terminal collaborative communication. This embodiment, through innovation and modeling optimization, achieves intelligent scheduling of multi-terminal collaborative communication traffic, completely resolving the pain points of existing technologies. Traffic utilization efficiency and link load balancing both meet multi-terminal collaborative communication standards. 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: Cross-domain converged communication multi-protocol adaptation scenario (corresponding to multi-protocol adaptive compatibility module) Implementation steps Step 1: Fault-related data collection: Collect various protocol data (communication protocol formats, transmission instructions, and interaction processes of different domains), terminal protocol requirement data (protocols supported by terminals in each domain and protocol adaptation parameters), and link protocol adaptation data (cross-domain link supported protocols and adaptation capabilities) for cross-domain converged communication scenarios, and build a multi-protocol compatible data resource pool.

[0041] Step 2: Multi-protocol feature extraction and parsing: Multi-protocol feature extraction and parsing is used to build a multi-protocol feature library, extract the core features of various protocols (protocol format, transmission rules, interaction logic), and improve TF-IDF to achieve accurate parsing and feature matching of multiple protocols, clarifying the differences and adaptation points of different protocols.

[0042] Step 3: Protocol Adaptive Adaptation: Adaptive protocol adaptation is adopted. Based on the protocol parsing results and terminal protocol requirements, a protocol adaptation model is constructed, and the protocol interaction parameters (transmission rate, data format) are dynamically adjusted to achieve seamless adaptation and efficient interaction of different domain protocols without the need for manual configuration of protocol parameters.

[0043] Step 4: Protocol compatibility verification and optimization: Verify the accuracy of protocol parsing, adaptation efficiency, and interaction stability to ensure seamless compatibility of multiple protocols and meet the needs of cross-domain converged communication; collect protocol interaction feedback data, optimize feature extraction parameters and adaptation parameters, and improve the multi-protocol adaptation effect.

[0044] Step 5: Continuous optimization: Collect protocol change data and terminal access data for cross-domain converged communication scenarios, dynamically update the multi-protocol feature library and protocol adaptation model, optimize parameters, improve the adaptability to multi-protocol changes in cross-domain converged scenarios, and ensure continuous and seamless compatibility of multiple protocols.

[0045] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed adaptation and manual configuration," this paper constructs an integrated closed-loop model of "data acquisition - protocol modeling - parsing modeling - adaptation modeling." It uses the multi-protocol compatibility requirements, protocol characteristics, terminal protocol requirements, and link adaptation characteristics of cross-domain converged communication as core inputs, overcoming the limitations of difficult multi-protocol compatibility and low adaptation efficiency. Protocol feature modeling achieves accurate characterization of the core features of multiple protocols; parsing modeling achieves accurate parsing and feature matching of multiple protocols; adaptation modeling achieves seamless adaptation and efficient interaction of multiple protocols; and parameter optimization modeling achieves precise adjustment of protocol adaptation, filling the gap in adaptive compatibility modeling of multi-protocols in cross-domain converged communication. The modeling process focuses on the multi-protocol compatibility requirements of cross-domain converged scenarios, which is completely different from the fixed adaptation and manual configuration 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 meets the development needs of the strategic emerging industry of new mobile communication networks.

[0046] Multi-protocol feature extraction and parsing, through multi-protocol feature extraction, feature library construction, and improved TF-IDF modeling, significantly improves protocol parsing accuracy and feature recognition capabilities compared to traditional single-protocol parsing and manual identification modes, completely solving the problems of ambiguous protocol parsing and inability to identify protocol differences, and providing accurate basis for protocol adaptation; Protocol adaptive adaptation, through protocol adaptation modeling and dynamic parameter adjustment, significantly improves protocol adaptation efficiency and interaction stability compared to traditional fixed adaptation and manual configuration modes, completely solving the problems of difficult multi-protocol compatibility and cumbersome adaptation, achieving seamless multi-protocol adaptation, and greatly reducing manual costs; The synergistic effect of these two types enables cross-domain converged communication to achieve "accurate multi-protocol parsing, adaptive adaptation, and seamless interaction," achieving a qualitative improvement in multi-protocol compatibility and adaptation efficiency compared to existing technologies, fully meeting the needs of cross-domain converged communication.

[0047] Existing technologies employ a multi-protocol processing mode of "fixed protocol adaptation + manual configuration," lacking multi-protocol feature extraction and parsing, and adaptive protocol adaptation. Different protocols cannot be seamlessly compatible, requiring manual configuration of protocol parameters during terminal access, resulting in low adaptation efficiency and failing to meet the multi-protocol adaptation requirements of cross-domain converged communication. This embodiment, through innovation and modeling optimization, achieves adaptive compatibility of multiple protocols in cross-domain converged communication, completely resolving the pain points of existing technologies. Protocol parsing accuracy and adaptation efficiency both meet cross-domain converged communication standards, and there is no overlap with existing technologies, current technical directions, or implementation scenarios. Its innovation is prominent, its practicality is strong, and it aligns with the development needs of strategic emerging industries.

[0048] Example 4: Multi-scenario integrated intelligent communication platform (industrial + multi-terminal + cross-domain) collaborative management scenario (integrating three core modules) Implementation steps Step 1: Intelligent Business Traffic Scheduling: Collect business traffic data, terminal access data, and link load data from the multi-scenario fusion platform. Using two components of the intelligent business traffic scheduling module, extract multi-dimensional traffic features, achieve optimal matching between traffic and link resources through dynamic traffic allocation, achieve link load balancing through load balancing control, and output an optimized traffic scheduling solution.

[0049] Step 2: Dynamic monitoring of communication quality: Collect platform communication link quality data, service transmission quality data, and terminal feedback data. Use two components of the dynamic communication quality monitoring module to quantify the overall communication quality level through a precise quality assessment formula. Achieve real-time identification and early warning of anomalies through quality anomaly early warning and output a quality monitoring and early warning solution.

[0050] Step 3: Multi-protocol adaptive compatibility: Collect various protocol data from the platform, terminal protocol requirement data, and link protocol adaptation data. Utilize two aspects of the multi-protocol adaptive compatibility module to achieve accurate protocol parsing through multi-protocol feature extraction and parsing, and to achieve seamless multi-protocol interaction through protocol adaptive adaptation, outputting a multi-protocol adaptation solution.

[0051] 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 traffic scheduling, quality monitoring, and multi-protocol adaptation, and output the full-domain collaborative management and control instructions of the multi-scenario fusion platform to achieve efficient communication across multiple scenarios, services, terminals, and protocols, ensuring the stable and efficient operation of the platform.

[0052] Step 5: Full-process verification and optimization: Verify the platform's traffic utilization efficiency, quality compliance rate, and protocol adaptation efficiency; collect business feedback from various scenarios; optimize the parameters of the three major modules; achieve continuous optimization and scenario expansion of platform communication; and meet the development needs of strategic emerging industries for multi-scenario integration, high reliability, and multi-protocol compatibility, 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-level control," this paper constructs an integrated, full-domain modeling logic encompassing "traffic scheduling, quality monitoring, multi-protocol adaptation, and multi-module fusion." It takes the business diversity, terminal heterogeneity, protocol diversification, and high reliability 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 for traffic scheduling, quality monitoring, and multi-protocol adaptation. Traffic scheduling modeling enables efficient traffic allocation, quality monitoring modeling achieves precise quality control, and multi-protocol adaptation modeling achieves seamless multi-protocol compatibility, filling the gap in full-domain collaborative management modeling for multi-scenario fusion intelligent communication platforms. The modeling process focuses on the multi-scenario fusion, high reliability, and multi-protocol compatibility requirements 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 current modeling logic, representing a completely new modeling direction that complies with the invention priority examination policy.

[0054] The six core features of this invention achieve synergistic efficiency in a multi-scenario integrated intelligent communication platform: Two aspects of traffic scheduling enable dynamic allocation and load balancing of traffic across multiple scenarios, significantly improving traffic utilization efficiency and link stability compared to traditional independent traffic management modes, and greatly enhancing multi-scenario service transmission efficiency; two aspects of quality monitoring enable precise assessment and anomaly warning of communication quality across multiple scenarios, significantly improving quality controllability and reliability compared to traditional quality monitoring modes, effectively mitigating transmission interruption risks; two aspects of multi-protocol adaptation enable precise parsing and adaptive adaptation of multiple protocols across multiple scenarios, significantly improving protocol adaptation efficiency and compatibility compared to traditional protocol processing modes, supporting multi-scenario integrated communication; these six features, working in conjunction with the three main modules, achieve comprehensive optimization of the multi-scenario integrated intelligent communication platform in terms of "efficient scheduling, precise monitoring, and multi-protocol compatibility," representing a qualitative leap in communication automation compared to existing technologies, fully meeting the multi-scenario integrated communication needs of strategic emerging industries, and conforming to the requirements of the invention priority examination policy of "promoting industrial transformation and upgrading and enhancing core industrial competitiveness."

[0055] Existing communication automation methods suffer from problems such as independent and singular modules, lack of collaborative management and control, absence of dynamic allocation and load balancing of service traffic, accurate assessment and anomaly warning of communication quality, multi-protocol feature extraction and parsing, and adaptive adaptation. They also suffer from rigid traffic scheduling in multi-scenario integrated communication, lagging quality monitoring, and difficulties in multi-protocol compatibility, making it difficult to meet the high efficiency, high reliability, and multi-protocol compatibility requirements of multi-scenario integrated intelligent communication platforms. This embodiment, through three core modules and six core integrated innovations, achieves traffic scheduling and quality monitoring with multi-protocol adaptation in communication automation, completely solving the pain points of existing technologies. It significantly improves traffic utilization efficiency, quality controllability, and multi-protocol compatibility in 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 can be widely applied to various communication automation scenarios such as industry, multi-terminal, and cross-domain applications.

[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 multi-protocol adaptation method for automated communication traffic scheduling and quality monitoring, characterized in that, Includes the following steps: S1: Intelligent scheduling and processing of service traffic. It collects communication network service traffic data, terminal access data and link load data. Through dynamic allocation of service traffic and traffic load balancing control, it realizes accurate allocation, load balancing and dynamic scheduling of service traffic, and outputs traffic scheduling optimization scheme. S2: Dynamic monitoring and processing of communication quality, real-time collection of communication link quality data, service transmission quality data and terminal feedback data, and through accurate communication quality assessment and quality anomaly early warning, to achieve accurate quantification, real-time monitoring and anomaly early warning of communication quality, and ensure stable and compliant communication quality. S3: Multi-protocol adaptive compatibility processing integrates various protocol data, terminal protocol requirement data and link protocol adaptation data in the communication network. Through multi-protocol feature extraction and parsing, and protocol adaptive adaptation, it achieves accurate parsing, adaptive adaptation and seamless interaction of multiple protocols, forming a closed loop of traffic-quality-protocol collaborative management and control for the entire automated communication process. The accurate communication quality assessment in step S2 includes a quality assessment formula, which is as follows: The constraints are , This is a comprehensive evaluation value for communication quality. , , The weighting coefficients for transmission rate, signal strength, and packet loss rate are respectively ( ), For the normalized value of transmission rate, This is the normalized value of the signal strength. This is a standardized value for the packet loss rate (inverse mapping). This is the minimum threshold for achieving communication quality, set according to the service type and scenario requirements.

2. The method according to claim 1, characterized in that, The dynamic allocation of service traffic in step S1 includes the following sub-steps: extracting multi-dimensional features of service traffic, constructing a traffic allocation model, and achieving optimal matching of service traffic and link resources through traffic evaluation and dynamic scheduling.

3. The method according to claim 1, characterized in that, The traffic load balancing control in step S1 is based on the link load status and traffic change trend to build a load balancing model, dynamically adjust the traffic allocation ratio, avoid link overload or resource idleness, and achieve link load balancing.

4. The method according to claim 1, characterized in that, The precise communication quality assessment in step S2 quantifies the overall communication quality level through a quality assessment formula, and combines multi-dimensional quality indicators to achieve precise assessment and source analysis of communication quality, providing data support for quality optimization.

5. The method according to claim 1, characterized in that, The quality anomaly warning in step S2 is based on the quality assessment results and anomaly characteristics. An anomaly warning model is constructed to achieve accurate identification, classification and real-time warning of quality anomalies, and to avoid the risk of quality deterioration in advance.

6. The method according to claim 1, characterized in that, The multi-protocol feature extraction and parsing in step S3 involves constructing a multi-protocol feature library, extracting the core features of various protocols, and achieving accurate parsing and feature matching of multiple protocols.

7. The method according to claim 1, characterized in that, The protocol adaptive adaptation in step S3 involves constructing a protocol adaptation model based on the protocol parsing results and terminal protocol requirements, dynamically adjusting protocol interaction parameters, and achieving seamless adaptation and efficient interaction of multiple protocols.

8. The method according to claim 1, characterized in that, The minimum communication quality threshold Flexible adjustment based on the scenario, industrial heterogeneous communication Multi-terminal collaborative communication Cross-domain converged communication The weighting coefficients can be dynamically adjusted according to business priorities.

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 heterogeneous communication, multi-terminal collaborative communication, cross-domain converged communication, and smart grid communication, to achieve traffic scheduling, quality monitoring, and multi-protocol adaptation.

10. A multi-protocol adaptation system for automated traffic scheduling and quality monitoring in communication, characterized in that, include: The module includes a business traffic intelligent scheduling module, a communication quality dynamic monitoring module, a multi-protocol adaptive compatibility module, a multi-source data acquisition module, and a collaborative management and control engine module. The traffic scheduling module performs the functions of claims 1-2, the quality monitoring module performs the functions of claims 4-5, and the protocol adaptation module performs the functions of claims 6-7. Each module achieves real-time data interaction through a high-speed communication bus, thereby completing automated traffic scheduling and quality monitoring with multi-protocol adaptation.

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