A system and method for intelligent fault diagnosis and dynamic resource allocation of communication automation

Abstract

This invention belongs to the field of communication automation technology and discloses an intelligent fault diagnosis and dynamic resource allocation system and method for communication automation. The invention covers the entire process of communication automation from fault diagnosis and resource allocation to service adaptation by constructing three core modules: intelligent fault diagnosis, dynamic allocation of transmission resources, and multi-service priority adaptation. By employing core technologies such as intelligent fault diagnosis, dynamic resource allocation, and multi-service priority adaptation, it overcomes the industry bottlenecks of traditional communication automation, including "lagging fault diagnosis, rigid resource allocation, and poor service adaptability." It achieves accurate and rapid fault diagnosis, dynamic and efficient allocation of transmission resources, and intelligent priority adaptation of multiple services, significantly improving the operational stability, resource utilization, and multi-service adaptability of communication automation. It is suitable for various scenarios such as industrial internet, intelligent communication, and emergency communication, and particularly solves the technical problems of inaccurate fault diagnosis, serious resource waste, and frequent multi-service conflicts.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to an intelligent fault diagnosis and dynamic resource allocation system and method for communication automation. Background Technology

[0002] In the current field of communication automation, with the rapid development of emerging scenarios such as the Industrial Internet, intelligent communication, and emergency communication, communication demands exhibit the core characteristics of "high stability, high efficiency, and multi-service." The requirements for communication fault diagnosis, transmission resource allocation, and multi-service adaptation are becoming increasingly stringent. However, existing technologies still face specific and unresolved practical problems in the three sub-scenarios of "intelligent fault diagnosis, transmission resource allocation, and multi-service 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. Fault diagnosis is lagging and inaccurate, lacking intelligent diagnosis and tracing: Existing communication fault diagnosis mostly adopts the "manual investigation + passive alarm" mode, lacking intelligent and accurate fault diagnosis, and also lacking a fault tracing and location mechanism; resulting in inaccurate fault identification, low diagnostic efficiency, difficulty in tracing the source, inability to quickly locate the fault location and cause, and easy to cause problems such as fault expansion and excessively long communication interruption time, affecting the operational stability of communication automation.

[0003] 2. Rigid and wasteful resource allocation, lacking dynamic allocation and load balancing: Existing transmission resource allocation mostly adopts the "fixed allocation + manual adjustment" mode, lacking dynamic resource allocation optimization and resource load balancing mechanism; resulting in a mismatch between resource allocation and business needs, with some link resources overloaded and some link resources idle, low resource utilization, serious waste, and increased operating costs of communication automation.

[0004] 3. Poor multi-service adaptability and frequent conflicts, lack of priority sorting and adaptation: Existing multi-service transmission mostly adopts the "unordered transmission + simple queuing" mode, lacking multi-service priority sorting and service transmission adaptation mechanism; resulting in high-priority services (such as emergency communication and industrial control) not being able to be transmitted first, frequent conflicts between multiple services, and inconsistent transmission quality, making it difficult to meet the automation needs of multi-scenario multi-service communication.

[0005] Existing communication automation methods have not achieved core innovations in "intelligent fault diagnosis, dynamic allocation of transmission resources, and multi-service priority adaptation," especially in the areas of fault diagnosis modeling, resource allocation solution, and multi-service adaptation modeling solution, which have significant gaps and cannot solve the aforementioned specific problems. Summary of the Invention

[0006] To address the three specific problems raised in the background technology, the present invention aims to provide an intelligent fault diagnosis and dynamic resource allocation system and method for communication automation. This system enables accurate and rapid diagnosis of communication faults, dynamic and efficient allocation of transmission resources, and intelligent priority adaptation of multiple services. It solves the problems of lagging and inaccurate fault diagnosis, rigid and wasteful resource allocation, and poor adaptability to multiple services. The entire process emphasizes innovation and modeling and solving without involving rules for intellectual activities. This improves the operational stability, resource utilization, and multi-service adaptability of communication automation, and further perfects the communication automation technology system in highly stable, highly efficient, and multi-service scenarios.

[0007] The present invention is implemented through the following specific technical solution: (I) Intelligent Fault Diagnosis Module This module enables accurate identification, rapid diagnosis, and source tracing of communication faults. It constructs an intelligent fault diagnosis modeling system to solve problems such as lagging and inaccurate fault diagnosis, difficulty in source tracing, and fault escalation. It improves the operational stability of communication automation, provides technical support for communication automation fault management, and meets the development needs of strategic emerging industries such as the Industrial Internet and new mobile communication networks.

[0008] Modeling Approach: Abandoning the traditional extensive modeling approach of "manual troubleshooting and passive alarms," ​​we construct an integrated modeling logic of "fault data acquisition - equipment status modeling - fault feature modeling - diagnostic optimization modeling - source tracing and location modeling - verification optimization modeling." Combining communication link fault characteristics (fault type, fault severity), equipment operating status characteristics (operating parameters, abnormal indicators), and fault environment characteristics (link load, external interference), we establish equipment status models, fault feature models, diagnostic optimization models, source tracing and location models, and verification optimization models. We design intelligent fault accurate diagnosis and fault source tracing and location to achieve intelligent fault diagnosis.

[0009] First, deploy fault monitoring and data acquisition components to collect communication link fault data (fault type, fault duration), equipment operating status data (operating parameters, abnormal indicators), and fault characteristic data (fault signals, characteristic parameters) to build an intelligent fault diagnosis data resource pool. Then, design intelligent and precise fault diagnosis, extracting core features of fault characteristics, equipment operating status, and fault environment to build a diagnostic optimization model, achieving accurate fault identification, type determination, and rapid diagnosis, shortening diagnosis time. Next, design fault tracing and localization, building a fault tracing model based on fault data, equipment operating data, and link status data to achieve accurate fault tracing, location positioning, and cause analysis, providing support for fault handling. Finally, build a verification and optimization model to quantify fault diagnosis accuracy, diagnosis time, and tracing precision, dynamically optimizing parameters and diagnostic strategies to ensure the effectiveness of intelligent fault diagnosis.

[0010] 1: Intelligent and accurate fault diagnosis To address the issues of "lagging fault diagnosis and inaccurate identification" in existing technologies, an integrated model is constructed that models fault characteristics, equipment operating status, and fault environment. This model enables accurate fault identification, type determination, and rapid diagnosis, resolving the core pain points of fault diagnosis and filling the technological gap in accurate intelligent fault diagnosis for communication automation.

[0011] 2: Fault source tracing and location To address the problems of "difficulty in fault tracing and inability to pinpoint the cause" in existing technologies, an integrated model of fault data, equipment operation data, and link status modeling is constructed to achieve accurate fault tracing, location positioning, and cause analysis, solving the core pain point of fault tracing and filling the technological gap in accurate fault tracing and positioning in communication automation.

[0012] (ii) Dynamic allocation module for transmission resources This module is designed to dynamically allocate, load balance, and optimize the scheduling of transmission resources. It constructs a dynamic allocation modeling system for transmission resources, which solves the problems of rigid resource allocation, serious waste, and low resource utilization. It improves the resource utilization efficiency and economy of communication automation, provides technical support for resource optimization in communication automation, and meets the development needs of strategic emerging industries such as the Industrial Internet and information technology services.

[0013] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed allocation and manual adjustment," we construct an integrated modeling logic of "resource data collection - resource status modeling - load characteristic modeling - allocation optimization modeling - load balancing modeling - verification optimization modeling." Combining transmission resource status characteristics (resource occupancy rate, available resource quantity), link load characteristics (load rate, data throughput), and service resource demand characteristics (resource demand quantity, demand priority), we establish resource status models, load characteristic models, allocation optimization models, load balancing models, and verification optimization models. We design dynamic resource allocation optimization and resource load balancing to achieve dynamic allocation of transmission resources.

[0014] First, deploy resource monitoring and data acquisition components to collect transmission resource occupancy data (resource occupancy rate, occupancy duration), business resource demand data (demand quantity, priority), and link load data (load rate, throughput) to build a dynamic allocation data resource pool for transmission resources. Then, design dynamic resource allocation optimization, extracting core features of resource status, link load, and business resource demand to build an allocation optimization model. Combined with resource allocation calculation formulas, this achieves dynamic allocation and optimized scheduling of transmission resources, improving resource utilization. Next, design resource load balancing, building a load balancing model based on resource occupancy status and link load data to achieve resource load balancing across multiple links and devices, avoiding resource overload and idleness. Finally, build a verification optimization model to quantify resource utilization, load balancing degree, and business satisfaction, dynamically optimizing parameters and allocation strategies to ensure the effectiveness of dynamic transmission resource allocation.

[0015] 3: Optimization of dynamic resource allocation To address the issues of "rigid resource allocation and mismatch with business needs" in existing technologies, an integrated model is constructed that models resource status, link load, and business resource requirements. Combined with resource allocation calculation formulas, this model enables dynamic allocation and optimized scheduling of transmission resources, solving the core pain point of unreasonable resource allocation and filling the technological gap in dynamic allocation and optimization of communication automation transmission resources.

[0016] 4: Resource load balancing To address the problems of "uneven resource load and serious waste" in existing technologies, an integrated model for resource occupancy status, link load and load balancing is constructed to achieve resource load balancing across multiple links and devices, solve the problems of resource overload and idleness, and fill the technological gap in intelligent resource load balancing for automated communication transmission.

[0017] (III) Multi-service priority adaptation module This module's core functionality includes intelligent priority sorting of multiple services, transmission parameter adaptation, and conflict coordination. It constructs a multi-service priority adaptation modeling system to address issues such as poor multi-service compatibility, frequent conflicts, and inconsistent transmission quality. This enhances the multi-service adaptation capabilities and transmission quality of communication automation, providing technical support for multi-service transmission in communication automation and meeting the development needs of strategic emerging industries such as new mobile communication networks and information technology services.

[0018] Modeling Approach: Abandoning the traditional crude modeling approach of "disordered transmission and simple queuing," we construct an integrated modeling logic of "business data collection - business feature modeling - priority modeling - adaptation optimization modeling - conflict coordination modeling - verification optimization modeling." Combining the characteristics of multiple business types (business type, urgency), business transmission requirements (transmission rate, latency requirements), and priority parameter characteristics (priority weight, adaptation standards), we establish business feature models, priority models, adaptation optimization models, conflict coordination models, and verification optimization models. We design multi-service priority sorting and business transmission adaptation to achieve multi-service priority adaptation.

[0019] First, deploy business monitoring and data acquisition components to collect data on multiple business types (business type, urgency), business priority parameters (weight, adaptation standards), and business transmission requirements (transmission rate, latency requirements), building a multi-business priority adaptation data resource pool. Then, design a multi-business priority ranking system, extracting core features of business type, urgency, and transmission requirements to build a priority model, enabling intelligent ranking and dynamic priority adjustment of multiple services, ensuring high-priority services are transmitted first. Next, design business transmission adaptation; based on business priority and transmission requirement data, build a business adaptation model to achieve dynamic adaptation of business transmission parameters and coordination of multi-business conflicts, improving transmission quality. Finally, build a verification and optimization model to quantify priority ranking accuracy, transmission adaptability, and conflict occurrence rate, dynamically optimizing parameters and adaptation strategies to ensure the effectiveness of multi-business priority adaptation.

[0020] 5: Prioritization of multiple services To address the issues of "disordered transmission of multiple services and failure to prioritize high-priority services" in existing technologies, an integrated model is constructed that models service types, urgency levels, and transmission requirements. This model enables intelligent sorting and dynamic priority adjustment of multiple services, resolving the problem of chaotic sorting and filling the technological gap in intelligent priority sorting of multiple services in communication automation.

[0021] 6: Service transmission adaptation To address the issues of poor multi-service adaptability and frequent conflicts in existing technologies, an integrated model is constructed that combines service priority, transmission requirements, and service adaptation modeling. This model enables dynamic adaptation of service transmission parameters and coordination of multi-service conflicts, resolving the problems of multi-service adaptation and conflicts, and filling the technological gap in intelligent adaptation of multi-service transmission in communication automation.

[0022] Beneficial effects 1. Intelligent and accurate fault diagnosis: Abandoning the crude approach of manual troubleshooting, it constructs an integrated system of fault characteristics, equipment operating status and fault environment modeling, which significantly improves the accuracy and efficiency of fault diagnosis, completely solves the problems of lagging fault diagnosis and inaccurate identification, focuses on innovation in accurate and rapid fault diagnosis, and meets the development needs of strategic emerging industries such as industrial Internet and new mobile communication networks. 2. Fault source tracing and localization: Construct an integrated system of fault data, equipment operation data and link status modeling, which significantly improves the accuracy and efficiency of fault source tracing and localization, completely solves the problems of difficulty in fault source tracing and inability to locate the cause, and fills the technical gap in accurate fault source tracing and localization; 3. Dynamic Resource Allocation Optimization: Construct an integrated system for modeling resource status, link load, and business resource requirements. Combined with resource allocation calculation formulas, resource utilization and allocation rationality are significantly improved, completely solving the problems of rigid resource allocation and mismatch with business needs. Focusing on the innovation of efficient and dynamic resource allocation, it meets the development needs of strategic emerging industries in information technology services. 4. Resource load balancing: Construct an integrated system for modeling resource occupancy status, link load, and load balancing, significantly improving resource load balancing and completely solving the problems of uneven resource load and serious waste, filling the technical gap in intelligent load balancing of transmission resources; 5. Multi-service priority ranking: Construct an integrated system for modeling service type, urgency, and transmission requirements. The accuracy and rationality of priority ranking are significantly improved, completely solving the problems of disordered transmission of multiple services and failure to prioritize high-priority services. Focusing on intelligent management and control innovation of multi-service priority, it meets the development needs of strategic emerging industries in new mobile communication networks. 6. Service Transmission Adaptation: Construct an integrated system for service priority, transmission requirements and service adaptation modeling, which significantly improves service adaptability and transmission quality, completely solves the problems of poor multi-service adaptability and frequent conflicts, fills the technical gap in intelligent adaptation of multi-service transmission, and meets the requirements of the invention priority examination policy. Attached Figure Description

[0023] Figure 1 Intelligent Fault Diagnosis Module Workflow Diagram Detailed Implementation

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

[0025] Example 1: Industrial Internet Fault Management and Resource Optimization Scenario Implementation steps Step 1: Data Acquisition and Parameter Setting: Collect communication link fault data, equipment operating status data, fault characteristic data, transmission resource usage data, service resource demand data, link load data, multi-service type data, and service priority parameters for the industrial internet scenario, and set the total available transmission resources. Set business resource allocation weights (Industrial Control Business) (Monitoring services) (Ordinary data services) Set resource redundancy allocation correction value .

[0026] Step 2: Intelligent Fault Diagnosis: Employing intelligent fault diagnosis and fault source tracing, core features of fault characteristics, equipment operating status, and fault environment are extracted to construct a diagnostic optimization and source tracing model. This enables accurate identification, rapid diagnosis, and source tracing of industrial internet communication faults, outputting fault diagnosis reports and handling instructions to ensure the stable operation of the industrial internet.

[0027] Step 3: Dynamic Allocation of Transmission Resources: Employing dynamic resource allocation optimization and load balancing, core features of resource status, link load, and service resource requirements are extracted to construct an allocation optimization and load balancing model. This is achieved through a resource allocation calculation formula based on dynamic resource allocation optimization. This enables dynamic allocation, optimized scheduling, and load balancing of transmission resources, improving resource utilization and avoiding resource waste.

[0028] Step 4: Multi-service priority adaptation: Employing multi-service priority sorting and service transmission adaptation, the core features of service type, urgency, and transmission requirements are extracted to construct a priority sorting and service adaptation model. This enables intelligent sorting, dynamic priority adjustment, transmission parameter adaptation, and conflict coordination of multiple services in the Industrial Internet, ensuring priority transmission for high-priority services such as industrial control.

[0029] Step 5: Full-process verification and optimization: Verify the accuracy of fault diagnosis, diagnosis time, resource utilization, load balancing, priority sorting accuracy, and business adaptability to ensure that the high stability, high efficiency, and multi-service requirements of the industrial internet are met; collect feedback data from each module, optimize parameters and diagnosis, allocation, and adaptation strategies, and improve the adaptability to industrial internet scenarios.

[0030] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "manual troubleshooting, fixed allocation, and disordered transmission," this paper constructs an integrated closed-loop model of "fault diagnosis, resource allocation, and business adaptation." It takes the high stability, high efficiency, multi-service requirements, fault characteristics, resource characteristics, and business characteristics of industrial internet fault management and resource optimization as core inputs, overcoming the limitations of lagging and inaccurate fault diagnosis, rigid and wasteful resource allocation, and poor multi-service adaptability. Fault diagnosis modeling achieves accurate and rapid fault diagnosis and source tracing; resource allocation modeling achieves dynamic and efficient resource allocation and load balancing; and business adaptation modeling achieves priority adaptation and conflict coordination among multiple services, filling the gap in integrated fault-resource-business collaborative modeling for the industrial internet. The modeling process focuses on the high stability, high reliability, and high efficiency requirements of the industrial internet scenario, 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 meets the development needs of strategic emerging industries such as the industrial internet and new mobile communication networks, as well as the requirements of invention priority examination policies.

[0031] Efficiency Enhancement Principle Intelligent fault diagnosis, through fault characteristic, equipment operating status, and fault environment modeling, significantly improves the accuracy and efficiency of fault diagnosis compared to traditional manual troubleshooting, completely solving the problems of delayed and inaccurate fault diagnosis, and providing core support for the stable operation of the Industrial Internet. Fault source tracing and location, through fault data, equipment operating data, and link status modeling and source analysis strategies, significantly improves the accuracy and efficiency of fault source tracing compared to traditional passive alarm modes, completely solving the problems of difficult fault source tracing and inability to pinpoint the cause, and facilitating rapid fault handling. Dynamic resource allocation optimization, through resource status, link load, and business resource demand modeling and resource allocation calculation formulas, significantly improves resource utilization and allocation rationality compared to traditional fixed allocation modes, completely solving the problems of rigid resource allocation and mismatch with business needs. Resource load balancing, through resource occupancy status, link load, and load balancing modeling and dynamic adjustment strategies, improves the efficiency of resource allocation compared to traditional no-load allocation. The balanced mode significantly improves resource load balancing, completely resolving resource overload and idleness issues and reducing resource waste. Multi-service priority sequencing, through modeling of service type, urgency, and transmission requirements, and intelligent sequencing strategies, significantly improves the accuracy and rationality of priority sequencing compared to traditional disordered transmission modes, completely resolving the problems of disordered multi-service transmission and the inability to prioritize high-priority services. Service transmission adaptation, through modeling of service priority, transmission requirements, and service adaptation, and parameter optimization strategies, significantly improves service adaptability and transmission quality compared to traditional simple adaptation modes, completely resolving the problems of poor multi-service adaptability and frequent conflicts. These six synergistic effects enable the Industrial Internet to achieve "high stability, high efficiency, and multi-adaptability." Compared to existing technologies, operational stability, resource utilization, and multi-service adaptability are qualitatively improved, fully meeting the high requirements of the Industrial Internet and conforming to the requirements of the invention priority examination policy of "enhancing core industrial competitiveness and optimizing resource allocation."

[0032] Existing technologies employ a "manual troubleshooting + fixed allocation + disordered transmission" model, lacking intelligent fault diagnosis, fault source tracing, dynamic resource allocation optimization, resource load balancing, multi-service priority ranking, and service transmission adaptation. This results in inaccurate and delayed fault diagnosis, significant resource waste, and frequent multi-service conflicts, failing to meet the high stability, high efficiency, and multi-service requirements of the Industrial Internet. It is prone to problems such as communication interruptions, resource idleness, and delays in high-priority service transmission. This embodiment, through innovation and model optimization, achieves intelligent fault diagnosis, dynamic allocation of transmission resources, and multi-service priority adaptation for the Industrial Internet, completely resolving the pain points of existing technologies. The fault diagnosis accuracy, resource utilization, and multi-service adaptability all meet the standards for Industrial Internet scenarios. Furthermore, it does not 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 invention priority examination policies.

[0033] Example 2: Emergency Communication Failure Rapid Handling and Resource Guarantee Scenario (corresponding to intelligent fault diagnosis and dynamic allocation of transmission resources modules) Implementation steps Step 1: Relevant Data Collection: Deploy monitoring and data collection components to collect communication link fault data, equipment operation status data, fault characteristic data, transmission resource usage data, emergency service resource demand data, and link load data in emergency communication scenarios. Construct a fault diagnosis and resource allocation data resource pool to meet the needs of "rapid response and priority protection" in emergency communication.

[0034] Step 2: Intelligent Fault Diagnosis: Employing intelligent fault diagnosis and fault source tracing, core features of fault characteristics, equipment operating status, and fault environment are extracted to construct a diagnostic optimization and source tracing model. This enables accurate identification, rapid diagnosis, and source tracing of emergency communication faults, outputting fault diagnosis reports and rapid response instructions to minimize fault handling time and ensure uninterrupted emergency communication.

[0035] Step 3: Dynamic allocation of transmission resources: By adopting dynamic resource allocation optimization and resource load balancing, the core features of resource status, link load and emergency service resource requirements are extracted to construct an allocation optimization and load balancing model. Combined with the resource allocation calculation formula, dynamic allocation, optimized scheduling and load balancing of transmission resources are realized, giving priority to ensuring the resource requirements of emergency services, improving resource utilization efficiency and avoiding resource waste.

[0036] Step 4: Full-process collaborative management and control: Integrate the results of intelligent fault diagnosis and dynamic allocation of transmission resources, output emergency communication fault handling and resource guarantee collaborative management and control instructions, realize the linkage between fault diagnosis and resource allocation, and ensure rapid response, stable operation and priority resource guarantee of emergency communication.

[0037] Step 5: Continuous optimization: Verify the accuracy of fault diagnosis, diagnosis time, resource utilization, load balancing, and emergency service guarantee rate; collect feedback data from emergency communication scenarios, optimize parameters and diagnosis and allocation strategies, improve adaptability to emergency communication scenarios, and ensure rapid handling of emergency communication faults and efficient resource guarantee.

[0038] Modeling Innovation Principles Abandoning the traditional, extensive modeling approach of "manual troubleshooting and fixed allocation," this paper constructs an integrated closed-loop model of "fault diagnosis, resource allocation, and emergency support." It uses rapid response, priority, and high stability requirements for emergency communication fault handling and resource support, along with fault characteristics, resource characteristics, and emergency business needs, as core inputs. This overcomes the limitations of lagging and inaccurate fault diagnosis, rigid resource allocation, and insufficient emergency resource support. Fault diagnosis modeling achieves accurate and rapid fault diagnosis and tracing; resource allocation modeling achieves dynamic and efficient resource allocation and load balancing; and emergency support modeling achieves priority support for emergency business resources, filling the gap in integrated modeling of emergency communication faults, resources, and support. The modeling process focuses on the scenario requirements of rapid response and priority support in emergency communication, 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 aligns with the development needs of new mobile communication networks and the strategic emerging industries of the Industrial Internet.

[0039] Efficiency Enhancement Principle Intelligent fault diagnosis, through fault characteristic, equipment operating status, and fault environment modeling, significantly improves the accuracy and efficiency of fault diagnosis compared to traditional manual troubleshooting, completely solving the problems of delayed fault diagnosis and inaccurate identification, and providing support for rapid handling of emergency communication faults; Fault source tracing and location, through fault data, equipment operating data, and link status modeling and source analysis strategies, significantly improves the accuracy and efficiency of fault source tracing and location compared to traditional passive alarm modes, completely solving the problems of difficult fault source tracing and inability to locate the cause, and assisting in rapid fault handling; Dynamic resource allocation optimization, through resource status, link load, and emergency service resource demand modeling and resource allocation calculation formulas... Compared to the traditional fixed allocation model, this approach significantly improves resource utilization and allocation rationality, completely resolving the issues of rigid resource allocation and mismatch with emergency service needs, and prioritizing emergency service resources. Resource load balancing, through resource occupancy status, link load, and load balancing modeling and dynamic adjustment strategies, significantly improves resource load balancing compared to the traditional no-load-balancing model, completely resolving resource overload and idleness issues, and ensuring efficient utilization of emergency resources. These four synergistic effects enable emergency communications to achieve "rapid response, priority guarantee, and stable operation." Compared to existing technologies, fault handling efficiency, resource utilization, and emergency support capabilities are qualitatively improved, fully meeting the needs of emergency communications.

[0040] Existing technologies employ a "manual troubleshooting + fixed allocation" model, lacking intelligent fault diagnosis, fault source tracing, dynamic resource allocation optimization, and resource load balancing. This results in lagging and inaccurate fault diagnosis, rigid resource allocation, and insufficient emergency resource support, failing to meet the demands for rapid response, priority protection, and high stability in emergency communications. Such technologies are prone to problems such as untimely fault handling, insufficient emergency service resources, and communication interruptions, impacting emergency response efforts. This embodiment, through innovation and model optimization, achieves intelligent fault diagnosis and dynamic allocation of transmission resources for emergency communications, completely resolving the pain points of existing technologies. Fault handling efficiency, resource utilization, and emergency support capabilities all meet emergency 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.

[0041] Example 3: Enterprise-level multi-service communication adaptation and resource optimization scenario (corresponding to dynamic allocation of transmission resources and multi-service priority adaptation module) Implementation steps Step 1: Data Acquisition and Scenario Adaptation: Collect data on transmission resource usage, business resource requirements, link load, multiple service types, business priority parameters, and business transmission requirements for enterprise-level multi-service communication scenarios. Adapt to the enterprise-level communication requirements of "multi-service, high efficiency, and low conflict" and build a resource allocation and business adaptation data resource pool.

[0042] Step 2: Dynamic allocation of transmission resources: By adopting dynamic resource allocation optimization and resource load balancing, the core features of resource status, link load and service resource requirements are extracted to construct an allocation optimization and load balancing model. Combined with the resource allocation calculation formula, dynamic allocation, optimized scheduling and load balancing of transmission resources are realized, thereby improving resource utilization, avoiding resource waste and reducing enterprise communication operating costs.

[0043] Step 3: Multi-service priority adaptation: Employing multi-service priority sorting and service transmission adaptation, core features of service type, urgency, and transmission requirements are extracted to construct a priority sorting and service adaptation model. This enables intelligent sorting, dynamic priority adjustment, transmission parameter adaptation, and conflict coordination of enterprise-level multi-services, ensuring priority transmission of core services (such as finance and office) and reducing the occurrence rate of multi-service conflicts.

[0044] Step 4: Resource and Service Co-optimization: Integrate the results of dynamic allocation of transmission resources and multi-service priority adaptation to achieve co-optimization of resource allocation and service adaptation, balance resource utilization and service transmission quality, and ensure efficient, stable and low-conflict operation of enterprise-level multi-service communication.

[0045] Step 5: Continuous optimization: Verify resource utilization, load balancing, priority sorting accuracy, business adaptability, and conflict occurrence rate; collect data on changes in enterprise-level multi-service communication requirements, optimize parameters and allocation, and adaptability strategies to improve adaptability to enterprise-level multi-service communication scenarios.

[0046] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed allocation and disordered transmission," this paper constructs an integrated closed-loop model of "resource allocation - service adaptation - collaborative optimization." It takes the multi-service, high-efficiency, low-conflict requirements, resource characteristics, service characteristics, and transmission requirements of enterprise-level multi-service communication as core inputs, overcoming the limitations of rigid and wasteful resource allocation, poor multi-service adaptability, and frequent conflicts. Resource allocation modeling achieves dynamic and efficient resource allocation and load balancing; service adaptation modeling achieves priority adaptation and conflict coordination among multiple services; and collaborative optimization modeling achieves balanced optimization of resource allocation and service adaptation, filling the gap in integrated modeling of enterprise-level multi-service communication resources and services. The modeling process focuses on the scenario requirements of enterprise-level multi-service, high-efficiency, and low-conflict scenarios, completely different from the single-module, extensive modeling approach and technical direction of existing technologies. It also has no overlap with the current modeling logic, representing a completely new modeling direction that aligns with the development needs of the Industrial Internet and information technology services for strategic emerging industries.

[0047] Efficiency Enhancement Principle Dynamic resource allocation optimization, through modeling resource status, link load, and business resource requirements, and using resource allocation calculation formulas, significantly improves resource utilization and allocation rationality compared to the traditional fixed allocation mode, completely solving the problems of rigid resource allocation and mismatch with business needs, and reducing enterprise communication operating costs. Resource load balancing, through modeling resource occupancy status, link load, and load balancing, and using dynamic adjustment strategies, significantly improves resource load balancing compared to the traditional no-load-balancing mode, completely solving the problems of resource overload and idleness, and ensuring efficient resource utilization. Multi-service priority sequencing, through modeling service type, urgency, and transmission requirements, and using intelligent sequencing strategies, compared to traditional... The system unifies the disordered transmission mode, significantly improving the accuracy and rationality of priority sorting, and completely solving the problems of disordered transmission of multiple services and failure to prioritize core services. Service transmission adaptation, through service priority, transmission requirements, and service adaptation modeling and parameter optimization strategies, significantly improves service adaptability and transmission quality compared to the traditional simple adaptation mode, completely solving the problems of poor multi-service adaptability and frequent conflicts. These four synergistic effects achieve collaborative optimization of enterprise-level multi-service communication resources and services. Compared to existing technologies, resource utilization, service transmission quality, and conflict control capabilities are qualitatively improved, fully meeting the needs of enterprise-level multi-service communication and complying with the invention priority examination policy requirements.

[0048] Existing technologies employ a "fixed allocation + disordered transmission" model, lacking dynamic resource allocation optimization, resource load balancing, multi-service priority ranking, and service transmission adaptation. This results in rigid and wasteful resource allocation, poor multi-service adaptability, and frequent conflicts, failing to meet the demands of enterprise-level multi-service communication for high efficiency and low conflict. It easily leads to problems such as idle resources, delayed core service transmission, and transmission stuttering caused by multi-service conflicts, increasing enterprise communication operating costs. This embodiment, through innovation and modeling optimization, achieves dynamic allocation of transmission resources and multi-service priority adaptation for enterprise-level multi-service communication, completely resolving the pain points of existing technologies. Resource utilization, service transmission quality, and conflict control capabilities all meet enterprise-level 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.

[0049] Example 4: Multi-scenario converged communication automated management and control platform (industrial + emergency + enterprise) scenario (integrating three core modules) Implementation steps Step 1: Intelligent Fault Diagnosis: Collect communication link fault data, equipment operation status data, and fault characteristic data from the multi-scenario fusion platform. Utilize two components of the intelligent fault diagnosis module to achieve accurate identification, rapid diagnosis, and source tracing of communication faults across multiple scenarios. Output fault diagnosis reports and handling instructions to ensure stable operation of communication across multiple scenarios.

[0050] Step 2: Dynamic allocation of transmission resources: Collect data on transmission resource usage, business resource demand, and link load from the platform. Using two components of the dynamic allocation module, combined with the optimized resource allocation calculation formula, the system achieves dynamic allocation, optimized scheduling, and load balancing of transmission resources across multiple scenarios. It outputs resource allocation instructions to improve platform resource utilization and avoid resource waste.

[0051] Step 3: Multi-service priority adaptation: Collect multi-service type data, service priority parameters, and service transmission requirement data from the platform. Utilize two components of the multi-service priority adaptation module to achieve intelligent sorting, dynamic priority adjustment, transmission parameter adaptation, and conflict coordination for multiple scenarios and services. Output service adaptation results to ensure that high-priority services in each scenario are transmitted first.

[0052] 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 fault diagnosis, resource allocation, and service adaptation, and output the full-domain collaborative management and control instructions of the multi-scenario fusion platform. This enables full-domain management and control of faults, resources, and services across multiple scenarios, links, and services, ensuring stable, efficient, low-conflict, and highly adaptable operation of the platform.

[0053] Step 5: Full-process verification and optimization: Verify the accuracy of fault diagnosis, diagnosis time, resource utilization, load balancing, priority sorting accuracy, and business adaptability. Collect business feedback from various scenarios, optimize the parameters and diagnosis, allocation, and adaptation strategies of the three major modules, and achieve continuous optimization and scenario expansion of the platform's automated communication management and control. This meets the needs of strategic emerging industries for multi-scenario integration, high efficiency, stability, and adaptability, as well as the requirements of the invention priority examination policy.

[0054] Modeling Innovation Principles Abandoning the traditional, crude modeling approach of "independent modules and single-level control," this paper constructs an integrated, full-domain modeling logic encompassing "fault diagnosis, resource allocation, service adaptation, and multi-scenario fusion." It uses the multi-service requirements, fault characteristics, resource characteristics, service characteristics, and transmission requirements of the multi-scenario fusion platform as core inputs, overcoming the limitations of traditional communication automation modules being independent, lacking coordination, and insufficient scenario adaptability. The deep integration of these three core modules enables closed-loop management of the entire fault-resource-service process. Fault diagnosis modeling achieves accurate and rapid fault diagnosis and tracing across multiple scenarios. Resource allocation modeling enables dynamic and efficient resource allocation and load balancing across multiple scenarios. Service adaptation modeling achieves priority adaptation and conflict coordination across multiple scenarios and services, filling the gap in multi-scenario fusion communication automation fault-resource-service collaborative full-domain modeling. The modeling process focuses on the multi-scenario fusion, high efficiency, stability, adaptability, and intelligent development needs of strategic emerging industries. It is completely different from the single-module, single-scenario modeling approach and technical direction of existing technologies, and has no overlap with the current modeling logic, representing a completely new modeling direction that complies with the invention priority examination policy.

[0055] Efficiency Enhancement Principle The six core aspects of this invention achieve synergistic efficiency in a multi-scenario converged communication automated management and control platform: Two aspects of fault diagnosis enable accurate and rapid diagnosis and tracing of communication faults across multiple scenarios. Compared to traditional manual troubleshooting, the accuracy, efficiency, and tracing precision of fault diagnosis are significantly improved, completely resolving the problems of delayed and inaccurate fault diagnosis and difficulty in tracing, ensuring stable operation of communication across multiple scenarios; Two aspects of resource allocation enable dynamic and efficient allocation and load balancing of transmission resources across multiple scenarios. Compared to traditional fixed allocation modes, resource utilization, allocation rationality, and load balancing are significantly improved, completely resolving the problems of rigid and wasteful resource allocation and uneven load distribution, improving the platform's resource utilization efficiency; Two aspects of service adaptation enable multi-scenario and multi-industry... Compared to the traditional disordered transmission mode, the priority matching and conflict coordination of services significantly improves the accuracy of priority sorting and service compatibility, and significantly reduces the conflict rate, completely solving the problems of poor multi-service compatibility and frequent conflicts, and ensuring the priority transmission of high-priority services. The six features work synergistically with the three major modules to achieve full-domain optimization of the multi-scenario converged communication automation management and control platform, which is characterized by "high stability, high efficiency, high adaptability, and low conflict". Compared with existing technologies, the level of communication automation management and control has achieved a qualitative leap, fully meeting the high efficiency, stability, adaptability, and intelligence requirements of multi-scenario converged communication in strategic emerging industries, and conforming to the requirements of the invention priority examination policy of "promoting industrial transformation and upgrading, enhancing the core competitiveness of industries, and optimizing resource allocation".

[0056] Existing communication automation methods suffer from problems such as independent and singular modules, lack of collaborative management and control, and poor scenario adaptability. They lack intelligent fault diagnosis, fault source location, dynamic resource allocation optimization, resource load balancing, multi-service priority ranking, and service transmission adaptation. Multi-scenario integrated communication suffers from lagging fault diagnosis, serious resource waste, and frequent multi-service conflicts, making it difficult to meet the high stability, high efficiency, high adaptability, and low conflict requirements of a multi-scenario integrated communication automation management platform. This embodiment, through three core modules and six core integrated innovations, achieves intelligent fault diagnosis and dynamic resource allocation in communication automation, completely solving the pain points of existing technologies. It significantly improves the operational stability, resource utilization, and multi-service adaptability 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 in Strategic Emerging Industries (2021 Edition)" and the requirements of the invention priority examination policy. It can be widely applied to multiple communication automation scenarios such as industrial internet, emergency communication, and enterprise-level communication.

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

Claims

1. A method for intelligent fault diagnosis and dynamic resource allocation of communication automation, characterized in that, Includes the following steps: S1: Intelligent fault diagnosis and processing, collects communication link fault data, equipment operating status data and fault characteristic data, and achieves accurate identification, rapid diagnosis and source location of communication faults through intelligent fault accurate diagnosis and fault source location, and outputs fault diagnosis reports and handling instructions; S2: Dynamic allocation and processing of transmission resources. It collects transmission resource occupancy data, service resource demand data and link load data. Through dynamic resource allocation optimization and resource load balancing, it realizes dynamic allocation, load balancing and optimized scheduling of transmission resources, and outputs resource allocation instructions. S3: Multi-service priority adaptation processing, collects multi-service type data, service priority parameters and transmission requirement data, and realizes intelligent priority sorting, transmission parameter adaptation and conflict coordination of multi-services through multi-service priority sorting and service transmission adaptation, forming a closed loop of communication automation full-process fault-resource-service collaborative management and control. Wherein, the resource dynamic allocation optimization in step S2 contains resource allocation accounting formula, the formula is , the constraint condition is , is the optimal resource allocation amount, is the jth type of service resource demand quantization value, is the jth type of service resource allocation weight ( ), is the average link load rate, is the resource redundancy allocation correction value, is the total available transmission resource amount, which is set according to the resource configuration of the communication system.

2. The method of claim 1, wherein, The intelligent fault accurate diagnosis in step S1 includes the following sub-steps: extracting fault features, equipment operating status features and link status features, constructing an intelligent fault diagnosis modeling system, and realizing accurate fault identification, type determination and rapid diagnosis.

3. The method of claim 1, wherein, The fault tracing and location in step S1 is based on fault data, equipment operation data and link status data to build a fault tracing model, so as to achieve accurate fault tracing, location and cause analysis.

4. The method of claim 1, wherein, The resource dynamic allocation optimization in step S2 involves extracting service resource demand characteristics, link load characteristics, and resource occupancy characteristics to construct a resource allocation modeling system, thereby achieving dynamic allocation, optimized scheduling, and efficient utilization of transmission resources.

5. The method according to claim 1, characterized in that, The resource load balancing in step S2 is based on resource occupancy status and link load data to build a load balancing model, realize resource load balancing of multiple links and multiple devices, and avoid resource overload and waste.

6. The method according to claim 1, characterized in that, In step S3, the multi-service priority sorting extracts service type features, service urgency features, and transmission requirement features to construct a priority sorting model, thereby realizing intelligent sorting and dynamic priority adjustment of multiple services.

7. The method according to claim 1, characterized in that, The service transmission adaptation in step S3 involves constructing a service adaptation model based on service priority and transmission requirement data to achieve dynamic adaptation of service transmission parameters and coordination of multi-service conflicts.

8. The method according to claim 1, characterized in that, Total available transmission resources The resource allocation weights for industrial control services can be flexibly adjusted according to the communication system configuration. Emergency communication services Ordinary data transmission services Resource allocation weights can be dynamically adjusted based on the urgency of the business.

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 communication, smart terminal communication, emergency communication, and enterprise-level communication, to achieve intelligent fault diagnosis, dynamic allocation of transmission resources, and multi-service priority adaptation.

10. A communication-automated intelligent fault diagnosis and dynamic resource allocation system, characterized in that, include: Intelligent fault diagnosis module, dynamic allocation module for transmission resources, multi-service priority adaptation module, multi-source data acquisition module, and collaborative management and control engine module; The fault diagnosis module executes the method of claims 1-2, the resource allocation module executes the method of claims 4-5, and the service adaptation module executes the method of claims 6-7. Each module achieves real-time data interaction through a high-speed communication link, thereby completing intelligent fault diagnosis and dynamic resource allocation with communication automation.

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