A low-latency transmission and heterogeneous network collaborative management system and method for communication automation

Abstract

This invention belongs to the field of communication automation technology and discloses a low-latency transmission and heterogeneous network collaborative management system and method for communication automation. The invention proposes six unique innovations by constructing three core modules: low-latency transmission optimization, adaptive energy consumption management, and heterogeneous network collaborative adaptation, covering the entire process of communication automation from transmission latency optimization and energy consumption management to heterogeneous network adaptation. By employing core technologies such as low-latency transmission scheduling, adaptive energy consumption adjustment, and heterogeneous network collaborative adaptation, it overcomes the industry bottlenecks of traditional communication automation, including "high transmission latency, crude energy consumption management, and poor heterogeneous network adaptability." It achieves low-latency optimization of communication transmission, adaptive and precise energy consumption management, and efficient collaborative adaptation of heterogeneous networks, significantly improving the transmission efficiency, energy economy, and heterogeneous network adaptability of communication automation. It is suitable for various scenarios such as industrial control communication, vehicle-to-everything (V2X) communication, and edge computing communication, and particularly solves the technical problems of excessively high transmission latency, serious energy waste, and poor heterogeneous network collaboration.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a low-latency transmission and heterogeneous network collaborative management 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 industrial control, vehicle networking, and edge computing, communication demands exhibit the core characteristics of "low latency, low energy consumption, and heterogeneity." The requirements for communication transmission latency, energy consumption management, and heterogeneous network collaboration are becoming increasingly stringent. However, existing technologies still face specific and unresolved practical problems in the three sub-scenarios of "low-latency transmission, energy consumption management, and heterogeneous network 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. High transmission latency and lack of latency scheduling and prediction optimization: Existing communication transmission mostly adopts the "fixed link transmission + passive latency adjustment" mode, which lacks low latency transmission scheduling and latency prediction optimization mechanism. This results in large transmission latency fluctuations and overall high latency, which cannot accurately match the low latency service requirements of industrial control, vehicle networking and other applications. It is easy to cause problems such as delayed service response and data transmission lag, which affects the transmission efficiency of communication automation.

[0003] 2. Inefficient energy management with no adaptive adjustment or optimized allocation: Existing communication equipment energy management mostly adopts the "fixed power operation + manual adjustment" mode, lacking adaptive energy adjustment and an optimized energy allocation mechanism; this leads to serious energy waste and low energy efficiency, and makes it impossible to dynamically adjust energy consumption according to link load and service needs, which does not meet the requirements of green and low-carbon development and also increases the operating cost of communication automation.

[0004] 3. Poor adaptability of heterogeneous networks, lack of collaborative adaptation and interface matching: Existing heterogeneous network (such as 5G, WiFi, industrial Ethernet) collaboration mostly adopts the "independent operation + manual adaptation" mode, lacking heterogeneous network collaborative adaptation and dynamic network interface matching mechanism; resulting in poor data communication between heterogeneous networks, low collaborative efficiency, poor interface adaptability, inability to achieve efficient collaborative transmission of heterogeneous networks, and difficulty in meeting the automation needs of heterogeneous communication in multiple scenarios.

[0005] Existing methods for communication automation lack core innovations in "low-latency transmission optimization, adaptive energy consumption management, and heterogeneous network collaborative adaptation," particularly in areas such as latency scheduling modeling, energy consumption optimization solutions, and heterogeneous network collaborative modeling, 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 information technology services), proposing an innovation-driven method for low-latency transmission and heterogeneous network collaborative management. This fills existing technological gaps, facilitates the upgrade of communication automation towards "low latency, low energy consumption, and high collaboration," and meets the requirements of the invention priority examination policy. Summary of the Invention

[0006] Addressing the three specific problems raised in the background art, the present invention aims to provide a low-latency transmission and heterogeneous network collaborative management system and method for communication automation. This system achieves low-latency optimization of communication transmission, adaptive and precise control of energy consumption, and efficient collaborative adaptation of heterogeneous networks. It solves the problems of high transmission latency, inefficient energy consumption control, and poor heterogeneous network adaptability. The entire process emphasizes innovation and modeling solutions, without involving rules for intellectual activities. It improves the transmission efficiency, energy economy, and heterogeneous network adaptability of communication automation, further perfecting the communication automation technology system for low-latency, low-energy, and highly collaborative scenarios. This aligns with the development direction of strategic emerging industries and the requirements of the invention priority examination policy.

[0007] The present invention is implemented through the following specific technical solution: (a) Low-latency transmission optimization module This module is designed to achieve low-latency scheduling, latency prediction, and dynamic optimization of communication transmission. It constructs a low-latency transmission optimization modeling system to solve problems such as high transmission latency, large latency fluctuations, and inability to match the needs of low-latency services. This improves the real-time performance and efficiency of communication transmission, provides technical support for automated low-latency transmission, 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 "fixed link, passive adjustment," we construct an integrated modeling logic of "transmission data acquisition - link state modeling - latency characteristic modeling - scheduling optimization modeling - latency prediction modeling - verification optimization modeling." Combining transmission link state characteristics (bandwidth, latency, bit error rate), service latency requirement characteristics (latency threshold, latency sensitivity), and transmission data characteristics (data volume, transmission priority), we establish link state models, latency characteristic models, scheduling optimization models, latency prediction models, and verification optimization models. We design low-latency transmission scheduling and transmission latency prediction optimization to achieve low-latency transmission optimization.

[0009] First, deploy transmission monitoring and data acquisition components to collect communication transmission latency data (real-time latency, latency fluctuation), service latency requirement data (latency threshold, sensitivity), and transmission link status data (bandwidth, bit error rate), constructing a low-latency transmission optimization data resource pool. Then, design low-latency transmission scheduling, extracting the core characteristics of link status, service latency requirements, and transmission data to build a scheduling optimization model. Combined with latency optimization calculation formulas, this enables dynamic selection of transmission links, orderly scheduling of data transmission, and latency optimization, reducing transmission latency. Next, design transmission latency prediction optimization, building a latency prediction model based on transmission latency data and link status data to achieve accurate transmission latency prediction, latency fluctuation early warning, and dynamic optimization adjustment, avoiding sudden latency changes. Finally, construct a verification optimization model to quantify transmission latency, latency fluctuation, and transmission efficiency, dynamically optimizing parameters and scheduling strategies to ensure the effectiveness of low-latency transmission optimization.

[0010] 1: Low-latency transmission scheduling To address the issues of "high transmission latency and large latency fluctuations" in existing technologies, an integrated model is constructed that combines link status, service latency requirements, and transmission data modeling. Combined with latency optimization calculation formulas, this model enables dynamic selection of transmission links and orderly data scheduling, solving the core pain points of low-latency transmission and filling the technological gap in intelligent scheduling of low-latency transmission in communication automation.

[0011] 2: Optimization of transmission delay prediction To address the problems of "unpredictable latency and difficulty in dealing with sudden latency changes" in existing technologies, an integrated model of transmission latency, link status, and latency prediction is constructed to achieve accurate prediction, fluctuation warning, and dynamic optimization of transmission latency. This solves the problem of passive latency management and fills the technological gap in accurate prediction and optimization of transmission latency in communication automation.

[0012] (II) Energy Consumption Adaptive Control Module The core of this module is to realize the adaptive adjustment of energy consumption of communication equipment and the dynamic optimization and allocation of energy consumption. It constructs an adaptive energy consumption management and control modeling system to solve the problems of extensive energy consumption management, serious energy waste, and low energy efficiency. It improves the energy economy and green and low-carbon level of communication automation, provides technical support for the low-energy operation of communication automation, and meets the development needs of strategic emerging industries such as industrial internet and information technology services.

[0013] Modeling Approach: Abandoning the traditional extensive modeling approach of "fixed power and manual adjustment," we construct an integrated modeling logic of "energy consumption data acquisition - equipment energy consumption modeling - load characteristic modeling - energy consumption adjustment modeling - energy consumption allocation modeling - verification and optimization modeling." Combining the energy consumption characteristics of communication equipment (power, energy efficiency), link load characteristics (load rate, data throughput), and service energy consumption demand characteristics (energy consumption threshold, energy saving requirements), we establish equipment energy consumption models, load characteristic models, energy consumption adjustment models, energy consumption allocation models, and verification and optimization models. We design adaptive energy consumption adjustment and optimized energy consumption allocation to achieve adaptive energy consumption management and control.

[0014] First, deploy energy consumption monitoring and data acquisition components to collect energy consumption data (power, energy consumption), link load data (load rate, throughput), and service energy consumption demand data (energy consumption threshold, energy saving requirements) from communication equipment, and build an energy consumption adaptive management and control data resource pool. Then, design an energy consumption adaptive adjustment model, extracting the core characteristics of equipment energy consumption, link load, and service energy consumption demand to achieve real-time adjustment of equipment energy consumption and dynamic power adaptation, suppressing energy waste. Next, design an energy consumption optimization allocation model based on equipment energy consumption status and link load data to achieve dynamic optimization allocation of energy consumption across multiple devices and links, balancing energy economy and transmission stability. Finally, build a verification optimization model to quantify energy efficiency, energy waste rate, and transmission stability, dynamically optimizing parameters and management strategies to ensure the effectiveness of energy consumption adaptive management and control.

[0015] 3: Adaptive Energy Consumption Adjustment To address the problems of "extensive energy consumption management and serious energy waste" in existing technologies, an integrated model is constructed to model equipment energy consumption, link load and service energy consumption requirements. This model enables real-time adaptive adjustment of equipment energy consumption and dynamic power adaptation, solving the core pain point of energy waste and filling the technological gap in adaptive and precise energy consumption adjustment in communication automation.

[0016] 4: Optimized energy consumption allocation To address the issues of "unreasonable energy consumption allocation and low energy efficiency" in existing technologies, an integrated model of device energy consumption status, link load, and energy consumption allocation is constructed to achieve dynamic optimization of energy consumption allocation across multiple devices and links, thereby solving the problem of low energy efficiency and filling the technological gap in multi-device energy consumption optimization allocation in communication automation.

[0017] (III) Heterogeneous Network Collaborative Adaptation Module This module enables efficient collaboration of heterogeneous networks, dynamic interface matching and transmission adaptation. It constructs a heterogeneous network collaboration and adaptation modeling system to solve problems such as poor heterogeneous network adaptability, low collaboration efficiency and poor interface adaptation. It improves the heterogeneous network collaboration capability and scenario adaptability of communication automation, provides technical support for heterogeneous transmission in multiple scenarios of communication automation, and meets the development needs of strategic emerging industries such as new mobile communication networks and information technology services.

[0018] Modeling Approach: Abandoning the traditional extensive modeling approach of "independent operation and manual adaptation," we construct an integrated modeling logic of "heterogeneous network acquisition - network state modeling - interface feature modeling - collaborative adaptation modeling - interface matching modeling - verification and optimization modeling." Combining heterogeneous network state characteristics (bandwidth, latency, stability), network interface adaptation characteristics (interface parameters, transmission protocols), and service transmission requirement characteristics (transmission rate, latency requirements), we establish network state models, interface feature models, collaborative adaptation models, interface matching models, and verification and optimization models. We design heterogeneous network collaborative adaptation and dynamic network interface matching to achieve heterogeneous network collaborative adaptation.

[0019] First, deploy heterogeneous network monitoring and data acquisition components to collect heterogeneous network status data (bandwidth, latency, stability), network interface adaptation data (interface parameters, protocol type), and service transmission requirement data (transmission rate, latency requirements), thus constructing a heterogeneous network collaborative adaptation data resource pool. Then, design heterogeneous network collaborative adaptation, extracting the core features of heterogeneous network status, interface adaptation, and service transmission requirements to build a collaborative adaptation model. This enables collaborative scheduling, data interoperability, and transmission adaptation of heterogeneous networks, improving collaborative efficiency. Next, design dynamic network interface matching, building an interface matching model based on heterogeneous network interface parameters and transmission requirement data. This achieves dynamic matching of network interfaces and optimization of adaptation parameters, ensuring smooth data transmission. Finally, construct a verification and optimization model to quantify collaborative efficiency, interface adaptability, and transmission smoothness, dynamically optimizing parameters and adaptation strategies to ensure the effectiveness of heterogeneous network collaborative adaptation.

[0020] 5: Heterogeneous network collaborative adaptation To address the issues of low efficiency in heterogeneous network collaboration and poor data interoperability in existing technologies, an integrated model is constructed to model the heterogeneous network status, interface adaptation, and service transmission requirements. This model enables efficient collaboration and transmission adaptation of heterogeneous networks, solves the problem of poor heterogeneous network collaboration, and fills the technological gap in intelligent collaborative adaptation of heterogeneous networks in communication automation.

[0021] 6: Dynamic matching of network interfaces To address the issues of poor compatibility and transmission lag in existing technologies, an integrated model is constructed that combines heterogeneous network interface parameters, transmission requirements, and interface matching. This model enables dynamic matching and optimization of network interface adaptation parameters, resolving the problem of poor interface compatibility and filling the technological gap in dynamic matching of heterogeneous network interfaces in communication automation.

[0022] Beneficial effects 1. Low-latency transmission scheduling: Abandoning the extensive approach of fixed links, it constructs an integrated system of link status, service latency requirements and transmission data modeling. Combined with latency optimization calculation formula, transmission latency and latency fluctuation are significantly reduced, completely solving the problems of high transmission latency and large latency fluctuation. It focuses on innovation in precise control of low-latency transmission, which meets the development needs of strategic emerging industries such as industrial internet and new mobile communication networks. 2. Transmission delay prediction optimization: Construct an integrated system of transmission delay, link status and delay prediction, which significantly improves the accuracy of delay prediction and the timeliness of delay fluctuation warning, completely solves the problems of unpredictable delay and difficulty in dealing with sudden delay changes, and fills the technical gap in accurate transmission delay prediction optimization; 3. Adaptive Energy Consumption Adjustment: Construct an integrated system for modeling equipment energy consumption, link load, and business energy consumption requirements. This significantly reduces equipment energy waste rate and significantly improves energy efficiency, thoroughly solving the problems of extensive energy consumption management and serious energy waste. It focuses on green and low-carbon energy consumption management innovation, which meets the development needs of strategic emerging industries in information technology services. 4. Energy consumption optimization and allocation: Construct an integrated system of equipment energy consumption status, link load and energy consumption allocation, which significantly improves the rationality and efficiency of energy consumption allocation, completely solves the problems of unreasonable energy consumption allocation and low energy consumption efficiency, and fills the technical gap in multi-device energy consumption optimization and allocation; 5. Heterogeneous Network Collaborative Adaptation: Construct an integrated system for modeling heterogeneous network status, interface adaptation, and service transmission requirements. This significantly improves heterogeneous network collaboration efficiency and data interoperability, completely solving the problems of low heterogeneous network collaboration efficiency and poor data interoperability. It focuses on efficient collaborative innovation in heterogeneous networks, meeting the development needs of strategic emerging industries in new mobile communication networks. 6. Dynamic matching of network interfaces: Construct an integrated system for matching heterogeneous network interface parameters, transmission requirements and interfaces, significantly improving interface adaptability and transmission smoothness, completely solving the problems of poor adaptability and transmission lag of heterogeneous network interfaces, filling the technical gap in dynamic matching of heterogeneous network interfaces, and meeting the requirements of the invention priority examination policy. Attached Figure Description

[0023] Figure 1 Low-latency transmission optimization module workflow diagram Figure 2Detailed Implementation of the Energy Consumption Adaptive Management Module Workflow Diagram Detailed Implementation

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

[0025] Example 1: Low-latency communication scenario for industrial control Implementation steps Step 1: Data Acquisition and Parameter Setting: Collect transmission latency data, service latency requirement data, transmission link status data, equipment energy consumption data, heterogeneous network status data, and interface adaptation data for low-latency communication scenarios in industrial control. Set the maximum allowable latency threshold for industrial control services. Set latency weights , , ( Set the redundancy delay correction value. .

[0026] Step 2: Low-Latency Transmission Optimization: Employing low-latency transmission scheduling and transmission latency prediction optimization, core features of link status, service latency requirements, and transmitted data are extracted to construct a scheduling optimization and latency prediction model. This is achieved through the latency optimization calculation formula for low-latency transmission scheduling. It enables dynamic selection of transmission links, orderly scheduling of data and optimization of latency, accurate prediction of transmission latency and early warning of fluctuations, and ensures low-latency transmission of industrial control services.

[0027] Step 3: Adaptive Energy Consumption Management: Adaptive energy consumption adjustment and optimized energy consumption allocation are adopted to extract the core characteristics of equipment energy consumption, link load and service energy consumption requirements, construct an energy consumption adjustment and allocation model, realize real-time adjustment of equipment energy consumption and optimized allocation of energy consumption of multiple devices, suppress energy waste, improve energy efficiency, and ensure low-energy operation of industrial control communication.

[0028] Step 4: Heterogeneous Network Collaborative Adaptation: By adopting heterogeneous network collaborative adaptation and dynamic network interface matching, the core features of heterogeneous network status, interface adaptation and service transmission requirements are extracted, and a collaborative adaptation and interface matching model is constructed to achieve efficient collaboration and dynamic interface matching of heterogeneous networks (industrial Ethernet, 5G private network) in industrial control scenarios, ensuring smooth data interoperability.

[0029] Step 5: Full-process verification and optimization: Verify transmission latency, latency fluctuation, energy efficiency, collaboration efficiency, and interface compatibility to ensure that the requirements of low latency, low energy consumption, and high collaboration in industrial control are met; collect feedback data from each module, optimize parameters and scheduling, control, and adaptation strategies, and improve the adaptability to industrial control communication scenarios.

[0030] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed links, fixed power, and manual adaptation," this paper constructs an integrated closed-loop model of "low-latency transmission, energy consumption management, and heterogeneous collaboration." It takes the low latency, low energy consumption, and high reliability requirements, link characteristics, energy consumption characteristics, and heterogeneous network characteristics of industrial control low-latency communication as core inputs, overcoming the limitations of high transmission latency, extensive energy consumption management, and poor heterogeneous collaboration. Low-latency transmission modeling achieves accurate optimization and prediction of transmission latency; energy consumption management modeling achieves adaptive adjustment and optimized allocation of energy consumption; and heterogeneous collaboration modeling achieves efficient collaboration and interface adaptation of heterogeneous networks, filling the gap in integrated modeling of low latency, energy consumption, and heterogeneous collaboration in industrial control communication. The modeling process focuses on the scenario requirements of low latency, high reliability, and low energy consumption in industrial control communication, completely different from the single-module, extensive modeling approach and technical direction of existing technologies. It has no overlap with the modeling logic of current open documents, representing a completely new modeling direction that 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 Low-latency transmission scheduling, through link status, service latency requirements, and transmission data modeling, and latency optimization calculation formulas, significantly reduces transmission latency and latency fluctuations compared to traditional fixed-link transmission modes, completely solving the problems of high transmission latency and large latency fluctuations, providing core support for low-latency transmission in industrial control services; Transmission latency prediction optimization, through transmission latency, link status, and latency prediction modeling, and fluctuation early warning strategies, significantly improves the accuracy of latency prediction and the timeliness of fluctuation early warnings compared to traditional passive latency adjustment modes, completely solving the problems of unpredictable latency and difficulty in handling sudden latency changes; Adaptive energy consumption adjustment, through equipment energy consumption, link load, and service energy consumption requirements modeling, and real-time adjustment strategies, significantly reduces equipment energy waste and significantly improves energy efficiency compared to traditional fixed-power operation modes, completely solving the problems of extensive energy consumption management and serious energy waste; Optimized energy consumption allocation, through equipment energy consumption status, link load, and energy consumption allocation modeling, and dynamic allocation strategies, significantly improves energy efficiency compared to traditional uniform energy consumption allocation models. The system significantly improves the rationality and efficiency of energy consumption allocation, completely solving the problems of unreasonable energy consumption allocation and low energy efficiency. Heterogeneous network collaborative adaptation, through modeling of heterogeneous network status, interface adaptation, and service transmission requirements, and collaborative scheduling strategies, significantly improves heterogeneous network collaborative efficiency and data interoperability compared to the traditional independent operation mode, completely solving the problems of low heterogeneous network collaborative efficiency and poor data interoperability. Dynamic network interface matching, through modeling of heterogeneous network interface parameters, transmission requirements, and interface matching, and parameter optimization strategies, significantly improves interface adaptability and transmission smoothness compared to the traditional manual interface adaptation mode, completely solving the problems of poor interface adaptation and transmission lag. These six synergistic effects enable industrial control communication to achieve "low latency, low energy consumption, and high collaboration." Compared to existing technologies, transmission efficiency, energy economy, and heterogeneous collaborative capabilities are qualitatively improved, fully meeting the high requirements of industrial control communication and conforming to the requirements of the invention priority examination policy of "enhancing the core competitiveness of industries and promoting green and low-carbon development."

[0032] Existing technologies employ a "fixed link transmission + fixed power operation + manual adaptation" model, lacking low-latency transmission scheduling, transmission latency prediction optimization, adaptive energy consumption adjustment, optimized energy consumption allocation, heterogeneous network collaborative adaptation, and dynamic network interface matching. This results in high transmission latency, significant energy waste, and poor heterogeneous network collaboration, failing to meet the low-latency, low-energy, and high-reliability requirements of industrial control communication scenarios. It is prone to problems such as delayed service response, excessive energy consumption, and data transmission interruptions. This embodiment, through innovation and modeling optimization, achieves low-latency transmission, adaptive energy consumption management, and heterogeneous network collaborative adaptation in industrial control communication, completely resolving the pain points of existing technologies. Transmission latency, energy efficiency, and collaborative efficiency all meet the standards for industrial control communication 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 the invention priority examination policy.

[0033] Example 2: Low-latency and high-reliability communication scenario in vehicle-to-everything (corresponding to low-latency transmission optimization and heterogeneous network collaborative adaptation modules) Implementation steps Step 1: Relevant Data Collection: Deploy monitoring and data collection components to collect transmission latency data, service latency requirement data, transmission link status data, heterogeneous network status data (5G, WiFi6), network interface adaptation data, and service transmission requirement data in the vehicle-to-everything (V2X) communication scenario, and build a low-latency transmission and heterogeneous collaborative data resource pool.

[0034] Step 2: Low-latency transmission optimization: Low-latency transmission scheduling and transmission latency prediction optimization are adopted. The core features of link status, service latency requirements and transmission data are extracted to build a scheduling optimization and latency prediction model. This enables dynamic selection of transmission links, orderly data scheduling and latency optimization, accurate prediction of transmission latency and early warning of fluctuations, and ensures low-latency and high-reliability transmission of vehicle networking services (such as autonomous driving command transmission).

[0035] Step 3: Heterogeneous Network Collaborative Adaptation: By adopting heterogeneous network collaborative adaptation and dynamic network interface matching, the core features of heterogeneous network status, interface adaptation and service transmission requirements are extracted, and a collaborative adaptation and interface matching model is constructed to achieve efficient collaboration and dynamic interface matching between 5G and WiFi 6 heterogeneous networks in the vehicle-to-everything (V2X) scenario, ensuring smooth and stable data communication between vehicles and base stations, and between vehicles.

[0036] Step 4: Full-process collaborative management and control: Integrate the results of low-latency transmission and heterogeneous network collaborative adaptation, output low-latency and high-reliability communication collaborative management and control commands for vehicle-to-everything (V2X) networks, realize the linkage between latency optimization and heterogeneous collaboration, and ensure low latency, high reliability, and high collaboration of V2X communication.

[0037] Step 5: Continuous optimization: Verify transmission latency, latency fluctuation, collaborative efficiency, interface compatibility, and transmission reliability; collect feedback data from vehicle networking scenarios, optimize parameters and scheduling, and adaptation strategies to improve adaptability to vehicle networking communication scenarios and ensure the stable operation of autonomous driving, vehicle collaboration, and other services.

[0038] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed links and manual adaptation," this paper constructs an integrated closed-loop model of "low-latency transmission - heterogeneous collaboration - reliable management and control." It takes the low latency, high reliability, and high collaboration requirements, link characteristics, heterogeneous network characteristics, and transmission requirements of vehicle-to-everything (V2X) communication as core inputs, overcoming limitations such as high transmission latency, poor heterogeneous collaboration, and insufficient transmission reliability. Low-latency transmission modeling achieves accurate optimization and prediction of transmission latency; heterogeneous collaboration modeling achieves efficient collaboration and interface adaptation of heterogeneous networks; and reliable management and control modeling achieves precise assurance of transmission stability, filling the gap in integrated modeling of latency and heterogeneous collaboration in V2X low-latency high-reliability communication. The modeling process focuses on the scenario requirements of autonomous driving and vehicle collaboration in V2X, 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 new mobile communication networks and the strategic emerging industries of the Industrial Internet.

[0039] Efficiency Enhancement Principle Low-latency transmission scheduling, through link status, service latency requirements, and transmission data modeling, and latency optimization calculation formulas, significantly reduces transmission latency and latency fluctuations compared to traditional fixed-link transmission modes, completely solving the problems of high transmission latency and large latency fluctuations, ensuring low-latency transmission in the vehicle-to-everything (V2X) network; transmission latency prediction optimization, through transmission latency, link status, and latency prediction modeling, and fluctuation early warning strategies, significantly improves the accuracy of latency prediction and the timeliness of fluctuation early warnings compared to traditional passive latency adjustment modes, completely solving the problems of unpredictable latency and difficulty in handling sudden latency changes, ensuring transmission reliability; heterogeneous network collaborative adaptation, through heterogeneous network status, interface adaptation, and service transmission requirements modeling... The collaborative scheduling strategy significantly improves the collaborative efficiency and data interoperability of heterogeneous networks compared to the traditional independent operation mode, completely solving the problems of low collaborative efficiency and poor data interoperability of heterogeneous networks. Dynamic network interface matching, through heterogeneous network interface parameters, transmission requirements, and interface matching modeling and parameter optimization strategies, significantly improves interface adaptability and transmission smoothness compared to the traditional manual interface adaptation mode, completely solving the problems of poor interface adaptation and transmission lag. These four collaborative effects enable vehicle-to-everything (V2X) communication to achieve "low latency, high reliability, and high collaboration." Compared to existing technologies, transmission efficiency, transmission reliability, and heterogeneous collaborative capabilities are qualitatively improved, fully meeting the low-latency and high-reliability communication requirements of V2X.

[0040] Existing technologies employ a "fixed link transmission + manual adaptation" model, lacking low-latency transmission scheduling, transmission latency prediction optimization, heterogeneous network collaborative adaptation, and dynamic network interface matching. This results in high transmission latency, poor heterogeneous network collaboration, and insufficient transmission reliability, failing to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) autonomous driving and vehicle collaboration. It is also prone to security risks such as delayed command transmission and data interoperability interruptions. This embodiment, through innovation and modeling optimization, achieves low-latency transmission and heterogeneous network collaborative adaptation in V2X communication, completely resolving the pain points of existing technologies. Transmission latency, transmission reliability, and collaborative efficiency all meet V2X 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.

[0041] Example 3: Low-power communication scenario for edge computing (corresponding to low-latency transmission optimization and adaptive power consumption management module) Implementation steps Step 1: Data Acquisition and Scenario Adaptation: Collect transmission latency data, service latency requirement data, transmission link status data, device energy consumption data, link load data, and service energy consumption requirement data for low-energy communication scenarios in edge computing, adapt to the "low latency, low energy consumption, and high efficiency" requirements of edge computing, and build a low-latency transmission and energy consumption management data resource pool.

[0042] Step 2: Low-latency transmission optimization: Low-latency transmission scheduling and transmission latency prediction optimization are adopted. The core features of link status, service latency requirements and transmission data are extracted to build a scheduling optimization and latency prediction model. This enables dynamic selection of transmission links, orderly data scheduling and latency optimization, accurate prediction of transmission latency and early warning of fluctuations, ensuring low-latency transmission of edge computing data and improving computing efficiency.

[0043] Step 3: Adaptive Energy Consumption Management: Adaptive energy consumption adjustment and optimized energy consumption allocation are adopted to extract the core characteristics of device energy consumption, link load and service energy consumption requirements, and construct an energy consumption adjustment and allocation model to realize real-time adjustment of edge computing device energy consumption and optimized allocation of energy consumption of multiple devices, suppress energy waste, improve energy efficiency, reduce edge computing operating costs, and meet the requirements of green and low-carbon development.

[0044] Step 4: Latency and Energy Consumption Co-optimization: Integrate the results of low-latency transmission and adaptive energy consumption management to achieve co-optimization of transmission latency and energy consumption, balance the requirements of low latency and low energy consumption, and ensure the efficient and energy-saving operation of edge computing communication.

[0045] Step 5: Continuous optimization: Verify transmission latency, latency fluctuation, energy efficiency, and transmission stability; collect data on changes in demand in edge computing scenarios, optimize parameters and scheduling and management strategies, and improve adaptability to low-energy communication scenarios in edge computing.

[0046] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "fixed link, fixed power," this paper constructs an integrated closed-loop model of "low-latency transmission - energy consumption management - collaborative optimization." It takes the low latency, low energy consumption, and high efficiency requirements, link characteristics, energy consumption characteristics, and computational requirements of edge computing's low-energy communication as core inputs, overcoming limitations such as high transmission latency, extensive energy consumption management, and the inability to coordinate latency and energy consumption. Low-latency transmission modeling achieves accurate optimization and prediction of transmission latency; energy consumption management modeling achieves adaptive adjustment and optimized allocation of energy consumption; and collaborative optimization modeling achieves balanced optimization of latency and energy consumption, filling the gap in integrated latency-energy consumption collaborative modeling for edge computing's low-energy communication. The modeling process focuses on the high-efficiency, low-energy consumption scenario requirements of edge computing, completely different from the single-module, extensive modeling approach and technical direction of existing technologies, and has no overlap with the current modeling logic. It represents a completely new modeling direction, aligning with the development needs of the Industrial Internet and information technology services for strategic emerging industries.

[0047] Efficiency Enhancement Principle Low-latency transmission scheduling, through link status, service latency requirements, and transmission data modeling, and latency optimization calculation formulas, significantly reduces transmission latency and latency fluctuations compared to traditional fixed-link transmission modes, completely solving the problems of high transmission latency and large latency fluctuations, and providing support for low-latency transmission of edge computing data; Transmission latency prediction optimization, through transmission latency, link status, and latency prediction modeling, and fluctuation early warning strategies, significantly improves the accuracy of latency prediction and the timeliness of fluctuation early warnings compared to traditional passive latency adjustment modes, completely solving the problems of unpredictable latency and difficulty in handling sudden latency changes; Adaptive energy consumption adjustment, through modeling equipment energy consumption, link load, and service energy consumption requirements, and real-time adjustment strategies, significantly improves the accuracy of latency prediction and the timeliness of fluctuation early warnings compared to traditional passive latency adjustment modes, and completely solves the problems of unpredictable latency and difficulty in handling sudden latency changes; Compared to the traditional fixed-power operation mode, the energy waste rate of the equipment is significantly reduced and the energy efficiency is significantly improved, completely solving the problems of extensive energy consumption management and serious energy waste. The optimized energy consumption allocation, through modeling of equipment energy consumption status, link load and energy consumption allocation, and dynamic allocation strategies, significantly improves the rationality and efficiency of energy consumption allocation compared to the traditional uniform energy consumption allocation mode, completely solving the problems of unreasonable energy consumption allocation and low energy efficiency. The four synergistic effects realize the coordinated optimization of edge computing communication latency and energy consumption. Compared with the existing technology, the transmission efficiency and energy economy are qualitatively improved, fully meeting the low latency and low energy consumption requirements of edge computing, and complying with the requirements of green and low-carbon development and invention priority examination policies.

[0048] Existing technologies employ a "fixed link transmission + fixed power operation" model, lacking low-latency transmission scheduling, transmission latency prediction optimization, adaptive energy consumption adjustment, and optimized energy allocation. This results in high transmission latency, significant energy waste, and a lack of coordination between latency and energy consumption, failing to meet the low-latency, low-energy, and high-efficiency requirements of edge computing. Consequently, they are prone to low computational efficiency and excessively high operating costs. This embodiment, through innovation and modeling optimization, achieves low-latency transmission and adaptive energy consumption control in edge computing communication, completely resolving the pain points of existing technologies. Both transmission latency and energy efficiency meet edge computing 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.

[0049] Example 4: Multi-scenario converged communication automated management and control platform (industrial + vehicle networking + edge computing) scenario (integrating three core modules) Implementation steps Step 1: Low-latency transmission optimization: Collect transmission latency data, service latency requirement data, and transmission link status data from the multi-scenario fusion platform. Use two components of the low-latency transmission optimization module to achieve low-latency scheduling, latency prediction, and dynamic optimization of communication transmission in multiple scenarios. Output a low-latency transmission optimization scheme to ensure low-latency transmission requirements in multiple scenarios.

[0050] Step 2: Adaptive Energy Consumption Management: Collect platform equipment energy consumption data, service energy consumption demand data, and link load data. Utilize two components of the adaptive energy consumption management module to achieve adaptive adjustment of energy consumption for communication equipment in multiple scenarios and dynamic optimization of energy consumption allocation. Output adaptive energy consumption management commands to improve the platform's energy economy and meet the requirements of green and low-carbon development.

[0051] Step 3: Heterogeneous Network Collaborative Adaptation: Collect heterogeneous network status data, network interface adaptation data, and service transmission requirement data from the platform. Utilize two components of the heterogeneous network collaborative adaptation module, combined with the latency optimization calculation formula for low-latency transmission scheduling, to achieve efficient collaboration of heterogeneous networks across multiple scenarios, dynamic interface matching, and transmission adaptation. Output heterogeneous network collaborative adaptation results to ensure smooth data interoperability across multiple scenarios.

[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 low-latency transmission, energy consumption management and heterogeneous collaboration, and output the full-domain collaborative management and control instructions of the multi-scenario fusion platform. This enables full-domain management and control of latency, energy consumption and collaboration across multiple scenarios, links and heterogeneous networks, ensuring the platform operates stably, efficiently, energy-saving and collaboratively.

[0053] Step 5: Full-process verification and optimization: Verify transmission latency, latency fluctuation, energy efficiency, collaboration efficiency, and interface compatibility; collect business feedback from various scenarios; optimize the parameters and control strategies of the three major modules; realize continuous optimization and scenario expansion of automated communication control of the platform; and meet the needs of strategic emerging industries for multi-scenario integration, high efficiency, energy saving, and collaborative development, 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 "low-latency transmission, energy consumption control, heterogeneous collaboration, and multi-scenario fusion." It uses the multi-service requirements, link characteristics, energy consumption characteristics, heterogeneous network characteristics, and latency requirements of a multi-scenario fusion platform as core inputs, overcoming the limitations of traditional communication automation modules being independent, lacking collaboration, and insufficient scenario adaptability. The deep integration of the three core modules achieves closed-loop control of the entire process of latency, energy consumption, and collaboration. Low-latency transmission modeling optimizes low-latency transmission across multiple scenarios; energy consumption control modeling enables adaptive energy consumption control across multiple scenarios; and heterogeneous collaboration modeling achieves efficient adaptation to heterogeneous networks across multiple scenarios, filling the gap in full-domain modeling of latency, energy consumption, and heterogeneous collaboration in multi-scenario fusion communication automation. The modeling process focuses on the multi-scenario fusion, high efficiency, energy saving, collaboration, 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 meets the requirements of 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 related to low-latency transmission optimize and predict low-latency transmission across multiple scenarios, significantly reducing transmission latency and latency fluctuations compared to traditional single-link transmission modes, effectively guaranteeing low-latency transmission needs across multiple scenarios; two aspects related to energy consumption management enable adaptive adjustment and optimized allocation of energy consumption for communication devices across multiple scenarios, significantly improving energy efficiency and greatly reducing energy waste compared to traditional fixed-power operation modes, effectively guaranteeing the platform's energy economy; and two aspects related to heterogeneous collaboration achieve efficient collaboration of heterogeneous networks across multiple scenarios. Compared to the traditional independent operation mode, the interface adaptation significantly improves collaborative efficiency and interface compatibility, effectively ensuring smooth data exchange across multiple scenarios. The six features work synergistically with the three major modules to achieve full-domain optimization of the multi-scenario integrated communication automation management and control platform, characterized by "low latency, low energy consumption, high collaboration, and high efficiency." Compared to existing technologies, the level of communication automation management and control has achieved a qualitative leap, fully meeting the high efficiency, energy saving, collaboration, and intelligence requirements of multi-scenario integrated communication in strategic emerging industries. This aligns with the requirements of the invention priority examination policy to "promote industrial transformation and upgrading, enhance core industrial competitiveness, and promote green and low-carbon development."

[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 low-latency transmission scheduling, transmission latency prediction and optimization, adaptive energy consumption adjustment, optimized energy consumption allocation, heterogeneous network collaborative adaptation, and dynamic network interface matching. Multi-scenario integrated communication suffers from high transmission latency, significant energy waste, and poor heterogeneous network collaboration, making it difficult to meet the low latency, low energy consumption, high collaboration, and high efficiency requirements of a multi-scenario integrated communication automation management and control platform. This embodiment achieves low-latency transmission and heterogeneous network collaborative management and control in communication automation through three core modules and six core integrated innovations, completely solving the pain points of existing technologies. It significantly improves the transmission efficiency, energy economy, and heterogeneous collaboration capabilities of multi-scenario communication. Furthermore, it has no overlap with existing technologies, current technical directions, or implementation scenarios, highlighting its innovations and strong practicality. It conforms to the relevant scope of the "Guidance Catalogue of Key Products and Services of Strategic Emerging Industries (2021 Edition)" and the requirements of the invention priority examination policy, and can be widely applied to multiple communication automation scenarios such as industrial control, vehicle networking, and edge computing.

[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 low-latency transmission and heterogeneous network collaborative management and control in communication automation, characterized in that, Includes the following steps: S1: Low-latency transmission optimization processing: Collects communication transmission latency data, service latency requirement data and transmission link status data. Through low-latency transmission scheduling and transmission latency prediction optimization, it realizes low-latency scheduling, latency prediction and dynamic optimization of communication transmission, and outputs low-latency transmission optimization scheme. S2: Adaptive energy consumption control processing, collects energy consumption data of communication equipment, energy consumption demand data of services and link load data, and realizes adaptive adjustment of energy consumption and dynamic optimization of energy consumption of communication equipment through adaptive energy consumption adjustment and optimized energy consumption allocation, and outputs adaptive energy consumption control instructions. S3: Heterogeneous network collaborative adaptation processing, collects heterogeneous network status data, network interface adaptation data and service transmission requirement data, and achieves efficient collaboration of heterogeneous networks, dynamic matching of interfaces and transmission adaptation through heterogeneous network collaborative adaptation and dynamic matching of network interfaces, forming a closed loop of latency-energy consumption-heterogeneous collaborative management and control for the entire process of communication automation. The low-latency transmission scheduling in step S1 includes a latency optimization calculation formula, which is as follows: The constraints are , For optimal transmission delay, This is the quantized value of the length of the i-th transmission link segment. The delay weight of the i-th link segment ( ), For average transmission rate, This is the transmission redundancy delay correction value. This is the maximum allowable latency threshold for the business, set according to the business latency requirements.

2. The method according to claim 1, characterized in that, The low-latency transmission scheduling in step S1 includes the following sub-steps: extracting transmission link status characteristics, service latency requirement characteristics, and transmission data characteristics, constructing a low-latency transmission scheduling modeling system, and realizing dynamic selection of transmission links, orderly scheduling of data transmission, and latency optimization.

3. The method according to claim 1, characterized in that, The transmission delay prediction optimization in step S1 is based on transmission delay data and link status data to build a delay prediction model, thereby achieving accurate prediction of transmission delay, early warning of delay fluctuations, and dynamic optimization adjustment.

4. The method according to claim 1, characterized in that, The adaptive energy consumption adjustment in step S2 involves extracting the energy consumption characteristics of communication equipment, link load characteristics, and service energy consumption demand characteristics to construct an adaptive energy consumption modeling system, thereby achieving real-time adjustment of equipment energy consumption, suppression of energy waste, and improvement of energy efficiency.

5. The method according to claim 1, characterized in that, The energy consumption optimization allocation in step S2 is based on the energy consumption status of the devices and the link load data to build an energy consumption allocation model, realize the dynamic optimization allocation of energy consumption of multiple devices and multiple links, and balance energy consumption economy and transmission stability.

6. The method according to claim 1, characterized in that, In step S3, heterogeneous network collaborative adaptation extracts heterogeneous network state features, interface adaptation features, and service transmission requirement features to construct a heterogeneous network collaborative modeling system, thereby realizing collaborative scheduling, data interoperability, and transmission adaptation of heterogeneous networks.

7. The method according to claim 1, characterized in that, The dynamic matching of network interfaces in step S3 involves constructing an interface matching model based on heterogeneous network interface parameters and transmission requirement data, thereby achieving dynamic matching of network interfaces, optimization of adaptation parameters, and ensuring smooth data transmission.

8. The method according to claim 1, characterized in that, The maximum allowable latency threshold for the service It can be flexibly adjusted according to the type of business, such as industrial control business. Vehicle-to-everything (V2X) services Ordinary data transmission services The latency weight can be dynamically adjusted based on the link latency sensitivity.

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 control communication, vehicle-to-everything (V2X) communication, edge computing communication, and smart terminal communication, to achieve low-latency transmission, adaptive energy consumption control, and collaborative adaptation of heterogeneous networks.

10. A low-latency transmission and heterogeneous network collaborative management system for automated communication, characterized in that, include: Low-latency transmission optimization module, energy consumption adaptive management and control module, heterogeneous network collaborative adaptation module, multi-source data acquisition module, and collaborative management and control engine module; The low-latency transmission module performs the functions of claims 1-2, the energy consumption management module performs the functions of claims 4-5, and the heterogeneous network adaptation module performs the functions of claims 6-7. Each module achieves real-time data interaction through a high-speed communication link, thereby completing automated low-latency transmission and collaborative management of heterogeneous networks.

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