A remote operation and maintenance intelligent primary and secondary integrated ring network box automated complete set of equipment

Abstract

This invention discloses a remote operation and maintenance intelligent primary and secondary integrated ring network box automated complete set of equipment, comprising: acquiring the operating status data of the ring network box; classifying and grading the operating status data; performing differentiated transmission processing on the data based on a multi-technology collaborative fusion mechanism to obtain processing results; and executing remote operation and maintenance operations based on the processing results. The differentiated transmission processing of data of different types and priorities based on the multi-technology collaborative fusion mechanism includes: allocating corresponding network resources and quality of service guarantees for data of different priorities through network slicing and QoS optimization technologies; providing multi-link transmission and automatic fault switching capabilities for data through multi-path transmission and network redundancy technologies; and dynamically adjusting compression strategies and transmission timing based on data content and network conditions through data semantic compression and intelligent transmission technologies. This equipment features high performance, high reliability, and high adaptability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network operation and maintenance communication, in particular to a remote operation and maintenance intelligent primary and secondary fusion ring network box automatic complete equipment. BACKGROUND

[0002] As a key device in the power distribution network, the operation state of the ring network box is directly related to the reliability and stability of power supply. The traditional operation and maintenance mode of the ring network box mainly relies on on-site operation of professional technicians, which has problems such as high professional threshold, large personnel demand, and long response time. Especially in remote areas, due to the shortage of professional technicians, the response time of the ring network box fault is too long, which seriously affects the reliability of power supply.

[0003] In recent years, with the development of 5G communication and AR (Augmented Reality) technology, remote operation and maintenance technology has been applied to ring network box maintenance. However, the current technology still faces many challenges: in the environment with weak network coverage in remote areas, the communication stability is difficult to guarantee; in the case of limited network resources, the resource competition problem of different priority services is prominent; the contradiction between big data transmission demand and limited bandwidth is increasingly prominent; in complex business scenarios, the communication reliability is difficult to meet the high standard requirements. These problems seriously restrict the wide application and effect of the ring network box remote operation and maintenance technology.

[0004] Although existing technologies have tried to use network slicing, multi-path transmission or data compression to solve some problems, due to the lack of systematic synergistic mechanism, there is a conflict between resource competition and optimization target among different technologies, which cannot achieve the best effect and is difficult to meet the demand of ring network box remote operation and maintenance in all scenarios and high reliability. Therefore, how to deeply integrate multiple communication technologies to build a high-performance, high-reliability, and highly adaptive ring network box remote operation and maintenance communication system has become a technical problem to be solved. SUMMARY

[0005] The present application at least provides a remote operation and maintenance intelligent primary and secondary fusion ring network box automatic complete equipment. This remote operation and maintenance intelligent primary and secondary fusion ring network box automatic complete equipment has the characteristics of high performance, high reliability, and high adaptability.

[0006] In a first aspect, the present application provides a ring network box remote operation and maintenance communication method, comprising: obtaining operation state data of a ring network box, the operation state data comprising device state data and field image data; classifying and grading the operation state data to obtain data of different types and different priorities; based on a multi-technology collaborative fusion mechanism, performing differential transmission processing on the data of different types and different priorities to obtain a processing result; wherein the multi-technology collaborative fusion mechanism comprises a cross-coordination mechanism of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology; based on the processing result, performing a corresponding ring network box remote operation and maintenance operation; wherein the differential transmission processing of the data of different types and different priorities based on the multi-technology collaborative fusion mechanism comprises: allocating corresponding network resources and quality of service guarantees for data of different priorities through network slicing and QoS optimization technology; providing multi-link transmission and automatic fault switching capability for data through multi-path transmission and network redundancy technology; and dynamically adjusting the compression strategy and transmission timing based on the data content and network status through data semantic compression and intelligent transmission technology.

[0007] In the present application, the multi-technology collaborative fusion mechanism is used to perform differential transmission processing on data of different types and different priorities, realizing deep collaboration and cross-enhancement of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology, and forming a "1+1+1>3" fusion effect. This method effectively solves the problems of weak network coverage in remote areas, resource contention of different priority services, limited bandwidth for large data transmission, and insufficient communication reliability in complex service scenarios, and significantly improves the reliability, real-time performance and adaptability of ring network box remote operation and maintenance.

[0008] According to the first aspect, in a possible implementation, the network slicing and QoS optimization technology comprises: creating three types of network slices, namely control instruction slice, state monitoring slice and routine maintenance slice; setting multiple QoS levels based on data urgency, and configuring different quality of service parameters for data of different levels; and monitoring the resource usage of each slice in real time, and dynamically adjusting the slice resource quota.

[0009] According to the first aspect, in a possible implementation, the multi-path transmission and network redundancy technology comprises: constructing a multi-path communication architecture comprising 5G, 4G, LoRa / NB-IoT and wired networks; selecting the optimal transmission path combination according to the data type and the quality status of each link; providing multi-path redundant transmission for critical data, and removing redundant information at the receiving end; and realizing automatic switching within milliseconds when a link fails, to ensure communication continuity.

[0010] According to the first aspect, in a possible implementation, the data semantic compression and intelligent transmission technology comprises: extracting key semantic information of data based on ring box device characteristics and service knowledge; applying a light compression, moderate compression or high compression strategy according to data type and importance; implementing incremental data transmission, only transmitting state change information; and actively adjusting data sampling frequency and transmission timing based on prediction analysis results.

[0011] According to the first aspect, in a possible implementation, the network slice and QoS optimization technology and the cross coordination mechanism of the multi-path transmission and network redundancy technology comprise: mapping service priorities of network slices to multi-path selection strategies to implement slice-aware path selection; dynamically adjusting multi-path resource allocation according to slice resource conditions to implement resource collaborative allocation; providing multi-link redundancy protection for key slices to enhance slice reliability; and reducing slice congestion risks by distributing the same slice service load through different physical links.

[0012] According to the first aspect, in a possible implementation, the network slice and QoS optimization technology and the cross coordination mechanism of the data semantic compression and intelligent transmission technology comprise: selecting a suitable compression level according to the slice type to which data belongs to implement a slice-aware compression strategy; dynamically adjusting the compression strategy according to slice resource utilization to implement slice resource adaptive compression; selecting the fineness of data feature extraction based on QoS levels to implement QoS-driven feature selection; and avoiding invalid data from occupying slice resources through semantic-level data filtering to maximize resource efficiency.

[0013] According to the first aspect, in a possible implementation, the multi-path transmission and network redundancy technology and the cross coordination mechanism of the data semantic compression and intelligent transmission technology comprise: adjusting the compression strategy according to the real-time conditions of each communication link to implement a link-aware compression strategy; distributing contents of different compression levels according to link characteristics to implement differentiated content distribution; selecting the most suitable transmission path based on data semantic importance to implement content-aware routing; and classifying data features according to importance, and transmitting the features through paths of different reliabilities to implement feature hierarchical transmission.

[0014] According to the first aspect, in a possible implementation, the method further comprises: establishing a unified resource management framework to implement collaborative scheduling of network resources, computing resources and storage resources; implementing multi-dimensional state awareness to simultaneously monitor network slice states, link quality and data characteristics; and executing preset technology linkage strategies according to different service scenarios to implement scenario-based collaborative work.

[0015] According to a first aspect, in a possible implementation, the different service scenarios include: an emergency control scenario: activating a control instruction slice, enabling multi-link redundant transmission, and using lossless compression; a large-scale monitoring scenario: mainly using a state monitoring slice, routing according to data importance, and applying a high proportion of compression; a weak network environment scenario: reducing resources of non-critical slices, activating all available links, and enabling the highest compression level; a security threat scenario: isolating suspicious slices, enabling secure links, and enhancing transmission priority of security-related data.

[0016] According to a second aspect, the application further provides a remote operation and maintenance intelligent primary-secondary fusion ring network box automatic complete equipment, comprising: a data acquisition module configured to acquire operation state data of a ring network box, wherein the operation state data includes device state data and field image data; a data classification and grading module configured to classify and grade the operation state data to obtain data of different types and different priorities; a fusion transmission module configured to perform differential transmission processing on the data of different types and different priorities based on a multi-technology collaborative fusion mechanism to obtain a processing result, wherein the multi-technology collaborative fusion mechanism includes a cross-coordination mechanism of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology; and an operation and maintenance execution module configured to execute a corresponding remote operation and maintenance operation of the ring network box based on the processing result; wherein the fusion transmission module is specifically configured to: allocate corresponding network resources and quality of service guarantees for data of different priorities through network slicing and QoS optimization technology; provide multi-link transmission and automatic fault switching capability for data through multi-path transmission and network redundancy technology; and dynamically adjust compression strategies and transmission opportunities based on data content and network conditions through data semantic compression and intelligent transmission technology.

[0017] According to a second aspect, in a possible implementation, the equipment further comprises: a fusion coordination center configured to coordinate resource requirements and performance targets of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology; the fusion coordination center includes: an inter-technology coordinator configured to coordinate cross-coordination between technologies; a resource scheduler configured to optimize network, computing, and storage resource allocation; a service coordinator configured to adjust system configuration according to service requirements; and a self-learning optimization engine configured to continuously improve system performance.

[0018] According to the second aspect, in a possible implementation, the device comprises a perception and data layer, an edge intelligence layer, a converged communication layer, and a cloud collaboration layer; the perception and data layer comprises an enhanced sensor network, an intelligent image acquisition system, a data preprocessing unit, and an acquisition controller; the edge intelligence layer comprises a high-performance edge computing unit, a semantic compression engine, an edge decision system, a multi-level cache system, and an AR content local rendering module; the converged communication layer comprises a network slice management module, a QoS control engine, a multi-path transmission controller, a link state monitoring system, and a secure communication module; and the cloud collaboration layer comprises a cloud data center, an AR content server, a remote expert collaboration platform, a resource scheduling center, and a business collaboration platform.

[0019] According to the second aspect, in a possible implementation, the device further comprises a network slice management system for dynamically creating and configuring network slices to achieve differentiated quality of service, a multi-path transmission management system for managing multiple communication links to achieve intelligent data distribution and redundant transmission, and a semantic compression and intelligent transmission system for extracting key features of data to achieve efficient compression and intelligent transmission.

[0020] According to the third aspect, the present application further provides an electronic device comprising a processor, a memory, and a bus, wherein the memory stores machine readable instructions executable by the processor, and the processor communicates with the memory through the bus when the electronic device is running, and the machine readable instructions are executed by the processor to perform the ring network box remote operation and maintenance communication method in the first aspect or any possible implementation of the first aspect.

[0021] According to the fourth aspect, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by the processor to perform the ring network box remote operation and maintenance communication method in the first aspect or any possible implementation of the first aspect.

[0022] In summary, the present application has the following advantages:

[0023] The application can keep the core function normal operation in the extreme weak network environment (bandwidth is as low as 0.2 Mbps), adaptively improve 96%, and the communication reliability is improved from 99.9% to 99.9999%; the number of devices supported by a single base station is improved from 800-1200 to 4500-5800, which is improved by 380%-480%, and the 150 Mbps original data stream is compressed to 28 Mbps, which reduces the bandwidth demand by 81%, and the multi-device simultaneous alarm processing capacity is improved by 460%; the end-to-end delay of AR remote guidance is reduced to 30-80 ms, which is lower than the perception threshold, and the annotation accuracy is improved by 62%-80%; the resource utilization efficiency is improved by 5.3 times compared with the traditional method, and the bandwidth utilization rate is 85-95%; the fault processing time is shortened from 2.5 hours to 25 minutes, the remote diagnosis accuracy is improved to 95%, the on-site personnel demand is reduced by 75%, the number of expert support devices is improved by 650%, the monthly traffic cost of each device is saved by 77%, which fully proves that the application has made breakthrough progress in solving the communication problem of ring network box remote operation and maintenance, and provides a foundation for the development of smart grid. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the embodiments will be briefly introduced below. The drawings herein are incorporated into the specification and form a part of the specification, which show the embodiments consistent with the present application, and are used to illustrate the technical solutions of the present application together with the specification. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and other related drawings can also be obtained by those skilled in the art without creative labor.

[0025] Figure 1 A flow chart of a ring network box remote operation and maintenance communication method provided by the present application is shown;

[0026] Figure 2 A whole architecture diagram of a ring network box remote operation and maintenance system provided by the present application is shown;

[0027] Figure 3 A structure schematic diagram of network slice and QoS optimization technology provided by the present application is shown;

[0028] Figure 4 A structure schematic diagram of multi-path transmission and network redundancy technology provided by the present application is shown;

[0029] Figure 5 A structure schematic diagram of data semantic compression and intelligent transmission technology provided by the present application is shown;

[0030] Figure 6 A working principle diagram of the multi-technology collaborative fusion mechanism provided by the application is shown.

[0031] Figure 7 A structural block diagram of the remote operation and maintenance intelligent primary-secondary fusion ring network box automatic complete equipment provided by the application is shown.

[0032] Figure 8 A structural block diagram of an electronic device provided by the application is shown. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described below in detail with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application and are not all the embodiments. The components of the present application described and shown in the drawings can be arranged and disposed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0034] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0035] Figure 1 A flowchart of a ring network box remote operation and maintenance communication method provided by the application is shown. As shown in the figure, Figure 1 the method comprises:

[0036] Step 101, obtaining the running state data of the ring network box, the running state data comprising device state data and field image data.

[0037] In the present embodiment, the running state data of the ring network box is mainly obtained through various sensors and image acquisition systems deployed on the ring network box. The device state data includes but is not limited to electrical parameters (such as voltage, current, power, power factor, etc.), temperature parameters, humidity parameters, mechanical state parameters (such as switch state, mechanical life, etc.), vibration parameters, etc. The field image data is obtained through the deployment of high-definition industrial cameras, infrared thermal imaging cameras and environmental panoramic cameras, etc., including device appearance state images, internal key component images, device operating environment images, etc.

[0038] Step 102, classifying and grading the running state data to obtain data of different types and different priorities.

[0039] In terms of data classification, the system classifies the running state data into multiple types according to the source and content characteristics of the data, such as real-time monitoring data (such as key parameters such as voltage, current, etc.), device state data (such as switch state, temperature, etc.), image and video data, alarm event data, historical statistical data, etc.

[0040] In terms of data classification, the system classifies the running state data into multiple types according to the source and content characteristics of the data, such as real-time monitoring data (such as key parameters such as voltage, current, etc.), device state data (such as switch state, temperature, etc.), image and video data, alarm event data, historical statistical data, etc.

[0041] Highest priority: emergency alarm data, safety-related data;

[0042] High priority: key control instructions, important state parameters;

[0043] Medium priority: ordinary monitoring data, general image data;

[0044] Low priority: historical record data, statistical analysis data.

[0045] The classification and classification of data are the basis for subsequent differentiated transmission processing, ensuring that the system can adopt appropriate transmission strategies according to the different characteristics and importance of data.

[0046] Step 103, based on the multi-technology collaborative fusion mechanism, the different types and different priority data are processed by differentiated transmission, and the processing result is obtained; wherein the multi-technology collaborative fusion mechanism includes the cross matching mechanism of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, data semantic compression and intelligent transmission technology.

[0047] In this embodiment, the multi-technology collaborative fusion mechanism is a systematic solution that deeply integrates three communication technologies, rather than simply stacking technologies. Through the cross matching mechanism and collaborative linkage strategy, it realizes complementary enhancement and collaborative optimization between technologies. Specifically, it includes:

[0048] Through network slicing and QoS optimization technology, different priority data is allocated with corresponding network resources and service quality guarantee; network slicing technology virtually divides 5G network resources into multiple logical networks (slices), providing customized services for different types of businesses. The system creates three types of network slices: control instruction slice (URLLC type, providing low latency and high reliability), state monitoring slice (eMBB type, providing high bandwidth) and routine maintenance slice (mMTC type, supporting a large number of connections). At the same time, based on the emergency level of data, multiple QoS levels are set to configure different service quality parameters for different levels of data, realizing resource allocation and service guarantee.

[0049] Multi-path transmission and network redundancy technology are used to provide multi-link transmission and automatic switching capability for data. Multi-path transmission technology builds a heterogeneous communication architecture including 5G, 4G, LoRa / NB-IoT and wired networks, and realizes intelligent distribution of data among multiple physical links. The system dynamically selects the optimal transmission path combination according to the data type and the quality of each link. For critical data, the system uses multi-path redundant transmission strategy to ensure reliable delivery of data. In the event of link failure, the system can automatically switch within milliseconds to ensure communication continuity.

[0050] Data semantic compression and intelligent transmission technology are used to dynamically adjust the compression strategy and transmission timing based on data content and network conditions. Data semantic compression technology extracts key semantic information of data based on ring box device features and business knowledge, and realizes efficient compression. The system applies a three-level compression strategy according to the data type and importance: light compression (lossless), moderate compression (small loss) and high compression (retaining core semantics). At the same time, the system realizes incremental data transmission, only transmitting state change information, which greatly reduces the data transmission volume. In addition, based on the prediction analysis results, the system can actively adjust the data sampling frequency and transmission timing to further optimize network resource usage.

[0051] The three technologies work together through a complex cross-coordination mechanism to form an organic whole, which specifically includes:

[0052] Cross-coordination mechanisms for network slicing and multipath transmission:

[0053] Slice-aware path selection: The business priority of network slices is mapped to the multi-path selection strategy, and URLLC slice business preferentially acquires the most stable communication link.

[0054] Resource coordination allocation: dynamically adjust multi-path resource allocation according to slice resource conditions, and find alternative resources through multi-path technology when slice resources are tight.

[0055] Link redundancy enhances slice reliability: provides multi-link redundancy protection for critical slices, and realizes physical isolation of slices through different physical networks.

[0056] Path dispersion reduces slice congestion: different physical links share the business load of the same slice to reduce the risk of slice congestion.

[0057] Cross-coordination mechanisms for network slicing and data semantic compression:

[0058] Slice-aware compression level: select the appropriate compression level according to the type of data belonging to the slice, URLLC slice data uses light compression, and mMTC slice data uses high compression.

[0059] Adaptive compression of slice resources: Real-time monitoring of slice resource utilization and dynamic adjustment of compression strategy, increasing compression ratio when slice resources are scarce.

[0060] QoS-driven feature selection: The granularity of data feature extraction is selected based on the QoS level, with higher QoS levels retaining more original features.

[0061] Maximize resource efficiency: Avoid invalid data consuming slice resources through semantic-level data filtering.

[0062] Cross-coordination mechanisms for multipath transmission and data semantic compression:

[0063] Link-aware compression strategy: Adjust the compression strategy according to the real-time status of each communication link, and use a high compression rate for low-quality links.

[0064] Differentiated content distribution: Content with different compression levels is distributed based on link characteristics, and key skeleton information is transmitted through reliable links.

[0065] Content-aware routing: Selects the most suitable transmission path based on the semantic importance of the data, and selects a highly reliable path for core semantic information.

[0066] Feature-based hierarchical transmission: Data features are classified according to their importance and transmitted through paths of varying reliability to ensure the reliable delivery of core information.

[0067] Through this multi-technology collaborative integration mechanism, the system can achieve differentiated transmission processing of data of different types and priorities, greatly improving the reliability, real-time performance, and adaptability of remote operation and maintenance communication for ring network boxes.

[0068] Step 104: Based on the processing results, perform the corresponding remote operation and maintenance of the ring network box.

[0069] In this embodiment, the remote operation and maintenance of the ring network box based on the processing results mainly includes the following types:

[0070] Data monitoring and status display: The system presents the processed ring network box operation status data to the operation and maintenance personnel, including real-time monitoring panel, equipment health status report, abnormal early warning information, etc., to help the operation and maintenance personnel understand the equipment operation status.

[0071] Remote control command execution: Maintenance personnel can send remote control commands through the system, such as switch operations and parameter adjustments. The system ensures that these control commands are accurately delivered and executed through a highly reliable channel.

[0072] AR Remote Guidance: When on-site maintenance is required, remote experts can provide AR augmented reality guidance to on-site personnel through the system, including visualization of maintenance steps, marking of fault points, operation demonstrations, etc., to improve maintenance efficiency and accuracy.

[0073] Fault diagnosis and handling: The system can perform intelligent fault diagnosis based on the collected data, propose handling suggestions, and guide the execution of fault handling operations on-site or remotely.

[0074] Preventive maintenance: Based on historical data analysis and trend prediction, the system proposes preventive maintenance suggestions, schedules preventive maintenance tasks, extends equipment lifespan, and reduces unplanned downtime.

[0075] Through this efficient remote operation and maintenance of ring network boxes, the system significantly improves the operation and maintenance efficiency and fault handling speed of ring network boxes, and reduces operation and maintenance costs.

[0076] It should be noted that the above-mentioned remote operation and maintenance communication method for ring network boxes may also include the following steps: establishing a unified resource management framework to achieve coordinated scheduling of network resources, computing resources and storage resources; implementing multi-dimensional status awareness, while monitoring network slice status, link quality and data characteristics; and executing preset technical linkage strategies according to different business scenarios to achieve scenario-based collaborative work.

[0077] The different business scenarios include: Emergency control scenario: activate control command slices, enable multi-link redundant transmission, and use lossless compression; Large-scale monitoring scenario: mainly use status monitoring slices, route according to data importance, and apply high-ratio compression; Weak network environment scenario: reduce non-critical slice resources, activate all available links, and enable the highest compression level; Security threat scenario: isolate suspicious slices, enable secure links, and enhance the transmission priority of security-related data.

[0078] Through this scenario-based technology linkage strategy, the system can automatically adjust the optimal resource configuration and transmission strategy for different business scenarios, further improving the adaptability and efficiency of remote operation and maintenance communication of ring network boxes.

[0079] Figure 2 This diagram illustrates the overall architecture of a remote operation and maintenance system for ring network enclosures provided by this invention. Figure 2 As shown, the system adopts a layered and collaborative overall architecture, consisting of a perception and data layer, an edge intelligence layer, a converged communication layer, and a cloud collaboration layer, from bottom to top. Each layer collaborates closely through vertical information and control flows. The core of the system is the convergence and coordination center, which is responsible for coordinating the resource requirements and performance goals of various technologies.

[0080] The perception and data layer mainly comprises an enhanced sensor network, an intelligent image acquisition system, a data preprocessing unit, and an acquisition controller, responsible for acquiring the operating status data of the ring network enclosure. The enhanced sensor network integrates multiple types of sensors, supporting on-demand activation and change-driven sampling; the intelligent image acquisition system supports multi-resolution, scene-adaptive image acquisition; the data preprocessing unit incorporates semantic analysis capabilities to achieve preliminary feature extraction; and the acquisition controller coordinates the operation of each sensor and optimizes the data acquisition strategy.

[0081] The edge intelligence layer mainly comprises a high-performance edge computing unit, a semantic compression engine, an edge decision-making system, a multi-level caching system, and an AR content local rendering module, responsible for preliminary processing and intelligent analysis of operational status data. The high-performance edge computing unit uses an industrial-grade multi-core processor to support edge intelligent analysis; the semantic compression engine integrates dedicated data feature extraction and compression algorithms for ring network boxes; the edge decision-making system supports local preliminary fault diagnosis and emergency response; the multi-level caching system implements hierarchical caching and storage management based on data importance; and the AR content local rendering module supports local generation of basic AR content, reducing network transmission burden.

[0082] The converged communication layer is the core layer of the system, mainly comprising a network slice management module, a QoS control engine, a multipath transmission controller, a link status monitoring system, and a secure communication module. It is responsible for implementing a multi-technology collaborative communication mechanism. The network slice management module handles slice negotiation and resource management with the 5G core network; the QoS control engine prioritizes and shapes data flows; the multipath transmission controller manages various communication links, enabling intelligent data distribution; the link status monitoring system evaluates the quality of each communication link in real time and supports predictive analysis; and the secure communication module provides end-to-end encryption and authentication functions.

[0083] The cloud-based collaboration layer mainly comprises a cloud data center, an AR content server, a remote expert collaboration platform, a resource scheduling center, and a business collaboration platform, responsible for remote expert guidance and global resource scheduling. The cloud data center receives, stores, and analyzes data from all network devices; the AR content server generates advanced AR interactive content, supporting tiered transmission; the remote expert collaboration platform supports multi-expert collaboration and remote guidance; the resource scheduling center globally coordinates the allocation of network and computing resources; and the business collaboration platform manages the collaborative operation and maintenance of multiple devices and tasks.

[0084] The convergence coordination center is the core of the system. Connecting all layers, it is responsible for coordinating the resource requirements and performance targets of network slicing and QoS optimization technologies, multipath transmission and network redundancy technologies, and data semantic compression and intelligent transmission technologies. The convergence coordination center includes: an inter-technology coordinator for coordinating cross-operation between technologies; a resource scheduler for optimizing the allocation of network, computing, and storage resources; a service coordinator for adjusting system configuration based on service needs; and a self-learning optimization engine for continuously improving system performance.

[0085] Figure 3 A schematic diagram of the network slicing and QoS optimization technology provided by this invention is shown. Figure 3 As shown, network slicing and QoS optimization technologies mainly include three functional modules: slice configuration management, QoS policy execution, and slice resource monitoring.

[0086] The slice configuration management module is responsible for dynamically creating and configuring three types of network slices: control command slices (URLLC), status monitoring slices (eMBB), and routine maintenance slices (mMTC). URLLC slices guarantee extremely low latency and high reliability, and are dedicated to emergency control commands; status monitoring slices provide high bandwidth guarantees and optimize image and sensor data transmission; and mMTC slices have dynamically allocated resources, suitable for routine maintenance and configuration updates. Furthermore, this module is also responsible for dynamically adjusting slice resource quotas based on service load to ensure optimal resource allocation.

[0087] The QoS policy enforcement module sets five QoS levels based on data urgency: emergency commands, fault alarms, video data, status data, and general data. It also configures differentiated QCI (QoS Class Identifier) ​​values ​​and ARP (Allocation and Retention Priority) parameters for different data levels to ensure service quality. Furthermore, this module can dynamically adjust the QoS policies for each service based on network load conditions, ensuring that critical services receive priority even when network resources are limited.

[0088] The slice resource monitoring module monitors the usage status of each slice resource in real time, including key indicators such as bandwidth utilization, latency performance, and packet loss rate, providing a basis for resource adjustments. Furthermore, this module can predict changes in resource demand based on historical data and business models, supporting proactive adjustments to resource configurations and preventing service quality degradation due to resource shortages.

[0089] Figure 4 A schematic diagram of the multipath transmission and network redundancy technology provided by this invention is shown. Figure 4 As shown, multipath transmission and network redundancy technology mainly includes four functional modules: link manager, intelligent router, failover controller, and data merger.

[0090] The link manager manages multiple communication links, including 5G, 4G, LoRa / NB-IoT, and wired networks, enabling the integration of multi-network resources. This module monitors performance parameters such as latency, bandwidth, and packet loss rate of each link in real time, building a link status database to provide a foundation for path selection. Furthermore, the link manager can dynamically activate or deactivate links, optimizing energy usage and extending device battery life.

[0091] The intelligent router uses deep learning algorithms to select the optimal transmission path or combination of paths based on data type and link conditions. For critical data, the system selects multiple paths simultaneously to ensure reliable data delivery; for general data, the system selects the most suitable single path to optimize resource usage. Furthermore, the intelligent router also considers factors such as data transmission deadlines and energy consumption requirements to achieve multi-objective optimized path selection.

[0092] When a link failure or severe performance degradation is detected, the failover controller can complete link switching within milliseconds, ensuring service continuity. This module employs a predictive switching strategy, proactively switching before link performance degrades to a critical level to avoid data loss. Furthermore, the failover controller maintains data transmission status to ensure that data packets transmitted during the switching process are not lost or duplicated.

[0093] The data merger is responsible for processing data packets from multipath transmissions, removing redundant information, and restoring the original data stream. This module employs a forward error correction (FEC) mechanism, which can recover complete data even if some data packets are lost, further improving system reliability. In addition, the data merger can also identify and process out-of-order data packets, ensuring that upper-layer applications receive the data stream in the correct order.

[0094] Figure 5 A schematic diagram of the data semantic compression and intelligent transmission technology provided by this invention is shown. Figure 5 As shown, data semantic compression and intelligent transmission technology mainly includes four functional modules: semantic analysis engine, adaptive compressor, intelligent transmission scheduler, and context reconstructioner.

[0095] The semantic analysis engine, based on knowledge of ring network enclosures, extracts key features and semantic information from the data. This module can identify correlations and redundancies between data, providing a foundation for efficient compression. Furthermore, the semantic analysis engine can identify data change patterns and abnormal trends, supporting early detection and warning of anomalies.

[0096] The adaptive compressor dynamically selects the most suitable compression strategy based on data type, importance, and network conditions. The system implements a three-level compression architecture: lightweight compression (lossless) is suitable for critical data such as control commands; medium compression (low loss) is suitable for general status data; and high compression (preserving key semantics) is suitable for large amounts of routine monitoring data. Furthermore, the adaptive compressor can also select compression algorithms based on data characteristics, such as using time-series data compression algorithms for device parameters and neural network compression for images.

[0097] The intelligent transmission scheduler is responsible for deciding when and what data to transmit, enabling incremental data transmission and transmitting only status change information, significantly reducing data transmission volume. This module identifies abnormal changes and triggers transmission based on a data change prediction model, ensuring timely reporting of important changes. Furthermore, the intelligent transmission scheduler incorporates an adaptive sampling rate adjustment mechanism based on importance, sampling key parameters frequently and general parameters less frequently to optimize data acquisition efficiency.

[0098] The context reconstructor reconstructs the complete original data in the cloud based on the received compressed data and context information. This module maintains a device state context model, enabling efficient data reconstruction and preserving the integrity of critical information even at high compression rates. Furthermore, the context reconstructor manages the synchronization and updating of context information, ensuring reconstruction accuracy.

[0099] Figure 6 A schematic diagram illustrating the working principle of the multi-technology synergistic integration mechanism provided by this invention is shown. Figure 6 As shown, the multi-technology collaborative integration mechanism forms a highly efficient and collaborative organic whole through the cross-cooperation and scenario-based linkage of three communication technologies.

[0100] The cross-cooperation between network slicing and multipath transmission is mainly reflected in four aspects: First, mapping the service priority of network slices to multipath selection strategies to achieve slice-aware path selection; second, dynamically adjusting multipath resource allocation based on slice resource status to achieve collaborative resource allocation; third, providing multi-link redundancy guarantees for critical slices to enhance slice reliability; and fourth, reducing slice congestion risk by sharing the service load of the same slice through different physical links.

[0101] The cross-cooperation between network slicing and data semantic compression is mainly reflected in four aspects: First, selecting an appropriate compression level based on the slice type to achieve slice-aware compression strategy; second, dynamically adjusting the compression strategy based on slice resource utilization to achieve slice resource adaptive compression; third, selecting the fineness of data feature extraction based on QoS level to achieve QoS-driven feature selection; and fourth, avoiding invalid data from occupying slice resources and maximizing resource efficiency through semantic-level data filtering.

[0102] The cross-cooperation of multipath transmission and data semantic compression is mainly reflected in four aspects: First, the compression strategy is adjusted according to the real-time status of each communication link to realize the link-aware compression strategy; second, content with different compression levels is distributed according to the characteristics of the link to realize differentiated content distribution; third, the most suitable transmission path is selected based on the semantic importance of the data to realize content-aware routing; and fourth, data features are classified according to importance and transmitted through paths with different reliability to realize feature-level transmission.

[0103] The collaborative strategy of the three technologies pre-sets optimized combinations of technical configurations for different business scenarios: In emergency control scenarios, the system activates control command slices, enables multi-link redundant transmission, and adopts lossless compression; In large-scale monitoring scenarios, the system mainly uses status monitoring slices, routes data according to its importance, and applies high-ratio compression; In weak network environment scenarios, the system reduces non-critical slice resources, activates all available links, and enables the highest compression level; In security threat scenarios, the system isolates suspicious slices, enables secure links, and enhances the transmission priority of security-related data.

[0104] Through this collaborative integration mechanism, the system achieves a fusion effect, significantly improving the reliability, real-time performance, and adaptability of remote operation and maintenance communication for ring network boxes.

[0105] The present invention also provides a remote operation and maintenance intelligent primary and secondary integrated ring network box automated complete set of equipment. Figure 7 The diagram shows the structural block diagram of the automated complete set of intelligent primary and secondary integrated ring network box remote operation and maintenance equipment provided by the present invention. The equipment includes a data acquisition module 701, a data classification and grading module 702, an integrated transmission module 703, and an operation and maintenance execution module 704.

[0106] The data acquisition module 701 is used to acquire the operating status data of the ring main unit, which includes equipment status data and on-site image data. In a specific implementation, the data acquisition module includes multiple types of sensors and an image acquisition system for comprehensively acquiring the operating status of the ring main unit.

[0107] The data classification and grading module 702 is used to classify and grade the operational status data to obtain data of different types and priorities. In specific implementation, this module divides the data into multiple types and priorities based on the data's source, content characteristics, importance, and urgency, providing a basis for subsequent differentiated transmission processing.

[0108] The converged transmission module 703 is used to perform differentiated transmission processing on data of different types and priorities based on a multi-technology collaborative fusion mechanism to obtain processing results. The multi-technology collaborative fusion mechanism includes a cross-cooperation mechanism of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology. In specific implementation, this module allocates corresponding network resources and quality of service guarantees for data of different priorities through network slicing and QoS optimization technology; provides multi-link transmission and automatic fault switching capabilities for data through multi-path transmission and network redundancy technology; and dynamically adjusts compression strategies and transmission timing based on data content and network conditions through data semantic compression and intelligent transmission technology.

[0109] The operation and maintenance execution module 704 is used to perform corresponding remote operation and maintenance operations on the ring network box based on the processing results. In specific implementation, this module performs remote operation and maintenance operations such as data monitoring and status display, remote control command execution, AR remote guidance, fault diagnosis and handling, and preventive maintenance based on the processed data.

[0110] In one possible implementation, the device further includes: a convergence coordination center for coordinating the resource requirements and performance targets of network slicing and QoS optimization technologies, multipath transmission and network redundancy technologies, and data semantic compression and intelligent transmission technologies; the convergence coordination center includes: an inter-technology coordinator for coordinating cross-operation between various technologies; a resource scheduler for optimizing the allocation of network, computing, and storage resources; a service coordinator for adjusting system configuration according to service requirements; and a self-learning optimization engine for continuously improving system performance.

[0111] In one possible implementation, the device includes a sensing and data layer, an edge intelligence layer, a converged communication layer, and a cloud collaboration layer; the sensing and data layer includes an enhanced sensor network, an intelligent image acquisition system, a data preprocessing unit, and an acquisition controller; the edge intelligence layer includes a high-performance edge computing unit, a semantic compression engine, an edge decision-making system, a multi-level caching system, and an AR content local rendering module; the converged communication layer includes a network slice management module, a QoS control engine, a multi-path transmission controller, a link status monitoring system, and a secure communication module; the cloud collaboration layer includes a cloud data center, an AR content server, a remote expert collaboration platform, a resource scheduling center, and a business collaboration platform.

[0112] In one possible implementation, the device further includes: a network slicing management system for dynamically creating and configuring network slices to achieve differentiated quality of service; a multi-path transmission management system for managing multiple communication links to achieve intelligent data distribution and redundant transmission; and a semantic compression and intelligent transmission system for extracting key data features to achieve efficient compression and intelligent transmission.

[0113] The present invention also provides an electronic device. Figure 8 A structural block diagram of an electronic device provided by the present invention is shown. For example... Figure 8 As shown, the electronic device includes a processor 801 and a memory 803, which are connected via a bus 802.

[0114] The processor 801 can be a central processing unit (CPU), a network processor (NP), a microprocessor, or a processor array, etc.

[0115] The memory 803 can be a read-only memory (ROM), a random access memory (RAM), or any other type of memory with the aforementioned memory performance. The memory 803 stores machine-readable instructions executable by the processor 801, which can be executed by the processor 801 to implement the aforementioned remote operation and maintenance communication method for the ring network enclosure.

[0116] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the above-described remote operation and maintenance communication method for ring network boxes.

[0117] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0118] The above are merely embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

[0119] To verify the above technical solution, the present invention provides the following embodiments to demonstrate the effectiveness of the present invention.

[0120] I. Test Environment and System Configuration

[0121] 1.1 Test Environment

[0122] This embodiment was deployed and tested in the Shijiazhuang power distribution network under the North China Electric Power Company. Three typical test scenarios were selected: dense ring network box area in urban area: 30 ring network boxes, with good 5G network coverage; urban-rural transition area: 15 ring network boxes, with average network coverage quality; remote mountainous area: 8 ring network boxes, with weak network coverage and frequent communication interruptions.

[0123] 1.2 Hardware Configuration

[0124] Ring network box end equipment: Edge computing unit: industrial-grade quad-core processor, 4GB RAM, 64GB storage; Sensor array: 12 types of key parameter sensors, including voltage, current, temperature, etc.; Image acquisition system: 1080p high-definition camera, supporting infrared night vision; Communication module: 5G main module, 4G and LoRa backup modules; Power system: 24VDC power supply, built-in 12-hour backup battery.

[0125] Cloud server: Computing resources: 64-core processor, 256GB RAM; Storage resources: 20TB high-performance storage; Network configuration: dual redundant 10 Gigabit network connection.

[0126] 1.3 Software Configuration

[0127] Edge side:

[0128] Operating System: Real-time Linux operating system; Edge Computing Framework: OpenEdge v1.5.2; Semantic Compression Engine: Self-developed TimeSeriesSemanticCompressor v2.1; Multipath Transmission Controller: Self-developed MultiPath-Controller v3.0.

[0129] Cloud:

[0130] Operating system: CentOS 8.4; Container platform: Kubernetes v1.22; Network slice management system: self-developed NetworkSlice-Manager v2.5; Remote expert system: self-developed AR-ExpertSystem v3.2.

[0131] 1.4 Network Configuration

[0132] 5G network configuration:

[0133] Three types of network slicing: URLLC (QCI=80, ARP=1), eMBB (QCI=82, ARP=2), mMTC (QCI=83, ARP=3); 5G SA mode, supporting network slicing and QoS differentiation; maximum bandwidth: uplink 50Mbps, downlink 200Mbps.

[0134] Backup network:

[0135] 4G LTE network: 10Mbps uplink, 50Mbps downlink; LoRa network: 2.5kbps, 10km coverage radius; Fixed network (in some areas): 50Mbps fiber optic connection.

[0136] II. Example 1: Remote Fault Handling in Remote Mountainous Areas with Weak Networks

[0137] 2.1 Scenario Description

[0138] At a remote power station in the western mountainous area of ​​the city, a ring main unit (EBox-R08) is installed. The area has weak and unstable 5G signal coverage, and the 4G signal is frequently interrupted, making it a typical weak network environment. The system detected abnormal temperature rise and current fluctuations in the ring main unit, requiring remote fault diagnosis and handling.

[0139] 2.2 Current Status of Communication

[0140] Poor network environment: 5G signal strength -105dBm, signal quality is extremely unstable;

[0141] Bandwidth is severely limited: available bandwidth fluctuates between 0.2-5 Mbps;

[0142] High network latency: average latency 200-500ms, peak latency up to 1500ms;

[0143] Frequent network outages: On average, there are 2-3 brief network interruptions per hour (30 seconds to 2 minutes).

[0144] 2.3 System Response and Processing

[0145] Fault detection and alarm triggering:

[0146] The edge computing unit detected a continuous rise in phase A temperature (from 45°C to 72°C within 15 minutes) and current fluctuations (±20%). The system performed semantic extraction on the fault data and identified the "possible heat generation caused by poor contact" pattern. It automatically triggered a high-priority alarm and started the emergency communication mode for weak network environments.

[0147] The multi-technology synergy and integration mechanism is automatically activated:

[0148] Network slicing and QoS optimization: Alarm data is allocated to URLLC slices with the highest priority (QCI=80); Multi-path transmission: Simultaneously activate 5G, 4G and LoRa communication paths; Semantic compression: Apply lightweight compression (30% compression rate) to alarm data and apply high semantic compression (preserving hotspot areas, 95% compression rate) to image data.

[0149] Data transmission implementation:

[0150] Key alarm data: transmitted in parallel via three paths, firstly via LoRa to transmit core alarm parameters (completed in 12 seconds); temperature curve data: transmitted via 4G network with semantically compressed historical temperature curves (completed in 5 seconds); thermal imaging images: transmitted via 5G network in blocks to transmit thermal imaging images of key areas (first block completed in 15 seconds, all blocks completed in 65 seconds).

[0151] Remote expert access and diagnosis:

[0152] The remote expert system notifies the on-duty expert within 3 seconds of receiving an alarm; the system proactively pushes the received core data to the expert without waiting for all data transmission to complete; the expert performs a preliminary diagnosis based on the received data and confirms that "the loose A-phase outgoing terminal has caused increased contact resistance".

[0153] AR remote guidance execution:

[0154] The system sends AR guidance information to on-site maintenance personnel via multi-path transmission: text commands: sent via LoRa (delivered within 2 seconds); simplified operation diagrams: sent via 4G (delivered within 8 seconds); detailed AR annotation guidance: transmitted via 5G tiered transmission (key annotations delivered within 10 seconds, complete AR content delivered within 35 seconds); on-site personnel receive guidance through AR devices and perform terminal tightening operations; operation feedback is returned through the same mechanism, the system verifies that the temperature begins to drop, and the fault is resolved.

[0155] 2.4 Performance Test Results

[0156]

[0157] 2.5 Communication Efficiency Analysis

[0158] In this example, through the synergistic integration of multiple technologies, the system achieves efficient communication in extremely weak network environments: improved bandwidth efficiency: through semantic compression, data that originally required 5MB is compressed to 300KB, achieving a compression rate of 94%; multi-path utilization: the combined utilization rate of the three communication paths reaches 95%, which is 320% higher than that of a single 5G channel; adaptability to weak network environments: even in extreme cases where the bandwidth drops to 0.2Mbps, the core functions can still work normally; resilience during network outages: during a brief 1-minute network outage, the edge continues to collect data and perform local analysis, and automatically transmits key information back to the cloud after the network is restored, achieving seamless connection.

[0159] III. Example 2: Centralized Operation and Maintenance Scenario for Large-Scale Ring Network Boxes

[0160] 3.1 Scene Description

[0161] In the city center business district, 30 densely distributed ring network boxes were deployed, requiring simultaneous remote status monitoring, data analysis, and operation and maintenance management of these devices. In this scenario, although network coverage was good, monitoring a large number of devices at the same time posed challenges to network resources and data processing capabilities.

[0162] 3.2 Communication Challenges

[0163] Massive data volume: The raw data stream generated simultaneously by 30 ring network boxes is approximately 150Mbps; Diverse services: Including various service types such as routine monitoring, periodic inspection, and fault early warning; Priority conflict: Resource contention issues when multiple devices simultaneously generate alarms; High monitoring density: Each device monitors an average of 40 parameter points, totaling 1200 monitoring points.

[0164] 3.3 System Response and Processing

[0165] Intelligent data collection and classification:

[0166] The system categorizes the 30 ring network boxes into three importance levels based on their importance and operating status. Low-frequency sampling (1Hz) is used for normally operating equipment, medium-frequency sampling (10Hz) is used for equipment with minor anomalies, and high-frequency sampling (100Hz) is used for equipment with obvious anomalies. The operating status data is divided into five categories according to content and importance, enabling refined data management.

[0167] Configuration of multi-technology collaborative integration mechanism:

[0168] Network slice resource allocation: Control Command Slice (URLLC): 10% bandwidth resource reserved; Status Monitoring Slice (eMBB): 70% bandwidth resource allocated; Daily Maintenance Slice (mMTC): 20% bandwidth resource allocated.

[0169] Multi-path transmission strategy: Critical control commands: 5G main path + 4G backup path; Important monitoring data: 5G dedicated channel; Ordinary data: Dynamically select 5G or 4G according to network load.

[0170] Semantic compression configuration: Control commands: Light compression (compression ratio 20-30%); Status data: Medium compression (compression ratio 70-80%); Image data: High compression (compression ratio 90-95%).

[0171] Adaptive resource scheduling:

[0172] The system monitors the overall network load in real time and found that the load reached 85% during the morning peak period (8:00-10:00). The fusion coordination center automatically activated the dynamic resource adjustment mechanism: improving semantic compression rate: increasing the compression rate of ordinary monitoring data to 85%; reducing the sampling rate of non-critical data: reducing the sampling frequency of normal equipment to 0.5Hz; optimizing slice resources: temporarily reducing the daily maintenance slice resources to 10% and increasing the status monitoring slice resources to 80%.

[0173] Emergency response test:

[0174] Simulate a scenario where three ring network enclosures simultaneously generate alarms to test the system's response capability; the system automatically allocates URLLC slice resources to the alarm data of the three devices; the multi-path transmission controller assigns an independent optimized transmission path to each device; and the semantic compression engine dynamically adjusts the compression strategy based on the alarm type.

[0175] 3.4 Performance Test Results

[0176]

[0177] 3.5 Resource Utilization Efficiency Analysis

[0178] In large-scale ring network enclosure centralized operation and maintenance scenarios, the system achieves efficient resource utilization through a multi-technology collaborative integration mechanism: Bandwidth utilization: increased from the traditional 40-60% to 85-95%, supporting more devices under the same physical resource conditions; Dynamic adjustment of slice resources: slice resource utilization reaches 92% during peak periods and remains above 75% during off-peak periods; Data compression efficiency: through semantic-level compression, the original data stream of 150Mbps is compressed to 28Mbps, with a comprehensive compression rate of 81%; Resource elasticity: the system can maintain service quality without significant degradation even when the sudden load increases by 400%.

[0179] Resource utilization efficiency in large-scale scenarios can be expressed by the following formula:

[0180] Resource utilization efficiency = (number of devices × average data volume) / actual bandwidth consumption; based on measured data, the resource utilization efficiency of the method of this invention is 5.3 times higher than that of the traditional method.

[0181] IV. Example 3: Remote AR-guided on-site maintenance scenario

[0182] 4.1 Scenario Description

[0183] In an industrial park on the outskirts of the city, a ring main unit (EBox-I12) triggered its grounding protection, requiring remote guidance from an expert to inexperienced maintenance personnel on-site for inspection and handling. This scenario places extremely high demands on the real-time transmission and interactive quality of AR content, posing challenges to the reliability and real-time performance of the communication system.

[0184] 4.2 Communication Challenges

[0185] High real-time requirements: AR remote guidance requires end-to-end latency of no more than 100ms; High bandwidth requirements: High-quality AR content transmission requires stable bandwidth of 10-20Mbps; Frequent interaction: Experts and on-site personnel need to interact and annotate frequently; Network fluctuations: Network quality in industrial areas fluctuates, with available bandwidth varying between 5-50Mbps.

[0186] 4.3 System Response and Processing

[0187] Remote AR guidance session setup:

[0188] On-site maintenance personnel initiate an AR remote guidance request via handheld device; the system automatically assesses network conditions: 5G signal -85dBm, current available bandwidth 15Mbps, network latency 25ms; the convergence coordination center reserves resources: activates eMBB slice, allocates 12Mbps bandwidth, and sets QoS priority to level 2.

[0189] Configuration of multi-technology collaborative integration mechanism:

[0190] Network slicing and QoS configuration: Real-time video stream: eMBB slice, QCI=82, priority 2; Voice interaction: URLLC slice, QCI=80, priority 1; Control commands: URLLC slice, QCI=80, priority 1; AR content data: eMBB slice, QCI=82, priority 2.

[0191] Multipath transmission configuration:

[0192] Video uplink: 5G main path + some key frames backed up via 4G; Voice interaction: 5G and 4G dual-path redundant transmission; AR content downlink: 5G main path, with transmission tiered according to content importance.

[0193] Semantic compression strategies:

[0194] Video stream: Region-aware compression, high-definition transmission of key areas, and low-resolution transmission of non-critical areas (overall compression rate of 75%); AR content: Layered transmission, basic framework first, and detailed content added later (adaptive compression rate of 65-85%).

[0195] Adaptive interaction guarantee mechanism:

[0196] The system detected a temporary drop in network bandwidth to 7Mbps and automatically activated adaptive strategies: increasing video compression rate to 85% to maintain clarity in critical areas; switching AR content transmission to a higher compression level to prioritize the basic framework and key annotations; the multipath controller transferred some non-critical data to the 4G network for transmission; after the network was restored, the system gradually restored its original configuration to ensure a continuous experience.

[0197] AR guidance process:

[0198] Remote experts received the on-site video stream and identified the grounding fault as caused by aging insulation at the cable termination. Experts then used the AR system for annotation and guidance: Marking checkpoints: The system ensures real-time (delay <50ms) and accurate (deviation <5mm) transmission of markings; Operational step guidance: The system breaks down operational steps into semantic units, prioritizing the transmission of the current step; Virtual demonstration: The system transmits the expert's virtual operational demonstration in layers to ensure smooth operation.

[0199] Network fluctuation response test:

[0200] Simulate a 30-second severe network fluctuation (bandwidth reduced to 2Mbps) to test the system's response capability. The system automatically switches to weak network mode: video is reduced to 480p, and only critical areas are transmitted; AR content is simplified to wireframes and text instructions; pre-loaded operation guides are retrieved locally from the edge device; the system completes full content synchronization and restores normal interaction within 30 seconds after the network recovers.

[0201] 4.4 Performance Test Results

[0202]

[0203] 4.5 AR Interaction Quality Analysis

[0204] This embodiment achieves a high-quality interactive experience during AR remote guidance through a multi-technology collaborative integration mechanism: Real-time performance guarantee: Through URLLC slicing and multi-path redundant transmission, the end-to-end latency of key interactive data is controlled within 50ms, which is lower than the human perception threshold (100ms); Bandwidth adaptability: The system can dynamically adjust the content transmission strategy within the range of 2-50Mbps to ensure the core interactive experience; Content continuity: Even during periods of severe network fluctuations, basic AR guidance functions remain available through edge preloading and hierarchical transmission; Priority guarantee: Through multi-level QoS policies, key interactive data (such as expert annotations) is prioritized for transmission under resource constraints.

[0205] User experience rating formula for AR interaction quality:

[0206] AR interaction quality score = 0.4 × real-time score + 0.3 × clarity score + 0.2 × continuity score + 0.1 × accuracy score; based on user testing, the AR interaction quality score of the method of this invention is 92 points (out of 100), while the traditional method is only 68 points, an improvement of 35%.

[0207] V. Effect Verification and Comparative Analysis

[0208] 5.1 Overall Performance Comparison

[0209] Through the above three typical scenario implementations, the remote operation and maintenance communication method for ring network boxes of the present invention demonstrates significant advantages over traditional methods in all key performance indicators:

[0210]

[0211] 5.2 Analysis of the Synergistic Effect of Three Communication Technologies

[0212] The key innovation of this invention lies in the deep integration of three technologies: network slicing and QoS optimization, multi-path transmission and network redundancy, and data semantic compression and intelligent transmission. Through practical examples, this integration demonstrates a significant synergistic effect.

[0213] Resource utilization optimization: The resource utilization rates of each technology when applied independently are as follows: network slicing (70%), multipath transmission (65%), and semantic compression (75%). The resource utilization rate after the three technologies are combined and integrated reaches 92%, which is 25% higher than the average of the single technologies.

[0214] Complementary performance enhancement: In weak network environments, network slicing alone is difficult to play a role, but when combined with semantic compression and multipath transmission, the system can still maintain core functions under extremely low bandwidth (0.2Mbps); in large-scale scenarios, multipath alone is difficult to support a large number of devices, but when combined with slice resource isolation and semantic compression, the system can support 5800 devices to access at the same time.

[0215] The system's adaptability has been greatly improved: the network environment it can adapt to has been expanded from the good urban networks of traditional methods to the weak network environment in remote areas; the types of services it can support have been expanded from basic monitoring to comprehensive operation and maintenance services, including advanced functions such as AR remote guidance and predictive maintenance.

[0216] Quantitative analysis of fusion effect:

[0217] Define a "fusion benefit index" to quantify the synergistic effects brought about by the integration of multiple technologies:

[0218] The fusion benefit index = fusion system performance / (weighted average of individual technical performance); through testing, it was found that the fusion benefit index is greater than 1 in different scenarios, including: remote weak network scenario: fusion benefit index = 1.85; large-scale monitoring scenario: fusion benefit index = 1.63; AR remote guidance scenario: fusion benefit index = 1.72; this fully demonstrates the fusion effect of the present invention.

[0219] 5.3 Analysis of Actual Operational Benefits

[0220] After deploying the remote operation and maintenance communication system for ring network boxes of this invention, the power distribution network has achieved significant economic and social benefits: improved operation and maintenance efficiency: fault handling time: reduced from an average of 2.5 hours to 25 minutes, an improvement of 83%; remote diagnosis accuracy: increased from 76% to 95%, an improvement of 25%; preventive maintenance success rate: increased from 65% to 92%, an improvement of 42%.

[0221] Human Resources Optimization: On-site personnel needs: reduced by 75%, saving approximately RMB 1.2 million in labor costs per region annually; Expert resource utilization: the number of devices that one expert can support simultaneously has increased from 20 to 150, an increase of 650%; Training cycle: the training time for new technical personnel has been shortened from 3 months to 1 month, an increase of 67%.

[0222] Network resource savings: Bandwidth consumption: Bandwidth requirements are reduced by 81% for the same monitoring scale; Traffic cost: The monthly traffic cost per device is reduced from 120 yuan to 28 yuan, saving 77%; Communication infrastructure investment: Remote areas can utilize existing network coverage without additional construction.

[0223] Social benefits: Power supply reliability: Power outage time is reduced by 78%, improving user satisfaction; Service equalization: Remote areas receive the same quality of power service as urban areas; Carbon emission reduction: Reduced on-site inspections and emergency response, reducing carbon emissions by approximately 200 tons per year.

[0224] VI. Conclusion

[0225] Through verification using three typical application scenarios, the effectiveness of the ring network enclosure remote operation and maintenance communication method of this invention has been fully demonstrated. Experimental results show that, through the deep integration and cross-cooperation of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology, the system successfully solves the communication problems faced in traditional ring network enclosure remote operation and maintenance.

[0226] This invention has the following outstanding advantages:

[0227] Full-scenario adaptability: It can provide high-quality remote operation and maintenance services from good urban networks to weak network environments in remote areas;

[0228] Reliability: Communication reliability reaches 99.9999%, meeting the high reliability requirements of power systems;

[0229] Efficient resource utilization: Bandwidth utilization efficiency is increased to 85-95%, significantly reducing communication costs;

[0230] Service quality differentiation guarantee: ensuring that critical services are given priority under any network conditions;

[0231] Significant economic and social benefits: improved operation and maintenance efficiency, reduced costs, and enhanced service quality.

[0232] The above-mentioned application in power distribution networks proves that this invention has created significant economic and social value in practical applications, provided technical support for the development of smart grids, and has broad application prospects.

Claims

1. A ring main unit remote operation and maintenance communication method, characterized in that, The method comprises the following steps: acquiring operation state data of the ring network box, wherein the operation state data comprises device state data and field image data; classifying and grading the operation state data to obtain data of different types and different priorities; based on a multi-technology collaborative fusion mechanism, differentially transmitting and processing the data of different types and different priorities to obtain a processing result; wherein the multi-technology collaborative fusion mechanism comprises a cross-coordination mechanism of network slicing and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology; based on the processing result, performing a corresponding remote operation and maintenance operation of the ring network box; wherein based on the multi-technology collaborative fusion mechanism, differentially transmitting and processing the data of different types and different priorities comprises: allocating corresponding network resources and quality of service guarantees for data of different priorities through network slicing and QoS optimization technology; providing multi-link transmission and automatic fault switching capability for data through multi-path transmission and network redundancy technology; and dynamically adjusting compression strategies and transmission opportunities based on data content and network conditions through data semantic compression and intelligent transmission technology; the cross-coordination mechanism of the network slicing and QoS optimization technology and the data semantic compression and intelligent transmission technology comprises: selecting a suitable compression level according to the slice type to which the data belongs, implementing a slice-aware compression strategy; dynamically adjusting the compression strategy according to the slice resource utilization, implementing a slice resource adaptive compression; selecting the fineness of data feature extraction based on the QoS level, implementing a QoS-driven feature selection; avoiding invalid data from occupying slice resources through semantic-level data filtering, and maximizing resource efficiency; the cross-coordination mechanism of the multi-path transmission and network redundancy technology and the data semantic compression and intelligent transmission technology comprises: adjusting the compression strategy according to the real-time condition of each communication link, implementing a link-aware compression strategy; distributing contents of different compression levels according to the link characteristics, implementing differentiated content distribution; selecting the most suitable transmission path based on the importance of data semantics, implementing content-aware routing; classifying data features according to importance, and transmitting the features through paths of different reliabilities, implementing feature hierarchical transmission.

2. The method of claim 1, wherein, The network slicing and QoS optimization technology comprises: creating three types of network slices, namely control instruction slice, state monitoring slice and routine maintenance slice; setting multiple QoS levels based on data urgency, and configuring differentiated quality of service parameters for data of different levels; monitoring the usage status of each slice resource in real time, and dynamically adjusting the slice resource quota; the multi-path transmission and network redundancy technology comprises: constructing a multi-path communication architecture comprising 5G, 4G, LoRa / NB-IoT and wired network; selecting the optimal transmission path combination according to the data type and the quality status of each link; providing multi-path redundant transmission for critical data, and removing redundant information at the receiving end; implementing millisecond-level automatic switching in the event of link failure, and ensuring communication continuity.

3. The method of claim 1, wherein, The data semantic compression and intelligent transmission technology includes: extracting key semantic information of data based on ring network box equipment characteristics and service knowledge; applying light compression, moderate compression or high compression strategies according to data types and importance; realizing incremental data transmission, only transmitting state change information; based on prediction analysis results, actively adjusting data sampling frequency and transmission timing; the cross cooperation mechanism of the network slice and QoS optimization technology and the multi-path transmission and network redundancy technology includes: mapping the service priority of the network slice to the multi-path selection strategy to realize slice-aware path selection; dynamically adjusting the multi-path resource allocation according to the slice resource status to realize resource collaborative allocation; providing multi-link redundancy protection for key slices to enhance slice reliability; by different physical links sharing the business load of the same slice, reducing the risk of slice congestion.

4. The method of claim 1, wherein, The method further includes: establishing a unified resource management framework to realize collaborative scheduling of network resources, computing resources and storage resources; implementing multi-dimensional state awareness, simultaneously monitoring network slice state, link quality and data characteristics; according to different business scenarios, executing preset technology linkage strategies to realize scenario-based collaborative work; the different business scenarios include: emergency control scenario: activate the control instruction slice, enable multi-link redundant transmission, and use lossless compression; large-scale monitoring scenario: mainly use the state monitoring slice, route according to data importance, and apply high proportion compression; weak network environment scenario: reduce non-key slice resources, activate all available links, and enable the highest compression level; security threat scenario: isolate suspicious slices, enable secure links, and enhance the transmission priority of security-related data.

5. A remote operation and maintenance intelligent primary-secondary fusion ring network box automatic complete equipment, characterized in that, The ring network box remote operation and maintenance communication method according to any one of claims 1-4, comprising: a data acquisition module for acquiring operation state data of the ring network box, the operation state data including device state data and field image data; a data classification and grading module for classifying and grading the operation state data to obtain data of different types and different priorities; a fusion transmission module for differentially transmitting and processing the data of different types and different priorities based on a multi-technology collaborative fusion mechanism to obtain a processing result; wherein the multi-technology collaborative fusion mechanism includes a cross cooperation mechanism of network slice and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology; an operation and maintenance execution module for executing corresponding ring network box remote operation and maintenance operations based on the processing result; wherein the fusion transmission module is specifically configured to: allocate corresponding network resources and quality of service guarantees for data of different priorities through network slice and QoS optimization technology; provide multi-link transmission and fault automatic switching capabilities for data through multi-path transmission and network redundancy technology; and dynamically adjust compression strategies and transmission timing based on data content and network status through data semantic compression and intelligent transmission technology.

6. The apparatus of claim 5, wherein, Further comprising: a fusion coordination center for coordinating resource requirements and performance targets of network slice and QoS optimization technology, multi-path transmission and network redundancy technology, and data semantic compression and intelligent transmission technology; The fusion coordination center comprises: an inter-technology coordinator for coordinating cross-connections between technologies; a resource scheduler for optimizing network, computing and storage resource allocation; a service coordinator for adjusting system configuration according to service requirements; and a self-learning optimization engine for continuously improving system performance.

7. The apparatus of claim 5, wherein, The device comprises a perception and data layer, an edge intelligence layer, a fusion communication layer and a cloud collaboration layer; the perception and data layer comprises an enhanced sensor network, an intelligent image acquisition system, a data preprocessing unit and an acquisition controller; the edge intelligence layer comprises a high-performance edge computing unit, a semantic compression engine, an edge decision system, a multi-level cache system and an AR content local rendering module; The fusion communication layer comprises a network slice management module, a QoS control engine, a multi-path transmission controller, a link state monitoring system and a secure communication module; the cloud collaboration layer comprises a cloud data center, an AR content server, a remote expert collaboration platform, a resource scheduling center and a service collaboration platform; The device further comprises: a network slice management system for dynamically creating and configuring network slices to achieve differentiated quality of service; a multi-path transmission management system for managing multiple communication links to achieve intelligent data distribution and redundant transmission; and a semantic compression and intelligent transmission system for extracting key features of data to achieve efficient compression and intelligent transmission.

8. An electronic device, comprising: It comprises: A processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, the machine readable instructions are executed by the processor to execute the ring network box remote operation and maintenance communication method of any one of claims 1 to 4.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program is executed by the processor to execute the ring network box remote operation and maintenance communication method of any one of claims 1 to 4.

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