A wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing

Abstract

This application provides a wireless ad hoc network data transmission method for dedicated power transformer data acquisition terminals supporting edge computing, relating to the field of wireless communication network technology. Based on a collaborative architecture, the collaborative architecture includes a terminal perception layer, an ad hoc network transmission layer, an edge computing layer, and a remote master station layer. The method includes: edge nodes assigning roles to each peer node in the ad hoc network transmission layer; collecting dedicated power transformer data, generating data frames, and sending them to the ad hoc network transmission layer; core relay nodes classifying and parsing the data frames and sending them to the edge computing layer; the edge computing layer preprocessing the data frames and forwarding them to the remote master station layer; the remote master station layer performing data analysis and storage, generating adjustment strategies based on the analysis results, and distributing them; edge nodes decomposing the adjustment strategies into lightweight tasks and distributing them to the terminal perception layer for execution via the ad hoc network transmission layer. This application deeply integrates edge computing and ad hoc networks to achieve low-latency, high-reliability, and low-load data transmission.

Description

Technical Field

[0001] This application relates to the field of wireless communication network technology, and in particular to a wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals that supports edge computing. Background Technology

[0002] In power systems, the acquisition, monitoring, and control of electricity consumption data from dedicated transformers are crucial for ensuring the safe and stable operation of industrial plants, commercial parks, and distributed power consumption scenarios. Dedicated transformer data acquisition terminals are responsible for the real-time acquisition and transmission of critical data such as current, voltage, power, and operating status from one or more dedicated transformers. Their performance directly impacts the power system's dispatching, energy efficiency management, and fault response capabilities. With the increasing complexity of power load management and distributed energy access, the number and distribution range of acquisition terminals are constantly expanding, placing higher demands on the real-time performance, reliability, and scalability of data transmission.

[0003] Existing dedicated transformer data acquisition systems primarily rely on two types of data transmission schemes: wired communication, including RS485 bus and fiber optic transmission; and wireless single-hop communication, such as 4G / 5G mobile networks and traditional WiFi networks. Wired deployments are time-consuming and costly, and in distributed transformer layouts, rapid expansion and flexible maintenance are difficult. Wireless single-hop communication often suffers from network coverage blind spots due to environmental obstructions, building barriers, or electromagnetic interference, and is highly dependent on carrier networks, resulting in high communication costs and limited network reliability. More importantly, traditional solutions typically upload massive amounts of raw data directly to the remote master station, causing heavy processing pressure on the master station, high network bandwidth consumption, and data processing latency often exceeding 500ms, failing to meet the requirements for real-time fault alarms and control command transmission. Furthermore, wireless ad hoc networking technology and edge computing are mostly used independently in existing solutions, lacking a coordination mechanism. Relay nodes usually need to perform full frame parsing for each message, resulting in high node processing load and high energy consumption. At the same time, node fault response is delayed, which can easily cause data transmission interruption, affecting system reliability and service continuity. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals that supports edge computing.

[0005] Firstly, this application provides a wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing, employing the following technical solution: A wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing, based on a collaborative architecture, the collaborative architecture including a terminal sensing layer, a self-organizing network transmission layer, an edge computing layer, and a remote master station layer; the method includes: Node hierarchy: Edge nodes in the edge computing layer dynamically assign roles to each peer node in the ad hoc network transmission layer and periodically broadcast node role information. The dynamic role assignment includes core relay nodes and backup relay nodes. Data frame acquisition: The dedicated transformer acquisition terminal of the terminal perception layer collects the dedicated power transformer data, generates corresponding data frames, and sends them to the self-organizing network transmission layer. The data frame includes the target terminal ID and the target identifier. Data frame processing: The relay nodes of the self-organizing network transmission layer receive data frames and perform pre-inspection and classification of the data frames based on the target identifier. The data frames that need to be pre-processed are sent to the edge computing layer, while other data frames are directly forwarded by hardware. The edge computing layer pre-processes the received data frames and sends the pre-processed data frames to the remote master station layer. Strategy distribution: The remote master station receives the processed data frames sent by the edge computing layer, performs data analysis and storage, generates adjustment strategies as needed based on the analysis results, and distributes them to the edge nodes of the edge computing layer. The edge nodes then decompose the adjustment strategies to generate several lightweight tasks. Task distribution: The edge node distributes the decomposed lightweight task to each dedicated transformer data acquisition terminal in the terminal perception layer through the self-organizing network transmission layer, and each dedicated transformer data acquisition terminal executes the lightweight task.

[0006] Optionally, the terminal perception layer includes a dedicated transformer data acquisition terminal, which integrates an ad hoc network communication module and a lightweight computing module. The ad hoc network communication module is used to establish a wireless ad hoc network connection with the ad hoc network transmission layer; the lightweight computing module is used to generate lightweight data frames based on the acquired dedicated power transformer data and execute lightweight tasks issued by the edge nodes. The self-organizing network transmission layer includes several core relay nodes and several backup relay nodes. The core relay nodes are used to perform pre-inspection and classification of data frames based on target identifiers, send data frames that need to be pre-processed to the edge computing layer, and forward other data frames directly through hardware. The backup relay nodes are used to synchronize the routing information of the core relay nodes in real time, and perform replacement and routing table updates when the core relay nodes fail. The edge computing layer includes several edge nodes, which are used to perform progressive preprocessing on the received data frames. The progressive preprocessing includes outlier detection, missing data repair, reduction and normalization, and sliding window deduplication. The remote master station layer includes a dedicated transformer data acquisition master station, which is used to receive preprocessed data frames, perform data analysis, generate adjustment strategies, and distribute them to edge nodes.

[0007] Optionally, data frames are pre-classified based on target identifiers, and data frames requiring preprocessing are sent to the edge computing layer, while other data frames are directly forwarded by hardware, including: The core relay node receives data frames and classifies them based on the target identifier. If the data frame is a directional data frame, it is parsed by the CPU and sent to the edge computing layer. If the data frame is a broadcast instruction data frame, it is parsed by the CPU and sent to the edge computing layer. If the data frame is a non-directional data frame, it is forwarded directly by hardware.

[0008] Optionally, the data frame may include a 2-4 byte target identifier segment in the frame header, and the data frame may contain a target identifier and a target terminal ID.

[0009] Optionally, in the node classification step, each peer node in the ad hoc network transmission layer reports its own signal strength and resource occupancy rate to the edge computing layer. The edge nodes of the edge computing layer dynamically classify the roles of each peer node in the ad hoc network transmission layer based on the above data, and periodically broadcast the role information of each peer node.

[0010] Optionally, after receiving a data frame and before performing progressive preprocessing, the edge computing layer further includes incremental computation: Incremental computation: After receiving data frames from the network transmission layer, the edge computing layer compares the received data frames with the corresponding pre-stored historical data frames. Data frames that are different from the historical data frames are recorded as data frames to be processed, and the data frames to be processed are used as new received data frames.

[0011] Optionally, after the edge computing layer preprocesses the received data frame, before sending the preprocessed data frame to the dedicated transformer data acquisition master station, it further includes: Transmission strategy classification: The edge nodes classify the preprocessed data frames according to priority; the priority includes first-level data frames, second-level data frames, and third-level data frames; The first-level data frame is core business data, the second-level data frame is ordinary business data, and the third-level data frame is non-critical data; When the preprocessed data frame is a first-level data frame, a dedicated bandwidth reservation and priority forwarding strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station. When the preprocessed data frame is a secondary data frame, a batch aggregation and timed transmission strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station; When the preprocessed data frame is a level 3 data frame, the idle time transmission strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station.

[0012] Optionally, the edge node is also used to perform adaptive hybrid routing, specifically including: the ad hoc network transport layer uploading link state information between peer nodes to the edge computing layer; the edge node determining the data transmission method adopted by the ad hoc network transport layer based on the link state information: when the destination address of the data frame is within the local communication area, the ad hoc network transport layer uses the optimized DSDV a priori routing protocol for data transmission; when the destination address of the data frame exceeds the local communication area, or when a link interruption occurs within the local communication area, the ad hoc network transport layer performs route discovery and reconstruction through the AODV reactive routing protocol; the edge node is also used to summarize the link state information of each peer node in the ad hoc network transport layer to optimize the routing table, and synchronize the optimized routing table to the core relay node and the backup relay node; The link status information includes packet loss rate, transmission delay, and bandwidth utilization.

[0013] Optionally, the edge node is also used to periodically send heartbeat detection signals to each peer node in the ad hoc network transport layer. When there is no response after three consecutive heartbeats, the channel is switched for verification. If there is still no response after the channel switching verification, the peer node is determined to be faulty. A backup relay node is triggered to fill the position of the faulty peer node. After filling the position, the backup relay node inherits the real-time synchronized routing table and performs a routing table broadcast update. The edge node is also used to report the fault information of the peer node to the remote master station layer and provide a reminder when it is determined that the peer node is faulty.

[0014] Optionally, the progressive preprocessing specifically includes: the outlier detection employs an improved 3D algorithm. Outlier detection is performed using the criteria and the Grubbs criterion; missing data repair is performed using linear interpolation; the reduction and standardization process is performed using the DL / T645 reduction and standardization method; and the sliding window deduplication process uses a sliding window of a preset duration to remove duplicate data.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application achieves refined division of labor and collaborative management of relay nodes in the transmission layer of ad hoc networks through an edge-driven dynamic node hierarchical and role allocation mechanism. Backup relay nodes synchronize routing information with core relay nodes in real time, enabling rapid replacement and routing table updates when a core relay node fails, effectively avoiding data transmission interruptions caused by single-point failures. Simultaneously, a dual fault detection mechanism combining heartbeat detection and channel switching verification ensures accurate fault determination. Combined with a dual-verification reporting process for fault information, it guarantees that the master station can promptly obtain fault status and trigger a response. This closed-loop fault handling mechanism significantly improves the system's transmission continuity and fault resistance in complex industrial environments, completely solving the technical problems of delayed node fault response and easy transmission interruption in traditional solutions, providing highly reliable communication guarantees for power data acquisition services.

[0016] 2. This application proposes a relay node frame header pre-inspection mechanism. By setting a target identifier segment in the data frame header, the relay node only needs to read the frame header identifier segment in hardware to complete the forwarding judgment. For non-directional forwarding packets, the hardware forwards them directly without CPU full-frame parsing; only directional packets and broadcast commands require CPU intervention. This mechanism significantly shortens the single-packet processing latency, significantly reduces the CPU utilization and energy consumption of the relay node, and effectively avoids packet accumulation. In distributed deployment scenarios requiring long-term stable operation, this lightweight processing mechanism extends the lifespan of the relay node, reduces equipment heat dissipation requirements and maintenance frequency, and improves the continuous operation capability and stability of the entire ad hoc network transmission layer.

[0017] 3. This application employs a combination of edge-progressive data preprocessing algorithms to perform outlier detection, missing data repair, reduction standardization, and sliding window deduplication at the edge, effectively eliminating redundant and abnormal data and significantly reducing network bandwidth pressure and the processing load on the remote master station. Based on this, an edge-driven differentiated transmission strategy processes data according to service priority: core service data uses dedicated bandwidth reservation and priority forwarding strategies to ensure real-time requirements are met; ordinary service data uses batch aggregation and timed transmission strategies; and non-critical data uses idle-time transmission strategies. This hierarchical mechanism achieves precise allocation and efficient utilization of bandwidth resources, ensuring rapid response to real-time services such as fault alarms, while avoiding network congestion caused by direct transmission of massive amounts of raw data to the master station, comprehensively improving the system's real-time performance and resource utilization efficiency.

[0018] 4. This application adopts wireless self-organizing network technology to replace the traditional wired communication solution, eliminating the need for complex cabling construction, significantly reducing construction and subsequent maintenance costs, and flexibly adapting to the rapid expansion needs of distributed transformer layout scenarios such as industrial plants and commercial parks. The edge computing layer completes data preprocessing, routing optimization, and fault handling locally, significantly reducing the amount of data uploaded to the remote master station, lowering operator network traffic costs and reducing the processing pressure on the master station server. Simultaneously, the system has an anti-interference design, effectively adapting to industrial electromagnetic interference environments. Furthermore, the edge node function can be carried by existing concentrators, intelligent converged terminals, or enhanced edge gateways, eliminating the need for additional dedicated equipment. It can be widely applied to various distributed deployment scenarios for dedicated transformer data acquisition, possessing excellent scalability, economy, and engineering practicality. Attached Figure Description

[0019] Figure 1 This is a flowchart of an embodiment of this application; Figure 2 This is a schematic diagram of the collaborative architecture of an embodiment of this application; Figure 3 This is a flowchart of the S3 data frame processing in an embodiment of this application; Figure 4 This is a flowchart of the S32 data frame classification in an embodiment of this application; Figure 5 This is a flowchart of the S35 transmission strategy division in an embodiment of this application. Detailed Implementation

[0020] The following combination Figures 1 to 5 This application will be described in further detail.

[0021] Example: This example discloses a wireless self-organizing network data transmission method for a dedicated transformer data acquisition terminal that supports edge computing, such as... Figure 1 and Figure 2 As shown, based on a collaborative architecture, the collaborative architecture includes a terminal perception layer, a self-organizing network transmission layer, an edge computing layer, and a remote master station layer. The method includes: edge nodes dynamically assigning roles to each peer node in the self-organizing network transmission layer; collecting power transformer data to generate data frames and sending them to the self-organizing network transmission layer; core relay nodes classifying and parsing the data frames and sending them to the edge computing layer; the edge computing layer preprocessing the data frames and forwarding them to the remote master station layer; the remote master station layer performing data analysis and storage, generating adjustment strategies based on the analysis results and issuing them; and edge nodes decomposing the adjustment strategies into lightweight tasks and issuing them to the terminal perception layer for execution through the self-organizing network transmission layer. This embodiment includes the following steps: This embodiment adopts a collaborative architecture including a terminal perception layer, a self-organizing network transmission layer, an edge computing layer, and a remote master station layer. Specifically: The terminal perception layer includes a dedicated transformer data acquisition terminal, which integrates a self-organizing network communication module and a lightweight computing module. The self-organizing network communication module is used to establish a wireless self-organizing network connection with the self-organizing network transmission layer. The lightweight computing module is used to generate lightweight data frames based on the acquired dedicated power transformer data and execute lightweight tasks issued by the edge nodes.

[0022] The self-organizing network transmission layer includes several core relay nodes and several backup relay nodes. The core relay nodes are used to perform pre-classification of data frames based on target identifiers, sending data frames requiring pre-processing to the edge computing layer, while other data frames are directly forwarded via hardware. The core relay nodes include forwarding-only core relay nodes and acquisition-and-forwarding core relay nodes. The forwarding-only core relay nodes receive data frames and perform pre-classification, directly forwarding non-directional data frames via hardware. The acquisition-and-forwarding core relay nodes receive data frames and perform pre-classification, performing CPU full-frame parsing and local data acquisition on directional data frames whose target identifiers match their own node, directly forwarding non-directional data frames via hardware, and reporting their own signal strength and resource occupancy to the edge computing layer. The backup relay nodes are used to synchronize the routing information of the core relay nodes in real time and perform replacement and routing table updates when a core relay node fails.

[0023] The edge computing layer includes several edge nodes. These edge nodes perform progressive preprocessing on received data frames, including outlier detection, missing data repair, protocol standardization, and sliding window deduplication. The edge nodes also aggregate link state information from peer nodes in the ad hoc network transport layer to optimize the routing table and synchronize the optimized routing table to the core relay node and backup relay node. Furthermore, the edge nodes periodically send heartbeat detection signals to each peer node in the ad hoc network transport layer. If three consecutive heartbeats fail to respond, the channel is switched for verification. If no response is received after channel switching verification, the peer node is deemed faulty; a backup relay node is triggered to fill the faulty peer node's position. After filling the position, the backup relay node inherits the real-time synchronized routing table and performs a routing table broadcast update. The edge nodes also report the fault information of a peer node to the remote master station layer and issue a notification when a peer node is determined to be faulty. In this embodiment, the backup relay node's filling time is within 100ms.

[0024] The remote master station layer includes a dedicated transformer data acquisition master station, which is used to receive preprocessed data frames, perform data analysis, generate adjustment strategies, and distribute them to edge nodes.

[0025] In this embodiment, the method specifically includes: S1 Node Hierarchy: Each peer node in the ad hoc network transmission layer reports its signal strength and resource utilization to the edge computing layer. The edge nodes in the edge computing layer dynamically classify the roles of each peer node in the ad hoc network transmission layer based on the above data, and periodically broadcast the role information of each peer node. The dynamic role classification includes core relay nodes and backup relay nodes. The core relay nodes include core relay nodes that only forward and core relay nodes that collect and forward data.

[0026] S2 Data Frame Acquisition: The dedicated transformer acquisition terminal of the terminal perception layer collects the power dedicated transformer data, generates corresponding data frames, and sends them to the self-organizing network transmission layer. The data frame sets a 2-4 byte target identifier segment in the frame header. The data frame contains the target identifier and the target terminal ID.

[0027] S3 Data Frame Processing: Relay nodes in the ad hoc network transport layer receive data frames and perform pre-classification based on target identifiers. Data frames requiring preprocessing are sent to the edge computing layer, while other data frames are directly forwarded via hardware. The edge computing layer preprocesses the received data frames and sends the preprocessed frames to the remote master station layer. The S3 data frame processing includes S31 transmission protocol partitioning, S32 data frame classification, S33 incremental computation, S34 data preprocessing, and S35 transmission strategy partitioning, such as... Figure 3 As shown.

[0028] S31 Transmission Protocol Division: The ad hoc network transmission layer uploads link state information between peer nodes to the edge computing layer. Based on this link state information, the edge nodes determine the data transmission method used by the ad hoc network transmission layer: when the destination address of the data frame is within the local communication area, the ad hoc network transmission layer uses the optimized DSDV a priori routing protocol for data transmission; when the destination address of the data frame exceeds the local communication area, or when a link interruption occurs within the local communication area, the ad hoc network transmission layer uses the AODV reactive routing protocol for route discovery and reconstruction. The link state information includes packet loss rate, transmission latency, and bandwidth utilization.

[0029] S32 Data Frame Classification: Relay nodes in the ad hoc network transport layer receive data frames and perform pre-classification based on the target identifier. Core relay nodes receive data frames and classify them based on the target identifier. If the data frame is a directional data frame, it is parsed by the CPU and sent to the edge computing layer according to the corresponding routing protocol. If the data frame is a broadcast command data frame, it is parsed by the CPU and sent to the edge computing layer according to the corresponding routing protocol. If the data frame is a non-directional data frame, it is directly hardware forwarded to the next node or remote master station layer according to the corresponding routing protocol. Figure 4 As shown.

[0030] S33 Incremental Calculation: After receiving data frames from the network transmission layer, the edge computing layer compares the received data frames with the corresponding pre-stored historical data frames. Data frames that are different from the historical data frames are recorded as data frames to be processed, and the data frames to be processed are used as new received data frames.

[0031] S34 Data Preprocessing: The received data frames undergo progressive preprocessing, specifically including: employing improved 3... Outlier detection is performed using the criteria and Grubbs criterion; missing data is repaired using linear interpolation; standardization processing is performed using the DL / T645 standardization method; and duplicate data is removed using a sliding window with a preset duration.

[0032] S35 transmission strategy division: such as Figure 5 As shown, the edge nodes classify the preprocessed data frames according to priority; the priority includes first-level data frames, second-level data frames, and third-level data frames. The first-level data frame is core business data, the second-level data frame is ordinary business data, and the third-level data frame is non-critical data; When the preprocessed data frame is a first-level data frame, a dedicated bandwidth reservation and priority forwarding strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station. When the preprocessed data frame is a secondary data frame, a batch aggregation and timed transmission strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station; When the preprocessed data frame is a level 3 data frame, the idle time transmission strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station.

[0033] S4 Strategy Deployment: The remote master station layer's dedicated transformer acquisition master station receives the processed data frames sent by the edge computing layer, performs data analysis and storage, generates adjustment strategies as needed based on the analysis results, and deploys them to the edge nodes of the edge computing layer. The edge nodes then decompose the adjustment strategies to generate several lightweight tasks.

[0034] S5 Task Distribution: Edge nodes distribute the disassembled lightweight tasks to each dedicated transformer data acquisition terminal in the terminal perception layer through the self-organizing network transmission layer, and each dedicated transformer data acquisition terminal executes the lightweight tasks.

[0035] A data acquisition scenario was selected for a dedicated transformer in an industrial plant. A four-layer collaborative architecture was built within the plant: "Terminal Sensing Layer - Self-Organizing Network Transmission Layer - Edge Computing Layer - Remote Master Station Layer". The terminal sensing layer deployed 30 dedicated transformer data acquisition terminals integrating LoRa self-organizing network modules, collecting current, voltage, and power data every 15 minutes. Fault data triggered real-time acquisition. The self-organizing network transmission layer adopted LoRa Ad-hoc networking, deploying 5 core relay nodes for forwarding only, 5 core relay nodes for both data acquisition and forwarding, and 5 backup relay nodes. The heartbeat detection cycle for the entire network was set to 30 seconds. The edge computing layer deployed 2 edge gateways, configured with quad-core ARM processors, supporting SM4 encryption and FHSS frequency hopping for interference suppression. The remote master station layer relied on the existing dedicated transformer data acquisition master station, adding incremental interaction interfaces with the edge nodes.

[0036] After the system powers on, node classification is first performed. Edge nodes use -85dBm as the acceptable signal strength threshold and collect their own signal strength and resource utilization reported by each peer node. After evaluation, 10 nodes in the central plant area and data aggregation hotspot area met the signal strength standard and had low resource utilization, and were designated as core relay nodes. Among them, 5 nodes are dedicated to high-frequency forwarding tasks as forwarding-only core relay nodes, while another 5 nodes located in densely populated collection areas also perform local data collection as collection and forwarding core relay nodes. The remaining 5 nodes with acceptable signal strength and redundant resources are designated as backup relay nodes, synchronizing routing information with the core relay nodes in real time. After role classification, edge nodes broadcast the entire network node role information every minute.

[0037] During daily operation, each dedicated transformer data acquisition terminal sets a 3-byte target identifier segment in the frame header when generating data frames. One byte identifies the target node type, and two bytes identify the target terminal ID. The core data of the frame body carries the DL / T645 terminal address code as the terminal's unique ID. The data frame is sent to the nearest core relay node via the LoRa module. Taking the No. 3 transformer terminal in the plant area as an example, it normally reports load data at a 15-minute cycle. During a sudden overcurrent fault, real-time acquisition is triggered, shortening the acquisition cycle to the second level. The fault data frame is sent to the nearest core relay node that only forwards data. This node reads the 3-byte target identifier segment in the frame header through its FPGA chip, determines that the target terminal ID of the frame points to a remote master station rather than an edge node in the local area, and then directly performs hardware forwarding. The single-message processing latency is only 0.8ms, without the need for CPU intervention in full-frame parsing.

[0038] At the routing level, each core relay node within the local communication area exchanges routing information every 30 seconds to maintain the optimized DSDV a priori routing. When the signal strength of a relay link between the No. 3 transformer terminal and the edge gateway attenuates to below -100dBm due to metal obstruction, the edge node determines that the link is interrupted and triggers the AODV reactive routing protocol to rebuild the transmission path. At the same time, the edge node summarizes the link status information of the entire network every 2 minutes, eliminates poor-quality links with a packet loss rate exceeding 5%, and synchronizes the optimized routing table to the core relay nodes and backup relay nodes.

[0039] After the data arrives at the edge computing layer, the edge nodes perform progressive preprocessing: first, they use an improved 3D processing method... Initial screening was performed using the criteria and the Grubbs criterion, followed by a final screening using a 95% confidence level Grubbs test on critical data to avoid misjudgments caused by sudden changes in industrial load. Missing data due to momentary communication interruptions were repaired using linear interpolation. Data formats were standardized according to the DL / T645-2007 specification. Finally, a 5-minute sliding window was used to remove duplicate data from periodically reported data. After preprocessing, the amount of data was significantly reduced compared to the original data collection.

[0040] Subsequently, a differentiated transmission strategy is implemented. The regular load data of transformer No. 3 belongs to the secondary data frame. The edge node aggregates it in batches at a 15-minute cycle and sends it to the remote master station. If transformer No. 3 suddenly experiences an overcurrent fault, the fault alarm data is marked as a primary data frame. The edge node immediately reserves 20% of the bandwidth for it and performs priority forwarding. The measured transmission latency is 75ms, and the entire process from the occurrence of the fault alarm to the master station's response takes 180ms. The tertiary data, such as historical logs and statistical reports, are transmitted in batches during the off-peak hours from 0:00 to 5:00 in the morning to avoid occupying business bandwidth.

[0041] The remote master station receives the pre-processed valid data and, upon analysis, discovers abnormal load fluctuations in the area where transformer No. 3 is located. It then generates a load balancing adjustment strategy and distributes it to the corresponding edge nodes. The edge nodes break down the strategy into lightweight tasks such as transformer switching control commands and data acquisition cycle adjustment commands, encapsulating them into command frames with target identifier segments, and distribute them through the ad hoc network transport layer. The core relay node, receiving the downlink command frame, reads the 3-byte target identifier segment in the frame header using its FPGA chip, determines that the target terminal ID matches the transformer No. 3 terminal, performs a frame header pre-check, and then distributes the command to that terminal. After receiving the lightweight task, the transformer No. 3 terminal performs load adjustment locally without needing to transmit the full data back, thus completing the strategy loop.

[0042] During the above operation, assuming that a core relay node in the central part of the plant experiences a communication failure due to electromagnetic interference, and the edge node receives no response after three consecutive heartbeats, it switches channels for verification. If no response is received, the node is determined to be faulty. The backup relay node completes replacement within 80ms, inherits the real-time synchronized routing table, performs a broadcast update, and then forwards data frames to the dedicated transformer acquisition terminal according to the routing table to ensure transmission continuity. The entire fault detection process takes 280ms. The edge node performs dual verification on the fault information (channel switching verification and redundant data comparison), encrypts it with SM4, and reports it to the remote master station. The master station triggers an operation and maintenance reminder within 500ms.

[0043] Through the above methods, this embodiment achieves the following technical effects in the industrial plant scenario: transmission continuity reaches 99.92%, meeting the plant's reliability requirements; fault alarm response latency is 180ms, meeting real-time requirements; it is 60% better than the traditional 4G direct transmission solution; single message processing latency is 0.8ms, relay processing volume is reduced by 88%, and there is no message backlog; it saves 70% on wired construction and maintenance costs, reduces data transmission volume by 52%, and reduces traffic tariffs by 50%; FHSS frequency hopping technology reduces the packet loss rate from 8% to 1.2%, effectively adapting to the electromagnetic interference environment of the plant.

[0044] In this embodiment, this application achieves refined division of labor and collaborative management of relay nodes in the transmission layer of an ad hoc network through an edge-driven dynamic node hierarchical and role allocation mechanism. Backup relay nodes synchronize routing information with the core relay nodes in real time, enabling rapid replacement and routing table updates when a core relay node fails, effectively avoiding data transmission interruptions caused by single-point failures. Simultaneously, a dual fault detection mechanism combining heartbeat detection and channel switching verification ensures accurate fault determination, and a dual-verification reporting process for fault information guarantees that the master station can promptly obtain fault status and trigger a response. This closed-loop fault handling mechanism significantly improves the system's transmission continuity and fault resistance in complex industrial environments, completely solving the technical problems of delayed node fault response and easy transmission interruption in traditional solutions, providing highly reliable communication guarantees for power data acquisition services.

[0045] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A wireless self-organizing network data transmission method for a dedicated transformer data acquisition terminal supporting edge computing, characterized in that, Based on a collaborative architecture, the collaborative architecture includes a terminal perception layer, a self-organizing network transmission layer, an edge computing layer, and a remote master station layer; the method includes: Node hierarchy: Edge nodes in the edge computing layer dynamically assign roles to each peer node in the ad hoc network transmission layer and periodically broadcast node role information. The dynamic role assignment includes core relay nodes and backup relay nodes. Data frame acquisition: The dedicated transformer acquisition terminal of the terminal perception layer collects the dedicated power transformer data, generates corresponding data frames, and sends them to the self-organizing network transmission layer. The data frame includes the target terminal ID and the target identifier. Data frame processing: The relay nodes of the self-organizing network transmission layer receive data frames and perform pre-inspection and classification of the data frames based on the target identifier. The data frames that need to be pre-processed are sent to the edge computing layer, while other data frames are directly forwarded by hardware. The edge computing layer pre-processes the received data frames and sends the pre-processed data frames to the remote master station layer. Strategy distribution: The remote master station receives the processed data frames sent by the edge computing layer, performs data analysis and storage, generates adjustment strategies as needed based on the analysis results, and distributes them to the edge nodes of the edge computing layer. The edge nodes then decompose the adjustment strategies to generate several lightweight tasks. Task distribution: The edge node distributes the decomposed lightweight task to each dedicated transformer data acquisition terminal in the terminal perception layer through the self-organizing network transmission layer, and each dedicated transformer data acquisition terminal executes the lightweight task.

2. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 1, characterized in that, The terminal perception layer includes a dedicated transformer data acquisition terminal, which integrates a self-organizing network communication module and a lightweight computing module. The self-organizing network communication module is used to establish a wireless self-organizing network connection with the self-organizing network transmission layer. The lightweight computing module is used to generate lightweight data frames based on the acquired dedicated power transformer data and execute lightweight tasks issued by the edge nodes. The self-organizing network transmission layer includes several core relay nodes and several backup relay nodes. The core relay nodes are used to perform pre-inspection and classification of data frames based on target identifiers, send data frames that need to be pre-processed to the edge computing layer, and forward other data frames directly through hardware. The backup relay nodes are used to synchronize the routing information of the core relay nodes in real time, and perform replacement and routing table updates when the core relay nodes fail. The edge computing layer includes several edge nodes, which are used to perform progressive preprocessing on the received data frames. The progressive preprocessing includes outlier detection, missing data repair, reduction and normalization, and sliding window deduplication. The remote master station layer includes a dedicated transformer data acquisition master station, which is used to receive preprocessed data frames, perform data analysis, generate adjustment strategies, and distribute them to edge nodes.

3. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 2, characterized in that, Data frames are pre-classified based on target identifiers. Data frames requiring preprocessing are sent to the edge computing layer, while other data frames are directly forwarded via hardware. The core relay node receives data frames and classifies them based on the target identifier. If the data frame is a directional data frame, it is parsed by the CPU and sent to the edge computing layer. If the data frame is a broadcast instruction data frame, it is parsed by the CPU and sent to the edge computing layer. If the data frame is a non-directional data frame, it is forwarded directly by hardware.

4. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 1, characterized in that, The data frame has a 2-4 byte target identifier field in the frame header, and the data frame contains the target identifier and the target terminal ID.

5. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 1, characterized in that, In the node classification step, each peer node in the ad hoc network transmission layer reports its signal strength and resource occupancy rate to the edge computing layer. The edge nodes of the edge computing layer dynamically classify the roles of each peer node in the ad hoc network transmission layer based on the above data and periodically broadcast the role information of each peer node.

6. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 2, characterized in that, After receiving data frames and before performing progressive preprocessing, the edge computing layer also includes incremental computation: Incremental computation: After receiving data frames from the network transmission layer, the edge computing layer compares the received data frames with the corresponding pre-stored historical data frames. Data frames that are different from the historical data frames are recorded as data frames to be processed, and the data frames to be processed are used as new received data frames.

7. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 1, characterized in that, After the edge computing layer preprocesses the received data frames, before sending the preprocessed data frames to the dedicated transformer data acquisition master station, it also includes: Transmission strategy classification: The edge nodes classify the preprocessed data frames according to priority; the priority includes first-level data frames, second-level data frames, and third-level data frames; The first-level data frame is core business data, the second-level data frame is ordinary business data, and the third-level data frame is non-critical data; When the preprocessed data frame is a first-level data frame, a dedicated bandwidth reservation and priority forwarding strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station. When the preprocessed data frame is a secondary data frame, a batch aggregation and timed transmission strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station; When the preprocessed data frame is a level 3 data frame, the idle time transmission strategy is adopted to send the preprocessed data frame to the dedicated transformer acquisition master station.

8. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 2, characterized in that, The edge nodes are also used to perform adaptive hybrid routing, specifically including: the ad hoc network transport layer uploads the link state information between each peer node to the edge computing layer; based on the link state information, the edge nodes determine the data transmission method adopted by the ad hoc network transport layer: when the destination address of the data frame is within the local communication area, the ad hoc network transport layer uses the optimized DSDV a priori routing protocol for data transmission; when the destination address of the data frame exceeds the local communication area, or when a link interruption occurs within the local communication area, the ad hoc network transport layer uses the AODV reactive routing protocol for route discovery and reconstruction; the edge nodes are also used to summarize the link state information of each peer node in the ad hoc network transport layer to optimize the routing table, and synchronize the optimized routing table to the core relay node and the backup relay node; The link status information includes packet loss rate, transmission delay, and bandwidth utilization.

9. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 2, characterized in that, The edge node is also used to periodically send heartbeat detection signals to each peer node in the ad hoc network transport layer. When there is no response after three consecutive heartbeats, the channel is switched for verification. If there is still no response after the channel switching verification, the peer node is determined to be faulty. A backup relay node is triggered to fill the faulty peer node. After filling the position, the backup relay node inherits the real-time synchronized routing table and performs a routing table broadcast update. The edge node is also used to report the fault information of the peer node to the remote master station layer and provide a reminder when it is determined that the peer node is faulty.

10. The wireless self-organizing network data transmission method for dedicated transformer data acquisition terminals supporting edge computing according to claim 2, characterized in that, The progressive preprocessing specifically includes: the outlier detection employs an improved 3D algorithm. Outlier detection is performed using the criteria and the Grubbs criterion; missing data repair is performed using linear interpolation; the reduction and standardization process is performed using the DL / T645 reduction and standardization method; and the sliding window deduplication process uses a sliding window of a preset duration to remove duplicate data.

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