Remote reporting method and system for ring main unit remote signaling and telemetry data

Abstract

The application belongs to the technical field of data acquisition, and particularly relates to a remote reporting method and system for remote signaling and telemetry data of ring network cabinets, which comprises the following steps: terminal equipment collects remote signaling and telemetry data, extracts a multi-dimensional feature vector identified by data type, event weight coefficient and time sequence correlation code, and determines a compression ratio for reporting according to the event weight coefficient; a gateway analyzes the vector, generates enhanced charging data records by fusing user identification, flow information and access point name, and maintains a state table; if the time sequence correlation code of continuous packets conforms to a preset mode and the weight average exceeds a limit, the gateway establishes a special GTP tunnel and configures quality of service parameters to adjust the lower limit of communication bandwidth; if the establishment condition is not triggered, the telemetry sequence variance is calculated, and a statistical result vector is reported and aggregated and labeled when the variance exceeds a limit or the buffer is full. The application guarantees the real-time performance and integrity of key information transmission while reducing transmission delay caused by network congestion.

Description

Technical Field

[0001] This invention relates to the field of data acquisition technology. More specifically, this invention relates to a method and system for remotely reporting remote signaling and telemetry data from ring main units. Background Technology

[0002] With the continuous advancement of smart grid construction, the intelligence level of distribution networks is constantly improving. Ring main units (RNBs), as crucial node devices in distribution networks, undertake functions such as power distribution, line control, and operational status monitoring. To achieve real-time monitoring and scientific dispatch of the distribution network, it is necessary to remotely report the remote signaling and telemetry data collected by the RNBs to the control center or master station system. Remote signaling data includes switch status and protection action signals, while telemetry data includes electrical parameters such as voltage, current, and power. As the deployment range of RNBs expands and the monitoring dimensions increase, the amount of data generated by terminal devices has increased significantly, placing considerable transmission pressure on the underlying communication network. Especially when conducting long-distance communication through limited bandwidth channels such as wireless cellular networks, the indiscriminate aggregation of massive amounts of data can easily cause network congestion, thereby affecting the stability and real-time performance of the distribution IoT system.

[0003] Existing remote data reporting mechanisms for ring main units typically employ fixed-frequency periodic polling or indiscriminate data upload strategies, which are ill-suited to adapt to dynamic changes in power grid operating conditions. When abnormal fluctuations occur in the distribution network where the ring main unit is located, or when critical events occur, critical remote signaling and telemetry data require highly reliable, low-latency communication resources to ensure rapid delivery to the master station system. However, existing transmission mechanisms lack the ability to identify the importance of data and the flexibility to configure communication channels, making it difficult to establish dedicated transmission links and quality assurance for high-value data in a timely manner, leading to the risk of delays or loss of critical information. On the other hand, during most periods of stable power grid operation, the large amount of routine monitoring data generated by the ring main unit changes relatively little. If all data is still reported according to the standard procedure and billing records are generated separately, it can easily lead to data redundancy, invalid occupation of communication channels, and increased forwarding load on the gateway side, as well as storage and computing costs for the billing system. Therefore, there is an urgent need for a remote reporting method for ring network cabinet telemetry and telecontrol data that can dynamically allocate network resources based on data characteristics and operating conditions, and aggregate low-value redundant data, so as to balance communication efficiency, transmission reliability and operation and maintenance costs. Summary of the Invention

[0004] To address the technical problems of high network resource consumption, insufficient protection of critical data transmission, and heavy billing processing load caused by indiscriminate reporting of data from ring network cabinets in existing technologies, this invention provides a method and system for remote reporting of remote signaling and telemetry data from ring network cabinets.

[0005] This invention provides a method for remotely reporting remote signaling and telemetry data of a ring network cabinet, comprising: S1: The terminal device collects remote signaling and telemetry data, extracts a multi-dimensional feature vector containing data type identifier, event weight coefficient, and time sequence association code; the terminal device determines the compression ratio according to the event weight coefficient, compresses the remote signaling and telemetry data, and encapsulates the compressed remote signaling and telemetry data and the multi-dimensional feature vector into a data packet for reporting; S2: The gateway device receives the data packet and parses the multi-dimensional feature vector, integrates the multi-dimensional feature vector, user identifier, traffic information, and access point name to generate and cache enhanced billing data records, and the gateway device maintains the status table for the terminal device; S3: When the timing association codes of a preset number of consecutive data packets meet the preset pattern and the average event weight coefficient exceeds the first threshold, the gateway device establishes a dedicated GTP tunnel to forward subsequent data packets. Based on the average event weight coefficient, the service quality parameters of the dedicated GTP tunnel are configured to adjust the lower limit of the communication bandwidth allocated to the terminal device. When the conditions for establishing a dedicated GTP tunnel are not triggered, the gateway device decompresses consecutive data packets from the same terminal device, extracts telemetry data sequences of the same dimension and calculates the variance. When the variance exceeds the second threshold or the cache reaches the preset upper limit, a statistical result vector is constructed and reported, and the corresponding enhanced billing data records are aggregated and labeled.

[0006] By adopting the above technical solution, this invention extracts multi-dimensional feature vectors containing event weight coefficients through terminal devices and performs differentiated compression. Combined with a dedicated GTP tunnel elastic scheduling mechanism based on time-series patterns and weighted averages on the gateway side, and variance-triggered statistical aggregation processing for low-priority data, it achieves deep coupling between underlying communication resource allocation and power grid operating conditions. Under fault conditions, dedicated tunnels and dynamic QoS configuration provide exclusive transmission links for high-weight data, eliminating transmission delays caused by network congestion and ensuring the real-time nature and integrity of critical information. Under stable operating conditions, edge-side decompression and aggregation of redundant data, along with billing record aggregation and labeling, reduces the total uplink communication traffic, lowers channel occupancy costs, and reduces the concurrent processing load of the backend system.

[0007] Preferably, the step of extracting a multidimensional feature vector containing a data type identifier, an event weight coefficient, and a time-series association code includes: collecting the digital status code and analog current data of the ring main unit, generating the data type identifier; calculating the absolute difference between the currently collected analog current data and a preset safety benchmark value, normalizing the absolute difference and mapping it to the event weight coefficient; reading the system time stamp as the time-series association code, and constructing the multidimensional feature vector using the data type identifier, the event weight coefficient, and the time-series association code.

[0008] Preferably, the step of determining the compression ratio based on the event weight coefficient to compress the remote signaling and telemetry data includes: comparing the event weight coefficient with a preset limit constant; when the event weight coefficient is less than or equal to the preset limit constant, using a trie pruning algorithm to remove redundant bit sequences in the remote signaling and telemetry data; and when the event weight coefficient is greater than the preset limit constant, using a basic lossless compression algorithm to compress the remote signaling and telemetry data.

[0009] By adopting the above technical solutions, this invention uses a trie pruning algorithm to deeply remove redundant bit sequences during the stable period and switches to a lossless compression algorithm to retain the original details during the fault period. This ensures that while minimizing daily bandwidth usage, the fidelity of the data is guaranteed when a fault occurs, providing complete waveform data support for accurate source tracing analysis in the background.

[0010] Preferably, the step of generating and caching enhanced billing data records by fusing multidimensional feature vectors, user identifiers, traffic information, and access point names includes: extracting the MAC address or Internet Protocol address of the terminal device as the user identifier; using the actual cumulative traffic consumption statistics as the traffic information; concatenating the user identifier, the traffic information, the access point name, and the multidimensional feature vectors into a data block; encapsulating the header and footer control fields of the data block to generate the enhanced billing data record; and writing the enhanced billing data record into the background system cache area.

[0011] Preferably, the gateway device is a terminal device maintenance status table, comprising: establishing a linked data table in memory with each terminal device identifier as an index; storing the parsed time sequence association code and the event weight coefficient as table entries in the linked data table in a cyclic overwrite manner according to the receiving time order, and limiting the number of table entries stored for each terminal device to not exceed a preset capacity limit.

[0012] Preferably, the step of extracting telemetry data sequences of the same dimension and calculating variance includes: separating analog telemetry data of the same dimension from continuous data groups of the same decompressed terminal device to form the telemetry data sequence; calculating the arithmetic mean of each data point in the telemetry data sequence; calculating the square of the difference between each data point and the arithmetic mean, summing all the squares and dividing by the total number of data points to obtain the variance.

[0013] Preferably, configuring the service quality parameters of the dedicated GTP tunnel based on the average event weight coefficient to adjust the lower limit of the communication bandwidth allocated to the terminal device includes: calculating the difference between the average event weight coefficient and the first threshold; adding the product of the preset basic service quality allocation rate, the difference, and a preset constant factor to obtain the service quality parameters; and configuring the service quality parameters in the control node of the dedicated GTP tunnel to adjust the lower limit of the communication bandwidth allocated to the terminal device.

[0014] By adopting the above technical solution, the present invention enables the guaranteed bit rate of the dedicated GTP tunnel to increase synchronously with the increase of the severity of the fault. By dynamically adjusting the lower limit of the communication bandwidth of the terminal equipment, the smooth passage of high-frequency characteristic data streams under extreme disturbance conditions is forcibly guaranteed, avoiding packet loss caused by critical data surges.

[0015] Preferably, the construction and reporting of the statistical result vector includes: traversing the extracted telemetry data sequence, calculating the maximum value, minimum value and arithmetic mean of the telemetry data sequence respectively; storing the maximum value, minimum value and arithmetic mean into an array in a preset order to construct the statistical result vector; and reporting the statistical result vector to the application server through a publishing mechanism.

[0016] By adopting the above technical solution, the present invention compresses the variable-length data stream that originally varied with the size of the time window into a constant-length vector. Without losing the power grid operation trend, it reduces the uplink transmission volume of low-priority monitoring data and optimizes the communication efficiency under long-cycle monitoring tasks.

[0017] Preferably, the step of aggregating and labeling the corresponding enhanced billing data records includes: matching the corresponding enhanced billing data records in the database based on the user identifier, the start time and end time of the cached continuous data group of the same terminal device; and updating the aggregation label field of the matched enhanced billing data records to a preset state, wherein the aggregation label field includes the aggregation batch number, the start time, the end time, and the associated index of the statistical result vector.

[0018] By adopting the above technical solution, the present invention realizes the logical encapsulation of fragmented enhanced billing records, enabling the billing backend to process historical records in batches through associated indexes, transforming individual settlements into consolidated settlements based on aggregation periods, reducing the IO load and redundant storage overhead of the billing database, and improving the operation and maintenance management performance in large-scale terminal access scenarios.

[0019] Secondly, the present invention provides a remote reporting system for remote signaling and telemetry data of ring main units, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned remote reporting method for remote signaling and telemetry data of ring main units is implemented.

[0020] By adopting the above technical solution, the remote reporting method of remote signaling and telemetry data of the ring network cabinet is generated into a computer program and stored in the memory so that it can be loaded and executed by the processor. The terminal equipment can then be made based on the memory and the processor for convenient use.

[0021] The technical solution of the present invention has the following beneficial technical effects:

[0022] This invention extracts a multi-dimensional feature vector containing data type identifiers, event weight coefficients, and time-series correlation codes, and dynamically determines the compression ratio based on the event weight coefficients. This enables differentiated compression and encapsulation reporting of remote signaling and telemetry data, effectively reducing the data transmission volume under normal operating conditions. The gateway device generates and caches enhanced billing data records by parsing the aforementioned multi-dimensional feature vectors. Simultaneously, it performs critical event determination by combining the time-series correlation codes and the average event weight coefficients recorded in the status table maintained for terminal devices. When preset triggering conditions are met, the gateway device establishes a dedicated GTP tunnel and configures corresponding quality of service parameters to ensure the real-time and reliable transmission of high-value, important data in complex network environments. For low-priority data that does not trigger the dedicated channel establishment conditions, the gateway performs aggregation processing using data caching, variance evaluation, and statistical result vector reporting. This alleviates the processing pressure on the communication network and billing backend caused by frequent low-value communications, thereby improving the overall utilization efficiency of communication network resources. Attached Figure Description

[0023] Figure 1 This is a flowchart of the remote reporting method for remote signaling and telemetry data of the ring network cabinet in this invention; Figure 2 This is a diagram comparing the measured current data with the preset safety benchmark value; Figure 3 This is a schematic diagram illustrating the linkage between the average event weighting coefficient and the allocated bandwidth. Figure 4 This is a diagram comparing the transmission delays of different reporting mechanisms under power grid disturbance events. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0025] This invention discloses a method for remotely reporting remote signaling and telemetry data of a ring main unit, referring to... Figure 1 This includes steps S1-S3: S1, terminal data multidimensional feature extraction and compression reporting.

[0026] In an optional embodiment, the terminal device collects remote signaling and telemetry data, extracts a multi-dimensional feature vector containing data type identifiers, event weight coefficients, and time sequence association codes; the terminal device determines the compression ratio based on the event weight coefficients, compresses the remote signaling and telemetry data, and encapsulates the compressed remote signaling and telemetry data and the multi-dimensional feature vectors into data packets for reporting.

[0027] In practice, the system first reads analog signals such as voltage and current through the analog-to-digital converter interface of the terminal device and converts them into digital telemetry values. Simultaneously, it reads the digital telemetry status of the relay protection switch through the GPIO interface. Subsequently, the data processing unit of the terminal device classifies the acquired raw data, distinguishing between analog signals and digital status signals, and generating a data type identifier accordingly. This data type identifier marks whether the data carried in the current data packet belongs to the telemetry, telemetry, or hybrid type, thus providing a classification basis for subsequent differentiated processing on the gateway side.

[0028] Next, the system calculates the absolute difference between the currently collected analog current data and the preset safety benchmark value, and performs linear normalization on this absolute difference based on the preset deviation normalization benchmark value, mapping the calculation result to an event weight coefficient between 0 and 1. This event weight coefficient is used to quantitatively characterize the degree to which the current power grid operating state deviates from normal operating conditions: the closer the coefficient is to 0, the closer the current operating condition is to normal operating conditions; the closer the coefficient is to 1, the more serious the deviation from normal operating conditions. Simultaneously, the terminal device obtains the current timestamp in milliseconds from the system's real-time clock through its built-in NTP or GPS timing module, using this timestamp as a time-series association code. At this point, the system combines the data type identifier, event weight coefficient, and time-series association code to construct a multi-dimensional feature vector. This feature vector serves as the core input parameter for subsequent compression strategy selection, gateway-side tunnel establishment determination, and billing record generation.

[0029] After feature vector extraction, the system determines the compression mode and corresponding compression parameters based on the event weight coefficient. The specific mechanism is as follows: the system compares the event weight coefficient with a preset limit constant. When the event weight coefficient is less than or equal to the preset limit constant, it indicates that the power grid is in a stable operating state, and the system triggers a high compression ratio mode to minimize data volume, thereby reducing bandwidth consumption. When the event weight coefficient is greater than the preset limit constant, it indicates that the power grid may be in an abnormal or faulty state, and the system switches to a lower compression intensity basic lossless compression mode to reduce compression processing latency and retain the complete details of key data, ensuring that the backend can perform accurate source tracing analysis of fault data. This operation can directly and dynamically balance bandwidth consumption and data fidelity based on the power grid's operating status. The system calls the corresponding compression algorithm to perform compression processing on the original binary data of remote signaling and telemetry, and writes multi-dimensional feature vectors, compression algorithm identifiers, compression levels, dictionary version numbers, and compression parameter fields into the data packet header so that the gateway can select the correct decompression method accordingly.

[0030] After compression, the system writes a multi-dimensional feature vector consisting of data type identifier, event weight coefficient, and time sequence association code into the data packet header. This header is then concatenated with the compressed data payload to form a complete data packet to be reported. Finally, the system reports the encapsulated data packet to the gateway device via the Transmission Control Protocol socket sending interface, thus completing the entire closed-loop processing on the terminal side from data acquisition, feature extraction, differentiated compression to encapsulation and reporting.

[0031] In one optional embodiment, the extraction of the multidimensional feature vector containing data type identifier, event weight coefficient, and time sequence association code includes: collecting the digital status code and analog current data of the ring main unit to generate a data type identifier; calculating the absolute difference between the currently collected analog current data and the preset safety benchmark value, normalizing the absolute difference and mapping it to the event weight coefficient; reading the system time stamp as the time sequence association code, and constructing the data type identifier, event weight coefficient, and time sequence association code into a multidimensional feature vector.

[0032] More specifically, the terminal equipment acquires remote signaling and telemetry data through sampling circuits. For remote signaling data, the terminal equipment reads the digital status codes of the ring main unit control terminals via the GPIO interface, such as the circuit breaker status and grounding switch status, where 1 indicates closed and 0 indicates open. To meet the millisecond-level resolution requirements of relay protection action signals and effectively filter transient levels when mechanical contacts close, the sampling frequency is set to 1000Hz to reduce jitter. For telemetry data, the terminal equipment acquires analog signals through an analog-to-digital converter, including output signals from temperature and humidity sensors and electrical telemetry signals from voltage and current transformers converted by sampling or transmission circuits, such as standard industrial signals from 4mA to 20mA, with a sampling accuracy of 12 bits or 16 bits.

[0033] In calculating the event weighting coefficient, this embodiment selects the analog current data output by the current transformer as the calculation object, and other telemetry parameters as the monitoring data reported along with it. The preset safety benchmark value is determined based on the benchmark current under normal operating conditions of the ring main unit, for example, set to 12mA. If the currently collected analog current data is 16.5mA, the terminal device calculates the absolute difference between the two, obtaining 4.5mA. The terminal device uses a linear normalization function... Calculate the event weight coefficients, where This is the currently collected analog current data. To preset a safety baseline value, The preset deviation normalization benchmark value, and It has the same dimensions as analog current data. When When the current is 20mA, the event weighting coefficient in this embodiment is 0.225. A comparison diagram of the measured current data collected by the terminal and the preset safety benchmark value is shown below. Figure 2 As shown.

[0034] To ensure data traceability in the time dimension, the terminal device reads the millisecond-level system timestamp, such as 1633072800123, through its built-in NTP or GPS timing module. This timestamp is then used as a time-series association code and packaged with the data type identifier and event weight coefficient into a multi-dimensional feature vector. For example, in JSON format {“type”:0, “weight”:0.225,“timestamp”:1633072800123}, the 'type' field is an integer enumeration value, where 0 represents the telemetry data type identifier (e.g., 1 represents the remote signaling data type identifier, 2 represents the mixed data type identifier), the 'weight' field takes a floating-point value, and the 'timestamp' field takes an integer value, thus completing the feature extraction and encapsulation.

[0035] In an optional embodiment, the above-mentioned compression of remote signaling and telemetry data based on the event weight coefficient includes: comparing the event weight coefficient with a preset limit constant; when the event weight coefficient is less than or equal to the preset limit constant, using a trie pruning algorithm to remove redundant bit sequences in the remote signaling and telemetry data; when the event weight coefficient is greater than the preset limit constant, using a basic lossless compression algorithm to compress the remote signaling and telemetry data.

[0036] More specifically, the system pre-defines a preset limit constant in the flash memory to determine the degree of anomalies. The value of this constant is calculated based on the 95% confidence interval of the maximum peak value of current fluctuations relative to the baseline value during the historical stable operation of the distribution network, and is determined to be between 0.2 and 0.5, typically set to 0.3. At the end of the acquisition cycle, the microprocessor reads the previously calculated event weight coefficient and compares it with the preset limit constant. If the event weight coefficient is less than or equal to 0.3, for example, a calculated value of 0.15, it indicates that the power grid is operating stably and data fluctuations are small. The terminal device will then switch to a high compression ratio mode, using a trie pruning algorithm to process the original remote signaling and telemetry bit streams. The core mechanism of this algorithm is to construct a trie structure from frequently occurring data bit streams in the historical cycle, such as consecutive zero-value bits or fixed-pattern status codes; when scanning the current data block, it replaces matching redundant bit sequences with shorter index codes and removes leaf nodes with access frequencies below a preset threshold, such as 5 times / minute, through a pruning mechanism, thereby compressing the overall data volume to 15% to 20% of its original size.

[0037] To ensure correct decompression by the gateway device, both the terminal device and the gateway device pre-store a basic dictionary tree with the same version number. The header of each data packet contains a compression algorithm identifier, a dictionary version number, and a pruning rule identifier. During decompression, the gateway device calls the same basic dictionary tree based on the compression algorithm identifier and dictionary version number, restores the corresponding dictionary tree state according to the pruning rule identifier, and then performs reverse decoding. If the terminal device generates new dictionary update entries during operation, these updates are sent as compression control fields along with the corresponding data packets, ensuring that the gateway device synchronously updates its local dictionary tree before decompression.

[0038] When the event weight coefficient exceeds a preset limit constant of 0.3, for example, a sudden short circuit causing the coefficient to rise above the preset limit constant, it indicates a fault condition in the power grid. To ensure the integrity of the fault data and the background decompression and analysis capabilities, the system calls basic lossless compression algorithms such as the Lempel-Ziv-Welch algorithm (LZW algorithm) or the Deflate algorithm. These algorithms perform light compression while preserving the detailed features of the original data. The compressed data volume accounts for about 50% to 60% of the original data volume, thus achieving a dynamic balance between bandwidth consumption and data fidelity based on the current safe operating status of the distribution network. Without this differentiated compression based on the event weight coefficient, the system would consume a large amount of unnecessary communication bandwidth when the power grid is operating smoothly, while under fault conditions, excessive compression intensity might lead to the loss of key data details, thereby affecting the background's accurate fault diagnosis and source tracing analysis.

[0039] S2, gateway data parsing and billing record maintenance.

[0040] In an optional embodiment, the gateway device receives data packets and parses multidimensional feature vectors. It then integrates the multidimensional feature vectors, user identifiers, traffic information, and access point names to generate and cache enhanced billing data records. The gateway device maintains a status table for the terminal device. When the timing association codes of a preset number of consecutive data packets conform to a preset pattern and the average event weight coefficient exceeds a first threshold, the gateway device establishes a dedicated GTP tunnel to forward subsequent data packets. Based on the average event weight coefficient, it configures the quality of service parameters of the dedicated GTP tunnel to adjust the lower limit of the communication bandwidth allocated to the terminal device.

[0041] In the actual execution process, the system first receives data packet byte streams from each terminal device via a listening socket. Following a preset data structure, the gateway device parses the data type identifier, event weight coefficient, and timing association code from the data packet header to reconstruct a multi-dimensional feature vector. Subsequently, the gateway device obtains the user identifier, cumulative traffic information, and access point name associated with the current communication session. This information is then integrated with the parsed multi-dimensional feature vector according to a preset format to generate an enhanced billing data record, which is then cached in a Redis in-memory database or a relational database. Simultaneously, the gateway device establishes a status table in memory, indexed by the identifier of each terminal device, to continuously record dynamic information such as the timing association code, event weight coefficient, and connection status of each terminal device.

[0042] For each terminal device, the gateway device stores the time-series correlation codes parsed from continuously received data packets into a first-in-first-out (FIFO) circular buffer and parses the timestamps corresponding to each time-series correlation code. The system determines the preset pattern of the time-series correlation codes: if the timestamps corresponding to a preset number of consecutive data packets monotonically increase, and the deviation between the difference of adjacent timestamps and the preset sampling period is within the preset time jitter interval, then the time-series correlation code is determined to conform to the preset pattern. For example, when the preset sampling period is 1 second and the time jitter interval is ±100 ms, if the difference between adjacent timestamps of five consecutive data packets falls between 900 ms and 1100 ms, then it is determined to conform to the preset pattern. This time-series pattern determination mechanism is set to filter out isolated spikes caused by instantaneous electromagnetic interference from sensors or abnormal analog-to-digital conversion, ensuring that core network resources are consumed to establish a dedicated tunnel only when a continuous real power grid physical disturbance is detected. Based on this, the gateway device calculates the average value of the event weight coefficients stored in the circular buffer. When the average value of the event weight coefficients exceeds a first threshold, the gateway device triggers the dedicated GTP tunnel establishment process. Specifically, the gateway device generates a dedicated GTP tunnel establishment request and sends it to the session management function node through a preset control interface. The session management function node configures a dedicated GTP tunnel for the corresponding terminal device based on the request. The gateway device uses the dedicated GTP tunnel to forward subsequently received data packets, thereby providing a dedicated transmission channel for high-value data.

[0043] Simultaneously with tunnel establishment, the gateway device calculates the difference between the average event weight coefficient and the first threshold. This difference is multiplied by a constant factor with rate conversion dimensions to obtain the compensation rate, which is then added to the preset basic service quality allocation rate to obtain service quality parameters such as the guaranteed bit rate or maximum guaranteed bit rate. These parameters are used to configure the dedicated GTP tunnel and forward subsequent data packets. A schematic diagram illustrating the linkage between the average event weight coefficient and allocated bandwidth within the sliding window is shown below. Figure 3 As shown.

[0044] In one optional embodiment, the above-mentioned generation and caching of enhanced billing data records by integrating multidimensional feature vectors, user identifiers, traffic information, and access point names includes: extracting the MAC address or Internet Protocol address of the terminal device as the user identifier; using the actual cumulative traffic consumption statistics as traffic information; concatenating the user identifier, traffic information, access point name, and multidimensional feature vectors into a data block; encapsulating the header and footer control fields of the data block to generate enhanced billing data records; and writing the enhanced billing data records into the background system cache area.

[0045] More specifically, when a gateway device, such as an edge computing gateway or a 5G user plane function node, receives a reported data packet, it uses a protocol parsing module to extract the source MAC address from the packet header, such as 00:1A:2B:3C:4D:5E, or an assigned static Internet Protocol address, such as 10.25.130.45, as a user identifier to locate the specific ring network cabinet terminal device. The traffic monitoring process of the gateway's underlying network interface reads the cumulative uplink and downlink bytes actually consumed by the terminal device in the current TCP or UDP connection, generating a cumulative traffic statistic, for example, a value of 15400 bytes. The gateway processor then aligns and concatenates the parsed user identifier, the aforementioned traffic statistic, the access point name in the communication network, such as njhwg.sgcc.net, along with a multi-dimensional feature vector extracted from the data packet containing data type identifiers, event weight coefficients, and timing correlation codes, according to a preset TLV structure (type-length-value structure) or a comma-separated ASCII string format, forming a complete continuous data block.

[0046] Subsequently, the system encapsulates the data block by adding header and tail control fields such as start delimiters, checksums, and terminators, and packages it to generate an enhanced billing data record that conforms to the pre-designed billing record format or is compatible with 3GPP extended fields. Once generated, the record is written to the high-speed RAM backend system cache allocated within the gateway device via memory access technology, such as a pre-allocated 128MB circular buffer, awaiting batch retrieval and settlement by the billing server at set intervals, such as every 15 minutes.

[0047] In one optional embodiment, the gateway device is a terminal device maintenance status table, including: establishing a linked data table in memory with each terminal device identifier as an index; storing the parsed time sequence association code and event weight coefficient as table entries in the linked data table in a cyclic overwrite manner according to the receiving time order, and limiting the number of table entries stored by each terminal device to no more than a preset capacity limit.

[0048] More specifically, during the startup initialization phase, the gateway device allocates independent storage space in DRAM using memory allocation functions or memory pool technology, for example, allocating 32MB as a terminal state tracking cache. The gateway establishes a hash table within the terminal state tracking cache, using each terminal device identifier as an index to maintain the corresponding terminal's state linked list. The terminal device identifier can be a MAC address, device serial number (UUID), or other unique device identifier. After receiving a new data packet and parsing out the multi-dimensional feature vector, the gateway combines the timing association code and event weight coefficients into a new state table entry and inserts this entry at the tail of the corresponding terminal's state linked list according to the data packet's arrival time.

[0049] To limit memory usage and retain recent state information, the gateway considers both the time window length for the aforementioned preset mode determination and the number of concurrent data streams in a single ring network unit, setting a maximum capacity for each terminal state list, for example, 64 entries. When the number of entries in a terminal state list reaches this capacity limit, subsequent new entries will overwrite the oldest entry in the list, thus forming a first-in-first-out (FIFO) cyclical update mechanism. In this way, the gateway can continuously maintain the timing association code and event weight coefficients of each ring network unit terminal within the most recent sliding window, providing a complete data foundation for subsequent timing mode determination and event weight average calculation.

[0050] In an optional embodiment, the above-mentioned configuration of service quality parameters for a dedicated GTP tunnel based on the average event weight coefficient to adjust the lower limit of communication bandwidth allocated to the terminal device includes: calculating the difference between the average event weight coefficient and a first threshold; adding the product of a preset basic service quality allocation rate, the difference, and a preset constant factor to obtain the service quality parameters; and configuring the service quality parameters at the control node of the dedicated GTP tunnel to adjust the lower limit of communication bandwidth allocated to the terminal device.

[0051] More specifically, when the average value of a set of event weight coefficients recently cached by the terminal exceeds a preset first threshold, for example, if the real-time statistical result is 0.65, exceeding the first threshold of 0.5, it indicates that a medium-to-high level power grid disturbance event has been detected. The resource scheduling module of the gateway device performs a subtraction difference operation: subtracting the first threshold of 0.5 from the actual average value of 0.65, the positive over-limit difference value is 0.15. The first threshold is set based on the statistical lower bound threshold of the event weight coefficients extracted under historical short-circuit faults and heavy load sudden change conditions.

[0052] To enable on-demand bandwidth expansion, a basic quality of service allocation rate is pre-configured in the system memory to ensure stable transmission of basic signaling. This value is preset to 512Kbps based on the minimum guaranteed bandwidth requirement of the network. Simultaneously, the system sets an amplification constant factor with the dimension of rate conversion, i.e., Kbps. This amplification constant factor is set based on the bandwidth allocation granularity of the 5G underlying core network resource blocks and the maximum transmission rate requirement of burst fault waveform data, and is configured between 1000Kbps and 5000Kbps, specifically 2000Kbps here. The scheduling module combines the over-limit difference of 0.15 with the constant factor. Multiplying these values ​​yields a compensation rate of 300Kbps. This compensation rate is then superimposed on the base rate of 512Kbps to calculate the service quality parameter, i.e., the target lower limit rate, as 812Kbps. After obtaining this bandwidth parameter, the gateway device sends a policy update request to the session management function node via a preset control interface. The session management function node, based on this request, configures the service quality parameters for the corresponding dedicated GTP tunnel through the core network control interface or the GTP control plane protocol, mapping the 812Kbps value to the guaranteed bit rate configuration parameter for the dedicated GTP tunnel. This closed-loop configuration process increases the lower limit of the underlying communication bandwidth allocated to this specific terminal device, reducing network congestion or packet loss during the uploading of high-frequency telemetry waveform data when a power grid fault occurs. If the service quality parameter is not dynamically adjusted based on the average event weight coefficient, the communication bandwidth will remain at a baseline level under fault conditions, failing to provide additional transmission resources to guarantee against sudden surges in fault data. This can lead to delays or loss of critical fault information due to network congestion, contradicting the requirements of smart distribution networks for rapid fault response.

[0053] After S2 completes the dedicated channel guarantee for critical event data, the system enters the aggregation and processing phase for low-priority routine data.

[0054] S3, channel elastic scheduling and low-priority data aggregation.

[0055] In an optional embodiment, when the conditions for establishing a dedicated GTP tunnel are not triggered, the gateway device decompresses continuous data packets from the same terminal device, extracts telemetry data sequences of the same dimension and calculates the variance. When the variance exceeds a second threshold or the cache reaches a preset upper limit, a statistical result vector is constructed and reported, and the corresponding enhanced billing data records are aggregated and labeled.

[0056] In the specific execution process, the system first queries the status table through the gateway device to determine whether the continuous data packets from the terminal device have met the triggering conditions for establishing a dedicated GTP tunnel. When the tunnel establishment conditions are not triggered, it indicates that the current power grid is operating stably and the corresponding event weight coefficient is at a low level. The system objectively defines such data packets as low-priority data, such as slowly changing temperature and humidity data and base load current data generated during routine inspections. For such low-priority data, the gateway device sequentially writes continuous data packets from the same terminal device into the local first-in-first-out (FIFO) buffer queue. For each data packet entering the buffer queue, the gateway device reads the compression algorithm identifier, compression level, dictionary version number, and compression parameters from its data packet header, and selects the corresponding decompression method based on the compression algorithm identifier.

[0057] Subsequently, the system performs differential decompression based on the compression algorithm identifier. When the compression algorithm identifier corresponds to a standard general lossless compression algorithm or a fast lossless compression algorithm, the gateway device calls the reverse decompression interface of the corresponding compression algorithm library to restore the data payload. When the compression algorithm identifier corresponds to a trie pruning algorithm, the gateway device synchronizes the local trie according to the dictionary version number, pruning rule identifier, and dictionary update items carried with the packet, and executes a dedicated reverse decoding process for the trie pruning algorithm to restore the data payload. After decompression, the gateway device extracts telemetry data of the same dimension from the restored plaintext data to form a telemetry data sequence within the current cache window, and uses the Welford online recursive algorithm to calculate the variance of the telemetry data sequence to avoid the memory overhead caused by storing all original data points. The core mechanism of this algorithm is that for each new data point received, only the current data point value, the existing data point count, the current mean, and the current cumulative square difference need to be used for recursive updates, without retaining the complete historical values ​​of processed data points in memory, thereby reducing space complexity from Reduce to .

[0058] When the latest calculated variance exceeds a preset second threshold, or when the number of data packets in the cache queue reaches a preset cache limit, the gateway device immediately triggers the data aggregation and reporting mechanism. After triggering, the gateway device performs statistical operations and constructs result vectors on the telemetry data sequences within the cache window, and performs aggregation annotation on the enhanced billing data records participating in the aggregation process, thereby completing the closed loop of compression aggregation and annotation of low-priority data.

[0059] In one optional embodiment, the above-mentioned extraction of telemetry data sequences of the same dimension and calculation of variance includes: separating analog telemetry data of the same dimension from continuous data groups of the same decompressed terminal device to form a telemetry data sequence; calculating the arithmetic mean of each data point in the telemetry data sequence; calculating the square of the difference between each data point and the arithmetic mean, summing all the squares and dividing by the total number of data points to obtain the variance.

[0060] More specifically, in the decompressed binary plaintext data, the parsing module, based on preset field offsets and field identifiers, removes non-target data fields such as discrete telemetry status codes, extracts analog telemetry data with the same dimensions, such as analog current measurements in mA, and sorts them in ascending order by timestamp to recover data containing continuous... Telemetry data sequence of data points For example, when At that time, a raw telemetry data sequence containing 100 analog current measurements was obtained.

[0061] Sum these 100 data points, assuming the total sum is 1250mA, and divide by the total number of data points (100) to obtain the arithmetic mean. The internal floating-point unit is used to traverse the sequence, processing each data point... Subtracting the average value of 12.5mA yields the deviation, which is then multiplied to obtain its squared value. The sum of these 100 squared deviations is then divided by the total sample size of 100 to output a floating-point variance. The variance value, as a statistical result, for example, a variance of 0.04 mA², can assess the degree of dispersion and jitter of telemetry parameters such as the ring main unit current deviating from a stable state within a time window in a single numerical form. Correspondingly, the second threshold is determined through experimental testing based on the normal fluctuation range of the monitored telemetry parameters. For example, the second threshold can be specifically set to 0.05 mA², and set to a threshold with the same dimension as the variance of the extracted telemetry data sequence. If all low-priority data is reported directly without using a variance evaluation mechanism, a large amount of redundant communication traffic will be generated during long-term stable operation of the power grid, which will not only occupy channel resources but also increase the number of records and storage burden of the billing system. However, through the variance-triggered aggregation reporting mechanism, the system only performs reporting when it detects a significant change in the statistical characteristics of the data or when the cache reaches its capacity limit, thereby effectively reducing the communication frequency of low-value data and the billing processing overhead.

[0062] In one optional embodiment, the above-mentioned construction and reporting of statistical result vector includes: traversing the extracted telemetry data sequence, calculating the maximum value, minimum value and arithmetic mean of the telemetry data sequence respectively; storing the maximum value, minimum value and arithmetic mean into an array in a preset order to construct a statistical result vector; and reporting the statistical result vector to the application server through a publishing mechanism.

[0063] More specifically, when the data aggregation and reporting mechanism is triggered, the gateway device initiates a full traversal of the extracted telemetry data sequence in the cache queue. The system employs a two-pointer circular comparison algorithm to scan each data point in the sequence sequentially: the first pointer maintains the maximum value of the data points scanned during the current traversal, initialized to the value of the first data point in the sequence; the second pointer maintains the minimum value of the data points scanned, also initialized to the value of the first data point. During the traversal, for each new data point read, the system compares it with the current maximum and minimum value pointers respectively. If the new data point is greater than the current maximum value, the maximum value pointer is updated; if it is less than the current minimum value, the minimum value pointer is updated. Simultaneously, the system sums all data points using an accumulator, and after the traversal is complete, the accumulated sum is divided by the total number of data points to obtain the arithmetic mean.

[0064] The three statistics mentioned above—maximum, minimum, and arithmetic mean—are stored in a double-precision floating-point array in a preset order to construct a statistical result vector. For example, the array is organized as follows: index 0 stores the maximum value, index 1 stores the minimum value, and index 2 stores the arithmetic mean, with corresponding example values ​​of 15.2mA, 9.8mA, and 12.5mA, respectively. After the result vector is constructed, the gateway device reports the statistical result vector to the cloud-based IoT application server via an HTTP POST request or an MQTT publishing mechanism. Using the statistical result vector instead of the original full data for reporting reduces the amount of reported data from... The original telemetry data points were reduced to three fixed statistical characteristic values, significantly reducing the amount of uplink communication data. If all data were reported directly... For example, given a large cache window, a number of original data points... When the data volume reaches several hundred, the amount of uplink data will increase linearly; while the statistical result vector compresses the data volume to a constant level that is independent of the window size, effectively ensuring the bandwidth saving effect of the low-priority data aggregation mechanism.

[0065] In an optional embodiment, the above-mentioned aggregation annotation of the corresponding enhanced billing data record includes: matching the corresponding enhanced billing data record in the database according to the user identifier, the start time and end time of the continuous data group of the same cached terminal device; updating the aggregation annotation field of the matched enhanced billing data record to a preset state, wherein the aggregation annotation field includes the aggregation batch number, start time, end time and the associated index of the statistical result vector.

[0066] More specifically, after the statistical result vector is reported, the gateway device queries the database for enhanced billing data records belonging to the current cache processing scope, based on the user identifier, the start and end times of this cached data group, and the aggregation batch number assigned by the system for this aggregation operation. The aggregation batch number is generated by the gateway device using a combination of timestamp and terminal device identifier, for example, by concatenating the last 4 bytes of the terminal device's MAC address with the Unix timestamp of the trigger time, ensuring that the batch number for each aggregation operation is unique within the system. The database query uses a composite index of user identifier and time range for matching. After locating the corresponding enhanced billing data record, the gateway device updates the aggregation label field of the corresponding record to a preset state, for example, by updating the boolean value of the aggregation label field from false to true, to indicate that this part of the data has been aggregated by the gateway edge node and does not need to be settled one by one by the billing system.

[0067] The structure of the aggregation label field includes: an aggregation batch number, used to uniquely identify this aggregation operation; the start and end times of the cached data group, used to define the time range of the original data participating in the aggregation; the association index of the statistical result vector, used to establish the correspondence between the aggregation label record and the reported statistical result vector; and the number of original data groups, used to record the number of original data groups covered by this aggregation operation, facilitating statistical verification by the billing system. After the update is completed, the gateway device clears the cache nodes of the corresponding terminal device and resets the status variables of the online variance calculation, restoring the system to its initial state to prepare for the next round of data aggregation. Through this aggregation labeling mechanism, the billing system can map multiple enhanced billing data records that originally required settlement one by one into one aggregation label record plus one statistical result vector, thereby reducing the number of processing entries and storage overhead in the billing backend.

[0068] The experiment was built on a real power distribution network monitoring and communication network environment. Core equipment included 50 intelligent ring main unit terminals and a 5G computing gateway deployed at the edge. Test data consisted of a full month's worth of actual power distribution network operation remote signaling and telemetry data from a municipal power supply bureau, covering stable operation conditions as well as simulated short-circuit faults and sudden load changes. The experiment included three groups: a baseline group using a fixed compression algorithm and a fixed lower limit for communication bandwidth; an ablation group incorporating only an event weight coefficient-based compression mechanism; and a complete scheme group incorporating both a compression mechanism and a service quality parameter adjustment mechanism based on the event weight mean.

[0069] During stable operation, the baseline group maintained a fixed compressed data volume ratio of 50%, with an average network bandwidth usage of 650Kbps and a packet loss rate of 12.4% during sudden failures. The ablation group optimized the compressed data volume ratio to 18% during stable operation and reduced daily bandwidth usage to 240Kbps. However, during sudden failures, due to the inability to increase network bandwidth, the data transmission latency reached 520ms and the packet loss rate was 5.3%. The complete solution group maintained an 18% compressed volume ratio and low bandwidth consumption during stable operation. When the average event weight coefficient exceeded the limit, the gateway increased the guaranteed bit rate of the dedicated tunnel from the basic 512Kbps to 812Kbps, reducing the average data transmission latency during failures to 42ms and the packet loss rate to 0.1%. The transmission latency test results of the three solutions under power grid disturbance events are as follows: Figure 4 As shown.

[0070] The compression mechanism can distinguish between stable grid operation and abnormal disturbance states. By removing redundant bit sequences through a pruning algorithm, it reduces daily basic bandwidth consumption and storage overhead. The quality of service parameter adjustment mechanism identifies data surges caused by sudden events and reduces network congestion and loss of high-frequency characteristic data by adjusting the rate lower limit of the dedicated tunnel's underlying control nodes. The integration of these two mechanisms in the complete solution achieves a balance between normalized compression and high-fidelity, low-latency transmission during fault periods, improving the real-time performance of distribution network situational awareness and the stability of the communication network.

[0071] Figure 2 This is a schematic diagram comparing the measured current data with the preset safety benchmark value. The horizontal dashed line in the background represents the preset safety benchmark value; the fluctuating solid line represents the analog current data collected in real time by the ring main unit terminal equipment. The image shows that the measured current waveform exhibits a sudden change far from the horizontal dashed line at a specific moment. This proves that the terminal side can accurately capture abnormal fluctuations in the analog quantity that deviate from normal operating conditions, and calculate the event weight coefficient used to quantify the degree of disturbance, thus providing accurate data input for the subsequent selection of dynamic compression strategies.

[0072] Figure 3 This diagram illustrates the linkage between the average event weight coefficient and allocated bandwidth. The dotted lines represent the average event weight coefficients statistically obtained within a continuous time window; the horizontal dotted lines represent the first threshold used to trigger resource scheduling; and the stepped solid lines represent the communication bandwidth allocated by the gateway device to the terminal device. Observing the image, it can be seen that when the dotted line crosses the horizontal dotted line, the stepped solid lines immediately jump upwards, and the increase in bandwidth is positively correlated with the difference between the dotted line exceeding the first threshold. This corresponds to the technical logic described in the specific implementation method of dynamically adjusting the dedicated tunnel service quality parameters based on the weight over-limit difference.

[0073] Figure 4 This diagram illustrates the comparison of transmission delays for different reporting mechanisms under power grid disturbance events. The dotted lines represent the baseline control group using fixed parameters; the short dashed lines represent the ablation experiment group introducing only a differentiated compression mechanism; and the thick solid lines represent the complete scheme group with both dynamic compression and elastic bandwidth scheduling mechanisms. The comparison shows that during the middle period of the simulated power grid disturbance, the transmission delay corresponding to the short dashed lines rapidly spikes to a high level, while the delay corresponding to the thick solid lines remains consistently at a very low level. This demonstrates that the present invention successfully mitigates data surges during fault periods through a dedicated tunnel bandwidth compensation mechanism, achieving synergistic optimization of efficient compression under normal conditions and high-fidelity, low-latency transmission during abnormal periods.

[0074] This invention also discloses a remote reporting system for remote signaling and telemetry data of ring main units, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the remote reporting method for remote signaling and telemetry data of ring main units according to this invention is implemented.

[0075] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0076] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for remotely reporting remote signaling and telemetry data of a ring main unit, characterized in that, include: S1: The terminal device collects remote signaling and telemetry data and extracts a multi-dimensional feature vector containing data type identifiers, event weight coefficients, and time sequence correlation codes; The terminal device determines the compression ratio based on the event weight coefficient, compresses the telemetry and teleindication data, and encapsulates the compressed telemetry and teleindication data with multidimensional feature vectors into data packets for reporting; S2: The gateway device receives the data packets and parses the multidimensional feature vectors, integrates the multidimensional feature vectors, user identifiers, traffic information, and access point names to generate and cache enhanced billing data records, and the gateway device maintains the status table for the terminal device; S3: When the time sequence association code of a preset number of consecutive data packets meets the preset pattern and the average event weight coefficient exceeds the first threshold, the gateway device establishes a dedicated GTP tunnel to forward subsequent data packets, configures the service quality parameters of the dedicated GTP tunnel based on the average event weight coefficient, and adjusts the lower limit of the communication bandwidth allocated to the terminal device; when the conditions for establishing a dedicated GTP tunnel are not triggered, the gateway device decompresses consecutive data packets of the same terminal device, extracts telemetry data sequences of the same dimension and calculates the variance, and constructs and reports a statistical result vector when the variance exceeds the second threshold or the cache reaches the preset upper limit, and aggregates and labels the corresponding enhanced billing data records.

2. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The step of extracting a multidimensional feature vector containing a data type identifier, an event weight coefficient, and a time-series association code includes: collecting the digital status code and analog current data of the ring main unit to generate the data type identifier; calculating the absolute difference between the currently collected analog current data and a preset safety benchmark value, normalizing the absolute difference and mapping it to the event weight coefficient; reading the system time stamp as the time-series association code, and constructing the multidimensional feature vector using the data type identifier, the event weight coefficient, and the time-series association code.

3. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The step of compressing remote signaling and telemetry data by determining the compression ratio based on the event weight coefficient includes: comparing the event weight coefficient with a preset limit constant; when the event weight coefficient is less than or equal to the preset limit constant, using a trie pruning algorithm to remove redundant bit sequences in the remote signaling and telemetry data; and when the event weight coefficient is greater than the preset limit constant, using a basic lossless compression algorithm to compress the remote signaling and telemetry data.

4. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The process of generating and caching enhanced billing data records by fusing multidimensional feature vectors, user identifiers, traffic information, and access point names includes: extracting the MAC address or Internet Protocol address of the terminal device as the user identifier; using the actual cumulative traffic consumption statistics as the traffic information; concatenating the user identifier, the traffic information, the access point name, and the multidimensional feature vector into a data block; encapsulating the data block with header and footer control fields to generate the enhanced billing data record; and writing the enhanced billing data record into the background system cache area.

5. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The gateway device is a terminal device maintenance status table, which includes: establishing a linked data table in memory with each terminal device identifier as an index; storing the parsed time sequence association code and the event weight coefficient as table entries in the linked data table in a cyclic overwrite manner according to the receiving time order, and limiting the number of table entries stored for each terminal device to not exceed a preset capacity limit.

6. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The step of extracting telemetry data sequences of the same dimension and calculating variance includes: separating analog telemetry data of the same dimension from continuous data groups of the same decompressed terminal device to form the telemetry data sequence; calculating the arithmetic mean of each data point in the telemetry data sequence; calculating the square of the difference between each data point and the arithmetic mean, summing all the squares and dividing by the total number of data points to obtain the variance.

7. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The method of configuring the service quality parameters of the dedicated GTP tunnel based on the average event weight coefficient to adjust the lower limit of the communication bandwidth allocated to the terminal device includes: calculating the difference between the average event weight coefficient and the first threshold; adding the product of the preset basic service quality allocation rate, the difference, and a preset constant factor to obtain the service quality parameters; and configuring the service quality parameters in the control node of the dedicated GTP tunnel to adjust the lower limit of the communication bandwidth allocated to the terminal device.

8. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The process of constructing and reporting the statistical result vector includes: traversing the extracted telemetry data sequence, calculating the maximum value, minimum value, and arithmetic mean of the telemetry data sequence respectively; storing the maximum value, minimum value, and arithmetic mean into an array in a preset order to construct the statistical result vector; and reporting the statistical result vector to the application server through a publishing mechanism.

9. The method for remotely reporting remote signaling and telemetry data of a ring main unit according to claim 1, characterized in that, The step of aggregating and labeling the corresponding enhanced billing data records includes: matching the corresponding enhanced billing data records in the database based on the user identifier, the start time and end time of the cached continuous data group of the same terminal device; and updating the aggregation label field of the matched enhanced billing data records to a preset state, wherein the aggregation label field includes the aggregation batch number, the start time, the end time and the associated index of the statistical result vector.

10. A remote reporting system for remote signaling and telemetry data of a ring main unit, characterized in that, include: The processor and memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the remote reporting method for remote signaling and telemetry data of the ring network cabinet according to any one of claims 1-9 is implemented.

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