Distribution network terminal data processing methods, devices and distribution network terminals

Abstract

This application provides a data processing method, apparatus, and terminal for a distribution network terminal, relating to the field of distribution network technology. The method includes: acquiring the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal; determining a target time period rule matching the current time according to a preset rule table; determining the target priority of the data to be processed based on priority determination logic in the target time period rule; allocating the data to be processed to a target buffer in a multi-level buffer for caching according to the target priority; determining a dynamic reporting strategy for multiple buffers based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal; and performing reporting processing on the data cached in the multi-level buffers according to the dynamic reporting strategy. This application aims to improve the intelligence and efficiency of data processing in distribution network terminals, achieving efficient and orderly data reporting.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a power distribution network terminal data processing method, device and power distribution network terminal. Background Technology

[0002] With the transformation of the global energy structure and the deepening of power system reform, smart distribution networks, as a key infrastructure supporting the efficient operation of modern power systems, are experiencing unprecedented rapid development. Distribution terminals, such as feeder terminal units (FTUs) and transformer terminal units (TTUs), serve as the basic units of smart distribution networks, undertaking the important tasks of data acquisition, status monitoring, and preliminary processing. They are widely distributed across various nodes of the distribution network, providing data support for grid dispatching, rapid fault response, and optimized operation. Therefore, the sophistication of distribution terminal data processing methods directly affects the overall efficiency and reliability of smart distribution networks.

[0003] Currently, data reporting from distribution terminals mainly adopts two modes: "fixed-period reporting" and "simple event-triggered reporting." In the fixed-period reporting mode, the distribution terminal sends data to the master station system at preset time intervals, regardless of data changes or network conditions. This method is simple and easy to implement but lacks flexibility. Simple event-triggered reporting, on the other hand, is based on the occurrence of specific events (such as voltage exceeding limits, switch changes, etc.) to trigger data reporting. While this can reduce invalid data transmission to some extent, its event judgment logic is relatively basic and fails to fully consider the complexity and dynamism of distribution network operation.

[0004] However, existing processing methods focus on data collection and transmission in the data processing flow. Faced with the complex and ever-changing operational needs of smart distribution networks, they suffer from low communication bandwidth utilization efficiency and rudimentary data management. This results in critical data not being transmitted in a timely and accurate manner during the operation of the distribution network, affecting the speed of fault handling and making it difficult to achieve operational optimization. Summary of the Invention

[0005] The data processing method, apparatus, and distribution network terminal provided in this application are intended to address the problems of existing processing methods that focus on data acquisition and transmission in the data processing flow. In the face of the complex and ever-changing operational needs of smart distribution networks, these methods suffer from low communication bandwidth utilization efficiency and rudimentary data management. The aim is to improve the intelligence and efficiency of data processing in distribution network terminals, and to achieve precise allocation of bandwidth resources and efficient and orderly data reporting.

[0006] In a first aspect, embodiments of this application provide a data processing method for distribution network terminals, including:

[0007] Obtain the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal;

[0008] Based on the preset rule table, determine the target time period rule that matches the current time. The rule table includes the bandwidth allocation strategy and priority determination logic corresponding to different time periods.

[0009] Based on the priority determination logic in the target time period rules, the target priority of the data to be processed is determined;

[0010] Based on the target priority, the data to be processed is allocated to the target cache area in the multi-level cache area for caching. The multi-level cache area includes multiple cache areas with different reporting priorities.

[0011] Based on the bandwidth allocation strategy in the target time period rules and the current status parameters of the uplink communication link of the distribution network terminal, a dynamic reporting strategy for multiple buffer zones is determined.

[0012] According to the dynamic reporting strategy, the data cached in the multi-level cache is reported.

[0013] In one possible implementation, the target priority of the data to be processed is determined based on the priority determination logic in the target time period rule, including: obtaining a priority determination result based on the measured value in the data to be processed and a preset event trigger threshold, wherein the priority determination result is used to indicate whether the data to be processed is high-priority event data; and determining the target priority of the data to be processed based on the priority determination logic in the target time period rule and the priority determination result.

[0014] In one possible implementation, the method further includes: obtaining the real-time occupancy status of the cache area corresponding to the preset reporting priority in the target cache area; determining the system processing status as a high-priority processing status based on the real-time occupancy status or priority determination result; performing persistent processing on the target data to be reported in the high-priority processing status to obtain persistent data, and the persistent processing instruction to write the target data to be reported to a non-volatile storage medium.

[0015] In one possible implementation, a dynamic reporting strategy for multiple buffer zones is determined based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal. This includes: determining the link status level of the uplink communication link based on the current status parameters of the uplink communication link of the distribution network terminal, wherein the current status parameters include at least one of instantaneous bandwidth value, signal strength indication, and packet loss rate; and determining the dynamic reporting strategy for multiple buffer zones based on the bandwidth allocation strategy in the target time period rule and the link status level, wherein the dynamic reporting strategy includes reporting order and reporting channel type.

[0016] In one possible implementation, a dynamic reporting strategy for multiple buffer zones is determined based on the bandwidth allocation strategy and link status level in the target time period rule, including: when the link status level indicates that the bandwidth is limited, the dynamic reporting strategy is determined to perform reporting processing on the buffer zone corresponding to the preset reporting priority in the multi-level buffer zone.

[0017] In one possible implementation, the method further includes: obtaining a data freshness index of cached data in the target cache; and performing replacement processing on the cached data in the target cache according to the data freshness index and a preset replacement threshold corresponding to the target cache to obtain an updated target cache.

[0018] In one possible implementation, the method further includes: obtaining the current occupancy rate of each cache in the multi-level cache; dynamically adjusting the capacity allocation ratio between the multi-level caches according to the bandwidth allocation strategy in the target time period rule and the current occupancy rate of each cache, to obtain the adjusted multi-level cache.

[0019] In one possible implementation, the method further includes: obtaining an update instruction for the rule table, authenticating the update instruction, and obtaining an authentication result; if the authentication result indicates that the authentication is successful, updating the rule table based on the update instruction, and performing an integrity check on the updated rule table to obtain a check result; if the check result indicates that the check is successful, using the updated rule table as a preset rule table; if the check result indicates that the check fails, performing a version rollback on the rule table to obtain a rolled-back rule table.

[0020] Secondly, embodiments of this application provide a power distribution network terminal data processing device, comprising:

[0021] The acquisition module is used to acquire the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal;

[0022] The first determining module is used to determine the target time period rule that matches the current time according to the preset rule table. The rule table includes the bandwidth allocation strategy and priority determination logic corresponding to different time periods.

[0023] The second determination module is used to determine the target priority of the data to be processed based on the priority determination logic in the target time period rule;

[0024] The caching module is used to allocate the data to be processed to the target cache area in the multi-level cache area according to the target priority. The multi-level cache area includes multiple cache areas with different reporting priorities.

[0025] The third determining module is used to determine the dynamic reporting strategy for multiple buffer zones based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal.

[0026] The processing module is used to perform reporting processing on the data cached in the multi-level cache area according to the dynamic reporting strategy.

[0027] Thirdly, embodiments of this application provide a power distribution network terminal, including: a memory and a processor;

[0028] The memory stores instructions that the computer executes;

[0029] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0030] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0031] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed, implements the first aspect and / or various possible implementations of the first aspect.

[0032] The data processing method, apparatus, and distribution network terminal provided in this application embodiment acquire the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal; determine a target time period rule matching the current time according to a preset rule table; determine the target priority of the data to be processed based on the priority determination logic in the target time period rule; allocate the data to be processed to the target buffer in the multi-level buffer according to the target priority; determine a dynamic reporting strategy for multiple buffers based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal; and perform multi-level buffer processing according to the dynamic reporting strategy. The method for processing data cached in the middle is based on a pre-defined rule table containing bandwidth allocation strategies and priority determination logic for different time periods. It matches the target time period rules according to the current time, and can dynamically allocate bandwidth according to the characteristics of the time period, such as ensuring the transmission of critical business during peak hours and avoiding resource waste during off-peak hours. It also optimizes the reporting strategy by combining link status, speeding up when the link is good and ensuring high-priority data during congestion, thereby improving bandwidth utilization efficiency. At the same time, the priority determination based on the time period rules divides the data into multi-level cache areas to achieve fine-grained management, and then reports in an orderly manner according to dynamic strategies to ensure that high-priority data is transmitted first, avoids disorderly data competition for bandwidth, meets the complex operation requirements of the distribution network, and solves the problem of rough data management. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0034] Figure 1 Flowchart of the data processing method for distribution network terminals provided in this application Figure 1 ;

[0035] Figure 2 Flowchart of the data processing method for distribution network terminals provided in this application Figure 2 ;

[0036] Figure 3 This is a schematic diagram of the structure of the distribution network terminal provided in this application;

[0037] Figure 4 Flowchart of the data processing method for distribution network terminals provided in this application Figure 3 ;

[0038] Figure 5 This is a schematic diagram of the structure of the power distribution network terminal data processing device provided in this application;

[0039] Figure 6 A schematic diagram of the structure of the electronic device provided in this application.

[0040] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0042] In existing technologies, data processing at distribution network terminals focuses on data acquisition and transmission. Current methods do not dynamically adjust to time-of-day characteristics. For example, using a fixed bandwidth allocation model cannot adapt to changes in business volume and demand at different times in a smart distribution network. Furthermore, they ignore the impact of time periods on data priority determination, making it difficult to accurately reflect data importance. Simultaneously, they lack comprehensive consideration of link status, and reporting strategies do not consider real-time link status, easily causing data transmission problems or wasting valuable network resources during network congestion. In addition, data caching and management mechanisms are imperfect, lacking multi-level caches with different reporting priorities, fine-grained cache management, and effective dynamic reporting strategies. This leads to disordered data management, failing to meet the complex and ever-changing needs of smart distribution networks, resulting in low communication bandwidth utilization efficiency and rudimentary data management.

[0043] To address the aforementioned issues, this application provides a data processing method, apparatus, and distribution network terminal. By pre-setting a rule table containing bandwidth allocation strategies and priority determination logic for different time periods, the method matches target time period rules based on the current time of the distribution network terminal. Based on this, the priority determination logic within the target time period rules determines the target priority of the data to be processed and allocates it to a target cache area within a multi-level cache area with different reporting priorities, achieving refined data classification and caching. Simultaneously, by combining the bandwidth allocation strategy in the target time period rules with the current status parameters of the distribution network terminal's uplink communication link, a dynamic reporting strategy for multiple cache areas is determined, and then reporting processing is performed on the cached data according to this strategy. In this way, bandwidth allocation and data priority determination can be dynamically adjusted according to the characteristics of different time periods, and a reasonable reporting strategy can be formulated based on the real-time status of the link, thereby improving the utilization efficiency of communication bandwidth, achieving refined data management, and better meeting the complex and ever-changing operational needs of smart distribution networks.

[0044] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0045] The execution entity of the power distribution terminal data processing method provided in this application embodiment can be a computing device such as a server or server cluster. The server can be a mobile phone, computer, tablet, or other device, such as a computer device serving as a power distribution terminal. This application embodiment does not impose any particular restrictions on the implementation method of the execution entity, as long as the execution entity can obtain the current time of the power distribution terminal and the data to be processed collected by the power distribution terminal; determine the target time period rule matching the current time according to a preset rule table, the rule table including bandwidth allocation strategies and priority determination logic corresponding to different time periods; determine the target priority of the data to be processed based on the priority determination logic in the target time period rule; allocate the data to be processed to the target cache area in the multi-level cache area for caching according to the target priority, the multi-level cache area including multiple cache areas with different reporting priorities; determine the dynamic reporting strategy for multiple cache areas based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the power distribution terminal; and perform reporting processing on the data cached in the multi-level cache area according to the dynamic reporting strategy.

[0046] Figure 1 Flowchart of the data processing method for distribution network terminals provided in this application Figure 1 The execution entity of this method can be a server storing the data processing method for distribution network terminals or other servers. This embodiment does not impose any special restrictions here. Figure 1 As shown, the method may include:

[0047] S101. Obtain the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal.

[0048] In this context, distribution network terminals can refer to various terminal devices installed in the distribution network, such as distribution transformer monitoring terminals and feeder terminals, which are responsible for collecting relevant data on the operation of the distribution network. Furthermore, terminals may have embedded processors, limited storage resources, and wireless or wired communication interfaces.

[0049] The current time can refer to the time information used to calibrate the data processing time, and can be represented in a standard time format. For example, this time information can be a complete timestamp in "year-month-day-hour-minute-second" format, or it can be simplified time information containing only "hour-minute". Furthermore, the current time can be obtained through a clock module inside the distribution network terminal, which can be synchronized with a standard time source to ensure time accuracy.

[0050] Data to be processed refers to various types of data collected by distribution network terminals, such as electrical quantity data like current, voltage, and power, as well as non-electrical quantity data like equipment status and environmental parameters. In one example, data to be processed is raw information collected by the terminal through various sensor interfaces that needs to be analyzed or sent to the superior master station system. This data is characterized by being multi-source and heterogeneous, with varying data types, generation frequencies, and business importance.

[0051] In some examples, the clock module of the distribution network terminal can periodically synchronize its time with a GPS or Network Time Protocol (NTP) server to ensure the accuracy of the current time. For example, a time calibration may be performed with the NTP server every hour.

[0052] For data acquisition, different types of data can be collected using different sensors. For example, current data can be acquired using a current transformer, voltage data using a voltage transformer, and equipment status data using a switch sensor. The acquired data can be stored locally in the distribution network terminal's memory, awaiting retrieval by the server. The server can obtain the current time and data to be processed from the distribution network terminal via wired communication (such as Ethernet) or wireless communication (such as 4G or 5G).

[0053] S102. Based on the preset rule table, determine the target time period rule that matches the current time. The rule table includes the bandwidth allocation strategy and priority determination logic corresponding to different time periods.

[0054] In this step, the preset rule table can refer to a pre-defined table or data structure that contains different time periods and their corresponding bandwidth allocation strategies and priority determination logic. For example, the rule table can be a structured data set pre-stored in the terminal's non-volatile memory. Furthermore, the rule table can support updates via local maintenance tools or remote communication.

[0055] A time period refers to a time interval defined based on the operation and management needs of the distribution network or the load characteristics of the communication network. Examples include normal operation periods, peak electricity consumption periods, off-peak nighttime periods, planned maintenance periods, or fault handling periods. It should be noted that time period divisions can be determined based on historical electricity consumption data and distribution network operation experience.

[0056] A bandwidth allocation strategy refers to a set of constraints imposed on the timing of transmission of data with different priorities and the proportion of communication resources that can be used within a specific time period in order to optimize the utilization of uplink communication links. For example, this strategy may include the proportion of bandwidth allocated to different types of data, bandwidth limits, etc. For instance, during peak hours, more bandwidth can be allocated to data related to power supply security (such as monitoring data of important equipment) to ensure its timely transmission; during off-peak hours, the bandwidth allocation for various types of data can be appropriately reduced to avoid resource waste.

[0057] Priority determination logic refers to the rules used to determine the priority of data to be processed. This logic depends not only on the type of data itself, but also on factors such as time period attributes and the urgency of the physical event represented by the data. For example, priority determination logic can be set based on factors such as the urgency and importance of the data. For instance, equipment fault alarm data has a higher priority than regular monitoring data, and data with high real-time requirements has a higher priority than data with low real-time requirements.

[0058] In some examples, the rule table can be a configuration file stored in a structured format (such as JSON or CSV) containing multiple "time period rule" entries. Further, each entry may include: a time period ID to uniquely identify the entry; a time period name describing the scope of the entry (such as normal operating hours); a start and end time field, in the format HH:MM, to define the effective time window of the entry; a bandwidth percentage field specifying the maximum allowable uplink bandwidth percentage for the highest priority data within that time period; and a policy description field defining the reporting order and triggering conditions for each priority data level.

[0059] Furthermore, in some embodiments, the specific matching process for determining the target time period rule may involve comparing the hour and minute information of the current time with the start and end time fields of each entry in the rule table. For example, if the current time is 14:30, after traversing the rule table, the "normal operation period" rule with a start and end time of "08:00" to "17:00" is selected as the target time period rule. If the current time simultaneously meets the time windows of multiple entries, the entry with the highest priority is selected as the target time period rule according to the preset entry priority. If the current time fails to match any preset time window, a default rule built into the program is used as the target time period rule.

[0060] S103. Determine the target priority of the data to be processed based on the priority determination logic in the target time period rule.

[0061] In this context, target priority refers to a level identifier assigned to the data to be processed based on the current time period and the characteristics of the data itself, used to guide subsequent caching and reporting. This level identifier indicates the urgency of the data and the transmission guarantee requirements.

[0062] Furthermore, the server analyzes and judges various characteristics of the data to be processed based on the priority determination logic in the target time period rules, thereby determining its target priority.

[0063] In some examples, if the priority determination logic specifies that equipment fault alarm data has the highest priority, regular monitoring data is divided into different levels according to the importance of the monitored object. When the data to be processed includes equipment fault alarm data, its target priority is directly set to the highest; for regular monitoring data, the priority is determined according to whether the monitored equipment is critical equipment (such as main transformer, important feeder, etc.), with monitoring data of critical equipment having a higher priority and monitoring data of non-critical equipment having a lower priority.

[0064] In other examples, the priority determination logic can be a decision tree or decision table containing "IF-THEN" rules. Taking the various attributes of the data to be processed as input, this logic outputs a defined priority. For example, the logic could specify that if the measured value of data exceeds a preset critical alarm threshold, or if the data originates from a specific protection action signal, its target priority is unconditionally set to "highest" or "real-time." The logic could also specify that during "normal operation periods," the target priority of regular measurement data from periodically sampled data is set to "medium" or "near real-time"; and during "off-peak nighttime periods," the target priority of non-electrical quantity data from environmental sensors is set to "lower" or "delayed."

[0065] Furthermore, the generation method of the data to be processed can be used as one of the input parameters for the priority determination logic. For example, state change data triggered by an interrupt can receive a higher base weight in the priority determination logic than periodically sampled data, even if the corresponding physical quantity does not reach the critical alarm threshold. By calculating the difference between the data collection timestamp and the current time, a data freshness index can be obtained. For some non-urgent data, its target priority can be dynamically downgraded as the freshness index increases.

[0066] S104. Based on the target priority, allocate the data to be processed to the target cache area in the multi-level cache area for caching. The multi-level cache area includes multiple cache areas with different reporting priorities.

[0067] A multi-level cache can refer to a storage area composed of multiple caches with different reporting priorities, used for temporarily storing data to be processed. This area can be multiple logically divided storage queues with clearly defined functions and management strategies within the terminal's memory space. Different levels of caches correspond to different data importance and processing urgency, thereby achieving data flow management.

[0068] The target buffer refers to the specific buffer instance selected from the multi-level buffers based on the target priority determined in S103, used to store the current data to be processed. Different reporting priorities indicate the order in which each buffer is served during subsequent reporting scheduling. In practical applications, the buffer storing high-target-priority data can be set to have the highest reporting priority, and its data will be sent first.

[0069] In some examples, the multi-level buffer can be specifically divided into three logical buffers: a real-time buffer, a near-real-time buffer, and a delayed buffer. The real-time buffer is configured to store data with the highest target priority, having the highest reporting priority. The near-real-time buffer is configured to store data with the medium target priority, having a medium reporting priority. The delayed buffer is configured to store data with the lowest target priority, having the lowest reporting priority. For example, device fault alarm data, due to its high target priority, will be allocated to the real-time buffer to ensure timely reporting and processing; while some routine monitoring data with low real-time requirements may be allocated to the delayed buffer, waiting for other high-priority data to be reported before processing.

[0070] Furthermore, the storage capacity of each cache area can be statically allocated or dynamically adjusted. In a static allocation scheme, a fixed amount of storage space (e.g., a 3:4:3 ratio) is pre-allocated to the real-time, near-real-time, and delayed areas based on the total memory size of the terminal. In a dynamic allocation scheme, the occupancy rate of each area is continuously monitored. When the occupancy rate of a certain area (e.g., the real-time area) exceeds a preset threshold, and the rules of the currently active time period allow it, some free space can be temporarily allocated from other areas (e.g., the delayed area) to cope with sudden data surges.

[0071] Furthermore, the specific operation of allocating data to be processed to the target cache can be to encapsulate the data content, the data timestamp, and the target priority tag into a data frame structure, and then add a pointer or copy of this data frame structure to the tail of the first-in-first-out queue corresponding to the target cache. For data entering the real-time area, a "non-flushable" flag can also be set in memory to prevent it from being accidentally deleted by subsequent cache cleanup operations.

[0072] S105. Based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal, determine the dynamic reporting strategy for multiple buffer zones.

[0073] The uplink communication link refers to the physical communication connection between the terminal and the upstream master station system, such as a wireless cellular link based on 4G / 5G technology or an Ethernet link based on fiber optic transmission. Current status parameters are a set of quantitative indicators that reflect the quality and service capability of the uplink communication link in real time or near real time. They indicate "how much data the channel can send and at what speed," reflecting the current operational status of the link.

[0074] Dynamic reporting strategy refers to the method and order in which data from different buffers in a multi-level buffer are reported, based on the preset bandwidth management requirements and real-time link quality for the current time period. In other words, dynamic reporting strategy is an execution strategy concerning "when to send how much data from which buffer, and how to send it." This strategy is not static but changes dynamically with the environment and rules.

[0075] In some examples, obtaining the current status parameters of the uplink communication link can be achieved by calling the AT command interface of the terminal's communication module to query and parse the returned signaling data, thereby obtaining a reference signal received power (such as the 4G RSRP value) or a signal strength indication such as the signal-to-interference-plus-noise ratio. Alternatively, within the most recent preset time window, the success rate and acknowledgment delay of data packets sent from the terminal to the master station can be statistically analyzed to estimate the effective uplink bandwidth and packet loss rate of the link. Another approach is to assess network congestion by sending Internet Control Message Protocol (ICP-IP) probe packets to a specific network address of the master station and measuring the round-trip time.

[0076] Furthermore, the server needs to comprehensively consider the bandwidth allocation strategy in the target time period rules and the current status parameters of the uplink communication link to determine the dynamic reporting strategy. For example, when the link bandwidth utilization is high, it may be necessary to prioritize the reporting of data in the high-priority buffer while appropriately reducing the reporting of low-priority data to avoid low-priority data occupying limited bandwidth; when the link signal-to-noise ratio is low, it may be necessary to adjust the data reporting method, such as adopting a more reliable encoding method or reducing the data transmission rate. This strategy, by combining time period rules and link status, achieves dynamic allocation of bandwidth resources, ensuring that critical data is transmitted first during high-load periods, thereby improving the utilization efficiency of communication bandwidth.

[0077] In one example, the server can monitor parameters such as bandwidth utilization, signal-to-noise ratio, and packet loss rate of the uplink communication link in real time. The specific process of determining the dynamic reporting strategy can involve comprehensively and weighting the acquired effective uplink bandwidth, packet loss rate, round-trip latency, and other status parameters to obtain a discrete link status level (e.g., Level 0: Excellent; Level 1: Average; Level 2: Limited). Then, this link status level is used as input to query the bandwidth allocation strategy in the target time period rule. For example, if the current target time period rule is "accident period," and its bandwidth allocation strategy stipulates that real-time zone data must occupy 100% of bandwidth resources, and the current link status level is "limited," then the generated dynamic reporting strategy might be: immediately suspend all reporting activities in the quasi-real-time zone and the delayed zone, and continuously send only the data buffered in the real-time zone using the highest retransmission mechanism and the highest priority queue until the real-time zone is cleared or the link status recovers.

[0078] S106. According to the dynamic reporting strategy, perform reporting processing on the data cached in the multi-level cache area.

[0079] In this step, performing the reporting process can refer to the process of extracting data from a multi-level buffer according to a determined dynamic reporting strategy, encapsulating the data into an application layer message through a selected communication channel and transmission protocol, and sending it to the upper-level system or other servers through the uplink communication link.

[0080] In some examples, based on the dynamic reporting strategy, when the strategy indicates sufficient bandwidth, three parallel reporting tasks are initiated: one task continuously retrieves data from the real-time queue and sends it directly using low-latency Transmission Control Protocol or User Datagram Protocol (UDP) messages; another task retrieves data from the near-real-time queue, performs field-level data compression (e.g., only reports changes instead of the full data), and then sends it in batches; the third task runs in the background with low priority, and when other queues are idle, retrieves historical data from the delayed queue and performs breakpoint-resumption reporting using long, low-priority packets.

[0081] When the policy instructs that only high-priority data should be reported, the reporting tasks in the near-real-time zone and the delayed zone are suspended or paused, and all communication resources are concentrated on sending real-time data. The real-time zone queue is checked; if the queue is not empty, the "retrieve data - encapsulate message - send" operation is executed in a loop, and confirmation is awaited. If the queue is empty, the system may briefly enter a sleep state or send a link-keeping message to wait for new high-priority data to arrive or for the policy to change.

[0082] Furthermore, during the reporting process, for data that is successfully sent and confirmed by the main station, the data record can be deleted from the corresponding cache. For data that fails to be sent or times out, a decision can be made based on the data's target priority and the preset retransmission strategy: immediate retransmission, delayed retransmission, or eventual discarding, and the corresponding audit logs will be recorded.

[0083] The power distribution network terminal data processing method provided in this application overcomes the shortcomings of existing technologies, such as fixed-period reporting modes ignoring network status and data value, coarse priority management, and rigid caching strategies, by introducing a time-period awareness mechanism based on a preset rule table and establishing a differentiated dynamic reporting system that is linked to time-period rules and real-time link status and includes multi-level priority buffers. This method can dynamically adjust the target priority of the data to be processed according to the current time period and specific event context, diverting it to different priority buffers for refined management. Simultaneously, by comprehensively considering the real-time monitored uplink status and the preset bandwidth allocation strategy for the current time period, it intelligently decides on a dynamic reporting strategy adapted to the current operating conditions. In complex power distribution network scenarios with limited bandwidth, it ensures low-latency transmission of high-priority critical alarm data, avoiding network congestion and loss of critical information caused by multi-terminal data flooding. This achieves efficient utilization of uplink communication bandwidth resources and intelligent and precise control over the reporting of massive, multi-source, heterogeneous data from the power distribution network, improving the adaptability and reliability of edge terminals under limited resource conditions.

[0084] Based on the above embodiments, when determining the target priority of the data to be processed, if only a preset static priority determination logic bound to a time period is relied upon, it may be possible that under certain sudden abnormal working conditions, highly urgent data cannot be identified as the highest priority in a timely manner due to the rule restrictions corresponding to the time period in which it is located, thereby delaying the reporting of critical alarm information.

[0085] To address this issue, the method described in S103 for determining the target priority of data to be processed based on the priority determination logic in the target time period rule may include: obtaining a priority determination result based on the measured value in the data to be processed and a preset event trigger threshold, wherein the priority determination result is used to indicate whether the data to be processed is high-priority event data; and determining the target priority of the data to be processed based on the priority determination logic in the target time period rule and the priority determination result.

[0086] In this embodiment, the measured value refers to the raw or preliminary processed physical quantity value directly collected from the primary equipment or environment of the distribution network without undergoing complex calculations. This value reflects the specific operating state of the distribution network at a certain moment. Unlike the data to be processed mentioned above, this specifically refers to numerical data that can be quantified and compared.

[0087] A preset event trigger threshold is a critical value pre-configured within the terminal to define whether the operating state is abnormal. It is a criterion for determining whether a physical event has occurred. This threshold can be a single upper limit (e.g., temperature > 85℃), a single lower limit (e.g., voltage < 180V), a rate of change threshold (e.g., current mutation rate > 200A / s), or a composite judgment condition composed of multiple conditions.

[0088] The priority determination result refers to a Boolean or enumerated conclusion obtained by comparing the measured value with the event trigger threshold. This result explicitly indicates whether the current piece of data to be processed originates from a high-priority event that requires special attention and priority processing. It is an intermediate conclusion used to assist in subsequent final priority determination.

[0089] In some examples, the measured value can be the current effective value of phase A current (e.g., 250A) acquired and calculated by the feeder terminal unit through a current transformer. The preset event trigger threshold can be an overcurrent alarm threshold, set to 200A. When this measured value is acquired, it is compared with the preset overcurrent alarm threshold. Because 250A is greater than 200A, the comparison logic outputs a value of True or a high priority result, indicating that the data is high-priority event data. Conversely, if the measured value is 150A, the output value is False or a low result.

[0090] Furthermore, the measured value can also be the top-layer oil temperature of the transformer collected by the distribution transformer monitoring terminal via an oil temperature sensor (e.g., 88℃). The preset event trigger threshold can be a high-temperature alarm threshold (85℃) and a temperature rise rate alarm threshold (5℃ / min). The priority determination result depends not only on whether the current temperature exceeds 85℃, but also on whether the temperature rise rate in the most recent minute exceeds 5℃ / min. As long as either condition is met, a determination result indicating high-priority event data will be obtained.

[0091] In other examples, the measured value can be a composite calculated value, such as the power factor calculated from the phase difference between voltage and current. A preset event trigger threshold can be a threshold where the power factor is below 0.8. When the calculated real-time power factor is below this threshold, the priority determination result is triggered.

[0092] In addition, to avoid frequent fluctuations in threshold judgment caused by measurement noise or instantaneous disturbances, the original measurement value can be low-pass filtered or median filtered before performing threshold comparison to obtain a smoother and more stable measurement value, which is then compared with the preset event trigger threshold.

[0093] In some examples, the target priority includes high priority, medium priority, and low priority; accordingly, based on the target priority, the data to be processed is allocated to the target cache in the multi-level cache for caching, including: in response to the target priority being high priority, the data to be processed is allocated to the real-time cache; in response to the target priority being medium priority, the data to be processed is allocated to the near-real-time cache; and in response to the target priority being low priority, the data to be processed is allocated to the deferred cache.

[0094] In this embodiment, it should be noted that the priority determination logic in the target time period rule provides a time-based basic priority framework; while the obtained priority determination result provides a real-time correction signal based on physical events.

[0095] In some examples, assuming the target time period rule is "nighttime off-peak hours," the basic priority determination logic stipulates that within this time period, the target priority of all periodically collected routine electrical quantity data (such as voltage and current) is "medium" (corresponding to the near-real-time zone). However, if the priority determination result for a certain current data is "indicated as high-priority event data" (e.g., due to current surge exceeding limits), then in this step, this priority determination result will override the default rule for "nighttime off-peak hours." Ultimately, the target priority of this data is forcibly determined to be "high" (corresponding to the real-time zone), and it will not be downgraded because it occurs at night.

[0096] In other examples, assuming the target time period rule is "accident handling period," the basic priority determination logic already stipulates that all electrical quantity data should be given "high" priority. In this case, regardless of whether the priority determination result is "high" or "low," the final determined target priority will be "high." In this scenario, the rule-based framework already provides the highest level of protection for emergency situations, while the event threshold determination exists as a redundant confirmation mechanism.

[0097] Furthermore, determining target priorities can also be a quantitative scoring process. For example, a basic priority framework assigns a base score to the data (such as a time-based weight score), while the priority determination result provides a correction coefficient or additional score (such as an event urgency score). Finally, the target priority is determined by calculating a comprehensive score and mapping it to a pre-defined score-priority mapping table. For example, a score above 90 corresponds to high priority, 60 to 89 corresponds to medium priority, and below 60 corresponds to low priority.

[0098] By introducing real-time priority determination results based on preset event trigger thresholds as correction input during the process of determining the target priority of data to be processed, and integrating them with the static priority determination logic in the target time period rules, dynamic management of data priority is achieved. This retains the planning capability of time period rules in optimizing bandwidth resource allocation at the macro level, while also providing the ability to instantly perceive and respond to sudden power grid events (such as short-circuit faults and equipment overloads). It can solve the problem of missed or delayed reporting of key alarm information that may be caused by rigid priority determination logic in existing technologies, and enhance the ability of distribution network terminals to identify and protect high-value, high-timeliness data in complex and ever-changing operating environments.

[0099] In practical applications, during the identification and priority reporting of high-priority event data, if abnormal conditions such as unexpected power outages of terminal equipment, system resets, or prolonged communication link interruptions occur, the identified but not yet successfully reported key data will be at risk of loss because it is only stored in volatile memory. This will prevent the master station system from knowing about the fault events that have occurred, resulting in blind spots in the monitoring of the distribution network's operating status and difficulties in tracing accidents. To address this, based on the above embodiments, the method may further include: obtaining the real-time occupancy status of the buffer corresponding to the preset reporting priority in the target buffer; determining the system processing status as a high-priority processing status based on the real-time occupancy status or priority determination result; performing persistent processing on the target data to be reported in the high-priority processing status to obtain persistent data, and instructing the persistent processing to write the target data to be reported to a non-volatile storage medium.

[0100] In this embodiment, the target cache area specifically refers to those cache areas that have been allocated data to be processed. The preset reporting priority refers to a predefined level in a multi-level cache system used to store the most urgent reported data. The cache area corresponding to the preset reporting priority refers to the logical cache area that undertakes the data storage task of the highest reporting priority, such as the real-time area, where the data stored has the highest requirements for timeliness and reliability.

[0101] Real-time occupancy status refers to a quantitative indicator of the fill level of the target cache at a given moment. This status reflects the proportion of data already stored in the cache relative to its total capacity, and is a direct basis for determining whether the system faces the risk of data backlog.

[0102] In some examples, the specific method for obtaining real-time occupancy status can be through the query interface provided by the terminal's cache management module, which reads the current queue length and maximum queue capacity of the real-time zone corresponding to the highest preset reporting priority. Occupancy status can be represented as a percentage value; for example, if the real-time zone currently stores 80 data entries and its total capacity is 100, then the real-time occupancy status is 80%. It can also be represented as a discrete status level, such as "Normal" (occupancy < 60%), "Warning" (occupancy 60%-80%), and "Alarm" (occupancy > 80%).

[0103] Furthermore, the cache management module can periodically poll the occupancy status of each cache area and maintain a status register. When the status is needed, the value of the register can be read directly, thereby avoiding frequent queue traversal operations and reducing CPU overhead.

[0104] The system processing status refers to the operating mode of the data processing method at the distribution network terminal during execution. Different system processing statuses correspond to different resource scheduling strategies and task execution priorities. Under normal circumstances, the system is in a routine processing state, processing data from each buffer area in a balanced manner according to a dynamic reporting strategy.

[0105] High-priority processing state is a special mode of system processing. In this state, the system will pause or degrade all non-critical tasks and prioritize computing, storage, and communication resources for processing and reporting the highest priority data. This state is a temporary, preemptive operating mode.

[0106] In some examples, determining high-priority processing status based on real-time occupancy can involve comparing the acquired real-time occupancy status with a preset high-water mark threshold. For instance, this high-water mark threshold might be set to 80% of the total real-time zone capacity. When the real-time zone's occupancy reaches or exceeds 80%, the system's processing status is automatically set to high-priority, regardless of whether any new events are triggered. This means that even without new urgent events, if the backlog of real-time data is nearing overflow, an emergency mode must be immediately activated to prioritize the cleanup of high-priority backlog data and prevent data loss.

[0107] In other examples, the process of determining the high-priority processing state based on the priority determination result can be that when the priority determination result indicates high-priority event data, the system processing state is immediately switched to the high-priority processing state, which reflects the immediate response mechanism to sudden failure events.

[0108] Furthermore, more granular control can be achieved by combining two conditions. For example, a high-priority processing state is determined only when the real-time occupancy exceeds a low threshold (e.g., 50%) and a priority determination result of "high" is received simultaneously. This combination of conditions can ensure timely response to genuine emergencies while avoiding overly sensitive triggering.

[0109] In addition, once the system's processing status is determined to be high-priority, a series of auxiliary actions can be performed. For example, a status change alarm message can be sent to the main station system to notify the main station terminal that it has entered emergency processing mode; or an audit log can be recorded locally, detailing the trigger time, trigger reason (whether it is event-triggered or resource-triggered), and the current snapshot of the occupancy of each cache area.

[0110] The target data to be reported refers to the critical data that has been identified as high priority but has not yet been successfully reported to the main station system when entering the high-priority processing state. Its scope includes, but is not limited to: the event data that triggered the current state switch; all high-priority data to be sent in the real-time area buffer queue; and system state context information associated with these high-priority events (such as voltage and current waveform snapshots before and after the fault).

[0111] Persistent storage refers to the process of writing or copying data from temporary, volatile storage media (such as memory) that are lost upon power failure to a non-volatile storage medium that can be stored long-term and is not lost upon power failure. Non-volatile storage media are physical storage devices that maintain the integrity of the stored data after a power outage. In the hardware environment of a power distribution network terminal, this can take the form of Flash memory chips, EEPROM chips, ferroelectric memory, or external SD memory cards, etc.

[0112] Persistent data refers to a copy of the data stored in non-volatile storage media after the persistence operation is completed. The existence of this copy ensures that this critical data will not be lost even if the system experiences an unexpected power outage or restart immediately after the persistence operation is completed.

[0113] In some examples, the specific operation of persisting the target data to be reported can be triggered by an interrupt service routine when the system processing state is determined to be a high-priority processing state. This interrupt service routine pauses the current reporting task and quickly writes all data frames (including data values, timestamps, and priority tags) and a status flag indicating that the system is currently in a high-priority processing state into a pre-defined, dedicated persistent storage partition in the Flash memory by calling the Flash memory driver interface. After the write operation is complete, the data index or file allocation table of that partition is updated.

[0114] Furthermore, the write strategy can employ a journal-based file system or a circular buffer. Because flash memory has a limited write lifespan, persistence processing does not perform a write for every piece of data, but rather uses a batch write or write-only strategy at critical nodes. For example, a full snapshot persistence is performed only when entering a high-priority processing state. Subsequently, if the state is not cleared but new high-priority data is generated, an incremental append write method is used, appending only the new data frames to the end of the log on the non-volatile storage medium.

[0115] In other examples, after a terminal device experiences an unexpected power outage and restarts, the system's initialization bootloader checks for valid persistent data in a specific partition of the non-volatile storage medium before loading the main application. If persistent data is detected and its high-priority processing status flag is valid, the bootloader or the main application's initialization module reads this persistent data back into the real-time buffer queue in memory and restores the system processing state to high-priority processing. This allows the terminal to seamlessly resume high-priority data reporting tasks from the point of interruption after a restart, ensuring the main station system ultimately receives this critical information.

[0116] Furthermore, the timing of persistent data cleanup needs to be clearly managed. For example, when data in the real-time zone receives confirmation from the master station after successful reporting, the corresponding persistent data in the non-volatile storage medium will be simultaneously deleted or marked as invalid, while the cache area is notified to delete the data in memory. When the system processing state returns from a high-priority processing state to a normal processing state, a persistent data cleanup operation can also be triggered to free up storage space.

[0117] By obtaining the real-time occupancy status of the target buffer corresponding to the preset reporting priority, the system processing status is determined to be a high-priority processing status based on the real-time occupancy status or priority determination result. The target data to be reported under this status is then written to a non-volatile storage medium for persistent processing. This improves the anti-loss protection capability of the distribution network terminal for key data to be reported and the integrity of the accident tracing link when encountering extreme conditions such as unexpected power outages or system resets. It also ensures the reliable retention and recovery reporting of fault event information.

[0118] However, when determining a dynamic reporting strategy, relying solely on the current state parameters of the uplink communication link without a comprehensive quantitative evaluation mechanism for various heterogeneous parameters will lead to an ambiguous decision-making process for the reporting strategy, making it difficult to accurately match complex and ever-changing network conditions, and failing to achieve a refined adaptation between the reporting behavior and the actual carrying capacity of the link. Therefore, based on the above embodiments, the method described in S105 for determining a dynamic reporting strategy for multiple buffers based on the bandwidth allocation strategy in the target time period rule and the current state parameters of the uplink communication link of the distribution network terminal may include: determining the link status level of the uplink communication link based on the current state parameters of the uplink communication link of the distribution network terminal, wherein the current state parameters include at least one of instantaneous bandwidth value, signal strength indication, and packet loss rate; and determining a dynamic reporting strategy for multiple buffers based on the bandwidth allocation strategy in the target time period rule and the link status level, wherein the dynamic reporting strategy includes reporting order and reporting channel type.

[0119] In this embodiment, the current state parameter can refer to a quantitative indicator that can reflect the uplink communication link transmission capability and quality in real time.

[0120] Instantaneous bandwidth refers to the actual available data transmission rate of the uplink within a recent short time window, measured in bps or kbps. This parameter can be calculated by measuring the amount of data successfully transmitted within a recent period (e.g., 10 seconds). Signal strength index (RSSI) refers to the quantified value of the network signal strength received by the terminal's wireless communication module, which can be read from the communication module (e.g., a 4G module). Packet loss rate refers to the proportion of data packets sent by the sender but not acknowledged by the receiver within a certain time window, out of the total number of packets sent. This parameter can be obtained by sending heartbeat packets to the master station or by performing a Ping test.

[0121] Link status level refers to a hierarchical identifier that characterizes the overall service capability of a link, derived from a comprehensive evaluation of the aforementioned various current status parameters. It can be a discrete level value, serving as a direct basis for subsequent policy decisions.

[0122] In one example, the above metrics are calculated together to output a link status level. This level is the result of comprehensively considering multiple metrics such as bandwidth, latency, and packet loss rate; for example, it can be defined as:

[0123] Level 0 (Excellent): Bandwidth > threshold 1 and latency < threshold 2;

[0124] Level 1 (General): Bandwidth is between threshold 1 and threshold 3;

[0125] Level 2 (Restricted): Bandwidth < Threshold 3 or Packet Loss Rate > Threshold 4.

[0126] In some embodiments, the link status level can be determined using a lookup table method: a multi-dimensional threshold table is predefined, mapping different ranges of instantaneous bandwidth, signal strength indicator, and packet loss rate to different levels. For example, when bandwidth > 100kbps, RSSI > -85dBm, and packet loss rate < 2% are simultaneously met, it is determined to be level 0 (excellent); when bandwidth < 50kbps or packet loss rate > 10%, it is determined to be level 2 (limited); and the rest are level 1 (general).

[0127] In some other examples, a weighted scoring method can be used: assign weight coefficients to each parameter, calculate the comprehensive score, and then let it fall into the corresponding grade range.

[0128] The reporting order can refer to the sequence in which data is sent from multiple buffers. For example, data can be reported in descending order of latency, i.e., real-time data is reported first (using a low-latency channel, such as a dedicated VPN), then near-real-time data is reported (with field-level compression), and finally delayed data is reported (batch packaged and reported).

[0129] The reporting channel type can refer to the category of physical communication link or logical transmission channel selected for data of different priorities. For example, high-priority data uses a high-reliability channel such as TCP, low-priority data uses a low-cost channel such as UDP, and real-time zone data uses a low-latency channel such as a dedicated VPN.

[0130] In some examples, when the link status level is excellent, the reporting order is determined to be full sequential reporting of real-time zone - near-real-time zone - delayed zone. The reporting channel type is that real-time zone data is sent through a dedicated TCP APN channel, near-real-time zone data is compressed and sent through a normal TCP channel, and delayed zone data is batch-packaged and sent through a UDP channel.

[0131] When the link status level is restricted, the reporting order is determined to be to report only real-time zone data, and the reporting channel type is a TCP channel with a high retransmission mechanism to ensure reliable delivery of critical data.

[0132] When the link status level is normal, the reporting order is determined as follows: real-time zone first, near-real-time zone reports as appropriate, and delayed zone is suspended. The reporting channel type can be flexibly configured according to the time period rules.

[0133] By determining the link status level based on at least one parameter among the instantaneous bandwidth value, signal strength indication, and packet loss rate of the uplink communication link, and coordinating the link status level with the bandwidth allocation strategy in the target time period rule, a dynamic reporting strategy including reporting order and reporting channel type is determined. This improves the quantitative matching accuracy and decision certainty of the dynamic reporting strategy for complex network conditions, realizes the fine-grained adaptation of reporting behavior and link real-time carrying capacity, and ensures differentiated transmission of key data under different link qualities.

[0134] Based on the above embodiments, if the strategy of parallel reporting across multiple buffers is maintained under extreme conditions of limited bandwidth, high-priority data will compete with low-priority data for scarce bandwidth resources, causing critical alarm information to be delayed or lost due to network congestion. Therefore, the method described above for determining a dynamic reporting strategy for multiple buffers based on the bandwidth allocation strategy in the target time period rule and the link status level may include: when the link status level indicates limited bandwidth, determining the dynamic reporting strategy as performing reporting processing on the buffer corresponding to the preset reporting priority among the multi-level buffers.

[0135] In this context, the link status level indicating bandwidth limitation can refer to a specific level determined in the above embodiments, which is a preset level indicating a severe shortage of uplink communication resources. For example, this level is bandwidth limitation (e.g., estimated bandwidth ≤ threshold), or level 2 (limited): bandwidth < threshold 3 or packet loss rate > threshold 4.

[0136] The buffer corresponding to the preset reporting priority can refer to the buffer with the highest reporting priority, i.e., the real-time buffer. For example, when the link state level indicates bandwidth limitation, only real-time buffer data is reported.

[0137] Performing reporting processing refers to the operation of extracting, encapsulating, and sending the data in the specific cache area to the main station system.

[0138] In some examples, when the link status level is determined to be Level 2 (Restricted), the following policy switch is immediately executed: suspend or halt all reporting threads or tasks in the near real-time zone and the delayed zone; centrally allocate all available communication socket handles or bandwidth resources to the real-time zone reporting tasks; and cyclically retrieve data frames from the real-time zone buffer queue and send them at the highest quality of service level.

[0139] Furthermore, during the reporting process performed only on the real-time zone, the data within the real-time zone can be further prioritized. For example, if there are multiple data entries in the real-time zone, they can be ordered according to the order of timestamps or the sub-priority of event severity levels to ensure that the earliest or most severe fault information is delivered to the main station first.

[0140] In other examples, when the link status level indicates bandwidth limitation, besides reporting only real-time data, the system does not directly discard data in the near-real-time and delayed areas. Instead, it records the reporting failure status and delays the next reporting attempt, or generates a data summary for temporary storage until the link status recovers before processing. As a further example, if a high-priority event is detected or the real-time buffer occupancy rate exceeds the high-water mark threshold, a high-priority processing state is triggered. In this state, reporting of cached data in the near-real-time and delayed buffers is suspended, and reporting of data cached in the real-time buffer is prioritized.

[0141] By determining the dynamic reporting strategy to only perform reporting processing on the buffer with the highest preset reporting priority among the multi-level buffers when the link status level indicates bandwidth limitation, the bandwidth resource focusing scheduling capability and critical data delivery guarantee capability of the distribution network terminal under extreme and severe communication conditions are improved. This ensures that in scenarios where rapid response is most needed, such as accidents or network congestion, high-priority alarm data can exclusively occupy the uplink channel without being disturbed by low-priority data, thereby minimizing the transmission delay and loss risk of critical information.

[0142] When managing data in multi-level caches, if each cache only adopts a simple "overflow and discard" strategy, newly arrived high-value data may be discarded because the cache is full of stale data, or low-value stale data may occupy cache space for a long time and cannot be cleaned up in a timely manner, resulting in low cache resource utilization efficiency. Therefore, based on the above embodiments, the method may further include: obtaining the data freshness index of cached data in the target cache; and performing replacement processing on the cached data in the target cache according to the data freshness index and the preset replacement threshold corresponding to the target cache to obtain the updated target cache.

[0143] The target cache, as defined above, refers to a specific cache instance selected based on target priority to store the data to be processed. In this step, it can refer to any one or all of the multi-level caches.

[0144] Data freshness metrics are parameters used to quantify the length of time data has been in existence from the time of collection to the current time. This metric reflects the time-sensitive value of the data. In some examples, this metric is quantified as the time elapsed since data collection (current system time - data timestamp), in seconds; the larger the value, the older the data.

[0145] In some examples, the data freshness metric can be obtained by iterating through each data frame in the target cache queue, reading its stored timestamp field, and performing a difference calculation with the current system time, using the calculated difference as the freshness metric for that data. Alternatively, an initial freshness score (e.g., a maximum of 100) can be appended to the data when it is enqueued, and a timer can be started to decrement the score at a fixed rate; during a query, the current score can be read directly.

[0146] The preset replacement threshold can refer to the critical value of buffer occupancy that triggers the replacement process. When the current occupancy of the target buffer reaches or exceeds this threshold, the replacement mechanism is activated. For example, the replacement trigger condition is: when the data occupancy of the near real-time buffer or the delayed buffer exceeds its replacement threshold (e.g., 80%), the replacement strategy is automatically started.

[0147] Replacement processing can be an operation that deletes or overwrites some stored data from the cache according to preset rules to free up storage space for new data. In one example, the replacement strategy is as follows: a freshness-first replacement algorithm is used; the system will perform the following steps: scan the corresponding logical cache; sort the data according to the freshness index, with the data with the largest index (i.e., the oldest) at the top; starting from the oldest data, remove or overwrite in sequence until the logical cache occupancy rate drops to a safe level (e.g., 70%).

[0148] The updated target cache is a cache that has undergone replacement processing, resulting in a safe occupancy level and the retention of relatively fresh data.

[0149] In some embodiments, the occupancy rate of the near real-time cache and / or the delayed cache can be monitored; in response to the occupancy rate exceeding a preset replacement threshold, the cached data is replaced based on the freshness index of the cached data in the near real-time cache and / or the delayed cache, and the freshness index is determined based on the collection timestamp of the cached data.

[0150] In some examples, the replacement process can adopt a freshness-first elimination strategy: when the occupancy rate of the near real-time zone reaches a preset replacement threshold of 80%, all data in the zone are sorted by freshness index, and deleted one by one starting from the oldest data until the occupancy rate drops to a safe level of 70%.

[0151] In other examples, for near real-time zones, a freshness and data importance weighting strategy can be adopted: in addition to freshness, different weights are assigned to different types of data, and the deletion order is determined after calculating the comprehensive elimination score.

[0152] In addition, due to the importance of the data in the real-time area, it is usually set to non-replaceable and will only be disposed of according to the administrator's highest-level policy in extreme cases (such as when all logical caches are full).

[0153] In some examples, the replacement process is not only executed when the replacement threshold is reached, but also when the system CPU is idle, a low-priority background cleanup task can be actively executed to maintain the health of the cache.

[0154] By obtaining the data freshness index of the cached data in the target cache area, and replacing the data in the target cache area according to the data freshness index and the preset replacement threshold, the dynamic recycling efficiency of cache space and the data value retention capability of the distribution network terminal in the multi-level cache management scenario are improved. This ensures that under the constraint of limited memory resources, old and low-value data can be eliminated in time, while freeing up storage space for newly arrived high-value data, and avoiding invalid discarding or loss of critical data due to cache overflow.

[0155] Furthermore, when managing multi-level caches, if the capacity allocation ratio of each cache remains static during system operation, it will lead to frequent replacement or discarding of certain types of data due to insufficient capacity in their corresponding caches at different times or under different operating conditions, while other caches will have a large amount of idle space that is not utilized, resulting in a structural waste and rigid configuration of overall cache resources. Therefore, based on the above embodiments, the method may further include: obtaining the current occupancy rate of each cache in the multi-level cache; dynamically adjusting the capacity allocation ratio between the multi-level caches according to the bandwidth allocation strategy in the target time period rule and the current occupancy rate of each cache, to obtain the adjusted multi-level cache.

[0156] The current occupancy rate refers to the percentage of storage space used by each cache area at the current moment relative to its currently allocated capacity. This metric reflects the storage pressure status of each cache area. Unlike the real-time occupancy status in the above embodiments, which focuses on triggering emergency responses, this focuses on the overall distribution of all cache areas to guide capacity allocation decisions.

[0157] In some examples, the current occupancy rate of each cache region can be obtained by periodically traversing the real-time, near-real-time, and delayed cache regions, reading the used capacity and currently allocated capacity of each region, and calculating the respective occupancy percentage. For example, the real-time cache occupancy rate is 30%, the near-real-time cache occupancy rate is 85%, and the delayed cache occupancy rate is 20%.

[0158] The bandwidth allocation strategy in the target time period rule not only specifies the uplink bandwidth ratio for each priority data type, but also implicitly includes the expected amount of data generated for different priorities and guidance on the capacity configuration of each buffer zone. For example, under the "incident period" rule, the system can temporarily allocate some capacity from the delayed zone to the real-time zone to cope with sudden surges of high-priority data.

[0159] The capacity allocation ratio refers to the proportion of capacity allocated to each level of cache in a multi-level cache area to the total cache capacity. For example, the ratio of real-time area: near-real-time area: delayed area is 3:4:3.

[0160] Dynamic adjustment refers to the process of redistributing capacity allocation ratios based on real-time monitored occupancy rates and the guidance of current time-period policies. For example, during system runtime, dynamic adjustments are made based on the currently active rule table policy and the total storage space utilization. Furthermore, a weighted round-robin algorithm can be used to dynamically adjust the capacity allocation ratios of each cache area based on the current occupancy rate and the bandwidth allocation policy in the target time-period rules.

[0161] In its implementation, the weighted round-robin algorithm dynamically adjusts the cache capacity allocation ratio based on the current occupancy rate and bandwidth allocation strategy. For example, the algorithm quantifies the resource demand of each cache by assigning weights (e.g., a weight of 0.6 for high-priority caches and 0.4 for low-priority caches) and dynamically adjusts the capacity according to the weight ratio. This algorithm improves cache space utilization through a flexible resource allocation strategy, ensuring that high-priority data receives priority storage space when resources are scarce.

[0162] The adjusted multilevel cache refers to a multilevel cache system in which the capacity boundaries of each level of cache have been updated after the capacity ratio has been redistributed.

[0163] In some examples, the triggering conditions for dynamic adjustment processing may include: a cache occupancy rate continuously exceeding a preset threshold (such as 90%); the rule table being switched to a new target time period rule; or the administrator actively triggering it via remote command.

[0164] Regarding the adjustment method, when the rule table switches to "Incident Period" and the real-time zone occupancy rate reaches 80% while the delayed zone occupancy rate is only 20%, the adjustment process can be as follows: temporarily allocate a portion of the delayed zone's capacity (such as 20% of the total capacity) to the real-time zone, changing the adjusted capacity ratio from the initial 3:4:3 to 5:4:1. After the incident period ends, the system restores the capacity ratio to the initial configuration according to the bandwidth allocation strategy for the normal operation period.

[0165] In some other examples, when the near-real-time zone occupancy rate consistently exceeds 85% while the real-time zone occupancy rate is low, and the current time period is a "normal operating period," a portion of the capacity can be temporarily allocated from the real-time zone to the near-real-time zone to alleviate the replacement pressure on the near-real-time zone.

[0166] In addition, the initial capacity of the logical buffer can be set to a fixed ratio (e.g., real-time buffer: near-real-time buffer: delayed buffer = 3:4:3).

[0167] In some examples, dynamic adjustment can employ a gradual adjustment strategy, where each adjustment does not exceed 10% of the total capacity, thus avoiding instability in cache management caused by a single large adjustment.

[0168] By obtaining the current occupancy rate of each cache in the multi-level cache area and dynamically adjusting the capacity allocation ratio between each cache area according to the bandwidth allocation strategy in the target time period rule, the flexibility of cross-regional allocation of cache resources and the overall space utilization rate of the distribution network terminal under the multi-level cache architecture are improved. This ensures that when the operating conditions change such as time period switching or data flood, the high-priority cache area can obtain immediate capacity expansion guarantee, while the redundant space released by the low-priority cache area when idle can be effectively reused, avoiding the structural waste of resources and the risk of local cache overflow caused by static capacity configuration.

[0169] Furthermore, the above method relies on a preset rule table to drive the data processing flow. If the rule table is corrupted or becomes invalid during the update process due to transmission errors, storage anomalies, or malicious tampering, it will cause the entire terminal's time period matching, priority determination, and reporting strategy decisions to fall into an erroneous state, seriously affecting the operational reliability of the distribution network automation system. Therefore, based on the above embodiments, the method may further include: obtaining an update instruction for the rule table and authenticating the update instruction to obtain an authentication result; if the authentication result indicates successful authentication, updating the rule table based on the update instruction and performing integrity verification on the updated rule table to obtain a verification result; if the verification result indicates successful verification, using the updated rule table as the preset rule table; if the verification result indicates failed verification, performing version rollback processing on the rule table to obtain a rolled-back rule table.

[0170] An update command refers to an operation request that triggers changes to the rule table content. This command can originate from the local configuration interface or a remote management center. Furthermore, the rule table supports dynamic updates via the local configuration interface or a remote management center (such as a distribution network master station system). Authentication refers to the process of verifying the legality and source credibility of the update command; that is, authorization authentication is required during the update process. The authentication result is a conclusive identifier output after the authentication operation is completed, indicating whether the update command has passed the legality verification.

[0171] In some examples, authenticating an update command can be achieved by verifying the digital signature carried within the command. The remote master system signs the digest of the update command using its private key, and the terminal, upon receiving the command, verifies the signature using the pre-stored master system public key. If the signature verification succeeds, the authentication result is successful; otherwise, authentication fails.

[0172] In other examples, for update commands initiated from the local configuration interface, the authentication method can be to verify whether the username and password entered by the operator match the preset authorization credentials.

[0173] Updating the rule table based on the update instruction can refer to the process of replacing the currently effective rule table content in the terminal memory with the new rule table content carried in the update instruction.

[0174] Integrity verification refers to the operation of verifying the correctness and integrity of updated rule table data. Furthermore, integrity verification is performed on the rule table before it is written or distributed, for example, through CRC checksums or digital signatures, to ensure data integrity and security. The verification result is a conclusive identifier output after the integrity verification operation is completed, indicating whether the updated rule table data is complete and error-free.

[0175] In some examples, integrity verification can be performed by calculating the cyclic redundancy check (CRC) code of the updated rule table data and comparing it with the expected CRC value attached to the update instruction. If they match, the verification result indicates successful verification; otherwise, it indicates verification failure.

[0176] In other examples, integrity checks may also include syntax parsing and validation of the structured format of the rule table. For example, for a JSON-formatted rule table, this involves verifying whether it conforms to the preset JSON Schema definition and whether the data types and value ranges of each field are valid.

[0177] Specifically, using the updated rule table as the preset rule table means that the new rule table, which has passed both authentication and integrity verification, is officially made into the "preset rule table" driving the data processing flow in this method. After this operation is completed, the subsequent S102 step will match the rules for the target time period based on the new rule table.

[0178] Version rollback refers to the process of automatically abandoning the current update operation and restoring the system to the stable version before the update when the new rule table verification fails. In other words, if verification fails, the system automatically rolls back to the previous valid version (managed by version number) and records an audit log. The rolled-back rule table is a stable version rule table that has passed verification before the update and has been re-implemented after version rollback.

[0179] In some examples, the specific implementation of version rollback processing can be that the terminal simultaneously stores the currently effective rule table and the previous effective version rule table in non-volatile memory. When the new rule table fails to be verified, the system re-marks the previous effective version rule table as the currently effective rule table and deletes or isolates the new rule table that failed verification.

[0180] In addition, the method also includes recording audit logs. In some examples, regardless of whether the update is successful or not, information such as the timestamp of the update operation, the source of the operation, the version numbers before and after the update, and the verification results are recorded to form a traceable operation log.

[0181] By performing authorization and authentication when obtaining rule table update instructions, performing integrity checks on the rule table after the update, and automatically performing version rollback when the check fails, the security protection and anomaly self-healing capabilities of the distribution network terminal during the dynamic update process of the rule table are improved. This ensures that only legally authorized and data-complete rule tables can take effect, while any abnormal rule tables caused by transmission errors, storage damage, or malicious tampering are effectively intercepted and automatically restored to the previous stable version. This ensures the continuous reliability and anti-interference of the core decision-making basis of the terminal data processing method.

[0182] As can be seen from the above embodiments, the power distribution network terminal data processing method provided in this application achieves intelligent and refined management of power distribution network smart terminal data by constructing a dynamically configurable rule table and a multi-level logical buffer, combined with a differentiated reporting strategy based on time period and event priority. This method uses a rule table as its core, defining time period types, triggering conditions, and reporting strategies through the rule table, and dynamically adjusting the logical buffer capacity allocation and reporting priority. Simultaneously, based on event priority judgment conditions (such as threshold exceeding limits, fault alarms) and communication resource status (such as bandwidth, link quality), a differentiated reporting strategy is executed, prioritizing the real-time performance of high-value data while also considering the batch transmission of low-priority data. This technical concept breaks through the limitations of existing fixed-period reporting and static classification, achieving dynamic optimization of bandwidth resources and precise management of data priorities.

[0183] This application applies to edge computing terminals (such as FTUs and TTUs) deployed in smart distribution networks. Application scenarios can include urban 10kV substations, rural distribution networks, and industrial park power supply systems. In complex network environments (such as limited GPRS / 4G wireless communication bandwidth and fiber optic link fluctuations), the terminal needs to collect multi-source heterogeneous data (such as voltage, current, environmental parameters, and event alarms) and transmit it to the master station system via an uplink communication channel. Distribution network operation periods exhibit significant dynamism (such as normal operation, off-peak periods, maintenance, and accident periods), and data priority varies with event type (such as threshold exceeding limits and equipment failure) and communication status (such as bandwidth availability).

[0184] Figure 2 Flowchart of the data processing method for distribution network terminals provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the data processing method for distribution network terminals is described in detail. This data processing method for distribution network terminals can be applied to distribution network terminals. Figure 3 The structural schematic diagram of the distribution network terminal provided in this application is as follows: Figure 3 As shown, the system architecture of the distribution network intelligent terminal is centered on a rule engine. Through the collaborative operation of various modules, it realizes intelligent lifecycle management of data from collection to reporting. The system architecture mainly includes the following functional modules:

[0185] Rule Management Module: As the control center of the system, it is responsible for the storage, loading, integrity verification (using CRC or digital signature), version control, and one-click rollback of rule tables. This module supports dynamic configuration through local interface or remote command and audits the source and content of all rule changes.

[0186] Time synchronization module: Provides a unified and reliable time reference for the entire system; adopts a primary and backup clock source strategy, giving priority to the use of local high-precision RTC (real-time clock) and automatically switching to the primary station for time synchronization or NTP (Network Time Protocol) as a backup, while recording the time synchronization source and error to ensure the accuracy of time period determination.

[0187] Data acquisition module: responsible for acquiring raw data from connected sensors / transformers; supports two modes: periodic acquisition (for routine monitoring) and event-triggered acquisition (for anomaly alarms), and adds high-precision timestamps to all data.

[0188] Data classification module: This is the primary execution unit of the rule engine. This module receives timestamped data from the data acquisition module and simultaneously queries the rule management module and the time synchronization module. Based on the dual rules of "current time period" and "event priority judgment condition", it dynamically classifies the data into real-time, near real-time, or delayed levels.

[0189] The cache management module virtualizes multiple levels of logical cache areas in the terminal memory, including real-time, near-real-time, and delayed areas. It receives data from the data classification module and is responsible for data enqueuing, storage, replacement, and persistence. This module maintains freshness metrics (such as data lifespan) and sets replacement thresholds and strategies (such as freshness-first replacement algorithms) for the non-real-time area to ensure dynamic and efficient utilization of cache space.

[0190] The reporting and scheduling module is the communication command center of the system. This module monitors the resource status of the uplink in real time (such as bandwidth, latency, and packet loss rate), and combines the policies in the rule management module with the priorities of each queue in the cache management module to dynamically decide the content, timing, and communication channel (such as 4G and fiber optic) for reporting, and executes differentiated reporting.

[0191] Priority control module: responsible for system exception response and resource preemption; when a high-priority event or real-time buffer occupancy alarm is detected, this module sends an instruction to the upper scheduling module to trigger the high-priority processing state, suspend low-priority tasks, and persist the system state and key data to ensure the continuity of transactions.

[0192] Audit and Replay Module: As the system's "black box," it records key logs such as rule changes, priority switching, cache replacement, and data reporting throughout the process, forming an immutable audit log that supports accurate querying and replay analysis of historical operations and fault scenarios after the fact.

[0193] Figure 4 Flowchart of the data processing method for distribution network terminals provided in this application Figure 3 ,like Figure 2 , Figure 3 , Figure 4 As shown, the method may include:

[0194] S201. Rule table preparation and loading.

[0195] The rule table is pre-stored in the non-volatile storage medium (such as Flash memory) of the smart terminal. The rule table is stored in a structured format (such as JSON or CSV) and contains the following fields: time period identifier (unique ID), time period name (such as "normal operation time period"), start and end time (defining the start and end time of the time period, in the format HH:MM), trigger condition (such as device threshold exceeding limit or master station instruction), real-time zone bandwidth ratio (indicating the proportion of uplink bandwidth that the real-time logical buffer can occupy under this time period, such as 0-100%), and priority strategy (defining the reporting order of each logical buffer under this time period).

[0196] Before being written to or distributed, the rule table undergoes integrity verification, such as through CRC check or digital signature, to ensure data integrity and security. If the verification fails, the system automatically rolls back to the previous valid version (managed by version number) and records the audit log. The rule table supports dynamic updates through a local configuration interface or a remote management center (such as the distribution network master station system), and authorization and authentication are required for updates.

[0197] S202. Establishment and initialization of multi-level logical cache areas.

[0198] Three logical cache areas are established within the smart terminal: a real-time area, a near-real-time area, and a delayed area. These logical cache areas are virtually partitioned in memory and managed through a cache management module. The capacity of the logical cache areas is dynamically configured according to device resources and supports a persistence mechanism.

[0199] Real-time zone: Used to store high-priority event data, such as fault alarms or threshold overrun data, with low access latency and no replacement characteristics (i.e., data is not overwritten under normal circumstances).

[0200] Near real-time area: Used to store medium-priority data, such as regular periodic measurements (voltage, current, etc.), and supports data compression and packaging;

[0201] Latent area: Used to store low-priority data, such as environmental parameters (temperature, humidity) or historical logs, allowing data to be reclaimed according to a replacement strategy when resources are insufficient;

[0202] The logical cache is initialized when the system starts and supports the persistence of critical states (such as high-priority event flags) to non-volatile storage to prevent loss in the event of power failure.

[0203] Furthermore, the capacity of the logical cache is dynamically allocated based on the urgency of data reporting and the overall utilization of system resources. This aims to ensure that high-priority data is not lost while maximizing the use of limited storage resources, as detailed below:

[0204] The initial capacity of the logical cache can be set to a fixed ratio (e.g., real-time area: near-real-time area: delayed area = 3:4:3, that is, the real-time area occupies 30% of the memory, the near-real-time area occupies 40%, and the delayed area occupies 30%). When the system is running, the cache management module will dynamically adjust the capacity based on the currently active rule table policy and the total storage space utilization rate. For example, under the "accident period" rule, the system can temporarily allocate some capacity from the delayed area to the real-time area to cope with sudden high-priority data floods. The triggering conditions for capacity adjustment include, but are not limited to: the occupancy rate of a certain logical cache continuously exceeds a preset threshold (e.g., 90%), or the rule table is switched to a new period type.

[0205] S203, Data Classification and Logical Cache Allocation.

[0206] The data acquisition module periodically or event-triggeredly acquires data from sensors (such as current transformers or temperature sensors) and adds a timestamp to each data entry.

[0207] Among them, periodic acquisition: collects conventional operating parameters (such as voltage, current, and power) at preset time intervals (such as every 5 seconds).

[0208] Event-triggered acquisition: When the sensor detects an anomaly or sudden change (such as the current instantaneously exceeding the safety threshold or the switch position changing), the regular periodic acquisition is immediately interrupted, and the event data is prioritized for real-time acquisition. This type of data usually has a higher priority.

[0209] The data collected in both modes will be appended with a high-precision timestamp and sent to the data classification module for processing. Data collected by event triggers usually has a higher priority.

[0210] The data classification module classifies the data based on the current time (provided by the time synchronization module, prioritizing the local RTC clock and using the master station as a backup) and the time period type in the matching rule table (e.g., matching "accident period" or "off-peak period"), combined with event priority judgment conditions.

[0211] High priority: Data is assigned to the real-time zone when one of the following conditions is met: the measured value exceeds a preset threshold (e.g., transformer temperature > 85°C) or a specific fault alarm is detected (e.g., current surge > 200A / s).

[0212] Medium priority: When the data is a regular periodic measurement (such as voltage value collected every 5 seconds), it is allocated to the near real-time zone;

[0213] Low priority: When the data is environmental parameters (such as data center humidity) or log data, it is allocated to the delayed area; for example, a piece of data: "Transformer temperature is too high", even if it comes from periodic collection, will be promoted to high priority because it meets the threshold condition;

[0214] After classification, the data is encapsulated into a structure (containing data value, timestamp, and priority flag) and added to the corresponding logical cache area; the cache management module records the freshness index of each data (such as the difference between the collection time and the current time) and sets replacement thresholds for the near real-time area and the delayed area (for example, when the logical cache area occupancy rate exceeds 80%, the replacement strategy is triggered).

[0215] Among them, the freshness index is quantified as the time elapsed from data collection to the current moment (current system time - data timestamp), with the unit being seconds; the larger this value, the older the data.

[0216] Replacement trigger: When the data occupancy rate of the near real-time zone or the delayed zone exceeds its replacement threshold (e.g., 80%), the cache management module automatically starts the replacement strategy;

[0217] Replacement strategy: A freshness-first replacement algorithm is used; the system will execute the following steps:

[0218] Scan the corresponding logical cache area; sort the data according to the freshness index, with the data with the largest index (i.e., the oldest) at the top; starting from the oldest data, remove or overwrite it in turn until the logical cache area occupancy rate drops to a safe level (e.g., 70%).

[0219] Due to the importance of the data in the real-time area, it is usually set to "non-flushable" and will only be disposed of according to the administrator's highest-level policy in extreme cases (such as when all logical buffers are full).

[0220] S204. Uplink communication resource monitoring and differentiated reporting strategy execution.

[0221] The reporting and scheduling module monitors uplink communication resource status in real time, including bandwidth estimation (via packet probing or link quality indicators), link status (such as GPRS / 4G signal strength or fiber optic connection status), and network load; the monitoring period is configurable (e.g., once every 1 second); based on the monitoring results and the real-time area bandwidth ratio in the rule table for the current time period, it executes differentiated reporting strategies:

[0222] When bandwidth is sufficient (e.g., estimated bandwidth > threshold): report data in descending order, i.e., report real-time zone data first (using low-latency channels, such as dedicated VPNs), then report near-real-time zone data (field-level compression can be performed), and finally report delayed zone data (batch packaged and reported).

[0223] When bandwidth is limited (e.g., estimated bandwidth ≤ threshold): only real-time zone data is reported, while near-real-time zone and delayed zone data are temporarily stored or only a summary (e.g., event count) is reported.

[0224] When the network is idle (such as during off-peak hours at night): trigger batch reporting of data in the delayed area, and map different communication channels for data of different priorities (for example, high-priority data uses a high-reliability channel such as TCP, and low-priority data uses a low-cost channel such as UDP).

[0225] During the reporting process, the system records the reporting information (including data ID, reporting time, and channel type) to the audit log;

[0226] The reporting and scheduling module monitors the following uplink communication resource status in real time by calling the network interface and communication module drivers at the operating system's underlying level:

[0227] Input source:

[0228] Instantaneous bandwidth: Calculated by measuring the amount of data successfully transmitted within a recent period (e.g., 10 seconds);

[0229] Link quality: Signal strength indication (RSSI) read from a communication module (such as a 4G module);

[0230] Network latency and packet loss rate: obtained by sending heartbeat packets to the main station or by ping testing;

[0231] Output: The module calculates the above metrics comprehensively and outputs a link status level. This level is the result of considering multiple metrics such as bandwidth, latency, and packet loss rate; for example, it can be defined as:

[0232] Level 0 (Excellent): Bandwidth > threshold 1 and latency < threshold 2;

[0233] Level 1 (General): Bandwidth is between threshold 1 and threshold 3;

[0234] Level 2 (Restricted): Bandwidth < Threshold 3 or Packet Loss Rate > Threshold 4.

[0235] This "link status level" is the direct basis for implementing differentiated reporting strategies.

[0236] The determination of states such as "sufficient bandwidth" and "limited bandwidth" relies on comparisons with preset thresholds. These thresholds can be configured according to the network environment and service requirements.

[0237] Bandwidth threshold: It is usually set as a percentage of the theoretical bandwidth of the communication link (for example, the "sufficient" threshold is 60% of the theoretical value, and the "restricted" threshold is 20% of the theoretical value); it can also be an absolute value set according to business needs (for example, to ensure smooth real-time data flow, bandwidth >100kbps is defined as "sufficient").

[0238] The specific values ​​of these thresholds are set by the configuration file during system initialization and can be dynamically adjusted via remote management commands to adapt to different network deployment environments.

[0239] S205. Triggering and management of high-priority processing states.

[0240] The priority control module monitors high-priority events or the real-time logic buffer occupancy in real time; when the triggering conditions for high-priority processing are met, the system automatically enters high-priority processing mode.

[0241] Immediately suspend or postpone data reporting in the low-priority logical buffer area (near-real-time area and delayed area), and concentrate reporting resources on uploading data in the real-time area;

[0242] The system status (such as high priority flags) and key data to be reported (such as unsent real-time area data) are persisted to non-volatile storage to ensure that the smart terminal can resume processing after restarting (for example, read the persisted data and continue reporting after restarting).

[0243] Simultaneously, a status alarm is sent to the remote management center, and an audit log (including trigger time, event type, and operation record) is recorded. After the status is cleared (e.g., real-time data reporting is completed or occupancy rate decreases), the system resumes the normal reporting process.

[0244] The triggering condition for high-priority processing states is two independent OR conditions; either one must be satisfied:

[0245] Event-driven: New data identified by the data classification module is marked as a "high-priority event" (such as threshold exceeding or fault alarm mentioned above).

[0246] Resource-driven: The cache management module detects that the occupancy rate of the real-time logical cache exceeds its high-water mark threshold (this threshold can be set independently, for example, to 80% of the total capacity of the real-time area); this means that even without new urgent events, the backlog of real-time data is about to fill the buffer and needs to be cleaned up immediately.

[0247] Once triggered, the system enters this state and performs operations such as prioritizing reporting and pausing low-priority tasks until the real-time data is cleared or its occupancy rate drops below the low water level threshold (e.g., 30%), at which point the system automatically exits this state.

[0248] S206, Audit and Replay.

[0249] The audit log module records the entire process log, including rule table change records, priority adjustment records, cache replacement records, and reporting records; the logs are stored in time series and support post-event query and fault playback (e.g., reproducing the data flow through the main site tools).

[0250] S207, Exception Handling.

[0251] When the latency zone remains under high occupancy (e.g., exceeding 95%) and the link is unavailable, the system triggers a "cache overflow alarm" and supports operational intervention (e.g., remotely adjusting the rule table or manually clearing the cache).

[0252] The following is a specific application case of a smart terminal in a 10kV urban substation:

[0253] This embodiment applies the aforementioned power distribution network terminal data processing method to a smart terminal (such as an FTU device) in a 10kV power distribution substation in a city, demonstrating the workflow and effectiveness of the method in a real-world environment. The scenario simulates typical operating periods and events of a power distribution substation, including normal operation, off-peak nighttime hours, maintenance periods, and accident periods, highlighting how the method improves the real-time performance and bandwidth utilization of critical data reporting. The smart terminal hardware configuration includes an embedded processor (such as an ARM Cortex-A series), 512MB of RAM, 4GB of Flash storage, and an uplink communication module (GPRS / 4G and fiber optic interfaces). The above configuration parameters are only preferred embodiments; the terminal can be dynamically adjusted according to actual resources.

[0254] The system's example configuration is as follows:

[0255] Example of equipment resources: The processor is a general-purpose embedded processor, and the memory and storage configuration varies depending on the model; the system has a GPRS / 4G module and an optical fiber FE interface for uplink communication.

[0256] Example of rule table configuration:

[0257] Accident period: Triggering condition "transformer temperature > 85℃ or current sudden change > 200A", real-time bandwidth ratio 100%, priority strategy: real-time data is prioritized for flushing;

[0258] Nighttime off-peak hours: 22:00–06:00, triggered by “master station command”, real-time zone bandwidth usage is 50%, and fiber optic reporting is allowed in the near real-time zone;

[0259] Maintenance period: 08:00–12:00, triggered by “master station command”, real-time zone bandwidth utilization is 100%, and only real-time zone reporting is allowed;

[0260] During holiday periods: The trigger condition is "master station command". The real-time zone bandwidth accounts for 30%, and the delayed zone allows batch transmission.

[0261] Logical cache configuration example:

[0262] Real-time zone: Capacity 100 data entries, storing high-priority events (such as temperature exceeding limits, current surges).

[0263] Near real-time zone: Capacity 200 data points, storing periodic measurement data (such as voltage and current, with a sampling period of 5 seconds).

[0264] Latent area: capacity 500 data entries, storage environment parameters (such as humidity, logs, sampling period 1 minute).

[0265] Logical cache replacement strategy: The delayed cache uses a replacement strategy based on data freshness, while the near real-time cache uses a freshness-first strategy (retaining the latest data).

[0266] Example execution process:

[0267] Example 1: Handling during the incident period;

[0268] Background: During normal operation, the smart terminal detected that the transformer temperature rose to 88°C (exceeding the threshold of 85°C) and the current suddenly increased to 250A.

[0269] Step S3: The data classification module matches the "accident period" (trigger condition met) according to the rule table, classifies the temperature and current data as high priority, and allocates them to the real-time zone.

[0270] Step S5: The priority control module detects a high-priority event and triggers a high-priority processing state: suspend the reporting of the near real-time zone and the delayed zone, prioritize the reporting of real-time zone data to the main station through a low-latency channel (such as a 4G dedicated APN), and persist the system state.

[0271] Result: Critical data was reported quickly, the main station triggered alarms and dispatched maintenance in a timely manner, while non-critical data was temporarily stored to avoid bandwidth contention.

[0272] Example 2: Nighttime off-peak period processing;

[0273] Background: During the off-peak hours of 22:00-06:00 at night, uplink bandwidth is limited (GPRS link bandwidth is estimated to be only 50kbps).

[0274] Step S4: The reporting and scheduling module executes a differentiated strategy based on the rule table (50% of the real-time zone bandwidth): only 50% of the real-time zone data is reported (the latest data is selected first), near-real-time zone data is reported in batches through fiber optic channel compression, and delayed zone data is uploaded in batches at the preset window (02:00 AM).

[0275] Results: Bandwidth consumption was effectively reduced, while ensuring the reporting of some critical data and overall system load balancing.

[0276] To verify the effectiveness of the method, a comparative test was designed:

[0277] Test environment: Simulating a 10kV substation data acquisition scenario, the terminal generates three types of data streams:

[0278] High priority (fault alarm, frequency 1 time / second, example value, actual configurable);

[0279] Medium priority (periodic measurement, frequency 1 time / 5 seconds, example value);

[0280] Low priority (log, frequency 1 time / minute, example value).

[0281] Test conditions: Uplink bandwidth is limited to a preset threshold (example value: 100kbps, actual value may vary depending on network environment); test duration is set according to requirements (example: 10 hours, actual duration may vary).

[0282] Comparison of approaches: Traditional fixed-period reporting method vs. the method of this invention.

[0283] Measurement metrics: average latency of high-priority data, bandwidth utilization, and peak cache utilization.

[0284] Test results: This method can significantly optimize overall bandwidth utilization while ensuring low latency for high-priority data.

[0285] This method can significantly optimize overall bandwidth utilization while ensuring low latency for high-priority data.

[0286] Peak cache usage: This method, through multi-level caching and replacement, is expected to result in more stable cache usage.

[0287] Results: Test data recording and analysis show that this method significantly optimizes bandwidth utilization while ensuring the real-time performance of high-priority data.

[0288] Operations and troubleshooting examples:

[0289] Abnormal scenario: The cache usage in the delayed area continuously exceeds 95% (due to network interruption), triggering a "cache overflow alarm" and recording an audit log.

[0290] Intervention measures: Maintenance personnel issue instructions via the remote management center to temporarily adjust the rule table (such as expanding the upload window for the delayed zone or reducing the sampling rate of low-priority data). The system automatically applies the new rules and resumes normal operation. Rule changes are recorded with version numbers, supporting rollback to prevent configuration errors.

[0291] The power distribution network terminal data processing method provided in this application introduces a time-based differentiated reporting strategy and a multi-level buffer. When bandwidth is tight, the system prioritizes high-value data and reports historical data in batches when idle, thereby smoothing network traffic, significantly improving bandwidth utilization efficiency, and effectively avoiding network congestion, thus achieving efficient and adaptive utilization of communication bandwidth.

[0292] By using the "event threshold determination" rule, the priority of data is dynamically increased (such as classifying periodic data exceeding the threshold into the real-time zone), breaking the limitations of traditional static classification. This mechanism combines high interpretability and flexibility, accurately ensuring the real-time nature of key information and realizing refined and dynamic management of data priority.

[0293] Furthermore, this method provides flexible cache scheduling and intelligent reporting decisions: the combination of a multi-level cache structure and preemptive reporting logic constitutes a lightweight scheduling system with rapid response and reasonable resource allocation; it can ensure immediate response to emergencies through preemption mechanisms, and reduce transmission overhead through batch aggregation, ensuring real-time performance while taking into account overall system efficiency. It also constructs a highly configurable and scalable edge intelligence framework: with a customizable rule table at its core, it allows users to flexibly define strategies according to specific scenarios without modifying the core code; this framework is highly practical for engineering implementation, easily adaptable to diverse power distribution scenarios, and provides a solid foundation for future functional expansion.

[0294] Figure 5 This is a schematic diagram of the structure of the distribution network terminal data processing device provided in this application, as shown below. Figure 5 As shown, the distribution network terminal data processing device 50 provided in this embodiment includes:

[0295] The acquisition module 501 is used to acquire the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal;

[0296] The first determining module 502 is used to determine the target time period rule that matches the current time according to a preset rule table. The rule table includes the bandwidth allocation strategy and priority determination logic corresponding to different time periods.

[0297] The second determining module 503 is used to determine the target priority of the data to be processed based on the priority determination logic in the target time period rule;

[0298] The caching module 504 is used to allocate the data to be processed to the target cache area in the multi-level cache area for caching according to the target priority. The multi-level cache area includes multiple cache areas with different reporting priorities.

[0299] The third determining module 505 is used to determine the dynamic reporting strategy for multiple buffer zones based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal.

[0300] The processing module 506 is used to perform reporting processing on the data cached in the multi-level cache area according to the dynamic reporting strategy.

[0301] In one possible implementation, the second determining module 503 can also be used to: obtain a priority determination result based on the measured value in the data to be processed and the preset event triggering threshold, the priority determination result being used to indicate whether the data to be processed is high-priority event data; and determine the target priority of the data to be processed based on the priority determination logic in the target time period rule and the priority determination result.

[0302] In one possible implementation, the second determining module 503 can also be used to: obtain the real-time occupancy status of the cache area corresponding to the preset reporting priority in the target cache area; determine the system processing status as a high-priority processing status based on the real-time occupancy status or priority determination result; perform persistent processing on the target data to be reported in the high-priority processing status to obtain persistent data, and the persistent processing instruction writes the target data to be reported to a non-volatile storage medium.

[0303] In one possible implementation, the third determining module 505 can also be used to: determine the link status level of the uplink communication link based on the current status parameters of the uplink communication link of the distribution network terminal, wherein the current status parameters include at least one of instantaneous bandwidth value, signal strength indication and packet loss rate; and determine a dynamic reporting strategy for multiple buffers based on the bandwidth allocation strategy in the target time period rule and the link status level, wherein the dynamic reporting strategy includes reporting order and reporting channel type.

[0304] In one possible implementation, the third determining module 505 can also be used to: determine the dynamic reporting strategy as performing reporting processing on the cache corresponding to the preset reporting priority in the multi-level cache when the link status level indication bandwidth is limited.

[0305] In one possible implementation, the processing module 506 can also be used to: obtain the data freshness index of the cached data in the target cache area; and perform replacement processing on the cached data in the target cache area according to the data freshness index and the preset replacement threshold corresponding to the target cache area to obtain the updated target cache area.

[0306] In one possible implementation, the processing module 506 can also be used to: obtain the current occupancy rate of each cache in the multi-level cache; dynamically adjust the capacity allocation ratio between the multi-level caches according to the bandwidth allocation strategy in the target time period rule and the current occupancy rate of each cache, so as to obtain the adjusted multi-level cache.

[0307] In one possible implementation, the processing module 506 can also be used to: obtain the update instruction of the rule table, authenticate the update instruction, and obtain the authentication result; if the authentication result indicates that the authentication is successful, update the rule table based on the update instruction, and perform integrity verification on the updated rule table to obtain the verification result; if the verification result indicates that the verification is successful, use the updated rule table as the preset rule table; if the verification result indicates that the verification fails, perform version rollback processing on the rule table to obtain the rolled-back rule table.

[0308] The power distribution network terminal data processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0309] Figure 6 This is a schematic diagram of the structure of the electronic device provided in this application, wherein the electronic device may be a power distribution network terminal. For example... Figure 6 As shown, the electronic device 60 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0310] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0311] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0312] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0313] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0314] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0315] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0316] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0317] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0318] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0319] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0320] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0321] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0322] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0323] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0324] It should be understood that the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover but not exclude inclusion. For example, a product or device that includes a series of components is not necessarily limited to those components that are explicitly listed, but may include other components that are not explicitly listed or that are inherent to such product or device.

[0325] As used in this application, the term "module" means any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code capable of performing the functions associated with that element.

[0326] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for processing data from a distribution network terminal, characterized in that, include: Obtain the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal; According to a preset rule table, a target time period rule matching the current time is determined. The rule table includes bandwidth allocation strategies and priority determination logic corresponding to different time periods. Based on the priority determination logic in the target time period rule, the target priority of the data to be processed is determined; According to the target priority, the data to be processed is allocated to the target cache area in the multi-level cache area for caching. The multi-level cache area includes multiple cache areas with different reporting priorities. Based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal, a dynamic reporting strategy for multiple buffer zones is determined. According to the dynamic reporting strategy, the data cached in the multi-level cache is reported.

2. The method according to claim 1, characterized in that, The step of determining the target priority of the data to be processed based on the priority determination logic in the target time period rule includes: Based on the measured values ​​in the data to be processed and the preset event triggering threshold, a priority determination result is obtained. The priority determination result is used to indicate whether the data to be processed is high-priority event data. Based on the priority determination logic in the target time period rule and the priority determination result, the target priority of the data to be processed is determined.

3. The method according to claim 2, characterized in that, The method further includes: Obtain the real-time occupancy status of the target cache area corresponding to the preset reporting priority; Based on the real-time occupancy status or the priority determination result, the system processing status is determined to be a high-priority processing status; The target data to be reported under the high-priority processing state is persisted to obtain persistent data. The persistence processing instruction is to write the target data to be reported to a non-volatile storage medium.

4. The method according to claim 1, characterized in that, The determination of the dynamic reporting strategy for the multiple buffer zones based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal includes: Based on the current status parameters of the uplink communication link of the distribution network terminal, the link status level of the uplink communication link is determined. The current status parameters include at least one of instantaneous bandwidth value, signal strength indication, and packet loss rate. Based on the bandwidth allocation strategy in the target time period rule and the link status level, a dynamic reporting strategy is determined for the multiple buffers. The dynamic reporting strategy includes the reporting order and the reporting channel type.

5. The method according to claim 4, characterized in that, The determination of a dynamic reporting strategy for the multiple buffers based on the bandwidth allocation strategy in the target time period rule and the link status level includes: When the link status level indicates that the bandwidth is limited, the dynamic reporting strategy is determined to perform reporting processing on the cache corresponding to the preset reporting priority in the multi-level cache.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain the data freshness index of the cached data in the target cache area; Based on the data freshness index and the preset replacement threshold corresponding to the target cache area, the data cached in the target cache area is replaced to obtain the updated target cache area.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the current occupancy rate of each cache area in the multi-level cache area; Based on the bandwidth allocation strategy in the target time period rule and the current occupancy rate of each cache area, the capacity allocation ratio between the multi-level cache areas is dynamically adjusted to obtain the adjusted multi-level cache areas.

8. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain the update instruction of the rule table, authenticate the update instruction, and obtain the authentication result; If the authentication result indicates that the authentication is successful, the rule table is updated based on the update instruction, and the integrity of the updated rule table is checked to obtain the verification result. If the verification result indicates that the verification was successful, the updated rule table will be used as the preset rule table. If the verification result indicates that the verification failed, then the rule table is rolled back to obtain the rolled-back rule table.

9. A data processing device for a power distribution network terminal, characterized in that, include: The acquisition module is used to acquire the current time of the distribution network terminal and the data to be processed collected by the distribution network terminal; The first determining module is used to determine the target time period rule that matches the current time according to a preset rule table, wherein the rule table includes bandwidth allocation strategies and priority determination logic corresponding to different time periods; The second determining module is used to determine the target priority of the data to be processed based on the priority determination logic in the target time period rule; The caching module is used to allocate the data to be processed to the target cache area in the multi-level cache area for caching according to the target priority. The multi-level cache area includes multiple cache areas with different reporting priorities. The third determining module is used to determine the dynamic reporting strategy for the multiple buffers based on the bandwidth allocation strategy in the target time period rule and the current status parameters of the uplink communication link of the distribution network terminal. The processing module is used to perform reporting processing on the data cached in the multi-level cache area according to the dynamic reporting strategy.

10. A distribution network terminal, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

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