Power distribution network real-time monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of power distribution network monitoring, and discloses a power distribution network real-time monitoring method and system based on the Internet of Things, and the method comprises the steps: obtaining city monitoring data and rural monitoring data; performing differential compression on the urban monitoring data and the rural monitoring data to generate compressed transmission data; extracting a transmission priority from the compressed transmission data, and determining a subpackage sending sequence; obtaining an optimized communication flow according to the subpackage sending sequence in combination with a pre-obtained optimal path, and transmitting the optimized communication flow to an edge node; performing edge node processing according to the optimized communication flow to obtain a processing result of the edge node; and optimizing a weight parameter according to the processing result, adjusting the number of data stream fragments, and generating a latest monitoring strategy. The method has the advantages that the real-time monitoring efficiency of the power distribution network in urban and rural areas can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network monitoring, and particularly relates to a power distribution network real-time monitoring method and system based on Internet of Things. BACKGROUND

[0002] With the continuous expansion and increasing complexity of modern power systems, the importance of real-time monitoring systems for power distribution networks is increasingly prominent. This system can provide comprehensive insights into the operating state of the power grid by collecting and analyzing data from different nodes, which is crucial for ensuring the stability and security of power supply services. Especially in the face of sudden failures, timely and accurate information acquisition and processing capabilities can greatly improve response speed, reduce power outage time, and optimize the allocation of maintenance resources. Therefore, developing efficient and reliable real-time monitoring systems for power distribution networks has become an important issue in the current power industry.

[0003] Currently, solutions for monitoring power distribution networks mainly rely on centralized data processing architectures. This method first deploys sensors at each monitoring point to collect key parameters such as voltage and current, and transmits these data to a central server through a communication network. On the central server, specific algorithms are used to analyze the collected data to identify potential problems or abnormal situations. In addition, to improve the reliability and stability of the system, redundant design is implemented, including backup power supply and multi-path data transmission scheme, to prevent any single failure from causing the entire system to fail.

[0004] However, in urban areas, due to well-developed infrastructure, extensive and stable network coverage, centralized data processing architectures can work well. However, in rural or remote areas, due to complex geographical environment, low level of development, and other factors, there are problems such as inadequate network facilities and limited communication bandwidth, which greatly increase the delay and loss rate of data transmission. At the same time, the layout of power distribution networks in rural areas is scattered, and the centralized processing mode will increase the burden on the central server, making it difficult to ensure effective monitoring in all areas. In summary, the real-time monitoring efficiency of power distribution networks in urban and rural areas is low. SUMMARY

[0005] The present application provides a power distribution network real-time monitoring method and system based on Internet of Things to improve the real-time monitoring efficiency of power distribution networks in urban and rural areas.

[0006] In a first aspect, to solve the above technical problems, the present application provides a power distribution network real-time monitoring method based on Internet of Things, comprising: obtaining urban monitoring data and rural monitoring data; differentially compressing the urban monitoring data and the rural monitoring data to generate compressed transmission data; extracting a transmission priority from the compressed transmission data, and determining a packet sending order; obtaining an optimized communication flow according to the packet sending order and a pre-acquired optimal path, and transmitting to an edge node; performing edge node processing according to the optimized communication flow, and obtaining a processing result of the edge node; optimizing a weight parameter according to the processing result, adjusting a data stream fragment number, and generating a latest monitoring strategy.

[0007] In an optional implementation, the acquiring of the urban monitoring data and the rural monitoring data comprises: acquiring the urban monitoring data by using a first urban sampling rate during a load peak time in an urban area; acquiring the urban monitoring data by using a second urban sampling rate during a non-load peak time in the urban area; acquiring the rural monitoring data by using a first rural sampling rate during a load peak time in a rural area; acquiring the rural monitoring data by using a second rural sampling rate during a non-load peak time in the rural area.

[0008] In an optional implementation, the differentiating compression of the urban monitoring data and the rural monitoring data to generate compressed transmission data comprises: performing high compression rate compression on the urban monitoring data to obtain urban compressed data; performing low compression rate compression on the rural monitoring data to obtain rural compressed data; extracting a data type marker from the urban monitoring data and the rural monitoring data, and adding a transmission priority to the urban compressed data and the rural compressed data according to the data type marker to obtain compressed transmission data.

[0009] In an optional implementation, the extracting of the transmission priority from the compressed transmission data and the determining of the packet sending order comprise: extracting a transmission priority from the compressed transmission data, and performing descending order sorting according to the size of the transmission priority to obtain a priority list; acquiring a real-time link state; performing priority scheduling according to the real-time link state and the priority list to obtain a packet sending order.

[0010] In an optional implementation, the obtaining of the optimized communication flow according to the packet sending order and the pre-acquired optimal path, and the transmitting to the edge node comprise: extracting high-priority data from the packet sending sequence according to a preset path weight; transmitting the high-priority data using a high-bandwidth path in the optimal path; determining remaining data in the packet sending sequence as low-priority data; transmitting the low-priority data using a low-bandwidth path in the optimal path; The optimized communication flow represents a communication flow after path optimization and sequence adjustment.

[0011] In an optional implementation, the edge node processing according to the optimized communication flow to obtain a processing result of the edge node includes: obtaining a load state of the edge node; migrating low-priority data in the optimized communication flow to a backup node when the load state exceeds a preset load threshold; extracting low-dynamic-class tasks from high-priority data in the optimized communication flow and migrating the low-dynamic-class tasks to the backup node; The processing result includes remaining data in the optimized communication flow and to-be-processed data of the backup node. The to-be-processed data includes the low-priority data and the low-dynamic-class tasks.

[0012] In an optional implementation, the weight parameter optimization according to the processing result and the adjustment of the data flow shard number to generate a latest monitoring strategy include: optimizing a task weight according to the processing result; adopting small shards for high-priority data to obtain a high-priority shard number; adopting large shards for low-priority data to obtain a low-priority shard number; The latest monitoring strategy includes the optimized task weight, the high-priority shard number, and the low-priority shard number.

[0013] In an optional implementation, the priority scheduling according to the real-time link state and the priority list to obtain a packet sending sequence includes: disabling a low-priority queue in the priority list when a bandwidth utilization of the real-time link state is greater than a preset threshold.

[0014] In an optional implementation, the weight parameter optimization according to the processing result to obtain an optimized task weight includes: setting a high weight parameter of a high-priority task as a preset first weight; The low-weight parameter of the low-priority task is set as a preset second weight; The first weight is greater than the second weight. The optimization task weight includes a high-weight parameter and the low-weight parameter.

[0015] In a second aspect, the present application provides a power distribution network real-time monitoring system based on Internet of Things, comprising: A data acquisition module is configured to acquire urban monitoring data and rural monitoring data. A data compression module is configured to perform differential compression on the urban monitoring data and the rural monitoring data to generate compressed transmission data. A packet sending sequence module is configured to extract transmission priority from the compressed transmission data and determine packet sending sequence. A communication optimization module is configured to obtain an optimized communication flow according to the packet sending sequence in combination with a pre-acquired optimal path and transmit the optimized communication flow to an edge node. An edge node module is configured to perform edge node processing according to the optimized communication flow to obtain an edge node processing result. A strategy updating module is configured to optimize weight parameters according to the processing result, adjust data flow fragmentation number, and generate a latest monitoring strategy.

[0016] In a third aspect, the present application further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power distribution network real-time monitoring method based on Internet of Things according to any one of the above.

[0017] In a fourth aspect, the present application further provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the power distribution network real-time monitoring method based on Internet of Things according to any one of the above when the computer program runs.

[0018] Compared with the prior art, the present application has the following beneficial effects: (1) The process of acquiring urban monitoring data and rural monitoring data ensures comprehensive monitoring of environmental conditions in different geographical regions. Various types of monitoring data, including air quality and noise level, are collected by sensor networks deployed in urban and rural areas. Different sampling frequencies are set for peak load periods in urban and rural areas to improve data processing efficiency. This step provides detailed basic data for subsequent data processing and analysis, improving the data integrity and effectiveness of the overall system.

[0019] (2) Differentiated compression of the urban monitoring data and the rural monitoring data to generate compressed transmission data. By using differentiated compression algorithms designed for different data characteristics, the amount of data can be effectively reduced, the transmission bandwidth requirement can be reduced, and the integrity of key information can be maintained. This method not only reduces the data transmission time, but also improves the transmission efficiency and ensures real-time performance.

[0020] (3) Extracting transmission priority from the compressed transmission data and determining the packet sending order. By evaluating the priority of the compressed data, it is identified which data needs to be processed and transmitted first. According to the packet sending order determined by the priority sorting, high-priority data can be processed in time, optimizing data flow management, enhancing system response speed and flexibility.

[0021] (4) According to the packet sending order combined with the pre-acquired optimal path, the optimized communication flow is obtained and transmitted to the edge node. Using intelligent routing algorithm combined with pre-calculated optimal path, the packet data is efficiently transmitted to the nearest or most suitable edge node. This process fully utilizes network resources, reduces delay, and improves data transmission reliability and efficiency.

[0022] (5) According to the optimized communication flow, the edge node processing result is obtained. Preliminary data processing tasks are performed on the edge node, and tasks are allocated to standby nodes. This distributed processing method reduces the burden of the central server, shortens the response time, makes local decision-making more rapid and accurate, and enhances the scalability and robustness of the entire system.

[0023] (6) According to the processing result, the weight parameter is optimized, and the number of data stream fragments is adjusted to generate the latest monitoring strategy. Based on the processing result returned by the edge node, the weight parameter and the number of data stream fragments of the monitoring system are dynamically adjusted to form the latest monitoring strategy. This method can continuously optimize system performance according to actual operation, improve monitoring accuracy and efficiency, and ensure that the system can adapt to changing actual environment to achieve continuous improvement and optimization. The entire process realizes closed-loop control from data acquisition to strategy update through automation, greatly improving the intelligent level and response ability of the environmental monitoring system. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flowchart of a real-time monitoring method for power distribution networks based on the Internet of Things provided by the first embodiment of the present application; Figure 2 is a structural diagram of a real-time monitoring system for power distribution networks based on the Internet of Things provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0025] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0026] With reference to Figure 1 The first embodiment of the present application provides a power distribution network real-time monitoring method based on Internet of Things, comprising the following steps: S11, acquiring urban monitoring data and rural monitoring data; S12, differentiating and compressing the urban monitoring data and the rural monitoring data to generate compressed transmission data; S13, extracting transmission priority from the compressed transmission data and determining packet sending order; S14, obtaining optimized communication flow according to the packet sending order combined with the pre-acquired optimal path and transmitting to the edge node; S15, performing edge node processing according to the optimized communication flow to obtain the processing result of the edge node; S16, optimizing weight parameters according to the processing result and adjusting the number of data stream fragments to generate the latest monitoring strategy.

[0027] In step S11, urban monitoring data and rural monitoring data are acquired.

[0028] In an embodiment, during the load peak time in the urban area, a first urban sampling rate is used for data acquisition to obtain urban monitoring data; during the non-load peak time in the urban area, a second urban sampling rate is used for data acquisition to obtain urban monitoring data; during the load peak time in the rural area, a first rural sampling rate is used for data acquisition to obtain rural monitoring data; during the non-load peak time in the rural area, a second rural sampling rate is used for data acquisition to obtain rural monitoring data.

[0029] It is worth mentioning that in the data acquisition stage, the collection of urban and rural monitoring data adopts a differentiated strategy: in the peak load time (such as 8-10 am and 18-20 pm on weekdays), the first city sampling rate (for example, 10 kHz) is adopted to capture the power grid fluctuations and equipment status in real time through high-frequency sampling; in the off-peak time, it is reduced to the second city sampling rate (such as 1 kHz) to reduce energy consumption and data redundancy. In the rural area, the first rural sampling rate (for example, 100 Hz) is adopted in the peak load time (such as irrigation period during busy season or holiday peak), while the second rural sampling rate (for example, 10 Hz) is used in the off-peak period (such as midnight or non-busy period). This method does not limit the specific time setting.

[0030] It is worth mentioning that the sensor arrangement strategy follows the following principles: urban area: high-density sensors (such as voltage, current, temperature and humidity sensors) are deployed on substations, commercial area distribution cabinets and key transmission lines, using modular embedded arrangement, combined with Internet of Things platform to realize plug and play; the line path avoids electromagnetic interference sources (such as near transformers), and is protected by metal pipes. Rural area: sensors are deployed at key nodes such as farmland irrigation distribution boxes and township center transformers to ensure coverage of farmland load change hotspots; the sensor spacing is expanded according to geographical dispersion, and low-power wide-area transmission is realized through LoRa or NB-IoT technology. This strategy dynamically adjusts the sampling rate and sensor layout to ensure real-time data while reducing network load, for example, high sampling rate during urban peak period can accurately capture voltage sag and other abnormalities, while low sampling rate during rural off-peak period prolongs device endurance, adapting to the low-frequency fluctuation characteristics of rural power grids.

[0031] In one embodiment, in the urban area, electrical sensors (monitoring voltage and current), temperature sensors are deployed, and their data is stored in compressed binary format during high-frequency peak period, and in ASCII text (such as {"Voltage": 220V, "Time": "2025-04-17 02:15"} ) during off-peak period; in the rural area, electrical sensors (monitoring voltage and current), accelerometers (UART output ASCII numerical values such as Acceleration_X=2.3g) and temperature and humidity sensors (I2C interface ASCII format) are deployed, and the data in the key period is stored in ASCII with precision (such as WaterLevel=1.2m).

[0032] In step S12, the urban monitoring data and the rural monitoring data are differentiated and compressed to generate compressed transmission data.

[0033] In one embodiment, the urban monitoring data is compressed at a high compression rate to obtain urban compressed data; the rural monitoring data is compressed at a low compression rate to obtain rural compressed data; a data type marker is extracted from the urban monitoring data and the rural monitoring data, and a transmission priority is added to the urban compressed data and the rural compressed data according to the data type marker, to obtain compressed transmission data.

[0034] In one embodiment, the high compression rate compression adopts a Gzip algorithm, and the process is as follows: first, a repeated substring in the urban monitoring data is scanned through an LZ77 sliding window algorithm, a pointer (offset and length) is used to replace the repeated sequence to reduce redundancy (for example, “abcdef_bcdefgh” is compressed to “abcdef_(6,5)gh”); then, Huffman coding is applied to the compressed data, a variable-length coding table is dynamically generated according to the character frequency (high-frequency characters are coded with short codes, and low-frequency characters are coded with long codes, for example, “A” is coded with “0” in the example), and finally a high compression rate binary stream is generated. This algorithm combines the advantages of LZ77 redundancy elimination and Huffman entropy coding, and is suitable for efficient compression of high-frequency and high-precision data in urban areas (such as power grid fluctuation data).

[0035] In one embodiment, the low compression rate compression of the rural monitoring data adopts a Huffman Coding algorithm, and the process is as follows: first, the frequency of each character or numerical value in the data is counted (such as the discrete value of a temperature and humidity sensor or the sampling sequence of a liquid level sensor), then a priority queue (a minimum heap based on frequency) is constructed, and a Huffman tree is generated by continuously merging the two nodes with the lowest frequency; then, short codes are assigned to high-frequency data and long codes are assigned to low-frequency data to form a coding table; finally, the original data is replaced with the corresponding binary code to generate compressed data. This algorithm ensures data lossless through adaptive coding, and is suitable for rural monitoring of high-precision data such as water quality, liquid level, etc. (such as pH value or water level fluctuation), which avoids information loss while ensuring low compression rate (such as compression ratio 1:1.5 to 1:2).

[0036] In one embodiment, data type identifiers (e.g., “voltage data”, “liquid level sensor”, “ASCII text”, etc.) are first extracted by analyzing the characteristics of urban and rural monitoring data (e.g., the Content-Type field of HTTP headers, packet contents, or sensor type markers). For example, binary high-compression data from urban electrical sensors are marked as “high-priority real-time power grid data”, while ASCII data from rural temperature and humidity sensors are marked as “low-priority environmental monitoring data”. Then, according to the importance and transmission requirements of data types, high-priority (e.g., 802.1p value 7) is assigned to urban compressed data (e.g., power grid fluctuation data) and low-priority (e.g., 802.1p value 3) is assigned to rural compressed data (e.g., non-real-time water quality data) based on IEEE 802.1p / Q or DSCP priority mapping rules. Priority encoding is achieved by modifying packet header fields (e.g., TOS / DSCP fields of IP packets or PRI fields of Ethernet frames), and is packaged with compressed data into a unified transmission unit. For example, in Go language, the SetDeadline of TCP packets is set using the net package or the Priority Code Point field of Ethernet frames is directly modified through the gopacket library. The final compressed transmission data contains priority labels, allowing network devices (e.g., Huawei QoS devices) to dynamically adjust the transmission order based on priority scheduling strategies (e.g., priority queue scheduling or frame preemption mechanisms), ensuring that critical urban data (e.g., power grid failure signals) are transmitted first, while rural low-priority data (e.g., non-real-time irrigation records) are processed later, thereby achieving differentiated transmission efficiency.

[0037] In step S13, the transmission priority is extracted from the compressed transmission data, and the packet sending order is determined.

[0038] In one embodiment, the transmission priority is extracted from the compressed transmission data, and the priority list is obtained by descending order sorting according to the size of the transmission priority; Obtain real-time link state; According to the real-time link state and the priority list, priority scheduling is performed to obtain the packet sending order.

[0039] When the bandwidth utilization of the real-time link state is greater than a preset threshold, the low-priority queue in the priority list is disabled.

[0040] It is worth mentioning that first, the preset transmission priority value (such as 802.1p=7 of urban power grid data and 802.1p=3 of rural irrigation data) is extracted from the packet header of compressed transmission data (such as the 802.1p field of the Ethernet frame or the DSCP field of the IP packet). Then, all data packets are sorted in descending order of priority value to generate a priority list (for example, power grid fluctuation data with priority 7 is placed before humidity data with priority 3). Subsequently, the current link state parameters are obtained by real-time monitoring tools (such as SNMP or traffic analyzers), including bandwidth utilization. When the bandwidth utilization exceeds the preset threshold (such as 85%), the system triggers the dynamic scheduling strategy.

[0041] In one embodiment, in the priority scheduling implementation, the system first extracts the transmission priority value (for example, power failure alarm data packets are marked as 802.1p=7 and rural irrigation records are marked as 802.1p=3) by analyzing the 802.1p priority field or DSCP label in the packet header. Then, using a preemptive priority scheduling algorithm, high-priority data packets (such as priority 7) are immediately inserted at the head of the priority queue (PQ), and low-priority data packets are temporarily stored in the low-priority queue. The scheduler monitors the link bandwidth utilization in real time, and if it detects that the bandwidth utilization exceeds the preset threshold (such as 90%), it triggers the dynamic queue disabling mechanism: by modifying the queue scheduling configuration of the switch or router, the transmission permission of the low-priority queue is directly closed (such as setting the queue with priority ≤4 to the blocked state). At this time, the scheduler only selects data packets from the high-priority queue (such as priority ≥5) for transmission, and ensures that data packets of the same priority are transmitted in the order of arrival through timestamp polling. For example, when the bandwidth occupancy rate is 90%, the system will forcibly discard irrigation record data packets and preferentially forward power grid data packets until the bandwidth returns to below the threshold, thereby achieving dynamic resource allocation and real-time guarantee.

[0042] It is worth mentioning that in the priority scheduling algorithm execution stage, the system first determines whether the bandwidth utilization exceeds the threshold (such as 80%) according to the real-time link state. If it does not exceed the threshold, the strict priority scheduling (SP) algorithm is adopted: the data packets are sent in turn according to the preset queue priority (such as queue 5> queue 6> queue 7). For example, the data packets of the high-priority queue 5 (power grid alarm data, 802.1p = 7) are preferentially selected and sent until the queue is empty; then the medium-priority queue 6 (rural smoke alarm, 802.1p = 6) and the low-priority queue 7 (irrigation record, 802.1p = 3) are processed in turn, and finally the sending order is [power grid alarm -> smoke alarm -> irrigation record]. If the bandwidth utilization exceeds the threshold (such as 90%), the preemptive scheduling + queue disabling mechanism is triggered: the high-priority data packets (such as 802.1p >= 5) will be directly inserted into the queue head, interrupting the sending of the current low-priority packets (such as power grid data preempting irrigation packets), and the low-priority queue (such as queue 7) is forcibly closed by hardware-level queue mapping (such as the qos lr-pq command of Huawei switch) to only keep the high / medium-priority queues (queues 5 and 6) to send according to the SP algorithm, at this time the sending order becomes [power grid alarm -> smoke alarm], and the low-priority packets are discarded until the bandwidth is restored. The whole process is dynamically adjusted through real-time link monitoring (such as SNMP to obtain bandwidth utilization) and hardware queue control (such as the priority scheduling strategy of the physical queue), ensuring that the end-to-end delay of key data such as power grid alarm is less than 10ms, while avoiding the occupation of scarce bandwidth resources by low-priority data.

[0043] In step S14, the optimized communication flow is obtained according to the packet sending order in combination with the pre-acquired optimal path, and is transmitted to the edge node.

[0044] In an embodiment, high-priority data is extracted from the packet sending order according to a preset path weight; The high-priority data is transmitted using a high-bandwidth path in the optimal path; The remaining data in the packet sending order is determined as low-priority data; The low-priority data is transmitted using a low-bandwidth path in the optimal path; The optimized communication flow represents the communication flow after path optimization and order adjustment.

[0045] It is worth noting that optimizing the communication flow refers to the data transmission process after adjusting the path selection and packet sending order. Specifically, the system first calculates the "optimal path" of multiple paths according to the real-time network state (such as bandwidth, delay) (for example, preferentially selecting high-bandwidth or low-delay paths), and binds high-priority data (such as power grid alarms) to these high-quality paths for transmission, while assigning low-priority data (such as non-real-time records) to low-bandwidth paths; at the same time, the sending order of data packets is also dynamically adjusted according to the priority list (such as sending emergency data first). Ultimately, this combination of "path optimization" and "order adjustment" ensures low-delay and high-reliability transmission of critical data, while reasonably utilizing network resources and improving overall communication efficiency (for example, power grid alarms are transmitted preferentially through fiber optic lines, while irrigation records are transmitted with delay through 4G networks).

[0046] It is worth noting that the path weight is a preset priority data proportion threshold (set to 30%), that is, the system determines the top 30% of high-priority data packets (such as power failure alarms, real-time monitoring videos) in the priority order of packet sending order as high-priority data, and these data packets will be transmitted through high-bandwidth paths in the optimal path (such as fiber optic lines or low-congestion links) to ensure low delay and high reliability; while the remaining 70% of low-priority data packets (such as non-real-time irrigation records, historical logs) are transmitted through low-bandwidth paths (such as 4G cellular networks or WiFi backhaul links). For example, if there are 100 data packets in the packet sending order, the system will mark the top 30 (according to priority order) as high-priority and assign them to high-bandwidth paths, and the remaining 70 will use low-bandwidth paths. This mechanism achieves resource isolation through fixed proportion division, avoiding delay of high-priority data due to bandwidth occupation by low-priority traffic, while optimizing overall communication efficiency.

[0047] It is worth noting that the process of obtaining the optimal path is achieved through the following detailed steps: first, the system collects the link state information of all nodes in the network in real time through the link state protocol (such as OSPF), including adjacency relationship, bandwidth, delay, availability and current load (such as interface traffic statistics obtained through SNMP). These information is synchronized to the whole network through the flooding mechanism (such as LSA of OSPF), forming a unified link state database (LSDB) and constructing a complete network topology graph. Then, Dijkstra algorithm is used to weight the cost of each link (such as bandwidth inverse, delay value), and gradually expand the path and select the path with the smallest total cost as the optimal path.

[0048] In one embodiment, the Dijkstra algorithm calculates the optimal path by the following process: first, set the initial cost of the source node (e.g., a data source server) to 0, and set the cost of all other nodes to "infinity" (indicating unexplored); then, constantly select the node with the smallest cost among the currently unprocessed nodes (initially the source node), calculate the new path cost of its adjacent nodes (e.g., the cost from the source to A + the link cost from A to B); if the new path cost is smaller than the original cost of the adjacent node (e.g., originally infinity or there is a better path), update its cost value and record the path to the node; repeat this process until the target edge node (e.g., a rural base station) is processed. Finally, the algorithm selects the path with the smallest total cost as the optimal path by tracing the recorded path in reverse. For example, if the total cost (calculated by bandwidth inverse or delay as weight) of the path A→B→C is lower than A→D→C, A→B→C is preferred, ensuring that critical data is transmitted through the "optimal" path with high bandwidth and low delay. This process achieves path optimization through dynamic weight calculation (e.g., link cost is smaller for higher bandwidth).

[0049] In step S15, edge node processing is performed according to the optimized communication flow, obtaining a processing result of the edge node.

[0050] In one embodiment, the load state of the edge node is obtained; When the load state exceeds a preset load threshold, low-priority data in the optimized communication flow is migrated to a backup node; Low-dynamic-class tasks are extracted from high-priority data in the optimized communication flow, and the low-dynamic-class tasks are migrated to a backup node; The processing result includes remaining data in the optimized communication flow and pending data of the backup node. The pending data includes the low-priority data and the low-dynamic-class tasks.

[0051] It is worth noting that edge node processing achieves efficient resource allocation through dynamic resource management and task migration mechanisms. The system first monitors the load state of the edge node (e.g., CPU, memory, and bandwidth usage) in real time, and when a critical indicator (e.g., CPU usage exceeds 80%) exceeds a preset threshold, the following operations are started: Firstly, low-priority data migration: non-urgent low-priority data (such as non-real-time irrigation records or historical logs) is migrated from the optimized communication flow to a backup node (such as a nearby idle edge node or cloud server), releasing main node resources through a dynamic load balancing strategy (such as a weighted round-robin algorithm) to avoid overload. Secondly, low-dynamic task migration: tasks with stable computing requirements (such as periodic data collection, static cache updates) are identified from high-priority data (such as fixed frame rate camera data collection in intelligent transportation systems), which are also migrated to backup nodes to ensure that the main node focuses on processing high-dynamic high-priority tasks (such as power grid alerts or real-time video analysis). Finally, the processing results are divided into two parts: the main node retains and prioritizes high-priority dynamic tasks that have not been migrated (such as emergency alerts or real-time analysis) to ensure low latency and high reliability; the backup node processes low-priority data and low-dynamic tasks that have been migrated (such as non-real-time logs or periodic data).

[0052] It is worth noting that in the step, the dynamic load balancing strategy (such as the weighted round-robin algorithm) monitors the load status of the edge nodes (such as CPU, memory usage) in real time, and dynamically adjusts task allocation to release resources when the main node load is too high (such as CPU exceeding 80%). The specific process is as follows: first, the system assigns a weight value to each node based on its current load and performance, with higher weights indicating that the node can handle more requests. For example, if the main node load is too high, its weight will be dynamically lowered, while the weight of the backup node (such as an idle edge node or cloud server) will be increased. Then, the weighted round-robin algorithm will allocate tasks according to the weight ratio: assuming the main node weight is 1 and the backup node weight is 3, the total weight is 4, and the new task has a 75% probability of being allocated to the backup node and only 25% to the main node. In this way, low-priority data (such as non-real-time logs) and low-dynamic tasks are preferentially migrated to backup nodes, while the main node retains high-priority dynamic tasks.

[0053] In step S16, the weight parameters are optimized according to the processing results, and the number of data stream shards is adjusted to generate the latest monitoring strategy.

[0054] In one embodiment, the weight parameters are optimized according to the processing results to obtain optimized task weights; small shards are used for high-priority data to obtain a high-priority shard number; and large shards are used for low-priority data to obtain a low-priority shard number; wherein the latest monitoring strategy includes optimized task weights, high-priority shard numbers, and low-priority shard numbers. The high weight parameter of the high-priority task is set to a preset first weight; the low weight parameter of the low-priority task is set to a preset second weight; wherein the first weight is greater than the second weight; wherein the optimized task weights include the high weight parameter and the low weight parameter.

[0055] It is worth noting that the monitoring strategy optimization realizes dynamic resource adaptation by adjusting the weight parameter and the number of shards. On the one hand, the weight parameter is optimized, such as the system presets the first weight of the high-priority task (such as power grid alarm) as 0.8 and the second weight of the low-priority task (such as historical log) as 0.2 (ensuring that the first weight is greater than the second weight), and then dynamically adjusts the weight ratio according to the task load state in the processing result (such as the current processing delay rate of the master node for high-priority tasks). If it is detected that the processing delay of high-priority tasks is increasing, the system can further increase its weight to 0.9, while reducing the low-priority weight to 0.1, to prioritize resource allocation. On the other hand, the number of shards is adjusted. Small shards (such as 256KB / shard) are used for high-priority data to reduce transmission delay (for example, real-time video analysis requires fast response); while large shards (such as 512KB / shard) are used for low-priority data to improve transmission efficiency (for example, batch transmission of non-real-time irrigation records). The final generated latest monitoring strategy contains two parts of optimized task weight and shard number configuration. The optimized task weight is the high weight parameter (0.8-0.9) and the low weight parameter (0.1-0.2), which is used for subsequent priority sorting of task scheduling; the shard number configuration is the high-priority shard number (such as 10 shards per second) and the low-priority shard number (such as 2 shards per second), which is dynamically adjusted according to the bandwidth. For example, when the processing result shows that the master node bandwidth occupancy rate is too high, the system can further reduce the high-priority shard to 50KB, while merging the low-priority shard to 2MB, to balance real-time performance and transmission efficiency. Through the dynamic combination of weight and shard number, the system ensures low-delay execution of critical tasks while maximizing network resource utilization.

[0056] It is worth noting that the task weight is used in the system to dynamically allocate computing and network resources, and by quantifying the priority of the task by a numerical value, it directly affects the resource scheduling strategy. For example, high weight parameters (such as 0.8-0.9 for power grid alarm tasks) will have priority to obtain resources such as CPU and bandwidth of the master node, ensuring that critical tasks (such as emergency alarms or real-time analysis) are executed with the lowest delay; while low weight parameters (such as 0.1-0.2 for historical logs) are allocated less resources, only to guarantee basic processing needs. This mechanism, combined with the shard strategy, enables the system to dynamically adjust the weight ratio according to real-time load (such as node resource occupancy rate), for example, by detecting that the master node is overloaded, further increasing the priority of high weight tasks, while limiting the resource occupancy of low weight tasks. Ultimately, the task weight, through the differentiated allocation of resources, balances the overall system efficiency and stability while ensuring the real-time performance of critical tasks.

[0057] It is worth mentioning that sharding is a technique that splits data or tasks into smaller, independent shards and distributes them to multiple nodes or servers for processing, aiming to improve system performance, scalability, and fault tolerance. Its core principles include data / task splitting, splitting data based on sharding strategies such as hash sharding, range sharding, or list sharding, for example, Apache IoTDB hashes device names to sequence slots (such as the default 1000 slots), then allocates them to different SchemaRegionGroup and DataRegionGroup, achieving distributed storage of metadata and data; MongoDB distributes data to multiple shards based on shard keys (such as timestamps or user IDs), enhancing storage and query capabilities. At the same time, sharding technology also involves load balancing, combining dynamic weight adjustment (such as weighted round-robin algorithm) or consistent hashing to ensure even distribution of data, such as high-priority tasks in edge computing using small shards (such as real-time video analysis) to reduce latency, and low-priority tasks using large shards (such as historical logs) to improve transmission efficiency; IP fragmentation solves network transmission problems by splitting large packets into small fragments that meet MTU restrictions. In addition, sharding has dynamic management capabilities, supporting expansion and fault tolerance, such as IoTDB dynamically migrating shards based on node load, and MongoDB balancing data distribution by configuring servers to record metadata. However, sharding technology also faces challenges such as cross-shard query complexity, data consistency (such as distributed transactions), and needs to rely on transaction logs and data synchronization mechanisms to solve them. Sharding achieves high throughput, low latency, and elastic expansion through parallel processing and resource distribution, and is a core means for distributed systems (such as databases, blockchains, and edge computing) to handle large-scale data and high concurrency.

[0058] In summary, the application discloses a power distribution network real-time monitoring method based on the Internet of Things, which aims to improve data processing efficiency by setting differentiated sensor distribution, sampling frequency, and data compression algorithm in urban and rural areas. And through distributed data transmission and node processing strategy, efficient real-time monitoring of power distribution network is realized.

[0059] Referring to Figure 2 The second embodiment of the application provides a power distribution network real-time monitoring system based on the Internet of Things, comprising: A data acquisition module is used to acquire urban monitoring data and rural monitoring data. A data compression module is used to compress the urban monitoring data and the rural monitoring data differently to generate compressed transmission data. A packet distribution sequence module is used to extract transmission priority from the compressed transmission data and determine packet distribution sequence. A communication optimization module is configured to obtain an optimized communication flow according to the packet sending sequence and a pre-acquired optimal path, and transmit the optimized communication flow to an edge node. An edge node module is configured to perform edge node processing according to the optimized communication flow, and obtain a processing result of the edge node. A policy updating module is configured to optimize a weight parameter according to the processing result, adjust a data flow fragment number, and generate a latest monitoring policy.

[0060] It should be noted that the real-time monitoring system for power distribution network based on Internet of Things provided by the embodiments of the present application is used to execute all process steps of the real-time monitoring method for power distribution network based on Internet of Things provided by the embodiments of the present application, and the working principles and advantages of the two are one-to-one corresponding, thus no longer being described in detail.

[0061] The embodiments of the present application further provide an electronic device. The electronic device comprises a processor, a memory, and a computer program, such as a data acquisition program, stored in the memory and executable on the processor. The processor implements the steps in the above various real-time monitoring methods for power distribution network based on Internet of Things when executing the computer program, such as the step S11 shown in the above. Figure 1 Alternatively, the processor implements the functions of the modules / units in the above various apparatus embodiments when executing the computer program, such as a data acquisition module.

[0062] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0063] The electronic device can be a desktop computer, a notebook computer, a palm computer, a smart tablet and the like. The electronic device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device, and do not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than the above, or combine certain components, or different components, for example, the electronic device can further include an input / output device, a network access device, a bus and the like.

[0064] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the electronic device, and connects various parts of the electronic device through various interfaces and lines.

[0065] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0066] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0067] It should be noted that the above-described device embodiments are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0068] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only for the specific embodiments of the present application and do not limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for real-time monitoring of power distribution networks based on the Internet of Things, characterized in that, include: Acquire urban and rural monitoring data; The urban monitoring data and the rural monitoring data are differentially compressed to generate compressed transmission data; The transmission priority is extracted from the compressed transmission data, and the packet sending order is determined; Based on the packet sending order and the pre-obtained optimal path, an optimized communication flow is obtained and transmitted to the edge node; Edge node processing is performed based on the optimized communication flow to obtain the processing results of the edge nodes; Based on the processing results, optimize the weight parameters, adjust the number of data stream shards, and generate the latest monitoring strategy; The step of obtaining an optimized communication flow based on the packet sending order and a pre-obtained optimal path, and transmitting it to the edge node, includes: High-priority data is extracted from the packet sending order according to a preset path weight; The high-priority data is transmitted using the high-bandwidth path in the optimal path; The remaining data in the packet sending order is determined to be low-priority data; The low-priority data is transmitted using the low-bandwidth path in the optimal path. The optimized communication flow refers to the communication flow after path optimization and sequence adjustment.

2. The method for real-time monitoring of power distribution networks based on the Internet of Things according to claim 1, characterized in that, The acquisition of urban and rural monitoring data includes: During peak load times in urban areas, data is collected using the first urban sampling rate to obtain urban monitoring data. During off-peak hours in urban areas, data is collected using a second urban sampling rate to obtain urban monitoring data. During the peak load period in rural areas, data was collected using the first rural sampling rate to obtain rural monitoring data. During off-peak hours in rural areas, data is collected using a second rural sampling rate to obtain rural monitoring data.

3. The method for real-time monitoring of power distribution networks based on the Internet of Things according to claim 1, characterized in that, The step of differentially compressing the urban monitoring data and the rural monitoring data to generate compressed transmission data includes: The urban monitoring data is compressed at a high compression rate to obtain compressed urban data; The rural monitoring data is compressed at a low compression rate to obtain compressed rural data; Data type tags are extracted from the urban monitoring data and the rural monitoring data, and transmission priorities are added to the urban compressed data and the rural compressed data according to the data type tags to obtain compressed transmission data.

4. The method for real-time monitoring of power distribution networks based on the Internet of Things according to claim 1, characterized in that, Extracting the transmission priority from the compressed transmission data and determining the packet transmission order includes: The transmission priority is extracted from the compressed transmission data and sorted in descending order according to the transmission priority to obtain a priority list; Obtain real-time link status; Priority scheduling is performed based on the real-time link status and the priority list to obtain the packet sending order.

5. The method for real-time monitoring of power distribution networks based on the Internet of Things according to claim 1, characterized in that, The step of processing edge nodes based on the optimized communication flow to obtain the processing results of the edge nodes includes: Obtain the load status of edge nodes; When the load exceeds a preset load threshold, low-priority data in the optimized communication stream is migrated to a backup node. Low-dynamic tasks are extracted from high-priority data in the optimized communication stream, and these low-dynamic tasks are migrated to a backup node. The processing result includes the remaining data in the optimized communication stream and the data to be processed from the backup node; The data to be processed includes the low-priority data and the low-dynamic tasks.

6. The method for real-time monitoring of power distribution networks based on the Internet of Things according to claim 1, characterized in that, The step of optimizing the weight parameters based on the processing result, adjusting the number of data stream shards, and generating the latest monitoring strategy includes: Based on the processing results, the weight parameters are optimized to obtain the optimized task weights; High-priority data is divided into smaller pieces to obtain the number of high-priority pieces; Large sharding is used for low-priority data to obtain the number of low-priority shards; The latest monitoring strategy mentioned above includes optimizing task weights, the number of high-priority shards, and the number of low-priority shards.

7. The method for real-time monitoring of power distribution networks based on the Internet of Things according to claim 4, characterized in that, The step of prioritizing and scheduling packets based on the real-time link status and the priority list to obtain the packet sending order includes: When the bandwidth utilization of the real-time link status exceeds a preset threshold, the low-priority queues in the priority list are disabled.

8. The method for real-time monitoring of power distribution networks based on the Internet of Things according to claim 6, characterized in that, The step of optimizing the weight parameters based on the processing result to obtain the optimized task weights includes: Set the high-priority task's high-weight parameter to the preset first weight; Set the low-priority task's low-weight parameter to the preset second weight; Wherein, the first weight is greater than the second weight; The optimized task weights include high-weight parameters and low-weight parameters.

9. A real-time monitoring system for power distribution networks based on the Internet of Things, characterized in that, For implementing the method as described in any one of claims 1-8, comprising: The data acquisition module is used to acquire urban monitoring data and rural monitoring data; The data compression module is used to perform differentiated compression on the urban monitoring data and the rural monitoring data to generate compressed transmission data; The packet ordering module is used to extract the transmission priority from the compressed transmission data and determine the packet sending order; The communication optimization module is used to obtain an optimized communication flow based on the packet sending order and a pre-obtained optimal path, and then transmit it to the edge node. An edge node module is used to perform edge node processing based on the optimized communication flow to obtain the processing results of the edge nodes; The strategy update module is used to optimize the weight parameters and adjust the number of data stream shards based on the processing results, and generate the latest monitoring strategy.

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