A power distribution internet of things communication and device management system based on dynamic optimization algorithm

Abstract

This invention discloses a power distribution IoT communication and equipment management system based on dynamic optimization algorithms. It adopts a four-layer architecture: a perception layer, an edge layer, a platform layer, and an application layer. The end devices in the perception layer are used to collect power distribution data and execute control commands. The edge devices in the edge layer, and their managed sub-devices, are used for protocol conversion, data preprocessing, and executing dynamic optimization algorithms. The platform layer is used for message parsing, algorithm optimization, device management, and data storage. The application layer provides a visual user interface. Through dynamic optimization algorithms and a full lifecycle device management scheme, the system significantly improves the reliability of power distribution IoT data transmission, optimizes network resource utilization, enhances equipment operating efficiency, ensures the real-time performance of critical services, and features strong compatibility, security, and concurrent processing capabilities.

Description

Technical Field

[0001] This invention relates to the field of power distribution Internet of Things (IoT) technology, and more specifically, to a power distribution IoT communication and equipment management system based on dynamic optimization algorithms. Background Technology

[0002] With the continuous advancement of smart grid construction, the scale of the distribution Internet of Things is expanding explosively. The number of smart integrated terminals, edge devices, and end devices (such as smart meters, switch controllers, and fault indicators) has increased significantly, and the communication interaction and full life cycle management needs between devices are becoming increasingly complex.

[0003] The core communication protocol in the power distribution IoT is the MQTT protocol, which is lightweight and has low bandwidth consumption, making it suitable for data transmission scenarios of IoT devices. Its core technologies include publish / subscribe communication modes, QoS (Quality of Service) level classification (QoS0 at most one transmission, QoS1 at least one transmission, QoS2 exactly one transmission), and Topic routing. Furthermore, power distribution IoT device management involves key aspects such as device registration, status updates, parameter configuration, and file management, which are the core support for ensuring stable system operation.

[0004] In existing technologies, communication and equipment management solutions for power distribution IoT are mainly based on the standard MQTT protocol. The core architecture consists of three layers: cloud platform, edge devices, and end devices. For communication, a fixed QoS level configuration is used. End devices upload data to edge devices via CoAP or MQTT protocols, which then forward the data to the cloud platform. The cloud platform issues control commands and parameter configurations via MQTT. For device management, edge devices register via MQTT CONNECT messages. The addition, deletion, and status updates of sub-devices are transmitted separately via standard Topic messages, which the platform receives, stores, and displays directly. In the data processing stage, the platform stores and displays all data in the order of receipt, without a priority scheduling mechanism. Parameter configuration relies on maintenance personnel manually operating according to equipment manuals or experience, and is then issued to the devices for activation.

[0005] However, in practical applications, due to the diverse types of distribution IoT devices, the large differences in data priorities, and the complex and ever-changing network environment, existing technologies have revealed many shortcomings: fixed QoS level allocation leads to the easy loss of critical data and excessive bandwidth consumption of ordinary data; frequent updates of sub-device status generate a large amount of redundant data, reducing equipment management efficiency; data transmission lacks priority distinction, affecting the response speed of critical services; manual configuration of equipment parameters is difficult to adapt to changes in equipment operating status and environment, resulting in low equipment operating efficiency and high energy consumption. Summary of the Invention

[0006] To address the technical problems in existing power distribution IoT technologies, such as the fixed allocation of QoS levels leading to easy loss of critical data and excessive bandwidth consumption of ordinary data; frequent updates of sub-device status generating a large amount of redundant data and reducing equipment management efficiency; lack of data transmission priority, affecting the response speed of critical services; and the difficulty of manually configuring equipment parameters to adapt to changes in equipment operating status and environment, resulting in low equipment operating efficiency and high energy consumption, this application proposes a power distribution IoT communication and equipment management system based on a dynamic optimization algorithm. The system adopts a four-layer architecture consisting of a perception layer, an edge layer, a platform layer, and an application layer. The perception layer includes several end devices, which are used to upload the collected power distribution data to the edge layer via the CoAP / MQTT protocol, and to issue control commands after the platform layer processes the operation instructions generated by the application layer. The edge layer includes several edge devices, and each edge device includes several sub-devices. As an intermediate node, it is used to perform protocol conversion, preprocess the power distribution data to generate standardized data, upload MQTT messages generated based on the standardized data to the platform layer via the MQTT protocol, and execute the dynamic optimization algorithm determined by the platform layer. The platform layer is used for message parsing, algorithm optimization, device management, and data storage, including: The communication protocol adaptation module is used for message parsing, topic adaptation, CoAP and MQTT protocol conversion, and TLS secure transmission. The dynamic optimization algorithm module is used for dynamic adaptation of QoS levels, aggregation and updating of sub-device status, and queue scheduling of data transmission. The device lifecycle management module is used to manage edge devices, sub-devices and terminal devices throughout the entire process, including edge device registration, sub-device management, terminal device file management, device model management and intelligent parameter configuration. The data storage and display module is used to store device information, operation logs, parameter configurations, and collected data using a database, and to visualize the stored data. The application layer provides a visual interface to support operations and maintenance personnel in configuring services, managing devices, querying data, viewing logs, and issuing operation commands to the platform layer.

[0007] The power distribution IoT communication and equipment management system based on dynamic optimization algorithms described in this invention achieves significant improvements in four core problems of existing technologies through dynamic optimization algorithms and a full lifecycle equipment management scheme. Specific effects are as follows: 1. Significantly improved data transmission reliability: The dynamic QoS adjustment algorithm dynamically allocates QoS levels based on data type, network conditions, and device communication quality. Combined with the gradient descent algorithm to calibrate weight coefficients, it reduces the packet loss rate of critical data (fault events, control commands, etc.) by more than 30%. It can still achieve reliable transmission in network congestion scenarios, solving the problem of easy loss of critical data caused by traditional fixed QoS level allocation. 2. Network resource utilization optimization: The sub-device status aggregation algorithm uses a sliding window to deduplicate and merge status messages, and handles devices with status jitter separately, reducing redundant messages by more than 70% and reducing network bandwidth overhead by 25%. This effectively alleviates the bandwidth pressure of concurrent communication of multiple devices and improves the platform's device management efficiency. 3. Real-time guarantee of critical business: The three-level queue scheduling mechanism prioritizes data according to the importance of the business, and adopts preemptive scheduling and differentiated bandwidth allocation to meet the real-time control requirements of the power distribution system and solve the problem of slow response of critical business caused by traditional no-priority scheduling. 4. Improved equipment operating efficiency: The intelligent parameter configuration scheme is based on the genetic optimization algorithm. It automatically generates the optimal parameter combination by combining the equipment model constraints and operating objectives (energy consumption coefficient + transmission reliability coefficient), which reduces equipment energy consumption by 15%-20% and improves transmission reliability by 10%-18%. It avoids the subjectivity and error of manual configuration and solves the problem of equipment operating in a non-optimal state. 5. Strong compatibility and security: The communication protocol adaptation module supports multiple protocols such as MQTT, CoAP, and DL / T 645-2007, which can quickly connect to existing power distribution IoT devices. At the same time, it adopts TLS 1.2 (RSA 2048-bit encryption) to ensure the confidentiality and integrity of data transmission, making it suitable for large-scale deployment in complex power distribution IoT scenarios. 6. Strong system concurrency processing capability: The platform supports concurrent access of 100 terminal devices, with device management response time ≤ 2.5 seconds, no lag or abnormal exit, meeting the application needs of the explosive growth in the number of power distribution IoT devices.

[0008] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0009] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0010] Figure 1 This is a schematic diagram of the structure of a power distribution Internet of Things communication and equipment management system based on a dynamic optimization algorithm according to a preferred embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the workflow of a communication protocol adaptation module according to a preferred embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the workflow of the dynamic QoS adjustment unit of the dynamic optimization algorithm module according to a preferred embodiment of the present invention. Figure 4 This is a schematic diagram of the workflow of the sub-device state aggregation and update unit of the dynamic optimization algorithm module according to a preferred embodiment of the present invention. Figure 5 This is a schematic diagram of the workflow of the data transmission priority scheduling unit of the dynamic optimization algorithm module according to a preferred embodiment of the present invention. Figure 6 This is a schematic diagram of the management process of the equipment lifecycle management module according to a preferred embodiment of the present invention; Figure 7 This is a schematic diagram of the data storage and display module according to a preferred embodiment of the present invention. Detailed Implementation

[0011] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0012] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0013] Figure 1 This is a schematic diagram of the structure of a power distribution Internet of Things (IoT) communication and equipment management system based on a dynamic optimization algorithm according to a preferred embodiment of the present invention. Figure 1 As shown, the power distribution Internet of Things communication and equipment management system 100 based on dynamic optimization algorithm described in this preferred embodiment adopts a four-layer architecture consisting of a perception layer 101, an edge layer 102, a platform layer 103, and an application layer 104, wherein: The perception layer 101 includes several end devices, used to upload the collected power distribution data to the edge layer via the CoAP / MQTT protocol, and to issue control commands after the platform layer processes the operation instructions generated by the application layer. The edge layer 102 includes several edge devices, each of which includes several sub-devices. As an intermediate node, it is used to perform protocol conversion, preprocess the power distribution data to generate standardized data, upload MQTT messages generated based on the standardized data to the platform layer via the MQTT protocol, and execute the dynamic optimization algorithm determined by the platform layer. The platform layer 103 is used for message parsing, algorithm optimization, device management, and data storage, including: The communication protocol adaptation module 131 is used for message parsing, topic adaptation, conversion between CoAP and MQTT protocols, and TLS secure transmission. The dynamic optimization algorithm module 132 is used for dynamic adaptation of QoS levels, aggregation and update of sub-device status, and queue scheduling of data transmission. The device lifecycle management module 133 is used to manage edge devices, sub-devices and terminal devices throughout the entire process, including edge device registration, sub-device management, terminal device file management, device model management and intelligent parameter configuration. The data storage and display module 134 is used to store device information, operation logs, parameter configurations and collected data using a database, and to visualize the stored data. The application layer 104 is used to provide a visual operation interface to support operation and maintenance personnel in configuring services, managing devices, querying data, viewing logs, and issuing operation commands to the platform layer.

[0014] according to Figure 1 Taking data collection and command issuance as an example, the system workflow is as follows: 1. Service Startup: Operations personnel configure the IP address and port of the HTTP file service and the IP address, port, username, and password of the MQTTBroker through a visual interface, start the service, and establish a connection; 2. Device Registration: The edge device sends a registration request to the platform via the CONNECT message of MQTT, carrying authentication information. After the platform verifies the information, it returns a CONNACK message to complete the registration. 3. Data Upload: The terminal device collects telemetry / telecommunications data and sends it to the edge device via the CoAP / MQTT protocol; the edge device completes protocol conversion and message standardization through the communication protocol adaptation module, then merges the status messages through the sub-device status aggregation algorithm, assigns QoS levels through the dynamic QoS adjustment algorithm, and uploads it to the platform; 4. Data Scheduling and Display: The platform distributes data to corresponding queues according to priority through a data transmission priority scheduling unit, stores it in the database, and displays it in a visual interface; 5. Command Issuance: Operations personnel issue control commands or parameter configuration instructions through the interface. The platform assigns QoS levels according to urgency priority and sends them to the edge devices via Topic / v1 / devices / {gatewayId} / command; 6. Response Feedback: After the edge device executes the command, it returns the response result through Topic / v1 / devices / {gatewayId} / commandResponse. The platform receives, stores and displays the response result.

[0015] Preferably, the communication protocol adaptation module 131 includes: The message parsing unit is used to parse and encapsulate the fixed header, variable header, and message body of MQTT messages; The Topic adapter unit is used to support standard Topic formats, including sub-device management, data reporting, and command issuance, while also extending the priority field for priority scheduling. The protocol conversion unit is used to convert between CoAP and MQTT protocols. The end device converts the power distribution data into TLV format data via CoAP and sends it to the edge device. The edge device converts the TLV format data into JSON format of MQTT protocol and uploads it to the platform layer. The platform layer converts the MQTT messages it sends and sends them to the end device via CoAP protocol. The TLS security element is used for authentication, data encryption, and integrity verification between application layer clients and platform layer servers using the TLS protocol.

[0016] This module complies with Q / EPRI 183-2025 "Communication Protocol for Distribution IoT Based on MQTT", achieving deep adaptation of the MQTT protocol with distribution IoT applications. It is compatible with multiple protocols, supports the parsing and conversion of protocols such as MQTT, CoAP, and DL / T 645-2007, extends the Topic priority field, and uses TLS 1.2 (RSA 2048-bit encryption) to ensure data transmission security.

[0017] Figure 2 This is a schematic diagram illustrating the workflow of a communication protocol adaptation module according to a preferred embodiment of the present invention. Figure 2As shown, for MQTT messages input from edge devices, the message parsing unit performs message parsing, specifically supporting the parsing and encapsulation of fixed headers, variable headers, and message bodies of MQTT data packets. Fixed header parsing includes the data packet type (CONNECT, PUBLISH, SUBSCRIBE, etc.), identifier bits (DUP, QoS, RETAIN), and remaining length (maximum 60KB, compliant with State Grid security gateway restrictions). Variable headers dynamically match content based on the data packet type; for example, CONNECT corresponds to a type 1 variable header, containing the protocol name, protocol level, connection identifier, and heartbeat duration. The Topic Adaptation Unit performs Topic adaptation, supporting standard Topic formats, including sub-device management ( / v1 / devices / {gatewayId} / topo / add, / v1 / devices / {gatewayId} / topo / delete), data reporting ( / v1 / devices / {gatewayId} / datas), and command issuance ( / v1 / devices / {gatewayId} / command). It also extends the priority field (e.g., / v1 / devices / {gatewayId} / command?priority=urgent) for priority scheduling. The Protocol Conversion Unit implements the conversion between CoAP and MQTT protocols. End devices send data to edge devices via CoAP (TLV format), and the edge devices convert the TLV format to MQTT JSON format before uploading it to the platform. Conversely, MQTT messages sent by the platform are converted and sent to end devices via CoAP. The TLS Security Unit uses the TLS 1.2 protocol (RSA). (2048-bit encryption) enables client-server authentication, data encryption, and integrity verification, preventing data from being eavesdropped on or tampered with.

[0018] Preferably, the dynamic optimization algorithm module 132 includes: The dynamic QoS adjustment unit 1321 is used to employ a multi-factor weighted decision algorithm to calculate a decision value based on the parameter values ​​of the extracted transmitted data and a preset weight coefficient adjustment rule, and to determine the QoS level based on the decision value according to a preset QoS level allocation rule. The sub-device status aggregation and update unit 1322 is used to collect sub-device status update requests for the configured time window after configuring the time window based on the preset time window configuration rules, and to perform deduplication and merging using the sliding window aggregation algorithm, and to send the status message of the sub-device according to the preset message sending rules. The data transmission priority scheduling unit 1323 is used to determine the priority of the transmitted message according to the preset priority division rule, configure the queue and allocate the bandwidth weight according to the optimization level, adjust the bandwidth weight according to the number of messages in the queues of different priorities based on the preset scheduling rule, and process the message according to the preset queue full processing rule when the number of messages in the queue reaches the configured upper limit.

[0019] Preferably, the dynamic QoS adjustment unit 1321 employs a multi-factor weighted decision algorithm, calculates a decision value based on the parameter values ​​of the extracted transmitted data and preset weight coefficient adjustment rules, and determines the QoS level based on the decision value according to preset QoS level allocation rules, including: Three core parameters of the transmitted data are extracted: data type weight W1, network bandwidth utilization U, and device communication quality coefficient Q. The decision value S is calculated based on the parameter values ​​of the extracted and transmitted data and the preset weight coefficient adjustment rules. The formula for calculating the decision value S is as follows: S = α × W1 + β × (1 - U) + γ × Q In the formula, α, β, and γ are weighting coefficients. The initial values ​​of α, β, and γ are determined empirically and satisfy α + β + γ = 1. When the U and Q values ​​in the calculation formula of the decision value S are updated according to a preset time interval, the values ​​of α, β, and γ are iterated according to a preset weighting coefficient adjustment rule. The weighting coefficient adjustment rule is as follows: The gradient descent method is used to iterate α, β, and γ according to the set calibration step size value until the fluctuation range of the decision value S is not greater than the set decision threshold. Based on a preset QoS level allocation rule, the QoS level is determined according to the decision value, wherein the QoS level allocation rule is as follows: When S≥k0, QoS2 is selected, and only one transmission is performed; When k1≤S<k0, select QoS1 and perform at least one transmission; When S < k1, QoS0 is selected, and at most one transmission is performed.

[0020] Figure 3 This is a schematic diagram illustrating the workflow of the dynamic QoS adjustment unit within the dynamic optimization algorithm module according to a preferred embodiment of the present invention. Figure 3As shown, the dynamic QoS adjustment unit in this preferred embodiment adopts a multi-factor weighted decision algorithm to achieve dynamic adaptation of QoS levels. Here, k0 and k1 are set to 0.5 and 0.8 respectively. Specifically, ① Parameter extraction: Real-time extraction of three core parameters of the data to be transmitted—data type weight W1 (telemetry data 0.8, teleindication data 0.7, event data 0.9, historical data 0.5), network bandwidth utilization U (U = current transmission rate / maximum bandwidth × 100%), and device communication quality coefficient Q (Q = 1 - packet loss rate - (actual transmission delay / baseline delay), with the baseline delay preset to 100ms); ② Decision value calculation: Calculation of the QoS decision value S = α × W1 + β × (1 - U) + γ × Q, where α, β, and γ are weight coefficients, satisfying α + β + γ = 1, with an initial configuration of α = 0.4, β = 0.3, and γ = 0.3; ③ QoS level allocation: S ≥ 0.8 When S < 0.5, QoS2 (only one transmission) is selected; when S < 0.8, QoS1 (at least one transmission) is selected; when S < 0.5, QoS0 (at most one transmission) is selected. ④ Dynamic calibration: The U and Q values ​​are updated every 5 seconds, and α, β, and γ are calibrated using a gradient descent algorithm (calibration step size 0.05, iteration to S fluctuation range ≤ 0.02). The dynamic QoS adjustment algorithm calculates the QoS decision value S based on multi-factor weighted decision, dynamically allocates QoS0 / QoS1 / QoS2 levels, and combines the gradient descent algorithm to calibrate the weight coefficients α, β, and γ, achieving a balance between data reliability and bandwidth utilization.

[0021] Preferably, after configuring a time window based on preset time window configuration rules, the sub-device status aggregation and update unit 1322 collects sub-device status update requests for the configured time window, performs deduplication and merging using a sliding window aggregation algorithm, and sends sub-device status messages according to preset message sending rules, including: The time window is configured based on a preset time window configuration rule, wherein the time window configuration rule is to configure the time window T according to the number N of sub-devices managed by the edge device; Within the time window T, status update requests from all sub-devices under the same device are collected. The status update requests include the sub-device ID, status value, and status change timestamp. Deduplicate the status update requests for the same sub-device, and generate a deduplicated message containing the sub-device ID, the final status value, and the earliest status change timestamp; when the number of status value switches of a sub-device within the time window T is not less than the preset number threshold, send its deduplicated message to the data transmission priority scheduling unit; otherwise, for sub-devices with the same status change, merge their deduplicated messages into one aggregated message and send it, where the aggregated message includes a list of sub-device IDs, a unified status value, and a status valid time interval composed of the window start time to the window end time.

[0022] Figure 4 It is a schematic diagram of the workflow of the sub-device status aggregation update unit of the dynamic optimization algorithm module according to the preferred embodiment of the present invention. As Figure 4 shown, the sub-device status aggregation update unit of this preferred embodiment uses a sliding window aggregation algorithm for status update. Specifically: ① Configure the time window: Configure the time window T according to the number of sub-devices N managed by the edge device - when N ≤ 50, T = 3 seconds; when 50 < N ≤ 100, T = 5 seconds; when N > 100, T = 8 seconds, and the window time can be manually adjusted (range 1 - 10 seconds); ② Collect status data: Within the time window T, collect the status update requests of all sub-devices under the same edge device (including sub-device ID, status value ONLINE / OFFLINE, status change timestamp); ③ Deduplication: Only retain the final status and the earliest change timestamp for multiple status changes of the same sub-device; ④ Message sending: When the window T ends, if the number of status switches of a certain sub-device within the window < 5 times, merge the same status changes in the deduplicated messages of different sub-devices into one aggregated message, including a list of sub-device IDs, a unified status value, and a status valid time interval (window start time - window end time); send the aggregated message through Topic / v1 / devices / {gatewayId} / topo / update; if the number of status switches of a certain sub-device within the window ≥ 5 times (determined as status jitter), then skip aggregation, send the deduplicated message of this sub-device in real time, and record the jitter log. This preferred embodiment realizes the deduplication and merging of status messages based on a sliding window (dynamically configuring the window time T according to the number of sub-devices), and transmits status jitter devices (switching ≥ 5 times within the window) separately in real time, effectively reducing redundant messages.

[0023] Preferably, the data transmission priority scheduling unit 1323 determines the priority of the transmitted messages according to a preset priority division rule, configures queues and allocates bandwidth weights according to the optimization level, adjusts bandwidth weights based on the number of messages in queues of different priorities according to preset scheduling rules, and processes messages based on preset queue full processing rules when the number of messages in the queue reaches the configured upper limit, including: The priority of the transmitted messages is determined according to the preset priority division rules. The priority division rules are to divide the transmitted messages into urgent priority, important priority and normal priority according to the data type and transmission delay time from shortest to longest, and store the priority identifier in the extension bit of the MQTT variable message header when the message is enqueued. Use a circular queue to store messages, and set upper limits for the number of messages and bandwidth weights for the emergency queue, important queue, and normal queue; Based on preset scheduling rules, bandwidth weights are adjusted according to the number of messages in queues of different priorities. Specifically, when the number of messages in the emergency queue exceeds a set first message threshold, the bandwidth of the important queue and the ordinary queue is used to increase its bandwidth share to a first percentage threshold; when the number of messages in the important queue exceeds a set second message threshold, the bandwidth of the ordinary queue is used to increase its bandwidth share to a second percentage threshold. When the number of messages in the emergency queue, important queue, and ordinary queue reaches the configured limit, the messages are processed based on the preset queue full handling rules. The queue full handling rules are as follows: the ordinary queue adopts the first-in-first-out method and discards the oldest message; the emergency queue and important queue adopt the tail-drop method and discard the latest message; and a platform alarm is triggered at the same time.

[0024] Figure 5 This is a schematic diagram illustrating the workflow of the data transmission priority scheduling unit of the dynamic optimization algorithm module according to a preferred embodiment of the present invention. Figure 5As shown, the data transmission priority scheduling unit adopts a three-level queue scheduling algorithm. When prioritizing messages, they are divided into three categories: urgent priority (control commands, real-time telemetry data, transmission delay ≤ 1 second), important priority (remote signaling changes, event data, transmission delay ≤ 3 seconds), and normal priority (historical frozen data, parameter query responses, transmission delay ≤ 10 seconds). When a message is enqueued, the priority identifier (urgent: 111, important: 101, normal: 001) is stored in the Bit7-Bit5 extension bits of the MQTT variable header. During queue configuration, a circular queue is used to store messages, with a default length of 100 queues for the urgent queue, 200 for the important queue, and 500 for the normal queue. Bandwidth weights are allocated to each queue level: 50% for the urgent queue, 30% for the important queue, and 20% for the normal queue. Preemptive scheduling is used for queue bandwidth scheduling, allocating differentiated bandwidth weights to ensure real-time transmission of critical data. That is, urgent queue messages can preempt the transmission resources of the important and normal queues (when the number of urgent queue messages > 50). When the number of messages in an important queue exceeds 100, the bandwidth share increases to 70%. Important queues can preempt ordinary queue resources (when the number of messages in an important queue exceeds 100, the bandwidth share increases to 40%). Messages in the message queue are scheduled and transmitted to the platform layer / edge device through the server MQTT Broker.

[0025] Preferably, the equipment lifecycle management module uses a genetic optimization algorithm for intelligent parameter configuration of the equipment model, specifically: Based on the parameter constraints of the equipment model determined by equipment model management, an optimization objective function F(x) is established, whose expression is: F(x) = k2 × energy consumption coefficient + k3 × transmission reliability coefficient k2 + k3 = 1 Energy consumption factor = Actual energy consumption / Rated energy consumption Transmission reliability coefficient = 1 - packet loss rate Initialize the parameter population, including setting the population size and iteration number threshold, and defining each individual as a set of parameter combinations for the device model; Genetic operations are performed, including selection using roulette wheel selection, crossover using single-point crossover, and mutation using random mutation. When the number of iterations reaches the iteration threshold, the parameter combination with the highest fitness is output.

[0026] Intelligent parameter configuration: This method combines genetic optimization algorithms to achieve optimal parameter configuration. The steps are as follows: ① Import the parameter constraints of the device model (e.g., communication baud rate 300-115200bps, parity bit support odd / even / none), and establish the optimization objective function F(x) = 0.3 × energy consumption coefficient + 0.7 × transmission reliability coefficient (energy consumption coefficient = actual energy consumption / rated energy consumption, transmission reliability coefficient = 1 - packet loss rate); ② Initialize the parameter population (population size 40, each individual represents one parameter combination); ③ Perform genetic operations: selection uses roulette wheel selection, crossover uses single-point crossover (crossover probability 0.7), and mutation uses random mutation (mutation probability 0.03); ④ After 30 iterations, output the parameter combination with the highest fitness, preset the parameters using the parameter_Set command, and activate the parameters using the parameter_Activate command. Figure 6 This is a schematic diagram of the management process of the equipment lifecycle management module according to a preferred embodiment of the present invention. Figure 6 As shown, the power distribution IoT full lifecycle equipment management process realizes full-process management of equipment registration (scanning / automatic), sub-equipment management, file management, intelligent parameter configuration, model management, and equipment decommissioning. Specifically: 1. Edge device registration: Supports QR code registration (operation and maintenance personnel scan the edge device's QR code to enter information) and automatic registration (the edge device sends clientId, Username, and Password (HMACSHA256 encrypted) to the platform via MQTT CONNECT message, and returns a CONNACK message after successful verification). 2. Sub-device management: Sub-device addition (edge ​​devices send sub-device information via / v1 / devices / {gatewayId} / topo / add, and the platform returns addResponse), deletion (edge ​​devices send a list of device IDs via / v1 / devices / {gatewayId} / topo / delete, and the platform returns deleteResponse), and status update (combined with the sliding window aggregation algorithm). 3. Terminal device file management: file distribution (the platform distributes file information via the acqFilesAdd command), query (the platform queries files via the acqFilesReq command), and deletion (the platform deletes files via the acqFilesDelete command). 4. Intelligent Parameter Configuration: Optimal parameter configuration is achieved by combining a genetic optimization algorithm. The steps are as follows: ① Import the parameter constraints of the device model (e.g., communication baud rate 300-115200bps, parity bit support odd / even / none), and establish the optimization objective function F(x) = 0.3 × energy consumption coefficient + 0.7 × transmission reliability coefficient (energy consumption coefficient = actual energy consumption / rated energy consumption, transmission reliability coefficient = 1 - packet loss rate); ② Initialize the parameter population (population size 40, each individual represents one parameter combination); ③ Perform genetic operations: selection uses roulette wheel selection, crossover uses single-point crossover (crossover probability 0.7), and mutation uses random mutation (mutation probability 0.03); ④ After 30 iterations, output the parameter combination with the highest fitness, preset the parameters using the parameter_Set command, and activate the parameters using the parameter_Activate command. 5. Equipment Model Management: Supports importing (Excel / JSON files), exporting (Excel / JSON files), adding, and deleting equipment models. Model data is used for parameter configuration and data parsing.

[0027] Preferably, the data storage and display module 134 includes: The data storage unit 1341 is used to use SQLite database and is divided into a device information table that stores the model parameters of the edge device ID and sub-device files, a data acquisition table partitioned by time series, an operation log table that stores command issuance records, response results and alarm information, and a parameter configuration table that stores historical configuration records. The display and export unit 1342 is used to visualize the data in the data acquisition table, operation log table and parameter configuration table using the service startup interface, device management interface, data query interface and log display interface.

[0028] Data storage: SQLite database is used, which is divided into a device information table (storing edge device ID, sub-device file, model parameters), a data acquisition table (partitioned by time series, 1 partition every 24 hours), an operation log table (storing command issuance records, response results, alarm information), and a parameter configuration table (storing historical configuration records). Visualization: Provides 4 types of interfaces: Service startup interface (configures HTTP file service and MQTTBroker connection), Device management interface (displays the status of side devices / sub-devices), Data query interface (real-time data curves and historical frozen data), and Log display interface (communication logs and operation logs, supporting filtering by time / device ID). Data export: Supports exporting historical data (Excel format), device models (JSON format), and operation logs (TXT format) for easy offline analysis.

[0029] Figure 7 This is a schematic diagram of the data storage and display module according to a preferred embodiment of the present invention. Figure 7 As shown, when the data storage and display module inputs device data / communication data / operation records, it stores them through the data storage unit 1341 using a SQLite database, and displays them through four types of visualization interfaces provided by the display and export unit 1342. At the same time, the data in the data acquisition table, operation log table and parameter configuration table of the data storage unit 1341 are filtered and converted into Excel / JSON / TXT format files.

[0030] Based on the preferred embodiments of the present invention, a preferred embodiment for a specific application is provided below: 1.1 Implementation Environment Server: Windows 10 operating system, Intel Core i5-12400 CPU, 16GB RAM, 1TB hard drive; Edge device: Intelligent converged terminal (supports MQTT 3.1.1 protocol and has a 4G communication module); Terminal equipment: 50 smart meters (supporting DL / T 645-2007 protocol), 30 switch controllers (supporting Modbus protocol), and 20 fault indicators (supporting CoAP protocol). Software environment: QT C++ 5.15, SQLite 3.40, Eclipse Mosquitto 2.0, OpenSSL 1.1.1.

[0031] 1.2 Implementation Steps 1. Deploy platform software: Install the power distribution IoT platform software developed with QT C++ on the server, and configure the SQLite database and Eclipse Mosquitto MQTT Broker; 2. Device Access: The edge devices and end devices are deployed at the power distribution site. The edge devices are connected to the MQTT Broker of the server via a 4G network, and the end devices are connected to the edge devices via the CoAP / Modbus protocol. 3. Algorithm parameter configuration: Set α=0.4, β=0.3, γ=0.3 for the dynamic QoS adjustment algorithm, T=5 seconds for the window time of the sub-device status aggregation algorithm, 50%:30%:20% for the queue bandwidth ratio of data transmission priority scheduling, and 40 for the population size and 30 for the genetic optimization algorithm; 4. Functional testing: Data upload test: The terminal device collects telemetry data such as voltage and current, and uploads it to the platform through the edge device. The platform successfully receives and displays the data, and the message volume is reduced by 72% after aggregation. Dynamic QoS Test: Simulating 85% network bandwidth usage, fault event data (S=0.92) was assigned QoS2 with a packet loss rate of 0.3%; historical data (S=0.45) was assigned QoS0, resulting in a 26% reduction in bandwidth usage. Control command test: A switch closing command (emergency priority) was issued with a transmission delay of 0.5 seconds, and the switch controller responded successfully; Parameter configuration test: The communication parameter combination (baud rate 9600bps, parity bit even) was generated by the genetic optimization algorithm and sent to the device. After that, the device transmission reliability was improved by 18% and the power consumption was reduced by 16%.

[0032] 1.3 Implementation Results 1. Key data packet loss rate ≤ 0.5%, network bandwidth consumption reduced by more than 25%; 2. Control command transmission delay ≤ 1 second, device management response time ≤ 2.5 seconds; 3. Equipment operating energy consumption is reduced by 15%-20%, and transmission reliability is improved by 10%-18%; 4. The platform supports concurrent access from 100 devices without any lag or abnormal exits.

[0033] As can be seen from the implementation results of the above embodiments, compared with the prior art, the present invention significantly improves the reliability of data transmission and the efficiency of equipment operation, significantly optimizes the utilization rate of network resources, fully guarantees the real-time performance of key services, and has strong system concurrent processing capabilities, which can meet the application needs of the explosive growth in the number of power distribution IoT devices.

[0034] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0035] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0036] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0037] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0038] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps are decomposable and / or recombinable. Such decomposition and / or recombination should be considered equivalent solutions to this disclosure. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0039] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A power distribution Internet of Things (IoT) communication and equipment management system based on dynamic optimization algorithms, characterized in that, The system adopts a four-layer architecture consisting of a perception layer, an edge layer, a platform layer, and an application layer, wherein: The perception layer includes several end devices, which are used to upload the collected power distribution data to the edge layer via the CoAP / MQTT protocol, and to issue control commands after the platform layer processes the operation instructions generated by the application layer. The edge layer includes several edge devices, and each edge device includes several sub-devices. As an intermediate node, it is used to perform protocol conversion, preprocess the power distribution data to generate standardized data, upload MQTT messages generated based on the standardized data to the platform layer via the MQTT protocol, and execute the dynamic optimization algorithm determined by the platform layer. The platform layer is used for message parsing, algorithm optimization, device management, and data storage, including: The communication protocol adaptation module is used for message parsing, topic adaptation, CoAP and MQTT protocol conversion, and TLS secure transmission. The dynamic optimization algorithm module is used for dynamic adaptation of QoS levels, aggregation and updating of sub-device status, and queue scheduling of data transmission. The device lifecycle management module is used to manage edge devices, sub-devices and terminal devices throughout the entire process, including edge device registration, sub-device management, terminal device file management, device model management and intelligent parameter configuration. The data storage and display module is used to store device information, operation logs, parameter configurations, and collected data using a database, and to visualize the stored data. The application layer provides a visual interface to support operations and maintenance personnel in configuring services, managing devices, querying data, viewing logs, and issuing operation commands to the platform layer.

2. The system according to claim 1, characterized in that, The communication protocol adaptation module includes: The message parsing unit is used to parse and encapsulate the fixed header, variable header, and message body of MQTT messages; The Topic adapter unit is used to support standard Topic formats, including sub-device management, data reporting, and command issuance, while also extending the priority field for priority scheduling. The protocol conversion unit is used to convert between CoAP and MQTT protocols. The end device converts the power distribution data into TLV format data via CoAP and sends it to the edge device. The edge device converts the TLV format data into JSON format of MQTT protocol and uploads it to the platform layer. The platform layer converts the MQTT messages it sends and sends them to the end device via CoAP protocol. The TLS security element is used for authentication, data encryption, and integrity verification between application layer clients and platform layer servers using the TLS protocol.

3. The system according to claim 1, characterized in that, The dynamic optimization algorithm module includes: The dynamic QoS adjustment unit is used to calculate a decision value based on the parameter values ​​of the extracted transmitted data and the preset weight coefficient adjustment rules using a multi-factor weighted decision algorithm, and to determine the QoS level based on the decision value according to the preset QoS level allocation rules. The sub-device status aggregation and update unit is used to collect sub-device status update requests for the configured time window after configuring the time window based on the preset time window configuration rules, and to perform deduplication and merging using the sliding window aggregation algorithm, and to send the sub-device status messages according to the preset message sending rules. The data transmission priority scheduling unit is used to determine the priority of the transmitted messages according to the preset priority division rules, configure the queue and allocate bandwidth weight according to the optimization level, adjust the bandwidth weight according to the number of messages in the queues of different priorities based on the preset scheduling rules, and process the messages based on the preset queue full processing rules when the number of messages in the queue reaches the configured upper limit.

4. The system according to claim 1, characterized in that, The dynamic QoS adjustment unit employs a multi-factor weighted decision algorithm. It calculates a decision value based on the parameter values ​​of the extracted transmitted data and preset weight coefficient adjustment rules. Then, based on a preset QoS level allocation rule, it determines the QoS level according to the decision value, including: Three core parameters of the transmitted data are extracted: data type weight W1, network bandwidth utilization U, and device communication quality coefficient Q. The decision value S is calculated based on the parameter values ​​of the extracted and transmitted data and the preset weight coefficient adjustment rules. The formula for calculating the decision value S is as follows: S = α × W1 + β × (1 - U) + γ × Q In the formula, α, β, and γ are weighting coefficients. The initial values ​​of α, β, and γ are determined empirically and satisfy α + β + γ = 1. When the U and Q values ​​in the calculation formula of the decision value S are updated according to a preset time interval, the values ​​of α, β, and γ are iterated according to a preset weighting coefficient adjustment rule. The weighting coefficient adjustment rule is as follows: The gradient descent method is used to iterate α, β, and γ according to the set calibration step size value until the fluctuation range of the decision value S is not greater than the set decision threshold. Based on a preset QoS level allocation rule, the QoS level is determined according to the decision value, wherein the QoS level allocation rule is as follows: When S≥k0, QoS2 is selected, and only one transmission is performed; When k1≤S<k0, select QoS1 and perform at least one transmission; When S < k1, QoS0 is selected, and at most one transmission is performed.

5. The system according to claim 1, characterized in that, The sub-device status aggregation and update unit configures a time window based on preset time window configuration rules, collects sub-device status update requests for the configured time window, performs deduplication and merging using a sliding window aggregation algorithm, and sends sub-device status messages according to preset message sending rules, including: The time window is configured based on a preset time window configuration rule, wherein the time window configuration rule is to configure the time window T according to the number N of sub-devices managed by the edge device; Within the time window T, status update requests from all sub-devices under the same device are collected. The status update requests include the sub-device ID, status value, and status change timestamp. For the same sub-device, the status update request is deduplicated, generating a deduplicated message containing the sub-device ID, the final status value, and the earliest status change timestamp. When the number of status value changes of the sub-device within the time window T is not less than a preset threshold, its deduplicated message is sent to the data transmission priority scheduling unit. Otherwise, for sub-devices with the same status change, their deduplicated messages are merged into an aggregated message and sent. The aggregated message includes a list of sub-device IDs, a unified status value, and a valid status time interval consisting of the window start time and the window end time.

6. The system according to claim 5, characterized in that, The data transmission priority scheduling unit determines the priority of transmitted messages according to a preset priority division rule, configures queues and allocates bandwidth weights according to the optimization level, and adjusts bandwidth weights based on the number of messages in queues of different priorities according to preset scheduling rules. Furthermore, when the number of messages in a queue reaches the configured upper limit, message processing is performed based on a preset queue full processing rule, including: The priority of the transmitted messages is determined according to the preset priority division rules. The priority division rules are to divide the transmitted messages into urgent priority, important priority and normal priority according to the data type and transmission delay time from shortest to longest, and store the priority identifier in the extension bit of the MQTT variable message header when the message is enqueued. Use a circular queue to store messages, and set upper limits for the number of messages and bandwidth weights for the emergency queue, important queue, and normal queue; Based on preset scheduling rules, bandwidth weights are adjusted according to the number of messages in queues of different priorities. Specifically, when the number of messages in the emergency queue exceeds a set first message threshold, the bandwidth of the important queue and the ordinary queue is used to increase its bandwidth share to a first percentage threshold; when the number of messages in the important queue exceeds a set second message threshold, the bandwidth of the ordinary queue is used to increase its bandwidth share to a second percentage threshold. When the number of messages in the emergency queue, important queue, and ordinary queue reaches the configured limit, the messages are processed based on the preset queue full handling rules. The queue full handling rules are as follows: the ordinary queue adopts the first-in-first-out method and discards the oldest message; the emergency queue and important queue adopt the tail-drop method and discard the latest message; and a platform alarm is triggered at the same time.

7. The system according to claim 6, characterized in that, The equipment lifecycle management module uses a genetic optimization algorithm for intelligent parameter configuration of the equipment model, specifically: Based on the parameter constraints of the equipment model determined by equipment model management, an optimization objective function F(x) is established, and its expression is: F(x) = k2 × energy consumption coefficient + k3 × transmission reliability coefficient k2 + k3 = 1 Energy consumption factor = Actual energy consumption / Rated energy consumption Transmission reliability coefficient = 1 - packet loss rate Initialize the parameter population, including setting the population size and iteration number threshold, and defining each individual as a set of parameter combinations for the device model; Genetic operations are performed, including selection using roulette wheel selection, crossover using single-point crossover, and mutation using random mutation. When the number of iterations reaches the iteration threshold, the parameter combination with the highest fitness is output.

8. The system according to claim 6, characterized in that, The data storage and display module includes: The data storage unit uses a SQLite database and is divided into a device information table that stores the model parameters of the edge device ID and sub-device files, a data acquisition table partitioned by time series, an operation log table that stores command issuance records, response results and alarm information, and a parameter configuration table that stores historical configuration records. The display and export unit is used to visualize the data in the service startup interface, device management interface, data query interface, and log display interface, and to export the data in the data acquisition table, operation log table, and parameter configuration table.

Images