Power distribution network one-transmitting double-receiving Internet of Things terminal modeling method

Abstract

The invention relates to the technical field of power distribution network Internet of Things terminal modeling, and discloses a power distribution network one-transmitting double-receiving Internet of Things terminal modeling method, which comprises the following steps: step 1, determining modeling boundaries and core elements, and determining division and data interaction ranges of real-time core and management core function modules; the core elements comprise operation, operation and maintenance, security isolation, master station interaction, distributed energy access and topological association data, and the priority, acquisition frequency and precision of each element are determined. A real-time core and management core dual-core architecture is creatively adopted, the function boundary and data interaction range of the two cores are clearly divided, the real-time core focuses on high-precision acquisition, rapid processing and regulation and control instruction response of operation data of power distribution equipment, and the management core focuses on comprehensive management and multi-dimensional analysis of operation and maintenance data and interaction adaptation with a master station. The delay problem caused by data mixing processing in a traditional single-core model is effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of distribution network Internet of Things (IoT) terminal modeling technology, specifically a method for modeling distribution network IoT terminals with one transmitter and two receivers. Background Technology

[0002] With the deep integration of the new energy revolution and the digital economy, the distribution network, as a key hub for energy transformation, is showing development characteristics of high penetration of distributed energy, diversified equipment types, complex operating conditions, and diversified business needs. As the core carrier for data collection, command execution, and information interaction in the distribution network, the standardization, accuracy, and security of its modeling directly determine the intelligent operation efficiency and power supply reliability of the distribution network.

[0003] Current modeling methods for IoT terminals in power distribution networks mainly suffer from the following technical pain points: 1. The model architecture design is unreasonable. Most of them adopt a single-core integrated modeling mode, and real-time control data and non-real-time operation and maintenance data are mixed and processed, resulting in high data processing latency and untimely response of core control tasks, which cannot meet the dual requirements of real-time control and efficient operation and maintenance of distribution network. 2. There is a lack of a unified standard adaptation system. Existing models are mostly built based on a single standard (such as IEC 61850 or IEC CIM), which makes it difficult to simultaneously meet the real-time control needs of the production control area and the operation and maintenance management needs of the management information area. There are problems of protocol incompatibility and inconsistent formats in cross-regional data interaction. 3. Insufficient accuracy and completeness of data acquisition. Measurement data lacks clear accuracy standards and efficient verification mechanisms, making it prone to data tampering, loss, or distortion, which affects the accuracy of control commands and the reliability of fault diagnosis. Summary of the Invention

[0004] The purpose of this invention is to provide a modeling method for IoT terminals with one generator and two receivers in a power distribution network, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network, comprising the following steps: Step 1: Determine the modeling boundaries and core elements, clarify the functional module division and data interaction scope of the real-time core and management core, and the core elements include operation, maintenance, security isolation, master station interaction, distributed energy access and topology association data, and determine the priority, collection frequency and accuracy of each element; Step 2: Extend the IEC 61850 standard and combine it with the power distribution scenario adaptation rules to build a real-time nuclear information model, add a dedicated logic node for real-time control, define service interfaces and data attributes with security attributes, and design logic nodes in layers according to acquisition, processing and output. Step 3: Expand the operation and maintenance data fields according to the IEC CIM standard, and establish a modular management core information model, covering equipment status monitoring, operation and maintenance data management and master station interaction adaptation modules; Step 4: Design one-way mapping rules between the real-time core and the management core, clarify the field correspondence, conversion format, triggering conditions and filtering mechanism, and set data validity verification based on the running threshold and integrity rules; Step 5: Construct the main site interaction adaptation model, including the interface specifications for the production control area and the adaptation protocol for the management information area; Step 6: Establish a dynamic model update mechanism to monitor changes in the distribution network and business needs, and automatically adjust model parameters, including version management, access control, and update verification processes.

[0006] As a preferred embodiment of the present invention, the operating data in step 1 is the operating data of the power distribution equipment, including electrical parameters such as voltage, current, power, and frequency, as well as equipment switch status data; the operation and maintenance data is the operation and maintenance management data, including equipment maintenance records, fault alarm information, battery status data, and environmental temperature and humidity data; the security isolation requirement clarifies the data transmission direction restrictions and access control strategies between the real-time core and the management core; the distributed energy access data includes distributed power output data, access point electrical parameters, and operating mode data; the topology association data covers the distribution network line connection relationship, equipment location distribution, and node association parameters.

[0007] As a preferred technical solution of the present invention, the service interface of the dedicated logic node for real-time control in step 2 supports real-time reception and feedback of control commands. The data attributes containing security attributes include data encryption identifier, access permission level and transmission security protocol type, as well as control parameter threshold, running status identifier and data verification field. The logic node designed in layers of acquisition, processing and output has independent data cache units in its acquisition layer, processing layer and output layer. The capacity of the cache unit is dynamically allocated according to the acquisition frequency and data volume determined in step 1.

[0008] As a preferred technical solution of the present invention, the real-time nuclear information model in step 2 includes a measurement data attribute specification. This specification clarifies the data acquisition accuracy, wherein the accuracy of voltage and current data is ≥0.2 level, and the accuracy of power data is ≥0.5 level. It also defines the data unit and binary storage format. The binary storage format adopts little-endian encoding, and the data verification adopts CRC32 cyclic redundancy check for integrity verification. The specific process is as follows: the stored binary data is segmented according to a preset byte length, and each segment of data is sequentially processed by cyclic redundancy check. A 32-bit check code is generated according to a fixed bit width rule and appended to the end of the corresponding data segment. The receiving end splits the data according to the same segmentation rule, extracts the check code, and performs a reverse verification operation on the data segment. If the check codes are completely consistent, it is determined that the data has not been tampered with or lost. If the check codes are inconsistent, a data retransmission mechanism is triggered.

[0009] As a preferred technical solution of the present invention, the process of expanding the operation and maintenance data fields in step 3 includes analyzing the requirements of the entire operation and maintenance process of power distribution network equipment, adding equipment fault early warning feature fields, operation and maintenance work order association fields, environmental monitoring data fields, and historical data traceability fields; each field adopts a standardized coding rule combining equipment type code + data category code + timestamp code, wherein the equipment type code is a 6-digit decimal number, the data category code is a 4-digit decimal number, and the timestamp code is a 14-digit year-month-day-hour-minute-second format; the historical data traceability field includes the data acquisition device number, data processing node identifier, and data transmission path record.

[0010] As a preferred technical solution of the present invention, the equipment status monitoring module of the modular management core information model in step 3 collects the power distribution equipment operation data in step 1 in real time and realizes fault early warning through threshold comparison. The threshold parameters are preset with multiple configuration schemes according to equipment type and operation scenario. The operation and maintenance data management module is responsible for the storage, classification, statistics and report generation of operation and maintenance management data in step 1, and supports data retrieval by equipment number, time range and data type. The main station interaction adaptation module realizes the data format conversion and protocol adaptation between the management core model and the management information area main station. Each module interacts with data through a standardized interface. The interface adopts the RESTful design specification and supports synchronous and asynchronous data transmission modes.

[0011] As a preferred technical solution of the present invention, the transmission triggering conditions in step 4 include timed triggering, threshold triggering, and event triggering; the preset time interval for timed triggering is configured in the range of 1-60s, and supports setting differentiated intervals according to different types of core data in step 1; threshold triggering is triggered when real-time core data reaches a preset threshold, and the threshold includes an upper limit threshold, a lower limit threshold, and a rate of change threshold; event triggering is triggered when specific events such as power distribution equipment failure or status change occur, and the event type is identified by the equipment status code, and after triggering, event-related core data is transmitted first.

[0012] As a preferred technical solution of the present invention, the interface specification for the production control area in step 5 requires a transmission rate of ≥100Mbps, a command response time threshold of <50ms, and data encryption using the national cryptographic standard SM4. The encryption key is updated daily, and a data fault tolerance mechanism is included. The fault tolerance mechanism supports data retransmission and breakpoint resumption functions, with a maximum of 3 retransmissions and retransmission intervals of 10ms, 20ms, and 30ms respectively. The management information area adaptation protocol supports batch uploading of operation and maintenance management data, processing of historical data query requests, and adaptation of operation and maintenance command issuance in step 1. Batch uploading adopts a block transmission method, with each block of data being <1MB in size.

[0013] As a preferred technical solution of the present invention, the triggering conditions for the dynamic update of the model in step 6 include the addition of distributed energy access, terminal function upgrade, or distribution network topology change; the update verification process includes functional verification, performance verification, and security verification; functional verification verifies the data processing and interaction functions of the real-time nuclear information model in step 2 and the management nuclear information model in step 3 by simulating the scenario of the master station issuing instructions and the terminal reporting data; performance verification tests the model's data processing latency, concurrent processing capability, and resource utilization rate, and the test environment includes operating scenarios under different load intensities; security verification detects the effectiveness of the model's security isolation and data transmission security by using simulated attack testing and data leakage detection methods.

[0014] As a preferred technical solution of the present invention, in step 6, the version management records the model creation time, update time, update content, and update responsible person, and establishes a version rollback mechanism. When the updated model has compatibility issues, it is rolled back to a historical stable version. The version file is stored in an encrypted manner, and the encryption method is asymmetric encryption. Specifically, it uses an RSA2048 key for encryption. The public key is stored in the distribution network master station security server and is used for key matching when the version file is encrypted. The private key is kept separately by the authorized management center through an offline storage device. Only authorized accounts can call the private key to decrypt the version file after identity authentication. After decryption, version viewing, updating, and rollback operations can be performed. The key pair is updated once a quarter and synchronized to all associated terminals and the master station system. The entire update process is recorded in operation logs and archived.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. Dual-core architecture design enables precise functional partitioning and improves operational efficiency: This invention innovatively adopts a dual-core architecture of real-time core + management core, clearly defining the functional boundaries and data interaction scope of the two cores. The real-time core focuses on high-precision acquisition, rapid processing and control command response of power distribution equipment operation data, while the management core focuses on comprehensive management, multi-dimensional analysis and master station interaction adaptation of operation and maintenance data. This effectively avoids the latency problem caused by mixed data processing in the traditional single-core model. Combined with the hierarchical design of logical nodes and independent cache unit dynamic allocation mechanism, as well as the high-speed transmission and rapid response design of the production control area interface, the response speed and data processing efficiency of core control tasks are significantly improved.

[0016] 2. Dual-standard extended adaptation enables cross-regional collaboration and ensures compatibility: This invention constructs a real-time core information model by extending the IEC 61850 standard, adding dedicated logic nodes for real-time control and data attributes with security attributes to meet the standardized requirements of real-time control in the production control region; it also extends the operation and maintenance data fields according to the IEC CIM standard to establish a modular management core information model, adapting to the business needs of operation and maintenance management in the management information region. At the same time, the constructed master station interaction adaptation model clarifies the interface specifications of the production control region and the adaptation protocol of the management information region, solving the problems of protocol incompatibility and inconsistent formats in cross-regional data interaction, and realizing seamless collaboration between the terminal and the master stations of the two regions.

[0017] 3. High-precision specifications and robust verification mechanisms ensure data reliability and improve control accuracy: This invention clearly defines the precision specifications for measurement data, adopts a little-endian binary storage format, and uses CRC32 cyclic redundancy check to perform segmented verification of the data. A retransmission mechanism is triggered when the reverse verification at the receiving end is inconsistent, ensuring data integrity throughout the entire process of acquisition, storage, and transmission. This effectively avoids data tampering, loss, or distortion, providing a reliable data foundation for precise control command formulation and accurate fault diagnosis, and improving the accuracy of fault early warning.

[0018] 4. Expanding Fields and Standardizing Coding to Optimize Operation and Maintenance Management and Reduce Operation and Maintenance Costs: This invention analyzes the requirements of the entire operation and maintenance process of power distribution network equipment, adds fields such as equipment fault early warning features, operation and maintenance work order association, environmental monitoring, and historical data traceability, and adopts standardized coding rules to enable operation and maintenance data to have a unified retrieval dimension and traceability capability. The operation and maintenance data management module of the management core supports multi-condition fast retrieval, and combined with multiple threshold configuration schemes of the equipment status monitoring module, it realizes real-time fault early warning and historical data traceability function, realizing rapid location and root cause analysis of operation and maintenance faults, and reducing operation and maintenance costs.

[0019] 5. Differentiated triggering and filtering mechanisms optimize resource allocation and save bandwidth costs: This invention designs differentiated transmission strategies based on timed triggering, threshold triggering, and event triggering. Dynamic timed intervals can be set according to the core data type. When key data reaches the threshold or a specific event occurs on the device, core data is transmitted first. At the same time, an invalid and redundant data is removed through a filtering mechanism, reducing unnecessary bandwidth occupation and storage resource consumption. This effectively solves the bandwidth congestion problem caused by indiscriminate data transmission in traditional modeling methods. Even in scenarios with high penetration of distributed energy access and a surge in data volume, it can still ensure the priority transmission and efficient processing of core data, improving bandwidth resource utilization.

[0020] 6. A multi-layered security protection system builds a solid security barrier to ensure operational safety: This invention constructs a security protection system from multiple dimensions, including inter-core isolation, data transmission, and version storage. It clearly defines the unidirectional data transmission restrictions and access control policies between the real-time core and the management core. Through hierarchical control of access permissions for data transmission channels and integrity verification of the transmission process, it prevents unauthorized data interaction and tampering. Version files are stored using RSA2048 asymmetric encryption, and public and private keys are stored separately and updated regularly. This multi-layered security design effectively resists security threats such as SQL injection, data tampering, unauthorized access, and DDoS attacks, ensuring the operational security of the distribution network's core data and terminal models, and avoiding system failures caused by data leakage and malicious attacks.

[0021] 7. Dynamic update mechanism enhances model adaptability and supports long-term development: The dynamic update mechanism established in this invention can monitor changes in scenarios such as new distributed energy access, distribution network topology changes, and terminal function upgrades in real time, automatically adjust model parameters, and ensure the stability and compatibility of the updated model through a triple verification process of function, performance, and security. The version management module supports model version rollback and regression, and can quickly restore to a historical stable version when compatibility issues occur during updates. This mechanism enables the model to have good scalability and adaptability, continuously adapt to the dynamic development needs of the distribution network, avoid model reconstruction due to scenario changes, extend the model's life cycle, reduce system upgrade costs, and provide strong support for the consumption of distributed energy, thereby improving power supply reliability. Attached Figure Description

[0022] Figure 1 This is an overall flowchart of a method for modeling a single-transmitter, dual-receiver IoT terminal in a power distribution network according to the present invention. Figure 2 This is a schematic diagram of the hierarchical structure of the real-time core information model in the modeling method for a single-transmitter, dual-receiver IoT terminal in a power distribution network according to the present invention. Figure 3 This is a schematic diagram of the one-way mapping rule between the real-time core and the management core in the modeling method of a distribution network single-transmitter dual-receiver IoT terminal of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] I. Implementation Example Design The core of this invention lies in achieving accurate modeling and efficient operation of IoT terminals in distribution networks through a dual-core architecture of real-time core + management core, standardized model construction, one-way mapping rules, cross-regional master station adaptation, and dynamic update mechanism. To verify the technical effect of this invention, a distribution network in a prefecture-level city in East my country (covering 12 districts and counties, 320 10kV lines, 5800 distribution devices, including 120 distributed photovoltaic access points and 80 energy storage terminals) was designed as the application scenario. Three sets of implementation examples (corresponding to distribution network scenarios of different complexities) and one set of control examples (using the traditional single-core modeling method) were designed. The superiority of this invention was verified through multi-dimensional index testing.

[0025] (I) Example 1: Basic distribution network scenario (no distributed energy access) 1. Modeling parameter configuration Step 1: Define the core elements Operating data: Voltage (sampling frequency 5s, accuracy 0.2), current (sampling frequency 5s, accuracy 0.2), power (sampling frequency 10s, accuracy 0.5), switch status (sampling frequency 1s). Operation and maintenance data: maintenance records (collected once a day), fault alarms (collected in real time), battery status (collected every 30 seconds), ambient temperature and humidity (collected every 60 seconds, using commercially available temperature and humidity sensor SHT30, measurement range -40~125℃ / 0~100%RH); Security isolation: Real-time core → Management core unidirectional data transmission, reverse access is prohibited (using the national cryptographic isolation gateway GAP-6000, supporting SM4 encrypted transmission). Topology association data: line connection relationship (static configuration, updated once per quarter), equipment location (GPS positioning data, accuracy ±5m, using Beidou positioning module BDS-M8T).

[0026] Step 2: Construction of Real-Time Nuclear Information Model Extend the IEC 61850 standard and add a new logical node "LLN0_CTRL" (dedicated to real-time control). The service interface adopts the IEC 61850-8-1 MMS protocol. Security attribute data: Encryption identifier "SM4-ENCRYPT", access permission level "Level 3 (Operation and Maintenance Level)", transmission protocol "TCP / IP+IEC 61850-9-2"; Layered design: Acquisition layer (10MB cache), processing layer (20MB cache), and output layer (15MB cache), all using DDR4-2666 memory modules; Measurement data specifications: voltage / current accuracy class 0.2, power accuracy class 0.5, binary little-endian encoding, CRC32 check (segment byte length 1024B).

[0027] Step 3: Construction of the Management Nuclear Information Model Expanded IEC CIM standard fields: Added fault warning feature field (code "0101"), maintenance work order association field (code "0201"), environmental monitoring field (code "0301"), and historical traceability field (code "0401"); Standardized coding: Equipment type coding (10kV circuit breaker "010001", transformer "020001"), data category coding (voltage "0001", fault alarm "0010"), timestamp coding (20240520143025); Modular configuration: Equipment status monitoring module (threshold configuration: voltage upper limit 10.5kV, lower limit 9.5kV), operation and maintenance data management module (using MySQL 8.0 database for storage), and main station interaction adaptation module (RESTful interface, supporting JSON format).

[0028] Step 4: One-way mapping rules Field correspondence: Real-time core "Voltage Data" → Management core "Operating Status Voltage Field", conversion format "Binary → Decimal"; Triggering conditions: timed trigger (5s for running data, 30s for maintenance data), threshold trigger (voltage > 10.5kV or < 9.5kV), event trigger (switch status change, status code "1001"); Validity verification: operation threshold verification (voltage 9.5~10.5kV), integrity verification (data field missing rate <0.1%).

[0029] Step 5: Main Site Interaction Adaptation Model Production control area interface: transmission rate 100Mbps, response time <40ms, SM4 encryption (key updated daily at 00:00), fault tolerance mechanism (retransmission 3 times, interval 10 / 20 / 30ms). Management Information Zone Protocol: Batch upload blocks are 512KB, and historical data can be queried by device number and time range.

[0030] Step 6: Dynamic Update Mechanism Triggering conditions: No distributed energy access, only responding to terminal function upgrades (such as firmware version updates); Version management: RSA2048 encryption, public key stored on the main site security server (Huawei USG6300), private key stored offline on an encrypted USB flash drive (Kingston DT4000G2). Update verification: Functional verification (simulating the main station issuing voltage adjustment commands), performance verification (simulating 100 data entries processed concurrently by a single terminal), and security verification (simulating SQL injection attacks).

[0031] 2. Experimental Equipment and Environment IoT terminal: Huawei iMaster NetEco 6000 distribution terminal (model DTU-6300); Master station system: NARI NSR-3000 distribution network master station; Testing tools: Spirent TestCenter C100 network performance tester, Metasploit Framework 6.0 security testing tool.

[0032] (II) Example 2: Distributed Energy Access Scenarios (including photovoltaic + energy storage) 1. Modeling parameter configuration Step 1: Add distributed energy access data: photovoltaic output (collection frequency 3s, accuracy 0.5 level, using Huawei SUN2000-17KTL-M0 inverter), access point voltage (collection frequency 2s, accuracy 0.2 level), and operating mode ("grid-connected" / "off-grid", collection frequency 1s). Step 2: Add "LLN0_DER" (dedicated to distributed energy regulation) to the real-time core logic node, and adjust the cache capacity to 15MB for the acquisition layer, 30MB for the processing layer, and 20MB for the output layer; Step 3: Add a distributed energy operation and maintenance field (code "0501") to the management core, and configure multiple threshold schemes for the equipment status monitoring module (grid-connected mode voltage 9.8~10.3kV, off-grid mode 9.5~10.5kV). Step 4 Triggering conditions: Timed trigger (distributed energy data 3s), threshold trigger (photovoltaic output change rate > 10% / min), event trigger (energy storage charging and discharging status change, status code "2001"); Step 6: Dynamically update trigger conditions: Adding distributed energy access (such as adding a new photovoltaic power station) or changing the distribution network topology (line capacity expansion and renovation). The remaining parameters are the same as in Example 1.

[0033] 2. Experimental Equipment and Environment Distributed energy equipment: Huawei SUN2000-17KTL-M0 photovoltaic inverter, BYD B-Box energy storage system; The remaining equipment is the same as in Example 1.

[0034] (III) Example 3: Complex power distribution network scenario (multi-area + high-density equipment) 1. Modeling parameter configuration Step 1: Core element expansion: Add "Regional partition identifier" (code "0601") to the topology association data, and configure the collection frequency according to regional differences (2s for core urban areas and 5s for suburban areas). Step 2: Real-time measurement data accuracy improvement: Voltage / current 0.1 level (using high-precision ABB T200 current transformer), CRC32 check segment byte length 2048B; Step 3: The core operation and maintenance data management module supports distributed storage (using a MongoDB 6.0 cluster), and a new "regional number" has been added as a search dimension; Step 4: One-way mapping filtering mechanism: Filter invalid data (such as data with instantaneous voltage fluctuations <0.01kV); Step 5: Production control area interface transmission rate 200Mbps, response time <30ms; Step 6 updates the verification process by adding "multi-terminal collaborative verification" (updating 100 terminals simultaneously). The remaining parameters are the same as in Example 2.

[0035] 2. Experimental Equipment and Environment High-precision instrument transformer: ABB T200 (accuracy class 0.1); Database cluster: MongoDB 6.0 (3-node cluster); The remaining equipment is the same as in Example 2.

[0036] (iv) Comparative Example: Traditional Single-Core Modeling Method It adopts the traditional IEC 61850 single-core model, without the distinction between real-time core and management core. Data storage and processing share a single core module, and dynamic updates are not supported. The master station interaction uses a single protocol (IEC 60870-5-104), and data verification uses CRC16. There is no security isolation mechanism.

[0037] Experimental equipment: Traditional distribution network terminal (model DTU-3000, commercially available ordinary model), and the rest of the environment is the same as in Example 1.

[0038] II. Experimental Data and Results (I) Experimental test indicators Seven core indicators were selected: data acquisition accuracy, transmission delay, data integrity, fault warning accuracy, main station interaction success rate, dynamic update compatibility, and security protection capability. Each indicator was tested 100 times, and the average value was taken.

[0039] (II) Summary Table of Experimental Data

[0040] (III) Supplementing Experimental Data (Dynamic Update Test in Example 3)

[0041] (iv) Supplementary experimental data (safety verification test)

[0042] III. Detailed Analysis and Explanation of Experimental Data (I) Data Acquisition Accuracy Analysis Data acquisition accuracy is the core foundation of distribution network modeling, directly affecting the accuracy of control commands and the reliability of fault diagnosis. Experimental data shows that the voltage and current acquisition accuracy of the three embodiments is better than 0.2 level, and the power acquisition accuracy is better than 0.5 level, which fully meets the requirements of claim 4 that "voltage and current data accuracy ≥ 0.2 level, power data accuracy ≥ 0.5 level", and is significantly better than the control example and industry standards.

[0043] Example 1, as a basic scenario, achieves voltage acquisition accuracy of 0.18 level, current of 0.19 level, and power of 0.42 level, representing improvements of 48.57%, 40.63%, and 44.00% respectively compared to the control example (voltage 0.35 level, current 0.32 level, power 0.75 level). This is attributed to the clearly defined core elements of acquisition frequency and accuracy in claim 2, and the strict implementation of the measurement data attribute specifications in claim 4—binary little-endian encoding reduces data conversion losses, and CRC32 cyclic redundancy check ensures that data is not distorted during acquisition and transmission. In Example 2, after adding distributed energy access data, the acquisition accuracy of distributed energy-related data was specifically optimized due to the addition of the "LLN0_DER" logical node (claim 2), resulting in a slight improvement in accuracy (voltage 0.17 level, current 0.18 level). Example 3 employs a high-precision current transformer (ABB). The T200 was optimized, and the segment byte length of the CRC32 check was improved (2048B), further reducing data loss during the check process. The voltage accuracy reached level 0.09 and the current accuracy reached level 0.08, which is 50.00% and 57.89% higher than that of Example 1, respectively. This fully verifies the positive effect of the "hierarchical design of logic nodes according to acquisition, processing and output" and "dynamic allocation of cache capacity" in claim 2 on the accuracy improvement.

[0044] The comparative example uses a traditional single-core model, which lacks clear specifications for measurement data attributes. The shared resources for data acquisition and processing lead to significant interference, and the use of only CRC16 checksum (with insufficient checksum length) fails to effectively detect data distortion. Therefore, the accuracy is far lower than that of the example. This demonstrates that the real-time core information model constructed by the present invention through claims 1-4, with standardized design in terms of acquisition frequency, data format, and checksum method, significantly improves the accuracy of data acquisition and provides a reliable data foundation for subsequent regulation and maintenance.

[0045] (II) Data Transmission Performance Analysis Data transmission latency and data integrity are key indicators for measuring the efficiency of interaction between the terminal and the master station. Experimental data show that the transmission latency of the three embodiments is all <50ms, which meets the requirement of "instruction response time threshold <50ms" in claim 8, and the data integrity is ≥99.97%, which is much higher than the 98.20% of the control example.

[0046] Example 1 achieved a transmission latency of 35ms and data integrity of 99.98%, representing a reduction of 58.82% and an improvement of 1.81 percentage points compared to the control example (85ms, 98.20%). This is mainly attributed to the one-way mapping rule designed in claim 4—timed triggering (differentiated intervals)—which reduced invalid data transmission, and threshold triggering and event triggering ensuring priority transmission of critical data and avoiding bandwidth consumption. Simultaneously, the SM4 encryption and fault tolerance mechanism (retransmission 3 times, intervals of 10 / 20 / 30ms) of the production control area interface in claim 8 ensured both transmission security and solved the data loss problem. Example 2, due to the addition of distributed... The energy data (collected at a higher frequency and with a larger data volume) had a slightly increased transmission latency of 42ms, but this was still far below the industry standard. The data integrity remained at 99.97%, indicating that the modular design of the management core in claim 3 (parallel processing of the equipment status monitoring module and the operation and maintenance data management module) effectively shared the data processing pressure and avoided data congestion. In embodiment 3, by increasing the transmission rate (200Mbps) and optimizing the filtering mechanism (filtering invalid instantaneous fluctuation data), the transmission latency was reduced to 28ms and the data integrity was improved to 99.99%, verifying the effectiveness of the "filtering mechanism" in claim 4 and the "block transmission" in claim 8.

[0047] The comparative example uses a single transmission protocol with no differentiated triggering or filtering mechanisms, resulting in the indiscriminate transmission of all data, leading to excessive bandwidth consumption and large transmission delays. Furthermore, it lacks a robust fault tolerance mechanism, making it impossible to effectively recover lost or tampered data, thus resulting in low data integrity. This fully demonstrates that the present invention, through the collaborative design of claim 4 (one-way mapping rule), claim 5 (modular management core), and claim 8 (master station interaction adaptation model), achieves both high efficiency and integrity in data transmission.

[0048] (III) Fault Early Warning and Operation and Maintenance Efficiency Analysis The accuracy of fault warning and the response time of operation and maintenance data retrieval are directly related to the operation and maintenance cost and power supply reliability of the distribution network. Experimental data show that the fault warning accuracy of Examples 1-3 is ≥96.5% and the response time of operation and maintenance data retrieval is <60ms, which are significantly better than the control example (82.1%, 120ms).

[0049] Example 1 achieved a fault warning accuracy of 96.5% and a retrieval response time of 45ms, representing improvements of 17.54% and a reduction of 62.50% respectively compared to the control example. This is attributed to the equipment status monitoring module of the management core in claim 3, which, through multiple preset threshold configuration schemes (claim 6), compares operational data with thresholds in real time to trigger fault warnings promptly. Simultaneously, the expansion of maintenance data fields (adding fault warning feature fields and historical data traceability fields) and standardized coding rules (equipment type coding + data category coding + timestamp coding) enable rapid location of target data in maintenance data retrieval. Example 2, by incorporating distributed energy operation data, offers more comprehensive fault warning dimensions (such as photovoltaic output anomaly warnings), increasing accuracy to 97.8%. Example 3 employs distributed database storage, adds regional numbers to the retrieval dimensions, reduces the response time to 38ms, and achieves an accuracy of 98.3%, fully validating the practicality of "the maintenance data management module supports multi-condition retrieval" in claim 3 and "standardized coding rules" in claim 5.

[0050] The comparative example lacks a dedicated fault warning module and relies solely on simple threshold comparison to determine faults. Furthermore, the operation and maintenance data lacks standardized coding, requiring the traversal of all data during retrieval. Consequently, the warning accuracy is low and the retrieval response time is long. This demonstrates that the present invention, through the design of claim 3 (management core information model extended fields and modular design) and claim 5 (standardized coding), significantly improves the fault warning capability and operation and maintenance efficiency of the distribution network, while reducing operation and maintenance costs.

[0051] (iv) Dynamic updates and compatibility analysis Dynamic update mechanism is the key to adapting to changes in distribution network topology and upgrades in business needs. Experimental data shows that the dynamic update error-free rate of Examples 1-3 is ≥99.8% and the rollback success rate is 100%, while the control example does not support dynamic update and cannot adapt to the dynamic changes in the distribution network.

[0052] Example 1 shows a 99.9% success rate for terminal function upgrades, with an average update time of 12.5 seconds and a 100% rollback success rate, verifying the effectiveness of the "version management, access control, and update verification process" in claim 6. RSA2048 asymmetric encryption ensures the security of version files, and the separate storage of public and private keys (claim 10) prevents unauthorized updates. The update verification process (functionality, performance, and security verification) ensures the stability of the updated model. Example 2 shows a 99.8% success rate for adding distributed energy access scenarios, with an average time of 18.3 seconds, indicating that the design of the "dynamic update trigger condition (adding distributed energy access)" in claim 6 accurately responds to new demands of the distribution network, and the one-way mapping rule has good adaptability. Example 3 shows a 90% success rate for multi-terminal collaborative updates (9 out of 10 tests), with an average time of 35.8 seconds and a 100% rollback success rate, proving that the dynamic update mechanism of this invention supports large-scale terminal collaborative upgrades, and the version rollback mechanism effectively solves compatibility issues after updates, fully meeting the dynamic adjustment needs of complex distribution networks.

[0053] In a practical application of a distribution network in a prefecture-level city in East China, Example 3 successfully handled three distribution network topology changes (line capacity expansion in March 2024, new photovoltaic power station in May, and energy storage terminal expansion in July). The model could run normally after each update without any data interaction anomalies or functional failures, fully verifying the practicality and reliability of the dynamic update mechanism of this invention.

[0054] (V) Analysis of safety protection capabilities Security protection capabilities are an important guarantee for power distribution network modeling. Experimental data show that the anti-attack success rate of Examples 1-3 is ≥99.5%, which is much higher than the 75.3% of the control example, thus meeting the requirements of "security isolation" in claim 1 and "encrypted storage" in claim 10.

[0055] Example 3 demonstrates a 98%, 99%, and 100% success rate in defending against SQL injection attacks, data tampering attacks, and unauthorized access attacks, respectively. It can also withstand DDoS attacks with a 10Gbps flow rate. In contrast, the control example can only defend against a few simple attacks and cannot withstand DDoS attacks. This is mainly due to the multiple security designs of this invention: the explicit security isolation requirement in claim 1 (one-way transmission between the real-time core and the management core, using the national cryptographic isolation gateway GAP-6000) prevents unauthorized access; the data attributes (encryption identifier, access permission level) with security attributes in claim 2 ensure secure data transmission; the SM4 encryption (key updated daily) of the production control area interface in claim 8 prevents data tampering; and the RSA2048 encryption of the version file and offline storage of the private key in claim 10 prevent unauthorized modification.

[0056] In practical applications in the power distribution network in East China, Example 3 successfully resisted three external attack attempts (SQL injection in April 2024, data tampering in June, and unauthorized access in August) without any data leakage or model tampering. In contrast, power distribution network terminals in the same region using traditional modeling methods experienced two data leakage incidents in 2024. This fully demonstrates that the security protection design of the present invention can effectively ensure the operational safety of the power distribution network.

[0057] (vi) Comprehensive performance analysis Considering all seven core indicators, Embodiments 1-3 of the present invention outperform the control example and industry standards in all test indicators. Among them, Embodiment 3 (complex scenario) has the best overall performance, which fully verifies the synergistic effectiveness of the technical solutions of claims 1-10.

[0058] The comparative example, due to its single-core architecture, lacks standardized model design, unidirectional mapping rules, dynamic update mechanism, and robust security protection, resulting in poor performance across various indicators and an inability to meet the operational requirements of modern distribution networks (especially those containing distributed energy resources and complex topologies). In contrast, this invention employs a complete process (claim 1) of "determining modeling boundaries and core elements → constructing a dual-core information model → designing unidirectional mapping rules → constructing a master station interactive adaptation model → establishing a dynamic update mechanism." Furthermore, each step incorporates standardized design (claims 2-3), high-precision requirements (claim 4), standardized coding (claim 5), modular architecture (claim 6), multi-trigger mechanism (claim 7), cross-regional adaptation (claim 8), multi-dimensional verification (claim 9), and high-security encryption (claim 10), forming a complete and practical modeling method for distribution network single-transmitter dual-receiver IoT terminals. The various technical features support and synergize with each other, ensuring the model's high precision, high efficiency, high security, and high adaptability.

[0059] Taking the application effect of a distribution network in a prefecture-level city in East China as an example, after adopting the modeling method of Embodiment 3 of the present invention, the average fault handling time of the distribution network was shortened from 45 minutes to 15 minutes, the power supply reliability rate increased from 99.78% to 99.92%, the operation and maintenance cost was reduced by 30%, and the distributed energy consumption rate was increased by 15%. This fully demonstrates that the present invention can effectively solve the problems of low accuracy, poor efficiency, weak security, and insufficient adaptability of traditional modeling methods, and has significant technical advantages and practical application value.

[0060] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network, characterized in that, Includes the following steps: Step 1: Determine the modeling boundaries and core elements, clarify the functional module division and data interaction scope of the real-time core and management core, and the core elements include operation, maintenance, security isolation, master station interaction, distributed energy access and topology association data, and determine the priority, collection frequency and accuracy of each element; Step 2: Extend the IEC 61850 standard and combine it with the power distribution scenario adaptation rules to build a real-time nuclear information model, add a dedicated logic node for real-time control, define service interfaces and data attributes with security attributes, and design logic nodes in layers according to acquisition, processing and output. Step 3: Expand the operation and maintenance data fields according to the IEC CIM standard, and establish a modular management core information model, covering equipment status monitoring, operation and maintenance data management and master station interaction adaptation modules; Step 4: Design one-way mapping rules between the real-time core and the management core, clarify the field correspondence, conversion format, triggering conditions and filtering mechanism, and set data validity verification based on the running threshold and integrity rules; Step 5: Construct the main site interaction adaptation model, including the interface specifications for the production control area and the adaptation protocol for the management information area; Step 6: Establish a dynamic model update mechanism to monitor changes in the distribution network and business needs, and automatically adjust model parameters, including version management, access control, and update verification processes.

2. The method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, The operational data in step 1 refers to the operational data of the power distribution equipment, including electrical parameters such as voltage, current, power, and frequency, as well as equipment switch status data; the maintenance data refers to maintenance management data, including equipment maintenance records, fault alarm information, battery status data, and environmental temperature and humidity data; the security isolation requirements specify the data transmission direction restrictions and access control strategies between the real-time core and the management core; the distributed energy access data includes distributed power output data, access point electrical parameters, and operating mode data; the topology association data covers the distribution network line connection relationships, equipment location distribution, and node association parameters.

3. The method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, In step 2, the service interface of the dedicated logic node for real-time control supports the real-time reception and feedback of control commands. The data attributes containing security attributes include data encryption identifier, access permission level and transmission security protocol type, as well as control parameter threshold, running status identifier and data verification field. The logic node designed in layers of acquisition, processing and output has independent data cache units in its acquisition layer, processing layer and output layer. The capacity of the cache unit is dynamically allocated according to the acquisition frequency and data volume determined in step 1.

4. The method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, In step 2, the real-time nuclear information model includes measurement data attribute specifications. These specifications clearly define the data acquisition accuracy, with voltage and current data accuracy ≥ 0.2 and power data accuracy ≥ 0.

5. They also define the data unit and binary storage format. The binary storage format uses little-endian encoding, and data verification uses CRC32 cyclic redundancy check for integrity verification. The specific process is as follows: the stored binary data is segmented according to a preset byte length. Each segment undergoes cyclic redundancy check processing sequentially. A 32-bit checksum is generated according to a fixed bit-width rule and appended to the end of the corresponding data segment. The receiving end splits the data according to the same segmentation rule, extracts the checksum, and performs a reverse verification operation on the data segment. If the checksums are completely consistent, it is determined that the data has not been tampered with or lost. If the checksums are inconsistent, a data retransmission mechanism is triggered.

5. The method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, The process of expanding the operation and maintenance data fields in step 3 includes analyzing the requirements of the entire operation and maintenance process of power distribution network equipment, and adding equipment fault early warning feature fields, operation and maintenance work order association fields, environmental monitoring data fields, and historical data traceability fields. Each field adopts a standardized coding rule combining device type code, data category code, and timestamp code. The device type code is a 6-digit decimal number, the data category code is a 4-digit decimal number, and the timestamp code is a 14-digit year-month-day-hour-minute-second format. The historical data traceability field includes the data acquisition device number, data processing node identifier, and data transmission path record.

6. The method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, In step 3, the modular management core information model's equipment status monitoring module collects real-time power distribution equipment operation data from step 1 and implements fault early warning through threshold comparison. The threshold parameters are preset with multiple configuration schemes according to equipment type and operating scenario. The operation and maintenance data management module is responsible for storing, classifying, statistically analyzing, and generating reports of operation and maintenance management data from step 1, and supports data retrieval by equipment number, time range, and data type. The main station interaction adaptation module realizes data format conversion and protocol adaptation between the management core model and the management information area main station. Each module interacts with data through standardized interfaces, which adopt the RESTful design specification and support synchronous and asynchronous data transmission modes.

7. The method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, The transmission triggering conditions in step 4 include timed triggering, threshold triggering, and event triggering; the preset time interval for timed triggering is configured in the range of 1-60s, and it supports setting differentiated intervals according to different types of core data in step 1. Threshold triggering is triggered when real-time core data reaches a preset threshold, which includes an upper threshold, a lower threshold, and a rate of change threshold. Event triggering occurs when specific events such as power distribution equipment failure or status change happen. The event type is identified by the equipment status code. After triggering, the core data related to the event is transmitted first.

8. The method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, In step 5, the interface specification for the production control area requires a transmission rate of ≥100Mbps, a command response time threshold of <50ms, and data encryption using the national cryptographic standard SM4. The encryption key is updated daily and includes a data fault tolerance mechanism that supports data retransmission and breakpoint resumption. The maximum number of retransmissions is 3, with retransmission intervals of 10ms, 20ms, and 30ms respectively. The management information area adaptation protocol supports batch uploading of operation and maintenance management data, processing of historical data query requests, and adaptation of operation and maintenance command issuance in step 1. Batch uploading adopts a block transmission method, with each block of data being <1MB in size.

9. A method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, The triggering conditions for dynamic model updates in step 6 include the addition of new distributed energy access, terminal function upgrades, or changes in distribution network topology. The update verification process includes functional verification, performance verification, and security verification. Functional verification verifies the data processing and interaction functions of the real-time nuclear information model in step 2 and the management nuclear information model in step 3 by simulating scenarios of master station issuing instructions and terminal data reporting. Performance verification tests the model's data processing latency, concurrent processing capability, and resource utilization rate. The test environment includes operating scenarios under different load intensities. Security verification detects the effectiveness of the model's security isolation and data transmission security by using simulated attack testing and data leakage detection methods.

10. A method for modeling a single-transmitter, dual-receiver IoT terminal in a distribution network according to claim 1, characterized in that, In step 6, the version management records the model creation time, update time, update content, and update responsibility, establishing a version rollback mechanism. When a compatibility issue arises after the update, the model is rolled back to a historical stable version. Version files are stored in encrypted form using asymmetric encryption, specifically RSA2048 keys. The public key is stored on the distribution network master station security server and used for key matching during version file encryption. The private key is separately stored by the authorized management center through offline storage. Only authorized accounts, after authentication, can use the private key to decrypt the version file. After decryption, version viewing, updating, and rollback operations can be performed. The key pair is updated quarterly and synchronized to all associated terminals and the master station system. The entire update process is logged and archived.

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