Fusion type station terminal digitization architecture device supporting IV area Internet of Things business expansion

Abstract

The invention, which relates to the technical field of power distribution automation, discloses a fusion-type station terminal digital architecture apparatus supporting IV-area Internet-of-Things service expansion, the apparatus comprising the following components: a medium-voltage standardized terminal, an Internet-of-Things module and a one-way communication isolation module. Through the architecture design that the medium-voltage standardized terminal solidifies the I-area service and the Internet of Things module expands the IV-area service, and in combination with the hardware modularization plug-and-play technology and the self-description model dynamic adaptation algorithm, iterative upgrading of the Internet of Things service after primary equipment wiring and fixing is achieved, the problem of high cost of overall replacement of a traditional DTU is avoided, and the overall replacement efficiency of the DTU is improved. Meanwhile, a protection system combining a dynamic trusted chain, hardware physical isolation and container logic isolation is constructed through the trusted security module, rapid deployment of emerging services in an IV region is supported on the premise of ensuring the security and controllability of core services in the I region, and the industrial pain point that security and expansibility of a traditional terminal are difficult to consider at the same time is solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution automation technology, specifically to a converged digital architecture device for station terminals that supports the expansion of IoT services in Zone IV. Background Technology

[0002] With the continuous deepening of smart grid construction, distribution automation technology, as a key means to improve grid operation efficiency and ensure power supply reliability, is undergoing a transformation from traditional automation to intelligent and IoT-based systems. In the distribution system, the station terminal, as a bridge connecting the master station and field equipment, undertakes the important tasks of data acquisition, processing, transmission, and execution of control commands. However, with the rapid development of IoT technology, the functional requirements of the distribution system for the station terminal are becoming increasingly diversified. Especially in Zone IV, the demand for IoT services such as equipment status monitoring, fault early warning, and environmental perception is increasing dramatically. The traditional station terminal architecture is simple and cannot meet the needs of security isolation and business expansion, becoming a bottleneck restricting the smart grid from moving towards IoT and intelligence.

[0003] Traditional station terminals are primarily designed for distribution automation services in Zone I, such as distribution monitoring and protection control. Their architecture is relatively closed, making it difficult to flexibly expand to support IoT services in Zone IV. Specifically, traditional technologies have the following shortcomings: First, poor service scalability. Traditional DTUs lack modularity and plug-and-play capabilities in their hardware design, often requiring complete equipment replacement for new IoT services, leading to high modification costs and maintenance difficulties. Second, insufficient security isolation. Traditional terminals lack effective isolation mechanisms when handling controlled and non-controlled services, posing security risks. Third, low data processing efficiency. Traditional terminals mostly adopt a centralized data processing mode, which is insufficient to meet the real-time and efficiency requirements of IoT services. Finally, limited algorithm analysis capabilities. Traditional terminals rely on simple threshold judgments for fault warnings and status assessments, making it difficult to guarantee accuracy and timeliness.

[0004] In view of the limitations of traditional station terminals in expanding IoT services in Zone IV, this invention proposes a converged digital architecture device for station terminals that supports the expansion of IoT services in Zone IV, which is of particular importance. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a converged digital architecture device for station terminals that supports the expansion of IoT services in Zone IV. It can solidify Zone I services through a medium-voltage standardized terminal and expand Zone IV services through IoT modules. Combined with hardware modular plug-and-play technology and self-describing model dynamic adaptation algorithm, it realizes iterative upgrades of IoT services after the primary equipment wiring is fixed, and significantly reduces the cost of transformation and maintenance.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a converged station terminal digital architecture device that supports the expansion of IoT services in Zone IV, the device comprising the following components: a medium-voltage standardized terminal, an IoT module, and a one-way communication isolation module;

[0007] The medium-voltage standardized terminal and the IoT module: achieve information interaction through a one-way communication isolation module;

[0008] The medium-voltage standardized terminal is responsible for power distribution automation services, including power distribution monitoring and protection control zone I services;

[0009] The IoT module is responsible for power distribution IoT services and extended functions, including primary equipment status monitoring, switchgear mechanical characteristic monitoring, and early fault insulation monitoring of power distribution lines (IV zone IoT services).

[0010] The one-way communication isolation module is used to realize one-way information transmission between the medium-voltage standardized terminal and the IoT module, ensuring the security of information interaction.

[0011] Furthermore, both the medium-voltage standardized terminal and the IoT module adopt a hardware modular design, including a base module, a data acquisition module, and a computing module;

[0012] The base module includes a main CPU, a power management unit, an edge computing resource expansion interface, a board identification and management unit, and a data collection and uploading unit.

[0013] The acquisition module includes at least one of the following: power frequency AC quantity acquisition board, high-speed AC quantity acquisition board, video acquisition board, and partial discharge acquisition board. Each board operates independently and is connected to the main CPU via an Ethernet communication bus.

[0014] The computing module is used to expand storage space and parallel computing power, and can deploy local models;

[0015] The hardware modules utilize secure and controllable chips, with clock speeds, memory, and storage meeting the requirements of edge computing services. Furthermore, the expansion module interfaces are uniformly designed, supporting plug-and-play and blade-style expansion. The board identification and management unit employs an optimized board identification algorithm based on an improved genetic algorithm. The algorithm formula is as follows:

[0016]

[0017] Where F(x) is the comprehensive score for board recognition, ranging from 0 to 1, with higher values ​​indicating better recognition performance. α, β, and γ are weighting coefficients, determined through gradient descent optimization using test data from 100 different types of boards, with α = 0.4, β = 0.35, and γ = 0.25. 识别 T represents the actual recognition time. 基准The theoretical minimum recognition time is calculated based on the data volume of the board interface and the bus transmission rate. E 校验 E represents the data validation error rate. max To allow the maximum error rate, C 兼容 To ensure compatibility with a certain number of board types, C 总 Given the total number of board types in the test sample, this algorithm achieves rapid identification and compatibility verification of newly added expansion boards by iteratively optimizing the identification parameters. Compared with traditional identification algorithms, the identification efficiency is improved by more than 30%, and the compatibility error rate is reduced to below 0.1%.

[0018] Furthermore, the trusted security module embeds a high-security national cryptographic algorithm to construct a trusted chain, performing trusted measurements on the controlled business applications and their execution environment. This ensures the security and controllability of the controlled business execution environment and the integrity of the applications themselves. The trusted security module also implements two-way identity authentication based on digital certificates to guarantee the reliability of terminal identities. Simultaneously, it encrypts and transmits controlled operation commands to ensure the reliability and authenticity of command issuance. The trusted measurement employs a multi-dimensional trusted evaluation algorithm based on dynamic weights, with the following formula:

[0019]

[0020] Where Trust is the reliability metric, ranging from 0 to 1, with values ​​≥0.8 indicating reliability; n is the metric dimension, n=5; w i The weights for each dimension were determined using the analytic hierarchy process (AHP): program integrity weight 0.3, environment variable weight 0.25, interface status weight 0.2, historical behavior weight 0.15, real-time resource usage weight 0.1, and S... i Let be the normalized score for the i-th dimension, λ be the decay coefficient, and t be the normalized score for the ith dimension. i The time interval from the last verification to the current time in the i-th dimension is given by the algorithm. By introducing a time decay factor, the algorithm solves the problem that traditional static metrics cannot reflect the real-time security status, thereby improving the reliability assessment response speed to within 100ms and reducing the false positive rate to below 0.5%.

[0021] Furthermore, the device employs a combination of physical hardware isolation and logical container isolation to achieve multi-service isolation. Physical hardware isolation uses hardware modules to securely isolate control-related and non-control-related services, allowing operation control services and production management services to access the control area and management area respectively. Logical container isolation uses container technology to run control-related and non-control-related services in different containers, forming resource silos that do not interfere with each other. Container resource scheduling uses a dynamic isolation algorithm based on service priority, with the following formula:

[0022]

[0023] Where R jThe amount of resources allocated to the j-th container, P j For the j-th container, the service priority is P = 5-10 for controlled services and P = 1-4 for non-controlled services, dynamically configured by the main station based on the service type. 总 δ represents the total available resources for the terminal, m represents the total number of containers, and δ j The resource adjustment factor is calculated based on the real-time load. When the load exceeds 80%, δ j By setting the value to 0.2, this algorithm ensures that controlled services receive priority allocation during resource competition, and the resource allocation deviation is controlled within 5%, thus solving the business lag problem caused by traditional static allocation.

[0024] Furthermore, the software platform of the IoT module supports an edge computing framework, configurable with six or more containers. Each container supports the deployment of multiple micro-application modules, including a primary equipment status monitoring module, a switchgear mechanical characteristic monitoring module, and a power distribution line early fault insulation monitoring module. The primary equipment status monitoring module employs a fault early warning algorithm based on an improved BP neural network, with the following formula:

[0025]

[0026] in The probability of a fault warning is z > 0.7, triggering a warning; f is the activation function; L is the number of neurons in the hidden layer; K is the dimension of the input features; w lk The weights are obtained through adaptive learning rate gradient descent training, with an initial learning rate of 0.01, decreasing by 10% every 100 iterations. k b represents the normalized eigenvalues. l As a bias term, this algorithm introduces feature importance weights, which improves the convergence speed by 40% compared to the traditional BP network and achieves a warning accuracy of over 92%.

[0027] Furthermore, the primary equipment status monitoring module is used to collect data from elbow-type head temperature, cabinet internal temperature, water immersion, smoke, and partial discharge sensors within the ring main unit / substation. This data is then transmitted to the distribution cloud master station via the MQTT protocol for equipment status assessment and fault early warning. The switchgear mechanical characteristic monitoring module is used to collect the current waveform when the switch opening and closing coils operate. This waveform data is transmitted to the distribution cloud master station via the MQTT protocol for waveform feature analysis to evaluate the operating mechanism status. The early fault insulation monitoring module for distribution lines is used to record waveforms of transient faults, derive judgment conclusions through edge computing, and transmit fault information and waveform recording data to the distribution cloud master station via the MQTT protocol. The switch mechanical characteristic analysis employs a status assessment algorithm based on waveform feature fusion, with the following formula:

[0028]

[0029] Where S is the state evaluation value, ω1-ω4 are the weights, determined through training with a fault case database, and are 0.3, 0.25, 0.25, and 0.2 respectively, I max To measure the maximum current, I nom For the rated current, t rise Let t be the current rise time. std For the standard rise time, σ(I) t ) represents the standard deviation of the current waveform, and E represents the integral value of the waveform energy. ref Using standard energy values, this algorithm integrates multi-dimensional waveform features to solve the problem of high false alarm rate in traditional single threshold judgment, thereby improving the accuracy of mechanical characteristic anomaly identification to over 95%.

[0030] Furthermore, the device can be installed in two ways: a discrete architecture installation and a centralized architecture installation. In the discrete architecture installation, the IoT unit is installed next to the common unit, communicates with each interval acquisition module through a network port, and reads data from each interval from the common unit through a single-phase isolation module. In the centralized architecture installation, the IoT unit is installed inside the centralized DTU enclosure, communicates with the centralized DTU through a network port, and communicates with the IV zone through a wireless module.

[0031] Furthermore, the device supports the fixed configuration of services on the terminal side after a one-time wiring of the device. The IoT services can be expanded and iterated for upgrades without changing the wiring. The service expansion and adaptation adopts a dynamic adaptation algorithm based on a self-describing model, and the formula is:

[0032]

[0033] Where Match represents the fit, q represents the total number of interface parameters, and a p Let δ(p,q) be the importance weight of the p-th parameter, with data format weight of 0.3, communication protocol weight of 0.25, power requirement weight of 0.2, interface type weight of 0.15, timing requirement weight of 0.05, and compatibility history weight of 0.05. Let δ(p,q) be the matching degree between the p-th parameter and the terminal's supported parameters. This algorithm automatically identifies the interface parameters of new services and evaluates their adaptability, enabling plug-and-play functionality during service expansion. The adaptation time is shortened to less than 30 seconds, improving efficiency by more than 80% compared to traditional manual configuration.

[0034] Furthermore, when the early fault insulation monitoring module of the power distribution line records transient faults, it employs a fault location algorithm based on the fusion of waveform characteristics and environmental factors, as shown in the formula:

[0035]

[0036] Where L is the distance from the fault point to the monitoring terminal, and v is the wave velocity, determined according to the cable type; for cross-linked polyethylene cables, v = 1.7 × 10⁻⁶. 8 m / s, Δt is the time difference between the arrival of the fault wave at both ends, k1, k2, and k3 are environmental correction coefficients, determined through field tests. The coefficients for soil resistivity ρ are k1 = 0.002, for ambient temperature θ are k2 = 0.001 / °θ, and for humidity h are k3 = 0.0005 / °h. ρ is soil resistivity, θ is ambient temperature, and h is relative humidity. This algorithm introduces environmental factor correction to solve the problem of traditional positioning methods being greatly affected by the environment. The positioning error is controlled within 50m, which is more than 40% more accurate than traditional methods.

[0037] Compared with existing technologies, this integrated station terminal digital architecture device that supports the expansion of IoT services in Zone IV has the following advantages:

[0038] I. This invention employs an architecture design that solidifies Zone I services using a medium-voltage standardized terminal and expands Zone IV services using an IoT module. By combining hardware modular plug-and-play technology and a self-describing model dynamic adaptation algorithm, it enables iterative upgrades of IoT services after the initial equipment wiring is fixed. This avoids the high cost of replacing the entire traditional DTU, reducing upgrade and maintenance costs by more than 60%. Simultaneously, by constructing a dynamic trusted chain through a trusted security module, a protection system combining hardware physical isolation and container logical isolation is built. This ensures the security and controllability of core services in Zone I while supporting the rapid deployment of emerging services in Zone IV, thus solving the industry pain point of traditional terminals struggling to balance security and scalability.

[0039] Second, the micro-application module of this invention achieves in-depth analysis of multi-dimensional data through a fusion algorithm. The primary equipment status monitoring module adopts an improved BP neural network, which integrates eight types of feature quantities such as temperature and partial discharge. The switch mechanical characteristic monitoring module uses a waveform feature fusion algorithm, which combines the current waveform of the opening and closing coils with a standard fingerprint database. The line fault insulation monitoring module introduces an environmental correction coefficient to achieve accurate fault location. All of the above algorithms are used to complete real-time analysis on the terminal side through edge computing. With the efficient transmission of the MQTT protocol, the transmission efficiency is improved by 40% compared with the traditional 104 protocol, enabling early detection of latent defects in equipment and rapid fault location, supporting the transformation of the distribution network from passive emergency repair to proactive operation and maintenance.

[0040] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0042] Figure 1 A framework for the startup and initialization process of a converged station terminal digital architecture device system that supports the expansion of IoT services in Zone IV;

[0043] Figure 2 This is a framework for the operation and dynamic expansion of a converged digital architecture device for station terminals that supports the expansion of IoT services in Zone IV. Detailed Implementation

[0044] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0045] Example 1

[0046] In a 10kV substation in the core business district of a city, in order to meet the requirements of power distribution reliability and intelligent management under high-density power load, the integrated substation terminal digital architecture device of the present invention was deployed. The substation undertakes the power supply task for important places such as surrounding shopping malls and office buildings, and has extremely high requirements for the stability of power distribution automation business and the scalability of IV zone IoT business.

[0047] The device is composed of a medium-voltage standardized terminal, an IoT module, and a one-way communication isolation module. The medium-voltage standardized terminal, as the core of distribution automation, continuously monitors the distribution and tracks the operating parameters of equipment such as transformers and switchgear in real time. Once an overload or voltage abnormality occurs, it immediately activates protection control and other Zone I services to quickly disconnect the faulty circuit and prevent the accident from escalating. The IoT module focuses on the in-depth expansion of Zone IV IoT services. On the one hand, it performs comprehensive status monitoring of primary equipment in the station. For example, through sensors installed in the ring main unit, it collects real-time data on temperature changes of elbow joints, humidity inside the unit, presence of abnormal conditions such as water immersion or smoke, and partial discharge data. On the other hand, it accurately collects the current waveforms when the opening and closing coils operate, capturing subtle changes during the operation, based on the mechanical characteristics of the switchgear. At the same time, it performs insulation monitoring for potential early faults in the distribution lines. Once a transient fault occurs, it immediately activates the waveform recording function to provide raw data for fault analysis.

[0048] Information interaction between the medium-voltage standardized terminal and the IoT module is strictly conducted through a one-way communication isolation module, which only allows data to be transmitted in a specified direction. This fundamentally eliminates potential risks from the IoT module side that could interfere with the core business of the medium-voltage standardized terminal, ensuring absolute security of information interaction.

[0049] In terms of hardware design, both the medium-voltage standardized terminal and the IoT module adopt a modular architecture. The base module, acquisition module, and computing module have clearly defined functions. The base module integrates core components such as the main CPU and power management unit. The board identification management unit uses a board identification optimization algorithm based on an improved genetic algorithm. The algorithm formula is as follows:

[0050]

[0051] Where F(x) is the comprehensive score for board recognition, α, β, and γ are weighting coefficients, and T... 识别 T represents the actual recognition time. 基准 E is the theoretical minimum recognition time. 校验 E represents the data validation error rate. max To allow the maximum error rate, C 兼容 To ensure compatibility with a certain number of board types, C 总 To ensure the total number of board types in the test sample and to quickly identify the various access acquisition boards, thus guaranteeing efficient adaptation when adding or replacing boards, the acquisition module is configured with power frequency AC quantity acquisition boards, high-speed AC quantity acquisition boards, and partial discharge acquisition boards according to the station's monitoring needs. These boards operate independently and are connected to the main CPU via an Ethernet communication bus, avoiding mutual interference during data acquisition. The computing module provides ample storage space and parallel computing power, supports the deployment of local analysis models, meets the real-time requirements of edge computing, and all hardware modules use secure and controllable chips. The expansion interfaces are standardized and unified, supporting plug-and-play and blade-type expansion, greatly reducing the difficulty of future hardware upgrades.

[0052] The device's built-in trusted security module is crucial for ensuring system security. It incorporates a highly secure national cryptographic algorithm to construct a trusted chain covering the entire terminal. This module employs a multi-dimensional trusted assessment algorithm based on dynamic weights, with the following formula:

[0053]

[0054] Where Trust is the trust metric, n is the metric dimension, and w i S represents the weights for each dimension. i Let be the normalized score for the i-th dimension, λ be the decay coefficient, and t be the normalized score for the ith dimension. iThe time interval from the last verification to the present is defined as the i-th dimension. Continuous trust measurement is performed on the applications and operating environment of the controlled business to detect abnormal states in a timely manner. At the same time, two-way identity authentication is achieved through digital certificates to ensure that the device and user identities of the access terminals are authentic and reliable. All control operation instructions are transmitted in encrypted form to prevent tampering or theft.

[0055] To handle multiple services operating simultaneously within the station, including power distribution monitoring, equipment monitoring, and fault early warning, the device employs a dual isolation mechanism combining physical hardware isolation and logical container isolation. Physical hardware isolation uses dedicated hardware modules to completely separate control-related protection and control services from non-control-related equipment status analysis services, connecting them to independent control and management areas respectively. Logical container isolation leverages container technology to encapsulate different services within their respective containers, forming independent resource silos. Furthermore, container resource scheduling utilizes a dynamic isolation algorithm based on service priority, with the following formula:

[0056]

[0057] Where R j The amount of resources allocated to the j-th container, P j Let R be the service priority of the j-th container. 总 δ represents the total available resources for the terminal, m represents the total number of containers, and δ j As a resource adjustment coefficient, it prioritizes the resource needs of core businesses such as protection and control when resources are scarce, ensuring that various businesses do not interfere with each other and operate stably.

[0058] The IoT module's software platform supports an edge computing framework, capable of running more than six containers simultaneously. Each container can deploy multiple micro-application modules. Among these, the primary equipment status monitoring module uploads collected sensor data to the power distribution cloud master station via the MQTT protocol. The master station analyzes the data using a fault early warning algorithm based on an improved BP neural network, as shown in the formula:

[0059]

[0060] in Let f be the probability of fault warning, L be the activation function, L be the number of neurons in the hidden layer, K be the dimension of the input features, and w be the input feature dimension. lk x is the weighting coefficient. k b represents the normalized eigenvalues. lAs a bias term, it anticipates potential equipment failures in advance. After the switchgear mechanical characteristic monitoring module uploads the collected current waveform data, the main station uses a state assessment algorithm based on waveform feature fusion to accurately evaluate the health status of the operating mechanism. After recording the waveform, the distribution line early fault insulation monitoring module quickly obtains a preliminary judgment conclusion through edge computing and then uploads the fault information and waveform data. When locating faults, it adopts a fault location algorithm based on the fusion of waveform features and environmental factors, combined with factors such as soil resistivity, ambient temperature and humidity at the time, to improve the accuracy of fault location.

[0061] The device is installed in a centralized architecture within the station. The IoT units are integrated into the centralized DTU enclosure, enabling high-speed communication with the DTU via Ethernet ports and data exchange with the IV zone via wireless modules. This saves station space and simplifies cabling. Notably, once the device is wired, the configuration services are permanently embedded in the terminal. Subsequent expansion of new IoT services requires only a software upgrade using a dynamic adaptation algorithm based on a self-describing model, as shown in the formula:

[0062]

[0063] Where Match represents the fit, q represents the total number of interface parameters, and a p δ(p,q) represents the importance weight of the p-th parameter, and δ(p,q) represents the matching degree between the p-th parameter and the terminal support parameters. No changes to the field wiring are required, which greatly reduces the impact of service expansion on power supply continuity.

[0064] Example 2

[0065] A large industrial park has multiple 10kV power distribution lines connecting various production workshops, warehousing centers, and office areas. The park has a wide variety of electrical equipment and large load fluctuations, creating an urgent need for intelligent monitoring and business expansion capabilities of the power distribution system. Therefore, the integrated digital architecture device for station terminals of this invention has been deployed.

[0066] The core components of the device remain the medium-voltage standardized terminal, the IoT module, and the one-way communication isolation module. The medium-voltage standardized terminal focuses on the power distribution automation business of the industrial park, monitoring parameters such as voltage, current, and power of each line in real time, and performing Zone I business such as line protection control to ensure the continuous and stable power supply for production. The IoT module undertakes a wide range of Zone IV IoT business, not only monitoring the status of primary equipment such as ring main units and transformers in the park and collecting data such as equipment surface temperature, operating noise, and partial discharge, but also monitoring the mechanical characteristics of switching equipment on the lines and collecting current waveforms during opening and closing; at the same time, it performs insulation monitoring for early faults in the power distribution lines and captures the waveform characteristics of transient faults.

[0067] The medium-voltage standardized terminal and the IoT module achieve secure data interaction through a one-way communication isolation module, ensuring that IoT business data can only be transmitted along the prescribed path, effectively preventing the impact of external attacks or misoperations on the core business of power distribution automation.

[0068] In terms of hardware, the modular design of the medium-voltage standardized terminal and IoT module fully adapts to the complex needs of the park. The main CPU of the base module has strong processing power, and the board identification and management unit can easily cope with the access needs of different manufacturers and types of acquisition boards in the park through the board identification optimization algorithm based on the improved genetic algorithm. In addition to the conventional power frequency acquisition board, the acquisition module is also equipped with a video acquisition board. Through the camera installed at the key equipment, it transmits video images of the equipment appearance and operating environment in real time. Each board is connected to the main CPU through the communication bus Ethernet to ensure stable and reliable data transmission. The computing module provides powerful edge computing support, which can quickly process video data and various sensor data to meet the park's requirements for real-time monitoring and rapid response. Moreover, the hardware module uses safe and controllable chips, has a unified expansion interface, and supports flexible plug-and-play and blade expansion, which is convenient for adding monitoring points according to the development of the park.

[0069] The trusted security module plays a crucial role in park scenarios. Its embedded high-security national cryptographic algorithm builds a solid trust chain. It adopts a multi-dimensional trust assessment algorithm based on dynamic weights to conduct strict trust measurement on applications and operating environments involved in production equipment control, ensuring that the execution environment of control commands is safe and reliable. At the same time, it achieves two-way identity authentication through digital certificates to prevent unauthorized devices from accessing the system. All control operation commands are transmitted in encrypted form to ensure the security of production control.

[0070] For multi-service isolation, the device adopts a combination of hardware physical isolation and container logical isolation. Hardware physical isolation connects the production line control business and non-control-related business such as energy management in the park to the control area and management area respectively to avoid mutual interference. Container logical isolation uses container technology to isolate different businesses in independent containers. Container resource scheduling adopts a dynamic isolation algorithm based on business priority to ensure that the emergency control business of the production line can obtain resources first and ensure the continuity of production.

[0071] The IoT module's software platform supports an edge computing framework and can be configured with more than six containers. Each container can deploy multiple micro-application modules. The primary equipment status monitoring module uploads collected data such as equipment temperature and partial discharge to the power distribution cloud master station via the MQTT protocol. The master station uses a fault early warning algorithm based on an improved BP neural network to detect potential equipment faults in advance. After the switch equipment mechanical characteristic monitoring module uploads current waveform data, the master station uses a status assessment algorithm based on waveform feature fusion to assess the health status of the switch operating mechanism. After the early fault insulation monitoring module of the power distribution line captures a transient fault, it quickly analyzes it through edge computing and combines it with a fault location algorithm based on waveform feature fusion and environmental factors, taking into account the differences in soil resistivity, temperature, and humidity in different areas of the park, to accurately locate the fault point, and then upload the fault information and waveform recording data to the master station.

[0072] The device is installed in a discrete architecture within the park, with the IoT unit installed next to the common unit. It communicates with the acquisition modules in each bay via a network port, and simultaneously reads data from each bay from the common unit through a single-phase isolation module. This adapts to the dispersed distribution of power distribution equipment in the park. Furthermore, the device supports the solidification of distribution services after a single wiring, and subsequent expansion and upgrades of IoT services do not require changes to the wiring. This can be achieved through a dynamic adaptation algorithm based on a self-describing model, which greatly reduces the cost of business expansion and the impact on production.

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A converged digital architecture device for station terminals that supports the expansion of IoT services in Zone IV, characterized in that, The device comprises the following components: a medium-voltage standardized terminal, an IoT module, and a one-way communication isolation module; The medium-voltage standardized terminal and the IoT module: achieve information interaction through a one-way communication isolation module; The medium-voltage standardized terminal is responsible for power distribution automation services, including power distribution monitoring and protection control zone I services; The IoT module is responsible for power distribution IoT services and extended functions, including primary equipment status monitoring, switchgear mechanical characteristic monitoring, and early fault insulation monitoring of power distribution lines (IV zone IoT services). The one-way communication isolation module is used to realize one-way information transmission between the medium-voltage standardized terminal and the IoT module, ensuring the security of information interaction.

2. The converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, The medium-voltage standardized terminal and IoT module both adopt a hardware modular design, including a base module, a data acquisition module, and a computing module; The base module includes a main CPU, a power management unit, an edge computing resource expansion interface, a board identification and management unit, and a data collection and uploading unit. The acquisition module includes at least one of the following: power frequency AC quantity acquisition board, high-speed AC quantity acquisition board, video acquisition board, and partial discharge acquisition board. Each board operates independently and is connected to the main CPU via an Ethernet communication bus. The computing module is used to expand storage space and parallel computing power, and can deploy local models; The hardware modules utilize secure and controllable chips, with clock speeds, memory, and storage meeting the requirements of edge computing services. Furthermore, the expansion module interfaces are uniformly designed, supporting plug-and-play and blade-style expansion. The board identification and management unit employs an optimized board identification algorithm based on an improved genetic algorithm. The algorithm formula is as follows: Where F(x) is the comprehensive score for board recognition, α, β, and γ are weighting coefficients, and T... 识别 T represents the actual recognition time. 基准 E is the theoretical minimum recognition time. 校验 E represents the data validation error rate. max To allow the maximum error rate, C 兼容 To ensure compatibility with a certain number of board types, C 总 This represents the total number of board types in the test sample.

3. The converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, The trusted security module embeds a high-security national cryptographic algorithm to construct a trusted chain and perform trusted measurement on the controlled business applications and execution environment. The trusted security module also implements two-way identity authentication based on digital certificates and encrypts the transmission of controlled operation commands. The trusted measurement employs a multi-dimensional trusted evaluation algorithm based on dynamic weights, with the following formula: Where Trust is the trust metric, n is the metric dimension, and w i S represents the weights for each dimension. i Let λ be the normalized score for the i-th dimension, and t be the decay coefficient. i The time interval from the last verification of the i-th dimension to the present.

4. The converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, The device employs a combination of physical hardware isolation and logical container isolation to achieve multi-service isolation. Physical hardware isolation uses hardware modules to securely separate control-related and non-control-related services, allowing operation control services and production management services to access the control area and management area respectively. Logical container isolation uses container technology to run control-related and non-control-related services in different containers, forming resource silos that do not interfere with each other. Container resource scheduling uses a dynamic isolation algorithm based on service priority, with the following formula: Where R j The amount of resources allocated to the j-th container, P j R represents the service priority of the j-th container. 总 δ represents the total available resources for the terminal, m represents the total number of containers, and δ j This is a resource adjustment coefficient.

5. The converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, The IoT module's software platform supports an edge computing framework and can be configured with six or more containers. Each container can deploy multiple micro-application modules. These micro-application modules include a primary equipment status monitoring module, a switchgear mechanical characteristic monitoring module, and a power distribution line early fault insulation monitoring module. The primary equipment status monitoring module employs a fault early warning algorithm based on an improved BP neural network, with the following formula: in Let f be the probability of fault warning, L be the activation function, L be the number of neurons in the hidden layer, K be the dimension of the input features, and w be the input feature dimension. lk x is the weighting coefficient. k b represents the normalized eigenvalues. l This is a bias term.

6. The converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, The primary equipment status monitoring module is used to collect data from elbow-type head temperature, cabinet internal temperature, water immersion, smoke, and partial discharge sensors within the ring main unit / substation. This data is then transmitted to the distribution cloud master station via the MQTT protocol for equipment status assessment and fault early warning. The switchgear mechanical characteristic monitoring module is used to collect the current waveform when the switch opening and closing coils operate. This waveform data is transmitted to the distribution cloud master station via the MQTT protocol for waveform feature analysis to evaluate the operating mechanism status. The early fault insulation monitoring module for distribution lines is used to record waveforms of transient faults, derive judgment conclusions through edge computing, and transmit fault information and waveform recording data to the distribution cloud master station via the MQTT protocol. The switch mechanical characteristic analysis employs a status assessment algorithm based on waveform feature fusion, with the following formula: Where S is the state evaluation value, ω1-ω4 are the weights, and I... max To measure the maximum current, I nom For the rated current, t rise Let t be the current rise time. std For the standard rise time, σ(I) t ) represents the standard deviation of the current waveform, and E represents the integral value of the waveform energy. ref This is the standard energy value.

7. The converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, The device can be installed in two ways: discrete architecture and centralized architecture. In discrete architecture, the IoT unit is installed next to the common unit, communicates with each interval acquisition module through a network port, and reads data from each interval from the common unit through a single-phase isolation module. In centralized architecture, the IoT unit is installed inside the centralized DTU enclosure, communicates with the centralized DTU through a network port, and communicates with the IV zone through a wireless module.

8. The converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, The device supports fixed service configuration on the terminal side after a one-time wiring of the equipment. The IoT service can be expanded and upgraded iteratively without changing the wiring. The service expansion and adaptation adopts a dynamic adaptation algorithm based on a self-describing model, and the formula is: Where Match represents the fit, q represents the total number of interface parameters, and a p Let δ(p,q) be the importance weight of the p-th parameter, and let δ(p,q) be the matching degree between the p-th parameter and the terminal support parameters.

9. A converged station terminal digital architecture device supporting the expansion of IoT services in Zone IV as described in claim 1, characterized in that, When the early fault insulation monitoring module of the power distribution line records transient faults, it adopts a fault location algorithm based on the fusion of waveform characteristics and environmental factors, and the formula is as follows: Where L is the distance from the fault point to the monitoring terminal, v is the wave velocity, Δt is the time difference between the arrival of the fault wave at both ends, k1, k2, and k3 are environmental correction coefficients, ρ is the soil resistivity, θ is the ambient temperature, and h is the relative humidity.

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