IOT edge communication base station for verification of flexible intelligent driving electric energy meter

Abstract

The invention discloses an IOT edge communication base station for flexible intelligent manufacturing driving electric energy meter verification, which relates to the field of electric energy information acquisition, and comprises a self-adaptive protocol conversion engine for supporting dynamic identification and conversion of Modbus, OPCUA and MQTT protocols, a streaming media intelligent slicing module for optimizing video stream processing based on a Gstreamer framework, docking with an RTSP / ONVIF protocol by adopting H.265 coding, and carrying out data processing on the streaming media intelligent slicing module. The dynamic encryption channel is used for carrying out edge side data encryption by adopting a national cipher SM9 algorithm; and the digital twin modeling engine is used for dynamically binding physical equipment data and a Unity 3D three-dimensional model through JSONSchema. According to the invention, efficient conversion and flexible adaptation of protocols are realized, low-delay video transmission is realized, a three-level security protection system is constructed, and the transmission security is ensured.

Description

Technical Field

[0001] The present invention relates to the field of electric energy information collection, and specifically to an IOT edge communication base station for driving the verification of electric energy meters by flexible manufacturing. Background Art

[0002] At present, industrial Internet of Things and edge computing technologies have been widely applied in many industrial scenarios such as intelligent manufacturing and energy power. However, there are some obvious defects in the existing industrial Internet of Things communication base station and edge computing integration system: Poor protocol conversion efficiency: When the traditional protocol conversion mechanism processes the conversion between industrial fieldbus protocols (such as Modbus, OPC UA) and Internet of Things protocols (such as MQTT), the efficiency is very low. Through actual tests, the average processing delay of its requests is as high as 1334.80 ms. Moreover, the resource occupancy rate of this conversion mechanism is relatively high, and the memory occupancy reaches 28 MB / second, which causes the performance of the system to drop significantly when dealing with high-concurrency industrial data.

[0003] Too high video stream transmission delay: In the video monitoring application in the industrial field, when using the traditional H.264 encoding combined with the RTSP protocol for video stream transmission, the end-to-end delay is generally above 500 ms. When the resolution of the video stream reaches 1080P and the bit rate is 4 Mbps, the delay even exceeds 800 ms; such a high delay seriously affects the effects of real-time monitoring and remote operation.

[0004] Insufficient security protection ability: The existing security protection system mainly relies on the RSA-2048 encryption algorithm and the static key management method. In the face of quantum computing attacks, the security of this encryption method will be seriously threatened. According to statistics, 76.6% of industrial network attack events are caused by key leakage or the cracking of encryption algorithms. In addition, the existing security protection system lacks an effective dynamic defense mechanism and cannot respond to new network attack means in a timely manner.

[0005] The existence of these problems severely restricts the further popularization and application of industrial Internet of Things and edge computing technologies in the industrial field. Therefore, there is an urgent need for an intelligent edge computing system that can support multi-protocol adaptation, low-delay streaming media processing, dynamic encryption, and digital twin modeling to solve the deficiencies in the existing technology and improve the performance and security of industrial Internet of Things and edge computing systems. Summary of the Invention

[0006] This application provides an IOT edge communication base station for driving the verification of electric energy meters by flexible manufacturing, which is used to solve the problems existing in the prior art such as low protocol conversion efficiency, high video delay, and insufficient security protection.

[0007] In view of the above problems, the present application provides an IOT edge communication base station for driving the verification of smart electricity meters in flexible manufacturing.

[0008] The first aspect of the present application provides an IOT edge communication base station for driving the verification of smart electricity meters in flexible manufacturing, including: an adaptive protocol conversion engine: supporting dynamic identification and conversion of Modbus, OPCUA, and MQTT protocols; A streaming media intelligent slicing module: optimizing video stream processing based on the Gstreamer framework, docking with the H.265 encoding and RTSP / ONVIF protocols, a dynamic encryption channel: encrypting edge-side data using the national cryptographic SM9 algorithm, a digital twin modeling engine: dynamically binding physical device data with a Unity3D three-dimensional model through JSONSchema, a microservice computing framework: the container image volume ≤ 380MB, supporting Docker / Kubernetes containerized deployment, a three-level security protection system: including DTLS1.3 encryption at the protocol layer, SM9 dynamic key rotation at the data layer, RBAC permission control and blockchain log storage at the application layer, an adaptive protocol conversion engine: dynamic decision tree classification, with an identification accuracy rate ≥ 99.2% and a parsing speed of 12,800 msg / s, a streaming media intelligent slicing module: H.265 encoding and latency optimization, reducing the latency from 320ms to 118ms, a dynamic encryption channel: the SM9 algorithm increases the key negotiation speed by 7 times, with an encryption throughput of 15MB / s, digital twin modeling: combining Unity3D and JSONSchema, with a modeling error ≤ 0.3%, edge-cloud collaboration: local processing of real-time data and cloud storage of non-real-time data, achieving efficient protocol conversion and flexible adaptation, and low-latency video transmission, and constructing a three-level security protection system to ensure the security of transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is an automatic recognition diagram of the current protocol features of the present invention; Figure 2 It is a three-level security protection architecture diagram of the present invention; Figure 3 It is a schematic diagram of the protocol conversion engine of the present invention; Figure 4 It is a three-level security protection system diagram of the present invention; Figure 5 It is a dynamic encryption channel diagram of the present invention; Figure 6 It is the startup process of the microservice framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0011] The embodiments of the present invention will be described below according to its overall structure.

[0012] The IOT edge communication base station for driving the verification of smart electricity meters by flexible manufacturing includes: Adaptive protocol conversion engine: supports dynamic identification and conversion of Modbus, OPC UA, and MQTT protocols, and the dynamic decision tree classification mechanism includes a feature extraction layer and a classification decision layer; The feature extraction layer includes: Modbus: extracts function codes 0x01 - 0x10; OPC UA: parses the 32-byte fixed message header; MQTT: analyzes the Topic hierarchical structure.

[0013] The classification decision layer includes: Realizes automatic protocol classification by constructing multi-way decision tree nodes; Supports unknown protocols to trigger machine learning models for feature library update; Furthermore, the above classification mechanism adopts a deployment architecture: Adopts a distributed design to connect to the MQTT Broker cluster; Supports Docker / Kubernetes containerized deployment; In the above structure, supporting unknown protocols to trigger machine learning models for feature library update is based on the above settings of the adaptive protocol conversion engine. In actual industrial scenarios, data messages of multiple protocols will be transmitted to the adaptive protocol conversion engine in real time. The decision tree will perform dynamic classification operations according to the characteristics of these real-time input messages. Moreover, the decision tree is based on a distributed design to connect to the MQTT Broker cluster and supports Docker / Kubernetes containerized deployment. This architecture enables the decision tree to flexibly handle protocol data processing requirements of different scales and complexities; The adaptive protocol conversion engine realizes automatic protocol feature recognition through dynamic decision tree classification, and the specific process includes the following steps: S100 Message reception, supports multi-format message input of Modbus RTU / ASCII, OPC UA Binary / XML, MQTT v3.1.1 / v5.0, and adopts an asynchronous IO queue mechanism to implement message buffer processing; S200 Protocol feature extraction. The protocols mainly include: Modbus protocol: Extract the function code range: 0x01 (Read Coils) to 0x10 (Preset Multiple Registers), and analyze the structural features of the data unit format (ADU); OPC UA protocol: Fixedly parse the first 32-byte message header (including fields such as message header, security header, request type), and identify the service type (such as ReadRequest, WriteRequest); MQTT protocol: Analyze the Topic hierarchical structure (such as / factory / line1 / machine1), and extract control bit features such as QoS level and retain flag; S300 The classification of OPCUA / Modbus / MQTT mainly performs the following operations: Perform preliminary protocol screening based on the feature extraction results; Exclude unmatched protocol types and enter the decision tree classification; S400 Dynamic decision tree classification. Among them, the architecture of the decision tree is: Root node: Protocol type identification Intermediate node: Feature parameter verification (such as Modbus function code range, OPC UA message header length) Leaf node: Protocol matching result; Specifically, when a new message is input, starting from the root node, according to the protocol features of the message, continuously perform feature parameter verification at the intermediate node, and then dynamically move to the corresponding leaf node to determine the protocol type. For example, different Modbus function codes will guide the message to different paths for matching, achieving the effect of dynamic classification.

[0014] S500 Protocol conversion, supporting two-way conversion between three protocols (such as Modbus→MQTT, OPC UA→Modbus), retaining the semantic integrity of the original data, and using zero-copy technology to improve the conversion efficiency; S600 MQTT Broker cluster, distributed deployment to achieve load balancing, supporting millions of concurrent connections; Provide QoS 0 / 1 / 2 message delivery guarantee. Among them, the Modbus protocol extracts function codes 0x01 - 0x10, the OPC UA protocol parses the 32-byte message header, and the MQTT protocol analyzes the Topic structure; Streaming Media Intelligent Slicing Module: Optimize video stream processing based on the Gstreamer framework, and use H.265 encoding to dock with RTSP / ONVIF protocols; Dynamic Encryption Channel: Use the national cipher SM9 algorithm to encrypt edge-side data; Digital Twin Modeling Engine: Dynamically bind physical device data and Unity3D 3D models through JSONSchema; Microservices Computing Framework: The container image volume ≤ 380MB, supporting Docker / Kubernetes containerized deployment; Three-level Security Protection System: Includes DTLS1.3 encryption at the protocol layer, SM9 dynamic key rotation at the data layer, RBAC permission control at the application layer, and blockchain log storage and evidence.

[0015] The streaming media intelligent slicing module includes a parallel transmission channel, achieving a 3.2-fold improvement in the video stream sharding transmission efficiency.

[0016] The three-level security protection system includes: Protocol layer: DTLS1.3 handshake encryption + message CRC32 check; The protocol layer mainly uses DTLS 1.3 handshake encryption: Use the DTLS variant of the TLS 1.3 protocol to implement transport layer encryption; Support 0-RTT (0-Round Trip Time) to resume sessions; The encryption algorithm suite includes AES-256-GCM, ChaCha20-Poly1305; Message CRC32 check: Perform cyclic redundancy check on the transmitted message; The check range covers the protocol header and the payload; Data layer: SM9 dynamic encryption + key rotation mechanism; The data layer uses the national cipher SM9 identity encryption algorithm; Support data block encryption and stream encryption modes; The key rotation mechanism includes: The key negotiation time ≤ 0.7 seconds (a 77% improvement over the traditional solution); The key rotation period ≤ 30 minutes; Support key version rollback and forward security protection; Application layer: RBAC permission control + blockchain log storage and evidence; RBAC permission control: Role-Based Access Control; Define 5-level access rights (Administrator / Engineer / Operator / Visitor / Restricted); Support fine-grained API interface permission control; Blockchain log deposit and proof: Adopt the Hyperledger Fabric consortium chain architecture; Log upload time to the chain ≤ 500ms; Support log integrity verification and anti-tampering verification; The digital twin modeling engine realizes 3D model rendering through the Unity3D engine, integrates ANSYS mesh generation technology, and supports dynamic binding of mechanical structure and fluid dynamics models.

[0017] The microservices computing framework adopts dependency pruning technology to delete redundant files during the container image building process, and the image volume compression rate ≥ 65%.

[0018] The dynamic encryption channel supports differential encryption strategies for sensitive data and ordinary data. IBE encryption is used for sensitive data, and AES-256 encryption is used for ordinary data; The system adopts an edge-cloud collaborative architecture. Real-time data (<100ms) is processed by the local rule engine, and non-real-time data (>5s) is synchronized to the cloud big data analysis platform through the 5G private network; When the system is deployed on the electricity meter production line, the edge node adopts the Huawei Atlas500 device, configured with a 4-core CPU / 32GB memory, and the network bandwidth is 1Gbps 5G private network; The system realizes anomaly detection through LSTM + rule engine.

[0019] Although the present application has been described in connection with specific features and their embodiments, it will be apparent that various modifications and combinations can be made thereto without departing from the spirit and scope of the present application. Accordingly, the present specification and the drawings are merely illustrative of the present application and are considered to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and variations therein. Although the embodiments of the present invention have been shown and described, the specific embodiments are merely explanations of the present invention and are not limitations thereof. The specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions and variations that do not make creative contributions to the embodiments as needed, but as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. Flexible intelligent manufacturing drives the IOT edge communication base station for electric energy meter verification, characterized by: include: Adaptive protocol conversion engine: supports dynamic identification and conversion of Modbus, OPCUA, and MQTT protocols; Streaming media intelligent slicing module: optimizes video stream processing based on the Gstreamer framework, uses H.265 encoding and connects to the RTSP / ONVIF protocol; Dynamic encryption channel: uses the national secret SM9 algorithm to encrypt edge-side data; Digital twin modeling engine: dynamically bind physical device data and Unity3D 3D models through JSONSchema; Microservice computing framework: container image size ≤ 380MB, supporting Docker / Kubernetes container deployment; Three-level security protection system: including DTLS1.3 encryption at the protocol layer, SM9 dynamic key rotation at the data layer, RBAC permission control at the application layer and blockchain log storage.

2. The IOT edge communication base station for flexible intelligent manufacturing drive electric energy meter verification according to claim 1 is characterized by: The adaptive protocol conversion engine realizes automatic identification of protocol features through dynamic decision tree classification. The specific process includes the following steps: S100 message reception; S200 protocol feature extraction; S300 OPCUA / Modbus / MQTT classification; S400 dynamic decision tree classification; S500 protocol conversion; S600 MQTTBroker cluster; Among them, the Modbus protocol extracts function codes 0x01-0x10, the OPCUA protocol parses the 32-byte message header, and the MQTT protocol analyzes the Topic structure.

3. The IOT edge communication base station for flexible intelligent manufacturing and power meter verification according to claim 1 is characterized by: The streaming media intelligent slicing module includes parallel transmission channels, which can increase the video stream segmentation transmission efficiency by 3.2 times.

4. The IOT edge communication base station for flexible intelligent manufacturing drive electric energy meter verification according to claim 1 is characterized by: The three-level safety protection system includes: Protocol layer: DTLS1.3 handshake encryption + message CRC32 check; Data layer: SM9 dynamic encryption + key rotation mechanism; Application layer: RBAC permission control + blockchain log storage.

5. The IOT edge communication base station for flexible intelligent manufacturing driving electric energy meter verification according to claim 1 is characterized in that: The digital twin modeling engine realizes three-dimensional model rendering through the Unity3D engine, and integrates ANSYS mesh generation technology to support dynamic binding of mechanical structure and fluid dynamics model.

6. The IOT edge communication base station for flexible intelligent manufacturing drive electric energy meter verification according to claim 1 is characterized by: The microservice computing framework adopts dependency pruning technology to delete redundant files during the container image building process, and the image volume compression rate is ≥65%.

7. The IOT edge communication base station for flexible intelligent manufacturing and power meter verification according to claim 1 is characterized by: The dynamic encryption channel supports differentiated encryption strategies for sensitive data and ordinary data. Sensitive data is encrypted using IBE, and ordinary data is encrypted using AES-256.

8. The IOT edge communication base station for flexible intelligent manufacturing and driving electric energy meter verification according to claim 1 is characterized in that: The IOT edge communication base station for the verification of the flexible intelligent manufacturing driven electric energy meter adopts an edge-cloud collaborative architecture. Real-time data (<100ms) is processed by a local rule engine, and non-real-time data (>5s) is synchronized to the cloud big data analysis platform through a 5G private network. When the IOT edge communication base station for the flexible intelligent manufacturing driving the electric energy meter calibration is deployed on the electric energy meter production line, the edge node adopts Huawei Atlas500 equipment, configured with 4-core CPU / 32GB memory, and the network bandwidth is 1Gbps 5G private network; The IOT edge communication base station for flexible intelligent manufacturing driving electric energy meter calibration realizes anomaly detection through LSTM+ rule engine.

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