Monitoring and early warning system for multi-energy complex power grid

Abstract

The invention discloses a monitoring and early warning system for a multi-energy complex power grid, and relates to the technical field of power grid monitoring, the monitoring and early warning system comprises a data acquisition layer, an edge calculation layer, a network transmission layer, an intelligent analysis layer and a decision application layer, the data acquisition layer is fixed on a power grid equipment wiring terminal and a new energy converter cabinet door through bolts; the output end of the power supply interface is directly connected with the edge computing layer through an RS485 bus, the edge computing layer is installed in a transformer substation control cabinet through a guide rail buckle, the power supply interface is connected with a direct-current power supply bus through an IP67 aviation plug, and the data interface is connected with a network transmission layer TSN switch through a gigabit Ethernet electric port. A main ring network of the network transmission layer is connected in series with a core switch through an ST connector locking single-mode optical fiber, a standby ring network fixes a 5G CPE antenna to a communication tower through a flange base, and a rack-mounted server of the intelligent analysis layer is fixed to a data center cabinet through bolts and has the functions of multi-source situation accurate sensing and dynamic cooperative efficient early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a monitoring and early warning system for complex multi-energy power grids. Background Art

[0002] With the deepening transformation of the energy structure, the large-scale integration of new energy sources such as wind power and photovoltaics into the power grid, coupled with the widespread use of energy storage facilities, has led to the modern power grid featuring multi-energy coupling, complex topology, and changing dynamic characteristics. Existing power grid monitoring and early warning systems are primarily designed based on traditional single-energy scenarios. Their architectures often adopt a centralized data processing model, with data collection primarily relying on grid-side electrical quantity monitoring. This model provides insufficient coverage of the output characteristics of new energy sources and the status of equipment. System levels are typically connected using standardized wired communication protocols, and data processing and analysis tasks are highly dependent on a central cloud platform or data center. Early warning decisions are often based on preset fixed thresholds or relatively static model rules.

[0003] However, the above-mentioned existing technologies have significant shortcomings when dealing with complex multi-energy power grids: first, the system architecture is difficult to adapt to the diversified data sources, regional decentralization and heterogeneous access protocols brought about by the high penetration of new energy, resulting in incomplete data collection and low transmission efficiency; second, the centralized processing mode is difficult to meet the real-time analysis and early warning response requirements required by the rapid changes in the power grid status under multi-energy coupling, and there is a risk of response delay; third, the traditional analysis model is not adaptable enough to the strong volatility of new energy and the complexity of multi-energy interactions, and the prediction accuracy is limited; in addition, the coordination efficiency between the various levels of the system is not high, and the cross-regional and cross-energy coordinated early warning and rapid control capabilities are relatively weak; finally, there are limitations in system scalability and maintenance convenience, which makes it difficult to support the flexible and rapid access and efficient on-site operation and maintenance of new energy storage facilities. To this end, we propose a monitoring and early warning system for complex multi-energy power grids. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the existing defects and provide a monitoring and early warning system for complex multi-energy power grids, which has accurate perception of multi-source situations, dynamic collaborative and efficient early warning, and can effectively solve the problems in the background technology.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a monitoring and early warning system for a multi-energy complex power grid, comprising a data acquisition layer, an edge computing layer, a network transmission layer, an intelligent analysis layer, and a decision application layer. The data acquisition layer is fixed to the power grid equipment terminal and the new energy converter cabinet door by bolts, and its output end is directly connected to the edge computing layer via the RS485 bus. The edge computing layer is installed in the substation control cabinet through a guide rail buckle, and the power supply interface is connected to the DC power bus via an IP67 aviation plug. The data interface is connected to the network transmission layer TSN switch via a Gigabit Ethernet electrical port. The backbone ring network of the network transmission layer is locked to the single-mode optical fiber in series with the core switch via an ST connector, and the standby ring network is fixed to the 5G CPE antenna on the communication tower via a flange base. The rack-mounted server of the intelligent analysis layer is fixed to the data center cabinet via bolts, wherein the mechanism model calculation module and the data-driven analysis module are connected in parallel via the PCIe bus, the dynamic arbitrator selects the output path via the relay contact, the decision application layer is connected to the energy storage converter control terminal via an optical isolation relay, and the feedback end is connected via the OPC The UA protocol is connected to the SCADA system, and its modular design enables reliable installation and low-latency transmission, enhancing system stability and efficiency.

[0006] Furthermore, the data acquisition layer includes an electrical quantity monitoring unit, a new energy characteristic monitoring unit and an equipment status monitoring unit. The electrical quantity monitoring unit is fixed to the grid bus terminal by bolts, and the output end is connected to the RS485 bus via a shielded twisted pair cable. The new energy characteristic monitoring unit is adsorbed on the photovoltaic inverter cabinet door through a magnetic base, and the optical fiber interface is connected to the SFP optical module via an armored optical cable. The equipment status monitoring unit is bonded to the wall of the transformer oil tank through thermal conductive glue and sends wireless data to the edge computing layer gateway via the LoRa module. The multiple installation methods adapt to different environments, improve deployment flexibility and monitoring coverage, and reduce maintenance costs.

[0007] Furthermore, the edge computing layer includes a protocol conversion module, a preprocessing module and an edge storage module connected via a backplane bus. The protocol conversion module is connected to the data acquisition layer through an M12 aviation connector (supporting Modbus / TCP) and is connected to the preprocessing module via the backplane bus. The preprocessing module contains an FPGA acceleration card fixed to the mainboard via a PCIe slot, and the output data is encapsulated through an IPsec tunnel. The edge storage module is connected to the mainboard via a SATA interface and a heat sink is attached via thermally conductive silicone. FPGA and IPsec technologies improve data processing speed and security, and reduce data loss and delay.

[0008] Furthermore, the network transmission layer includes a TSN switch, a 5G URLLC slicing module and a security encryption module. The TSN switch is installed in the cabinet through a DIN rail clip. The optical port is connected to the backbone ring network through an LC connector, and the electrical port is connected to the edge computing layer through an RJ45. The 5G URLLC slicing module fixes the CPE antenna through a flange base, and the control flow interface is directly connected to the decision application layer through a Mini-SAS HD connector. The security encryption module is embedded in the switch backplane through a slot, and the built-in national encryption SM4 chip encrypts data in real time. TSN and 5G technologies ensure highly reliable communication, and real-time encryption protects data integrity and privacy.

[0009] Furthermore, the intelligent analysis layer includes a mechanism model calculation module, a data-driven analysis module and a dynamic arbitrator. The GPU accelerator card of the mechanism model calculation module is fixed via a PCIe x16 slot, and the heat sink is directly connected to the CPU via a copper tube. The AI ​​coprocessor of the data-driven analysis module is soldered to the motherboard via a BGA package and interconnected via an NVLink interface. The dynamic arbitrator monitors new energy fluctuations via a voltage comparison circuit. When the threshold is exceeded, it switches to a data-driven path via a MOSFET switch. The dynamic arbitration automatically selects the optimal analysis mode, optimizes computing resources and improves response speed.

[0010] Furthermore, the decision-making application layer includes a collaborative control platform, an early warning console and a strategy generation module. The collaborative control platform is inserted into the cabinet via a slide rail, the command output end is connected to the energy storage converter via an optical isolation relay, the early warning console is wall-mounted via a VESA bracket, and the input interface is connected to the server via HDMI. The FPGA chip of the strategy generation module is attached to the cold plate via heat dissipation silicone grease, and data is exchanged through the DDR4 bus. Optical isolation and efficient heat dissipation ensure stable control, reduce interference and improve decision reliability.

[0011] Furthermore, the connection path between the data acquisition layer, edge computing layer, network transmission layer, intelligent analysis layer and decision application layer is that the LoRa signal of the data acquisition layer is converged to the edge computing layer gateway via a star topology, the pre-processed data is transmitted to the intelligent analysis layer via the TSN switch QoS queue, the arbitration result is directly written to the decision application layer shared memory via the RDMA network, and finally an instruction is generated to control the energy storage converter. The star topology and RDMA technology minimize data transmission delay and ensure end-to-end efficient control.

[0012] Furthermore, it also includes a partition coupling degree calculation unit and a cross-zone support channel. The partition coupling degree calculation unit maps the intelligent analysis layer topology database through shared memory, and the output end is connected to the arbitrator through the SPI bus. The cross-zone support channel runs through adjacent partitions through the MPO connector. The power mutual assistance instruction is encrypted by the HTTPS protocol and sent through the optical fiber. The response delay is ≤200ms. The low-latency mutual assistance mechanism quickly coordinates the partition power, thereby improving the system flexibility and fault response capabilities.

[0013] Furthermore, the edge computing layer shell is fixed to the guide rail by a spring clip, and tool-free disassembly can be achieved by pressing the release lever. The data acquisition layer magnetic base has a built-in Hall sensor. When a fault occurs, the positioning LED is triggered by LoRa to flash to indicate the maintenance location. Tool-free disassembly and LED positioning simplify the maintenance process, improve operational convenience and fault handling speed.

[0014] Furthermore, when a new energy storage partition is added, its edge node is connected to the backbone ring network via a pre-installed RJ45 interface, and the partition ID is registered to the intelligent analysis layer via the I²C bus. External meteorological data is converted by the OPC DA protocol gateway, embedded in the data bus through the USB-C socket, and the driver is dynamically loaded. The plug-and-play interface and protocol conversion enable flexible expansion and convenient integration of real-time data to enhance system intelligence.

[0015] Compared with the existing technology, the beneficial effects of the present invention are: the multi-energy complex power grid monitoring and early warning system has the following advantages: 1. The system uses the hardware-level fusion capabilities of the edge computing layer protocol conversion module (supporting multiple protocols such as Modbus / TCP) and combines three physical installation methods: magnetic suction / bolt / adhesive, to achieve unified access to heterogeneous data such as grid electrical quantity, new energy output characteristics, and equipment status. The FPGA accelerator card completes feature extraction and data dimensionality reduction on the edge side, reducing the amount of raw data by more than 40%. At the same time, IPsec tunnel encryption ensures the secure transmission of pre-processed data. This design breaks through the traditional system's reliance on a single data type and significantly improves the panoramic perception accuracy of wind, solar, and storage multi-energy hybrid scenarios. 2. The innovative use of a hardware arbitration circuit (MOSFET switch + voltage comparator) enables millisecond-level switching between the mechanism model and the AI ​​algorithm. When the fluctuation of the new energy exceeds the preset threshold, the system automatically switches the analysis path to the data-driven module within 10ms, using the AI ​​coprocessor with NVLink high-speed interconnect to learn the fluctuation pattern. In steady state, it switches to the GPU-accelerated physical model to ensure calculation accuracy. In testing, this mechanism reduces transient process prediction errors by 32% and reduces unnecessary AI computing power consumption by 60%, perfectly balancing computing efficiency and accuracy requirements. 3. Zone-to-zone direct connection channels built with MPO fiber optic connectors and the HTTPS encrypted transmission protocol enable encrypted transmission of power mutual assistance instructions between adjacent zones. A zone coupling calculation unit analyzes topological correlation strength in real time. When a zone experiences a power shortage, the system completes the entire process—from monitoring and identification to policy generation and cross-zone instruction issuance—within 200ms, a five-fold increase in response speed compared to traditional SCADA systems. Combined with direct control of the energy storage inverter by the decision-making layer's optical isolation relays, the system successfully mitigates over 90% of the risk of cascading failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] See also Figure 1 This embodiment provides a technical solution: a monitoring and early warning system for complex multi-energy power grids, including a data acquisition layer, an edge computing layer, a network transmission layer, an intelligent analysis layer, and a decision application layer. The data acquisition layer is bolted to the grid equipment terminal blocks and the new energy converter cabinet door. Its output end is directly connected to the edge computing layer via an RS485 bus. The edge computing layer is installed in the substation control cabinet via guide rail clips. The power supply interface is connected to the DC power bus via an IP67 aviation plug. The data interface is connected to the network transmission layer TSN switch via a Gigabit Ethernet electrical port. The backbone ring network of the network transmission layer is connected to the core switch in series with a single-mode optical fiber via an ST connector. The backup ring network is fixed to the 5G CPE antenna on the communication tower via a flange base. The rack-mounted server of the intelligent analysis layer is bolted to the data center cabinet. The mechanism model calculation module and the data-driven analysis module are connected in parallel via a PCIe bus. The dynamic arbitrator selects the output path via relay contacts. The decision application layer is connected to the energy storage converter control terminal via an optical isolation relay. The feedback end is connected to the SCADA system via the OPC UA protocol. The modular design achieves reliable installation and low-latency transmission, enhancing system stability and efficiency.

[0019] The data collection layer includes an electrical quantity monitoring unit, a new energy characteristic monitoring unit, and an equipment status monitoring unit. The electrical quantity monitoring unit is fixed to the grid bus terminal with bolts, and the output end is connected to the RS485 bus via a shielded twisted pair cable. The new energy characteristic monitoring unit is attached to the photovoltaic inverter cabinet door via a magnetic base, and the optical fiber interface is connected to the SFP optical module via an armored optical cable. The equipment status monitoring unit is bonded to the wall of the transformer oil tank with thermal conductive adhesive and sends wireless data to the edge computing layer gateway via the LoRa module. Multiple installation methods adapt to different environments, improve deployment flexibility and monitoring coverage, and reduce maintenance costs.

[0020] The edge computing layer includes a protocol conversion module, a pre-processing module and an edge storage module connected via the backplane bus. The protocol conversion module is connected to the data acquisition layer through an M12 aviation connector (supporting Modbus / TCP) and connected to the pre-processing module via the backplane bus. The pre-processing module contains an FPGA acceleration card fixed to the motherboard via the PCIe slot. The output data is encapsulated through an IPsec tunnel. The edge storage module is connected to the motherboard via a SATA interface and a heat sink is attached using thermally conductive silicone. FPGA and IPsec technologies improve data processing speed and security, reducing data loss and delay.

[0021] The network transmission layer includes TSN switches, 5G URLLC slicing modules and security encryption modules. The TSN switches are installed in the cabinet through DIN rail clips. The optical port is connected to the backbone ring network through the LC connector, and the electrical port is connected to the edge computing layer through the RJ45. The 5G URLLC slicing module fixes the CPE antenna through the flange base, and the control flow interface is directly connected to the decision application layer through the Mini-SAS HD connector. The security encryption module is embedded in the switch backplane through a slot, and the built-in national encryption SM4 chip encrypts data in real time. TSN and 5G technologies ensure highly reliable communication, and real-time encryption protects data integrity and privacy.

[0022] The intelligent analysis layer includes a mechanism model calculation module, a data-driven analysis module, and a dynamic arbitrator. The GPU accelerator card of the mechanism model calculation module is fixed via a PCIe x16 slot, and the heat sink is directly connected to the CPU via a copper tube. The AI ​​coprocessor of the data-driven analysis module is soldered to the motherboard via a BGA package and interconnected via an NVLink interface. The dynamic arbitrator monitors new energy fluctuations via a voltage comparison circuit. When the threshold is exceeded, it switches to the data-driven path through a MOSFET switch. The dynamic arbitrator automatically selects the optimal analysis mode, optimizes computing resources, and improves response speed.

[0023] The decision-making application layer includes a collaborative control platform, an early warning console, and a strategy generation module. The collaborative control platform is inserted into the cabinet via slide rails, the command output end is connected to the energy storage converter via an optical isolation relay, the early warning console is wall-mounted via a VESA bracket, and the input interface is connected to the server via HDMI. The FPGA chip of the strategy generation module is attached to the cold plate via thermal silicone grease, and data is exchanged through the DDR4 bus. Optical isolation and efficient heat dissipation ensure stable control, reduce interference, and improve decision reliability.

[0024] The connection path between the data acquisition layer, edge computing layer, network transmission layer, intelligent analysis layer and decision application layer is as follows: the LoRa signal of the data acquisition layer is converged to the edge computing layer gateway via a star topology, the pre-processed data is transmitted to the intelligent analysis layer via the TSN switch QoS queue, and the arbitration result is directly written into the shared memory of the decision application layer via the RDMA network, and finally an instruction is generated to control the energy storage converter. The star topology and RDMA technology minimize data transmission delay and ensure end-to-end efficient control.

[0025] It also includes a partition coupling calculation unit and a cross-zone support channel. The partition coupling calculation unit maps the intelligent analysis layer topology database through shared memory, and the output end is connected to the arbitrator via the SPI bus. The cross-zone support channel runs through adjacent partitions through the MPO connector. The power mutual assistance instruction is encrypted by the HTTPS protocol and sent through optical fiber. The response delay is ≤200ms. The low-latency mutual assistance mechanism quickly coordinates partition power, improving system resilience and fault response capabilities.

[0026] The edge computing layer shell is fixed to the guide rail by a spring clip, and tool-free disassembly can be achieved by pressing the release lever. The data acquisition layer magnetic base has a built-in Hall sensor. In the event of a fault, the positioning LED is triggered by LoRa to flash to indicate the maintenance location. Tool-free disassembly and LED positioning simplify the maintenance process, improve operational convenience and fault handling speed.

[0027] When a new energy storage zone is added, its edge node is connected to the backbone ring network via a pre-installed RJ45 interface, and the zone ID is registered to the intelligent analysis layer via the I²C bus. External meteorological data is converted by an OPC DA protocol gateway, embedded into the data bus via a USB-C socket, and the driver is dynamically loaded. The plug-and-play interface and protocol conversion enable flexible expansion and convenient integration of real-time data to enhance system intelligence.

[0028] The working principle of the monitoring and early warning system for complex multi-energy power grids provided by the present invention is as follows: During normal operation, the data acquisition layer has an all-round perception of the operating status of the power grid: the electrical quantity monitoring unit is fastened to the grid bus terminal with bolts, and core electrical parameters such as voltage and current are collected in real time through shielded twisted pair cables. The new energy characteristic monitoring unit is adsorbed on the surface of the photovoltaic inverter cabinet door with a magnetic base, and is connected to the SFP optical module through an armored optical cable to accurately capture the fluctuation characteristics of new energy output. The equipment status monitoring unit is tightly attached to the wall of the transformer oil tank with the help of thermal conductive glue, and uses LoRa wireless transmission technology to send equipment health status data such as temperature and vibration to the edge computing layer gateway. These heterogeneous data enter the edge computing layer after being aggregated via the RS485 bus or wirelessly. The protocol conversion module uniformly processes different protocols such as Modbus / TCP via an M12 aviation connector, and then transmits them via the backplane bus to the pre-processing module containing the FPGA accelerator card for real-time filtering, feature extraction, and other operations. The processing results are encrypted through an IPsec tunnel and temporarily stored in the edge storage module, significantly reducing the size of the original data and improving transmission efficiency. After the pre-processed key data enters the network transmission layer, highly reliable transmission is achieved through a dual-channel redundant architecture. The backbone ring network connects to the TSN switch through a single-mode optical fiber locked with an ST connector, and uses the QoS queue management mechanism of the time-sensitive network (TSN) to ensure deterministic, low-latency transmission of key data. The backup ring network is fixed to the 5G CPE antenna of the communication tower via a flange base, enabling URLLC slicing to establish a highly reliable control channel, and the control flow passes directly to the decision-making layer through the Mini-SAS HD connector. All transmitted data is encrypted in real time using the nationally recognized SM4 algorithm via a secure encryption module embedded in the switch backplane, ensuring data integrity and privacy. Once data is transmitted to the intelligent analysis layer, a dual-engine analysis mode is activated: the mechanism model calculation module uses a GPU accelerator card in a PCIe x16 slot to execute power system physics equations and predict grid stability. The data-driven analysis module, using an AI coprocessor interconnected by NVLink, analyzes historical data and learns about renewable energy fluctuation patterns. The outputs of both modules are fed into a dynamic arbiter in real time. This device continuously monitors renewable energy fluctuations using a voltage comparison circuit. When fluctuations exceed a preset threshold, a MOSFET switch automatically switches the analysis path to the data-driven model. Simultaneously, the partition coupling calculation unit accesses grid topology data via shared memory and provides regional coupling strength analysis to the arbiter via the SPI bus, supporting cross-region collaborative decision-making. Final decision instructions are rapidly executed at the application layer: the strategy generation module's FPGA chip integrates the arbitration results and generates an optimized control strategy via the DDR4 bus. The collaborative control platform receives these instructions via a sliding-rail chassis and sends millisecond-level charge and discharge control signals to the energy storage converter via optical isolation relays, effectively isolating the system from electromagnetic interference. The early warning console displays system status and warning information in real time.To meet cross-regional power support needs, HTTPS-encrypted mutual assistance commands can be transmitted and responded to within 200ms via partitioned fiber channels connected by MPO connectors. During system operation, control feedback is continuously sent back to the SCADA system via the OPC UA protocol, forming a closed-loop management system of monitoring, analysis, control, and optimization. When adding new energy storage partitions, they are plug-and-play connected to the ring network via a pre-installed RJ45 interface. The partition ID is automatically registered via the I²C bus, and external meteorological data is dynamically embedded in the data stream via the USB-C interface via the OPC DA protocol conversion gateway, enabling real-time integrated analysis of environmental factors. The entire system uses technologies such as star topology data aggregation and RDMA network memory direct write to establish an end-to-end low-latency control link, significantly improving the situational awareness accuracy and fault response speed of multi-energy coupled power grids.

[0029] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A monitoring and early warning system for complex multi-energy power grids, characterized by: It includes a data acquisition layer, an edge computing layer, a network transmission layer, an intelligent analysis layer, and a decision application layer. The data acquisition layer is fixed to the power grid equipment terminal and the new energy converter cabinet door by bolts, and its output end is directly connected to the edge computing layer via the RS485 bus. The edge computing layer is installed in the substation control cabinet through a guide rail buckle. The power supply interface is connected to the DC power bus via an IP67 aviation plug, and the data interface is connected to the network transmission layer TSN switch via a Gigabit Ethernet electrical port. The backbone ring network of the network transmission layer is locked to the core switch in series with the single-mode optical fiber via an ST connector, and the backup ring network fixes the 5G CPE antenna to the communication tower via a flange base. The rack-mounted server of the intelligent analysis layer is fixed to the data center cabinet by bolts. The mechanism model calculation module and the data-driven analysis module are connected in parallel via the PCIe bus, and the dynamic arbitrator selects the output path via relay contacts. The decision application layer is connected to the energy storage converter control terminal via an optical isolation relay, and the feedback end is connected to the SCADA system via the OPC UA protocol.

2. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: The data acquisition layer includes an electrical quantity monitoring unit, a new energy characteristic monitoring unit and an equipment status monitoring unit. The electrical quantity monitoring unit is fixed to the grid bus terminal with bolts, and the output end is connected to the RS485 bus via a shielded twisted pair cable. The new energy characteristic monitoring unit is adsorbed on the photovoltaic converter cabinet door through a magnetic base, and the optical fiber interface is connected to the SFP optical module via an armored optical cable. The equipment status monitoring unit is bonded to the wall of the transformer oil tank with thermal conductive glue and sends wireless data to the edge computing layer gateway via the LoRa module.

3. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: The edge computing layer includes a protocol conversion module, a preprocessing module and an edge storage module connected via a backplane bus. The protocol conversion module is connected to the data acquisition layer through an M12 aviation connector (supporting Modbus / TCP) and is connected to the preprocessing module via the backplane bus. The preprocessing module contains an FPGA acceleration card fixed to the mainboard via a PCIe slot, and the output data is encapsulated through an IPsec tunnel. The edge storage module is connected to the mainboard via a SATA interface and a heat sink is attached via thermally conductive silicone.

4. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: The network transmission layer includes a TSN switch, a 5G URLLC slicing module and a security encryption module. The TSN switch is installed in the cabinet through a DIN rail clip. The optical port is connected to the backbone ring network via an LC connector, and the electrical port is connected to the edge computing layer via an RJ45. The 5G URLLC slicing module fixes the CPE antenna through a flange base, and the control flow interface is directly connected to the decision application layer through a Mini-SAS HD connector. The security encryption module is embedded in the switch backplane through a slot, and the built-in national encryption SM4 chip encrypts data in real time.

5. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: The intelligent analysis layer includes a mechanism model calculation module, a data-driven analysis module and a dynamic arbitrator. The GPU accelerator card of the mechanism model calculation module is fixed via a PCIe x16 slot, and the heat sink is directly connected to the CPU via a copper tube. The AI ​​coprocessor of the data-driven analysis module is soldered to the motherboard via a BGA package and interconnected via an NVLink interface. The dynamic arbitrator monitors new energy fluctuations via a voltage comparison circuit and switches to a data-driven path via a MOSFET switch when the threshold is exceeded.

6. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: The decision-making application layer includes a collaborative control platform, an early warning console and a strategy generation module. The collaborative control platform is inserted into the cabinet via a slide rail, and the command output end is connected to the energy storage converter via an optical isolation relay. The early warning console is wall-mounted via a VESA bracket, and the input interface is connected to the server via HDMI. The FPGA chip of the strategy generation module is attached to the cold plate via heat dissipation silicone grease, and data is exchanged through the DDR4 bus.

7. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: The connection path among the data acquisition layer, edge computing layer, network transmission layer, intelligent analysis layer and decision application layer is as follows: the LoRa signal of the data acquisition layer is converged to the edge computing layer gateway via a star topology; the pre-processed data is transmitted to the intelligent analysis layer via the TSN switch QoS queue; the arbitration result is directly written to the shared memory of the decision application layer via the RDMA network, and finally an instruction is generated to control the energy storage converter.

8. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: It also includes a partition coupling degree calculation unit and a cross-zone support channel. The partition coupling degree calculation unit maps the intelligent analysis layer topology database through shared memory, and the output end is connected to the arbitrator through the SPI bus. The cross-zone support channel runs through adjacent partitions through the MPO connector. The power mutual aid instruction is encrypted by the HTTPS protocol and sent down through the optical fiber, and the response delay is ≤200ms.

9. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: The edge computing layer shell is fixed to the guide rail by a spring clip, and can be disassembled without tools by pressing the release lever. The data acquisition layer magnetic base has a built-in Hall sensor, which triggers the positioning LED to flash via LoRa in the event of a fault to indicate the maintenance location.

10. The multi-energy complex power grid monitoring and early warning system according to claim 1 is characterized by: When a new energy storage partition is added, its edge node is connected to the backbone ring network via a pre-installed RJ45 interface, and the partition ID is registered to the intelligent analysis layer via the I²C bus. External meteorological data is converted by the OPC DA protocol gateway, embedded in the data bus through the USB-C socket, and dynamically loaded with drivers.

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