A protocol conversion and communication adaptation system for a light storage and grid-connected device

Abstract

The present application relates to the technical field of power system communication, in particular to a kind of optical storage fills grid-connected device protocol conversion and communication adaptation system. Including: protocol conversion unit, for dynamic analysis photovoltaic inverter, energy storage converter, charging pile and heterogeneous protocol of grid-side equipment;Communication adaptation unit;Energy coordination unit;Grid interface unit;Man-machine interaction unit.The present application updates protocol mapping rule base dynamically through plug-in architecture module and machine learning algorithm, realizes the adaptive analysis of multiple heterogeneous protocols and variant protocols, improves the accuracy and generalization ability of protocol analysis, solves the problem of insufficient adaptability of traditional static rule base to non-standard protocol;The present application adopts distributed edge computing architecture to build bidirectional communication link, improves the real-time performance and security of communication link, improves the defects of high data delay and imperfect security mechanism in traditional communication architecture.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system communication, in particular to a protocol conversion and communication adaptation system for grid-connected photovoltaic storage and charging devices. BACKGROUND

[0002] With the large-scale grid connection of photovoltaic, energy storage and charging facilities, the protocol heterogeneity problem of photovoltaic inverters, energy storage converters, charging piles and other devices in photovoltaic storage and charging systems is increasingly prominent. Different manufacturers' devices use industrial protocols (such as Modbus, IEC60870-5-103, etc.) that differ in syntax, data format and communication rules, resulting in poor data interaction between devices, and an efficient protocol conversion and communication adaptation scheme is needed to realize the coordinated operation of the system. In the current photovoltaic storage and charging grid-connected scene, multi-device protocol analysis relies on static mapping rules, which cannot adapt to the dynamic changes of protocol version iteration and non-standard protocols, and the traditional centralized communication architecture has bottlenecks in data acquisition real-time and instruction interaction reliability, which cannot meet the efficiency requirements of power grid energy scheduling.

[0003] The prior art has the following technical defects in protocol conversion and communication adaptation, specifically: first, the traditional scheme relies on static rule library to parse protocols, and when facing non-standard protocols such as custom check bits and field order variations, the lack of intelligent learning ability leads to insufficient parsing adaptability, making it difficult to cope with protocol iteration updates; second, the data processing efficiency of the communication architecture is limited, and there is no standardized interaction process between edge devices and central control modules, real-time control data is prone to delay or packet loss in transmission, and the security encryption mechanism of the communication link is not perfect, which cannot meet the high security requirements of power grid scheduling; third, there is a lack of joint optimization mechanism for photovoltaic output, energy storage state and charging load in energy coordination and regulation, making it difficult to achieve dynamic balance between grid instruction response and energy utilization efficiency. In view of this, we propose a protocol conversion and communication adaptation system for grid-connected photovoltaic storage and charging devices. SUMMARY

[0004] The present application aims to provide a protocol conversion and communication adaptation system for grid-connected photovoltaic storage and charging devices to solve the problems of lack of intelligent learning ability in traditional schemes, which leads to insufficient parsing adaptability, difficulty in coping with protocol iteration updates, limited data processing efficiency of communication architecture, imperfect security encryption mechanism of communication link, and lack of joint optimization mechanism for photovoltaic output, energy storage state and charging load in energy coordination and regulation, making it difficult to achieve dynamic balance between grid instruction response and energy utilization efficiency.

[0005] To solve the above technical problems, the present application provides a protocol conversion and communication adaptation system for grid-connected photovoltaic storage and charging devices, which comprises:

[0006] A protocol conversion unit is configured to dynamically analyze heterogeneous protocols of photovoltaic inverters, energy storage converters, charging piles and grid-side devices, implement plug-and-play of multiple protocols through a plug-in architecture module, and automatically update a protocol mapping rule library based on a machine learning algorithm;

[0007] A communication adaptation unit is configured to implement real-time interaction of multiple device data by using a distributed edge computing architecture; the communication adaptation unit includes an edge computing node module and a central control module, a bidirectional connection is established between the edge computing node module and the central control module through an industrial-grade communication link, and real-time collection of multiple device data, protocol conversion, instruction interaction and closed-loop control are implemented;

[0008] An energy coordination unit is configured to perform multi-objective optimization on photovoltaic output, energy storage SOC, charging load and grid instructions, integrate an LSTM time series prediction model and a mixed integer programming algorithm, regulate energy storage charging and discharging power through a CAN bus, and dynamically adjust charging power by using a virtual electric meter technology;

[0009] A grid interface unit is configured to implement secure communication with a dispatching master station based on a national encryption SM2 and a national encryption SM4 algorithm, and establish an end-to-end encryption channel through a vertical encryption module;

[0010] A human-computer interaction unit is configured to provide visual interaction and remote control functions, construct a three-dimensional device state model by using a digital twin technology, and implement credible storage of charging data by using a blockchain technology.

[0011] As a further improvement of the technical solution, the plug-in architecture module includes a protocol analysis submodule, a data mapping submodule and a plug-in management submodule, wherein:

[0012] The protocol analysis submodule internally integrates a state machine analysis engine and supports syntax analysis of binary encoding protocols and function code protocols in the field of industrial automation;

[0013] The data mapping submodule constructs a protocol data model mapping library based on a knowledge graph technology, and dynamically maps device parameters (such as voltage, current and SOC) of different protocols to a unified data format;

[0014] The plug-in management submodule implements plug-and-play of industrial protocol plug-ins through a hot plug interface, supports monitoring of a running state of the plug-in and self-recovery (such as automatic restart of an abnormal plug-in) from faults.

[0015] As a further improvement of the technical solution, the protocol conversion unit automatically updates the protocol mapping rule library based on the machine learning algorithm, including the following steps:

[0016] S120.1, protocol message feature extraction:

[0017] Adopting the Transformer-XL network to encode the input protocol message sequence, and processing the long sequence context dependency through the relative position encoding mechanism, the formula is:

[0018] ;

[0019] Among them, , are query weight matrix and key value weight matrix, used for feature mapping; , are learnable position embedding vectors, capturing position dependency; is the offset between the current position and the context position, representing the relative position relationship; is the position and the context position protocol feature mapping correlation degree;

[0020] Through the above mechanism, the protocol feature vector is generated, solving the problem of position information loss of traditional Transformer for long messages (such as Modbus RTU long frame), and enhancing the distinguishability of different protocol features.

[0021] S120.2, dynamic generation of variant protocol rules:

[0022] A deep reinforcement learning framework is constructed, taking the protocol parser as the agent and the device end as the environment for interactive training, and dynamically adjusting the mapping rule generation strategy through the reward function, which is defined as:

[0023] ;

[0024] Among them, is the moment parsing accuracy, measuring the correctness of parsing; is the moment data throughput, measuring the efficiency of parsing; is the moment parsing error times, measuring the stability of parsing; is the weight coefficient and =1, used to balance different optimization objectives; is the reinforcement learning immediate reward of the protocol parser;

[0025] The agent generates adaptive mapping rules for variant protocols (such as Modbus ASCII with custom check bits, IEC60870-5-103 with field order variation) based on the reward function, improving the parsing ability of non-standard protocols.

[0026] S120.3, iterative optimization of global rule library:​

[0027] Edge nodes periodically update their local rule base Synchronized to the central control module, the multi-site rule data is aggregated using a federated learning algorithm. The aggregation formula is as follows:

[0028] ;

[0029] in, The total number of edge nodes represents the network size; For the first The amount of training data for each node reflects the contribution of the node's data. This represents the total amount of global data, used for weighted average calculation. A global protocol rule base for cross-regional aggregation.

[0030] This mechanism enables collaborative optimization of the global rule base while protecting the privacy of the original data on the device (without uploading message content), adapting to heterogeneous protocol environments across multiple sites, and enhancing the generalization capability of the rule base.

[0031] As a further improvement to this technical solution, the edge computing node module is installed at the local device site and connects to the local device through an industrial-grade communication interface, supporting real-time conversion between various heterogeneous industrial protocols and Ethernet protocols; the edge computing node module includes a local protocol processing submodule and an edge communication interface submodule, wherein:

[0032] The local protocol processing submodule is used to store the protocol mapping rules of the local device, enabling real-time acquisition and format standardization of device data (such as converting device-specific protocol data into a system-unified format).

[0033] When receiving raw protocol data from local devices, the hardware-accelerated parsing engine converts it into a system-unified data format.

[0034] When receiving system-unified format control commands from the central control module, the commands are converted into protocol commands executable by the local device through protocol inverse mapping logic.

[0035] The edge communication interface submodule supports bidirectional data interaction with the central control module, including real-time data uploading and control command reception.

[0036] Real-time data upload from the device: Transmits system-uniform format data converted by the local protocol processing submodule;

[0037] Control command reception: Forwards system-uniform format control commands issued by the central control module to the local protocol processing submodule;

[0038] The central control module is installed on the system management platform side, and is connected with multiple edge computing node modules through an industrial communication link, and supports cross-area centralized management of local device areas covered by each edge computing node module; the central control module includes a global rule management submodule and a data aggregation processing submodule, wherein:

[0039] The global rule management submodule is configured to store, update and distribute protocol mapping rules to the edge computing node modules across areas, and support version control (such as marking rule application scenarios and effective conditions) and conflict detection (identifying logical contradictions between rules) of the rules;

[0040] The data aggregation processing submodule is configured to receive device data uploaded by the edge computing node modules across areas, and perform protocol-independent standardized aggregation (such as integrating data according to device types and regional dimensions).

[0041] As a further improvement of the technical solution, the edge computing node module and the central control module perform real-time interaction and control of multiple device data, including the following steps:

[0042] S230.1, the edge computing node module connects multiple types of local devices (energy production / storage / consumption devices) through an industrial communication interface, collects device operation data (such as power, voltage, and working state) at a preset period, and performs local preprocessing on the raw data, including removing outliers, unifying parameter units (such as normalizing voltage units to V), adding device identification and timestamp information, and forming preliminary usable device state data;

[0043] S230.2, the edge computing node module converts the preprocessed device data from the original protocol format (such as device-specific industrial protocol) to a system-unified standardized data format (such as JSON) based on the built-in protocol mapping rule library; and encapsulates the converted data into a communication data frame containing priority, and distinguishes real-time control data (high priority) from regular monitoring data (low priority), providing priority scheduling basis for data transmission;

[0044] S230.3, the edge computing node module uploads the encapsulated standardized data frame to the central control module through the industrial communication link; and the central control module performs global integration processing on the data uploaded by the multiple edge nodes, including storing by region or device type, cross-node time synchronization calibration (ensuring data timestamp consistency), and generating device operation comprehensive indicators (such as total power and average operation efficiency of local device clusters);

[0045] S230.4, the central control module generates device control instructions (such as energy storage system charge and discharge power regulation, charging pile start and stop control) or rule update instructions based on the integrated global data and system control strategy; according to the instruction type and target device characteristics, select unicast or multicast transmission mode (such as unicast for specific device control instructions, and multicast for global rule update), and add transmission priority label for high timeliness instructions to ensure real-time transmission of critical instructions;

[0046] S230.5, after the edge computing node module receives the instruction issued by the central control module, the standardized instruction is converted into a raw protocol format recognizable by the target device based on the local protocol mapping rule library; the converted instruction is sent to the corresponding local device, and the local device execution result (such as instruction receiving status, execution completion status, exception code) is collected in real time to form an instruction execution feedback data frame temporarily stored in the edge node;

[0047] S230.6, the edge computing node module uploads the instruction execution feedback data frame to the central control module; the central control module verifies the instruction execution effectiveness based on the feedback result (such as whether the device state changes as expected according to the instruction), triggers an adaptive retransmission mechanism (such as increasing the number of retransmissions, improving the transmission priority) for the instructions that fail to execute, generates alarm information for abnormal situations that fail multiple times and records it to the system log, forming a closed-loop management of device control.

[0048] As a further improvement of the technical solution, the energy coordination unit comprises a prediction modeling module, a multi-objective optimization module and a coordinated control module, wherein:

[0049] The prediction modeling module integrates a long-short term memory network time series prediction model, and predicts the photovoltaic power generation power, charging load demand and energy storage residual capacity in the future period based on the historical data of photovoltaic output, charging load and energy storage SOC;

[0050] The multi-objective optimization module takes photovoltaic consumption, energy storage life and grid response as optimization objectives, constructs a multi-objective optimization model in combination with energy storage SOC constraints, charge and discharge power constraints and grid interactive power constraints, and solves the model by using a mixed integer programming algorithm to generate a distribution scheme of energy storage charge and discharge power and charging power;

[0051] The coordinated control module issues energy storage charge and discharge control instructions through a CAN bus, dynamically allocates charging pile power by using virtual meter technology, collects the data after control (actual energy storage power and charging pile load), and feeds back to the multi-objective optimization module to realize rolling optimization control.

[0052] As a further improvement of the technical solution, the multi-objective optimization module for multi-objective optimization comprises the following steps:

[0053] S320.1, collect real-time photovoltaic output, energy storage SOC, charging load and grid instruction data of the communication adaptation unit, eliminate abnormal values by 3σ criterion, and repair missing data by linear interpolation;

[0054] S320.2, call the long short-term memory network time series prediction model of the prediction modeling module, learn the preprocessed historical data sequence, and output the photovoltaic power generation power, charging load and remaining capacity prediction value of the future period;

[0055] S320.3, set the energy storage SOC operation interval, charging and discharging power limit value and grid interaction power threshold as the constraint condition, take maximizing photovoltaic consumption, prolonging energy storage life and improving grid response capability as the objective function, and construct a multi-objective optimization model;

[0056] S320.4, solve the multi-objective optimization model by using a mixed integer programming algorithm, generate executable instructions for energy storage charging and discharging power and charging pile power distribution, and mark the instruction priority (such as setting the grid emergency instruction as the highest priority);

[0057] S320.5, based on the real-time regulation data fed back by the collaborative regulation module, if there is a deviation from the optimization instruction, trigger the parameter update of the multi-objective optimization model, and re-execute steps S320.3-S320.4 to realize periodic rolling optimization.

[0058] As a further improvement of the technical solution, the longitudinal encryption module includes an encryption tunnel establishment submodule, a bidirectional identity authentication submodule and a hardware acceleration submodule, wherein:

[0059] The encryption tunnel establishment submodule constructs an end-to-end encryption channel based on IPsec-VPN technology, supports the collaborative application of national encryption SM2 and national encryption SM4 algorithms (SM2 is used for key exchange and SM4 is used for data encryption), and encapsulates network layer data in tunnel mode to encrypt the header and payload of IP data packets simultaneously to ensure the confidentiality of the communication link;

[0060] The bidirectional identity authentication submodule is built-in and based on a device digital certificate signed by the SM2 algorithm, and performs bidirectional identity legality verification with the dispatching master station (the sender's certificate contains a public key, and the receiver verifies the signature by the SM2 algorithm); and supports certificate validity check (such as certificate expiration time and CA agency signature verification) to prevent illegal devices from accessing the communication network;

[0061] The hardware acceleration submodule integrates a special encryption chip to perform hardware acceleration on the SM2 asymmetric encryption and SM4 symmetric encryption algorithms; supports national encryption algorithm pipeline processing, realizes parallel processing of encryption and decryption operations and data transmission, and improves communication efficiency.

[0062] As a further improvement of the technical solution, the human-computer interaction unit comprises a visual interaction module and a data storage module, wherein:

[0063] The visual interaction module adopts digital twin technology to construct a three-dimensional device state model, and maps the operating parameters (such as voltage, current, and SOC) of the photovoltaic device, energy storage device, and charging pile in real time; supports dynamic rendering of device state (such as normal state green and fault state red flashing), and provides an interactive operation interface (such as clicking the device model to retrieve real-time data and issue control instructions);

[0064] The data storage module realizes trusted storage of charging data based on blockchain technology, and realizes trusted storage of charging data based on blockchain technology. The charging order information (time, power, and cost), and the device interaction log are hashed and chained (such as using SHA-256 algorithm to generate data fingerprint); supports automatic execution of smart contract (such as triggering cost settlement contract after charging is completed), and provides a blockchain data query interface.

[0065] As a further improvement of the technical solution, the visual interaction module constructs a three-dimensional device state model, which comprises the following steps:

[0066] S510.1, obtain device real-time operating data from the communication adaptation unit, convert it to a unified format (such as JSON) through a protocol conversion interface, remove abnormal data and supplement time stamp;

[0067] S510.2, establish a three-dimensional geometric model of the device based on CAD drawings or point cloud data, and bind the preprocessed operating data and model parameters through digital twin mapping algorithm (such as mapping the energy storage SOC to the color gradient attribute of the battery model);

[0068] S510.3, generate a three-dimensional visual interface on the browser side using WebGL technology, support PC and mobile access; set up an interactive event response mechanism (such as displaying device detailed parameters on mouseover, and triggering remote control pop-up window by double-clicking).

[0069] Further, the data storage module realizes the charging data storage, which comprises the following steps:

[0070] S520.1, combine the charging start / end time, device ID, transaction amount and other key data, and generate a unique data hash value through SHA-256 algorithm;

[0071] S520.2, connect the blockchain network node through RPC interface, and pack the hash value and transaction information into a data block (including previous block hash, timestamp, and Merkle root);

[0072] S520.3, call the preset smart contract to verify the transaction legality (such as charging pile authority, account balance), and broadcast the block data to the block chain network after verification, complete data on-chain notarization.

[0073] Compared with the prior art, the present application has the following beneficial effects:

[0074] 1. The present application updates protocol mapping rule library dynamically through plug-in architecture module and machine learning algorithm, realizes adaptive analysis of heterogeneous protocols such as Modbus, IEC60870-5-103 and variant protocols, improves protocol analysis accuracy and generalization ability, and solves the problem of insufficient adaptability of traditional static rule library to non-standard protocols.

[0075] 2. The present application adopts a distributed edge computing architecture to construct a bidirectional communication link, optimizes real-time data transmission efficiency through a priority scheduling mechanism and a closed-loop control process, establishes an end-to-end encryption channel by combining the SM2 algorithm and the SM4 algorithm, improves the real-time performance and security of the communication link, and improves the defects of high data delay and imperfect security mechanism in the traditional communication architecture.

[0076] 3. The present application realizes the coordinated balance of photovoltaic consumption, energy storage life and grid response by integrating an LSTM time series prediction model and a mixed integer programming algorithm for multi-objective optimization, dynamically allocating energy storage charging and discharging power and charging load through CAN bus regulation and virtual electric meter technology, and solves the problem of lack of energy joint optimization mechanism in the traditional scheme. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 The system framework diagram of the present application is shown in the figure.

[0078] The meanings of the various labels in the figure are as follows:

[0079] 100, protocol conversion unit; 110, plug-in architecture module; 111, protocol analysis submodule; 112, data mapping submodule; 113, plug-in management submodule;

[0080] 200, communication adaptation unit; 210, edge computing node module; 211, local protocol processing submodule; 212, edge communication interface submodule; 220, central control module; 221, global rule management submodule; 222, data aggregation processing submodule;

[0081] 300, energy coordination unit; 310, prediction modeling module; 320, multi-objective optimization module; 330, coordinated regulation and control module;

[0082] 400, grid interface unit; 410, vertical encryption module; 411, encryption tunnel establishment submodule; 412, bidirectional identity authentication submodule; 413, hardware acceleration submodule;

[0083] 500. Human-computer interaction unit; 510. Visual interaction module; 520. Data storage module. Detailed Implementation

[0084] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0085] like Figure 1 As shown, this embodiment provides a protocol conversion and communication adaptation system for a photovoltaic energy storage and charging grid-connected device, including:

[0086] Protocol conversion unit 100 is used to dynamically parse the heterogeneous protocols of photovoltaic inverters, energy storage converters, charging piles and grid-side equipment. It realizes plug-and-play of multiple protocols through plug-in architecture module 110 and automatically updates the protocol mapping rule base based on machine learning algorithm.

[0087] In this embodiment, the plug-in architecture module 110 includes a protocol parsing submodule 111, a data mapping submodule 112, and a plug-in management submodule 113, wherein:

[0088] The protocol parsing submodule 111 integrates a state machine parsing engine, which supports syntax analysis of binary encoded protocols and function code protocols in the field of industrial automation.

[0089] The data mapping submodule 112 constructs a protocol data model mapping library based on knowledge graph technology, which dynamically maps device parameters (such as voltage, current, and SOC) of different protocols to a unified data format.

[0090] The plug-in management submodule 113 enables plug-and-play functionality for industrial protocol plug-ins through a hot-swappable interface, and supports plug-in operation status monitoring and fault self-healing (such as automatically restarting abnormal plug-ins).

[0091] In this embodiment, the protocol conversion unit 100 automatically updates the protocol mapping rule base based on a machine learning algorithm, including the following steps:

[0092] S120.1, Protocol Message Feature Extraction:

[0093] The Transformer-XL network is used to process the input protocol message sequence. Encoding is performed using a relative positional encoding mechanism to handle contextual dependencies in long sequences. The formula is as follows:

[0094] ;

[0095] in, , For querying the weight matrix and key-value weight matrix, used for feature mapping; , Capture positional dependencies by creating learnable positional embedding vectors; This represents the offset between the current position and the context position, indicating the relative positional relationship; For position With context position Protocol feature mapping correlation;

[0096] The above mechanism generates protocol feature vectors. This solves the problem of lost location information in long messages (such as Modbus RTU long frames) in traditional Transformers and enhances the distinguishability of different protocol features.

[0097] S120.2 Dynamic generation of mutation protocol rules:

[0098] A deep reinforcement learning framework is constructed, with a protocol parser as the agent and the device as the environment for interactive training. The mapping rule generation strategy is dynamically adjusted through a reward function, which is defined as follows:

[0099] ;

[0100] in, for Real-time parsing accuracy is used to measure the correctness of the parsing. for Real-time data throughput is used to measure parsing efficiency. for The number of errors in the parsing process is counted at all times to measure the stability of the parsing. The weighting coefficients and =1, used to balance different optimization objectives; Immediate rewards for reinforcement learning of the protocol parser;

[0101] The agent generates adaptation mapping rules for mutation protocols (such as ModbusASCII with custom check bits and IEC60870-5-103 with field order mutation) based on the reward function, thereby improving the parsing capability of non-standard protocols.

[0102] S120.3, Global rule base iterative optimization:

[0103] Edge nodes periodically update their local rule base Synchronize with the central control module 220, and aggregate multi-site rule data using a federated learning algorithm. The aggregation formula is as follows:

[0104] ;

[0105] wherein, is the total number of edge nodes, representing the network scale; is the training data volume of the th node, reflecting the data contribution of the node; is the total amount of global data, used for weighted average calculation; is the global protocol rule library aggregated across regions.

[0106] Through this mechanism, the collaborative optimization of the global rule library is realized under the premise of protecting the privacy of the original data at the protection device end (without uploading the message content), adapting to the multi-site heterogeneous protocol environment, and improving the generalization ability of the rule library.

[0107] As a further illustration of the present embodiment, the protocol analysis submodule 111 in the present embodiment adopts a hierarchical state machine design. The transmission layer state machine processes Modbus RTU frame check, IEC60870-5-103 frame synchronization, and other underlying protocol specifications. The application layer state machine analyzes the function code logic based on a state transition table. Taking the Modbus ASCII protocol as an example, the state machine covers core states such as start symbol detection, address analysis, function code identification, check processing, and frame end judgment. Bit operation analysis of binary protocols is realized through a hardware accelerator.

[0108] Further, the data mapping submodule 112 constructs a three-layer knowledge graph based on a Neo4j graph database, including a device entity layer (defining device physical properties), a protocol field layer (mapping register addresses to a unified data model), and a mapping rule layer (establishing bidirectional mapping through relational nodes), supporting dynamic updating of mapping rules through SPARQL language.

[0109] Further, the plug-in management submodule 113 adopts an OCP3.0 standard hot plug interface, monitors the power state of the plug-in through an I2C bus, triggers a hardware reset or a standby plug-in switching mechanism when an abnormal plug-in is detected, and ensures the stable operation of the industrial protocol plug-in.

[0110] As a further illustration of the present embodiment, the protocol conversion unit 100 extracts protocol message features through a Transformer-XL network. Its relative position encoding mechanism solves the problem of loss of position information in long messages, enhancing the distinguishability of different protocol features. A deep reinforcement learning framework takes the protocol parser as an agent, dynamically generates adaptive protocol rules through a reward function containing parsing accuracy, throughput, and error count. The edge node and the central control module 220 adopt a federated learning algorithm to aggregate multi-site rule libraries, realizing the collaborative optimization of global rules under the premise of protecting the privacy of the original data.

[0111] It can be understood that the protocol conversion unit 100 provides RS-485, Ethernet and other device access interfaces, is compatible with Modbus, IEC60870-5 and other industrial protocols, and interacts with the master station through a RESTAPI interface. The hardware design meets the GB / T17626 series electromagnetic compatibility standards, is suitable for industrial environments such as substations and charging stations, and the power consumption control and heat dissipation scheme meet the long-time stable operation requirements.

[0112] The communication adaptation unit 200 realizes real-time interaction of multi-device data by using a distributed edge computing architecture; the communication adaptation unit 200 includes an edge computing node module 210 and a central control module 220, and a bidirectional connection is established between the edge computing node module 210 and the central control module 220 through an industrial-grade communication link, realizing real-time collection, protocol conversion, instruction interaction and closed-loop control of multi-device data.

[0113] In the embodiment, the edge computing node module 210 is installed at a local device site, connects the local device through an industrial-grade communication interface, supports real-time conversion of multiple heterogeneous industrial protocols and Ethernet protocols; the edge computing node module 210 includes a local protocol processing submodule 211 and an edge communication interface submodule 212, wherein:

[0114] The local protocol processing submodule 211 is used to store the protocol mapping rules of the local device, and realizes real-time collection and format standardization of device data (such as converting device proprietary protocol data into a system unified format):

[0115] When receiving the original protocol data of the local device, the hardware acceleration parsing engine is used to convert it into a system unified data format;

[0116] When receiving the system unified format control instruction issued by the central control module 220, the protocol inverse mapping logic is used to convert it into a protocol instruction executable by the local device;

[0117] The edge communication interface submodule 212 supports bidirectional data interaction with the central control module 220, including device real-time data upload and control instruction reception:

[0118] Device real-time data upload: transmitting the system unified format data converted by the local protocol processing submodule 211;

[0119] Control instruction reception: forwarding the system unified format control instruction issued by the central control module 220 to the local protocol processing submodule;

[0120] Further, in the light storage and grid-connected system of the embodiment, the local device refers to a single terminal device directly connected by the industrial communication interface of the edge computing node module 210, including charging piles, energy storage converters, smart meters, etc., and its communication protocol covers Modbus, GB / T27930, CANopen, and other industrial standards. The system uses a unified standardized data format to realize cross-module data interaction, which is based on JSON architecture and contains device type identification, timestamp, and parameter set.

[0121] Further, the edge computing node module 210 is installed on site with local devices and connected to charging piles, energy storage converters, etc. through industrial communication interfaces, supporting real-time conversion of heterogeneous industrial protocols such as Modbus and CANopen and Ethernet protocols. The edge computing node module 210 includes a local protocol processing submodule 211 and an edge communication interface submodule 212: the local protocol processing submodule 211 uses FPGA hardware acceleration engine to realize high-speed parsing of protocol packets, disassembles raw protocols such as Modbus frames and CANopen data segments into basic data units, dynamically maps device-specific protocol fields to system standardized data format according to JSON format protocol mapping rule library, and supports remote updating of mapping rules by the central control module; the edge communication interface submodule 212 builds a bidirectional communication link with the central control module 220 based on the MQTT protocol, encapsulates standardized data into MQTT messages according to device type and node ID when uploading, receives central instructions and triggers the reverse conversion process of the local protocol processing submodule 211 when downloading, uses TLS encryption at the link layer to ensure data transmission security, and realizes bidirectional interaction of device real-time data uploading and control instruction receiving.

[0122] The central control module 220 is installed on the system management platform side and connected to multiple edge computing node modules 210 through industrial communication links, supporting cross-regional centralized management of local device areas covered by each edge computing node module 210; the central control module 220 includes a global rule management submodule 221 and a data aggregation processing submodule 222, wherein:

[0123] The global rule management submodule 221 is used to store, update, and distribute protocol mapping rules to cross-regional edge computing node modules 210, and supports version control (such as marking rule applicable scenarios and effective conditions) and conflict detection (identifying logical contradictions between rules) of rules;

[0124] The data aggregation processing submodule 222 is used to receive device data uploaded by cross-regional edge computing node modules 210 and perform protocol-independent standardized aggregation (such as integrating data according to device type and regional dimensions).

[0125] In this embodiment, the edge computing node module 210 interacts and controls the real-time multi-device data with the central control module 220, including the following steps:

[0126] S230.1, the edge computing node module 210 connects the local multi-type devices (energy production / storage / consumption devices) through the industrial communication interface, collects the device operation data (such as power, voltage, working state) according to the preset period, and performs local preprocessing on the original data, including eliminating outliers, unifying parameter units (such as normalizing the voltage unit to V), adding device identification and timestamp information, and forming preliminary usable device state data;

[0127] S230.2, the edge computing node module 210 converts the preprocessed device data from the original protocol format (such as device-specific industrial protocol) to the system-unified standardized data format (such as JSON) based on the built-in protocol mapping rule library; and encapsulates the converted data into communication data frames containing priority, and distinguishes real-time control data (high priority) from regular monitoring data (low priority), providing priority scheduling basis for data transmission;

[0128] S230.3, the edge computing node module 210 uploads the encapsulated standardized data frames to the central control module 220 through the industrial communication link (such as wireless private network, industrial Ethernet); and the central control module 220 processes the data uploaded by the multi-edge node globally, including storing by region or device type, cross-node time synchronization calibration (ensuring data timestamp consistency), and generating device operation comprehensive indicators (such as total power of local device cluster, average operation efficiency);

[0129] S230.4, the central control module 220 generates device control instructions (such as energy storage system charge / discharge power regulation, charging pile start / stop control) or rule update instructions based on the integrated global data and system control strategy; according to the instruction type and target device characteristics, select unicast or multicast transmission mode (such as unicast for specific device control instructions, and multicast for global rule update), and add transmission priority label for high-time-efficiency instructions to ensure real-time transmission of critical instructions;

[0130] S230.5, after receiving the instructions issued by the central control module 220, the edge computing node module 210 converts the standardized instructions into the original protocol format recognizable by the target device based on the local protocol mapping rule library; sends the converted instructions to the corresponding local device, and collects the local device execution results (such as instruction reception status, execution completion status, exception code) in real time, forms instruction execution feedback data frames, and temporarily stores them in the edge node locally;

[0131] S230.6, the edge computing node module 210 uploads the instruction execution feedback data frame to the central control module 220; the central control module 220 verifies the instruction execution effectiveness based on the feedback result (such as whether the device state is changed as expected according to the instruction), triggers an adaptive retransmission mechanism (such as increasing the number of retransmissions, improving the transmission priority) for the instructions with execution failure, generates alarm information for abnormal conditions with multiple failures and records it to the system log, forming a closed-loop management of device control.

[0132] As a further illustration of the present embodiment, the edge computing node module 210 in the present embodiment connects photovoltaic inverters, energy storage converters and other devices through industrial communication interfaces (such as RS-485, CANopen), and collects power, voltage, switching state and other operation data at a second-level cycle. The pre-processing link adopts a sliding window filtering mechanism to eliminate burst interference data, adds a unified timestamp to cross-node data through a clock synchronization protocol (IEEE1588v2) to ensure the time consistency of data from different devices. For non-standard units in the protocol field (such as kV converted to V), the unit standardization processing is automatically completed through a knowledge graph mapping library. At the same time, the hardware of the edge computing node module 210 in the present embodiment complies with the GB / T17626.4 industrial immunity standard, passes the 4-level test of electrical fast transient impulse group, and the power input end is configured with surge protection devices (SPD) to ensure stable operation in a strong electromagnetic interference environment.

[0133] As a further illustration of the present embodiment, the local protocol processing submodule 211 in the present embodiment converts ModbusRTU, IEC60870-5-103 and other protocol data into a unified data model in JSON format based on a state machine analysis engine. The data frame is prioritized according to business type:

[0134] The high priority includes energy storage charging and discharging instructions and charging pile emergency stop signals;

[0135] The medium priority is device state monitoring data, and the low priority is historical data query request;

[0136] The priority scheduling adopts a weighted round robin (WRR) algorithm, which allocates double transmission bandwidth for high-priority data, realizes parallel processing of different priority data through a hardware FIFO queue, and avoids delay of real-time control instructions.

[0137] As a further illustration of the present embodiment, the data aggregation processing submodule 222 of the central control module 220 in the present embodiment receives the data uploaded by the multi-edge nodes, stores it according to the region (such as the substation number) and the device type (photovoltaic / energy storage / charging), and generates comprehensive indicators such as the regional total power and the device operation efficiency through a distributed computing framework. The instruction generation link is based on the model predictive control (MPC) algorithm, combines the grid dispatching instruction (such as the peak-valley period electricity price signal) and the device state prediction data, generates the energy storage power regulation instruction (such as the charging power reduction of 20%) or the charging pile start-stop control instruction, and the instruction format follows the DL / T634.5104 standard. At the same time, the central control module 220 is deployed in the substation communication room, adopts a dual-machine hot standby architecture, and the master and standby servers are in real-time synchronization state through a heartbeat detection mechanism, which ensures the continuous operation of the system.

[0138] It should be noted that the communication adaptation unit 200 in the present embodiment adopts a dual-link redundancy architecture to ensure the reliability of instruction transmission: the wired link relies on the PRP protocol of the fiber ring network to build a main channel, which carries the stable transmission of high-priority instructions; the wireless link takes the 5.8GHz industrial private network as a backup link to deal with wiring limited scenarios;

[0139] Further, the central control module 220 dynamically selects the transmission strategy according to the characteristics of the instruction: for high-time-efficiency instructions such as fault tripping and emergency power regulation, the target edge node is directly addressed through unicast mode to ensure accurate instruction reach; for batch instructions such as global protocol rule updates, multicast mode is used to synchronously push to all edge nodes to improve rule deployment efficiency;

[0140] Further, the edge computing node receives the instruction and starts the protocol conversion and execution process, and the specific process is as follows: the local protocol mapping rule library is called to convert the standardized instruction into a device-specific protocol message (such as the Modbus write register frame format), which is issued to the target device through an industrial-grade communication interface; at the same time, the execution state feedback of the device (including instruction reception confirmation, execution progress code, exception code, etc.) is collected in real time and temporarily stored in the local cache;

[0141] In addition, the central control module 220 verifies the effectiveness of the instruction execution through the following double mechanism:

[0142] First, the device feedback message uploaded by the edge node is parsed, and the Modbus exception code, CANopen state bit and other identifiers are extracted to determine whether the instruction parsing is successful;

[0143] Second, the device sampling data before and after the instruction is issued (such as the power value of the energy storage converter and the start-stop state of the charging pile) are compared to verify whether the physical state meets the expectation;

[0144] If the expected state change is not detected within 100 ms, the system automatically triggers an adaptive retransmission process (the first retransmission interval is 100 ms, and subsequent intervals are exponentially increased); if the cumulative retransmission fails for 3 times, an alarm event is generated and recorded to the system log, and a manual intervention process is triggered synchronously to realize the integrity of the control loop.

[0145] It can be understood that the cooperative design of the edge computing node module 210 and the central control module 220 realizes the following technical advantages:

[0146] Protocol compatibility: compatible with more than ten industrial protocols such as Modbus and CANopen through standardized data formats, and only needs to update the local protocol mapping rules when new devices are connected, without modifying the system core architecture;

[0147] Real-time guarantee: hardware acceleration analysis and edge-side preprocessing mechanism to reduce data transmission volume and central computing pressure, and the instruction closed-loop delay control is within the industrial real-time standard;

[0148] System scalability: the global rule management function of the central control module 220 supports centralized configuration and rule synchronization of cross-regional edge nodes, and is suitable for large-scale deployment of the light storage and charging system.

[0149] The energy coordination unit 300 is used for multi-objective optimization of photovoltaic output, energy storage SOC, charging load and grid instruction, integrates an LSTM time series prediction model and a mixed integer programming algorithm, controls the charging and discharging power of the energy storage through the CAN bus, and dynamically adjusts the charging power by using a virtual electric meter technology;

[0150] In the embodiment, the energy coordination unit 300 includes a prediction modeling module 310, a multi-objective optimization module 320 and a coordination control module 330, wherein:

[0151] The prediction modeling module 310 integrates a long short-term memory network time series prediction model, and predicts the photovoltaic power generation power, charging load demand and energy storage residual capacity in the future period based on the historical data of the photovoltaic output, charging load and energy storage SOC;

[0152] As a further illustration of the embodiment, the prediction modeling module 310 is based on a long short-term memory network (LSTM) time series prediction model, realizes trend deduction of multi-dimensional data, and the specific process is as follows:

[0153] First, the real-time photovoltaic power, energy storage SOC, charging load and meteorological data of the communication adaptation unit 200 are collected, and the abnormal values are removed and the missing data are repaired through a statistical method to form a standardized time series;

[0154] Subsequently, the historical data sequence is learned by using an LSTM model, the model processes long sequence dependencies through a gating mechanism, and can capture the time sequence characteristics and periodicity of energy data; then the photovoltaic power generation power, charging load demand and remaining capacity prediction value of the energy storage in the future period are output, providing data support for optimization decision-making;

[0155] If the prediction accuracy is not up to standard, automatically switch to a backup prediction scheme such as exponential smoothing method to ensure data continuity.

[0156] The multi-objective optimization module 320 takes photovoltaic consumption, energy storage life, and grid response as optimization objectives, combines energy storage SOC constraints, charging and discharging power constraints, and grid interaction power constraints to construct a multi-objective optimization model, and uses a mixed integer programming algorithm to solve it, generating a distribution scheme of energy storage charging and discharging power and charging power;

[0157] The coordinated regulation module 330 issues energy storage charging and discharging control instructions through the CAN bus, dynamically allocates charging pile power using virtual meter technology, collects regulated data (actual energy storage power, charging pile load), and feeds back to the multi-objective optimization module 320 to realize rolling optimization control.

[0158] As a further description of the present embodiment, the coordinated regulation module 330 in the present embodiment transmits charging and discharging control instructions to the energy storage converter through the CAN bus, dynamically allocates charging pile power using virtual meter technology, and converts the optimization decision into a physical regulation executable by the device; at the same time, by collecting actual energy storage power, charging pile load, and the latest SOC data, it is returned to the multi-objective optimization module 320 to form a closed-loop control link of "prediction-optimization-regulation-feedback"; if the instruction execution fails or the deviation exceeds the limit, the adaptive retransmission mechanism or the alarm process is triggered to ensure the continuity of system regulation.

[0159] In the present embodiment, the multi-objective optimization module 320 performs multi-objective optimization including the following steps:

[0160] S320.1, collect real-time photovoltaic output, energy storage SOC, charging load and grid instruction data of the communication adaptation unit 200, remove outliers by 3σ rule, and repair missing data by linear interpolation;

[0161] As a further description of the present step, the present embodiment includes the following steps of removing outliers by 3σ rule and repairing missing data by linear interpolation:

[0162] First, 1-minute level real-time data stream is obtained from the communication adaptation unit 200, including photovoltaic output, energy storage SOC, charging load and grid instruction data;

[0163] Then, the statistical analysis method is used to identify outliers: calculate the statistical characteristics of the data sequence (such as mean, standard deviation), and mark the data points deviating from the normal range (such as a 50% drop in photovoltaic power);

[0164] Next, the median in the window is used to replace the outliers to avoid the interference of the outliers on the subsequent analysis;

[0165] Finally, for consecutive missing data points (such as 3 consecutive sampling points without data), linear interpolation is used to repair the adjacent valid data points to ensure the integrity and continuity of the data.

[0166] S320.2, calling the long short-term memory network time series prediction model of the prediction modeling module 310, learning the preprocessed historical data sequence, and outputting the photovoltaic power generation power, charging load and remaining capacity prediction value of the future period;

[0167] As a further description of this step, the embodiment performs trend extrapolation through the long short-term memory network time series prediction model, and the specific process is as follows: first, the LSTM time series prediction algorithm of the prediction modeling module 310 is called: as a well-known deep learning model, the algorithm captures the long-term dependence relationship in the time series through the gating mechanism (input gate, forget gate, output gate);

[0168] Then, the preprocessed historical data sequence (such as the power and meteorological data of the previous 72 hours) is input into the model, and the model automatically learns the periodic characteristics (such as the daily periodicity of photovoltaic output and the peak-valley characteristics of charging load) in the data;

[0169] Next, the prediction results of the future 4 hours, 15 minutes granularity are output, including the trend prediction of photovoltaic power generation power, charging load demand and remaining capacity of energy storage; at the same time, the algorithm generates a prediction uncertainty quantification index (such as a confidence interval) to evaluate the reliability of the prediction result;

[0170] Finally, the system monitors the prediction accuracy (such as the deviation between the predicted value and the actual value) in real time, and if the accuracy is not up to standard (such as the deviation of the predicted value is greater than 15% for 3 times in a row), it automatically switches to the standby prediction scheme such as the exponential smoothing method, which ensures the robustness of the prediction link.

[0171] S320.3, set the energy storage SOC operating range, charging and discharging power limit, and grid interaction power threshold as constraint conditions, and construct a multi-objective optimization model with the maximum photovoltaic consumption, the longest energy storage life and the highest grid response capability as the objective function;

[0172] As a further description of this step, the specific process of setting the multi-objective constraint condition and constructing the multi-objective optimization model in the embodiment is as follows:

[0173] First, the physical constraint system shown below is constructed:

[0174] Energy storage operation constraints: Set the SOC safety interval (such as 30%-90%) to avoid the impact of overcharging and over-discharging on the service life of energy storage; limit the upper limit of charging and discharging power (such as 80% of the rated power) to match the actual capacity of the energy storage device;

[0175] Grid interaction constraints: Set the grid interaction power threshold according to the capacity of the distribution network (such as ±100kW) to ensure that the system operation does not exceed the carrying capacity of the grid;

[0176] Device state constraints: Combine real-time device state (such as the number of charging piles in use, the cleanliness of photovoltaic panels) to dynamically adjust the constraint boundary (such as tightening the power upper limit of charging piles during charging peak hours);

[0177] Then, define the following multi-objective optimization system:

[0178] Photovoltaic power consumption goal: Maximize local consumption of photovoltaic power, reduce light waste, and improve renewable energy utilization rate;

[0179] Energy storage life goal: Optimize the charging and discharging strategy (such as avoiding deep charging and discharging, balancing SOC fluctuations) to extend the service life of the energy storage device and reduce the total life cycle cost of the system;

[0180] Grid response goal: Quickly track grid instructions (such as peak shaving and frequency modulation signals) to achieve friendly interaction between the system and the grid and improve the stability of the power system;

[0181] Finally, balance the priority of multiple objectives through a weight dynamic allocation mechanism: According to the type of grid dispatching instructions (such as emergency peak shaving and regular regulation) and the current working condition of the system (such as photovoltaic high production period and low SOC state of energy storage), the weight coefficient of each objective is adjusted in real time (such as when the grid emergency instruction is given, the weight of the grid response goal is increased to 0.6 and the weight of the photovoltaic power consumption goal is reduced to 0.3) to ensure that the optimization result meets the current business demand.

[0182] S320.4, use a mixed integer programming algorithm to solve the multi-objective optimization model, generate executable instructions for energy storage charging and discharging power and charging pile power distribution, and mark the priority of the instructions (such as setting the grid emergency instruction as the highest priority);

[0183] As a further description of this embodiment, the specific process of using a mixed integer programming algorithm to solve the multi-objective optimization model and generate executable instructions in this embodiment is as follows:

[0184] First, the mixed integer programming algorithm is used to solve the complex optimization problem of mixed discrete variables (such as charging pile start-stop state) and continuous variables (such as energy storage power);

[0185] Then, set the solution timeout threshold (such as 30 seconds), and give priority to the real-time performance of the instruction generation (if the solution is not completed within the timeout, the optimization result of the previous period is used as the temporary scheme);

[0186] Next, the mixed integer programming algorithm outputs the energy storage charge and discharge power instruction (precision 1 kW) and the charging pile power allocation scheme (power upper limit is dynamically divided according to charging pile groups) based on the constraint conditions and the objective function; at the same time, the system marks the priority according to the instruction type: the grid emergency instruction (such as the frequency support signal) is set to the highest priority, and the instruction issuance is required to be completed within 1 second; the energy storage life protection instruction (such as adjustment when the SOC approaches the safety boundary) is set to the intermediate priority; the conventional power allocation instruction (such as photovoltaic excess electricity consumption) is set to the low priority;

[0187] Finally, the optimization result is converted into a communication format recognizable by the equipment: the energy storage instruction is packaged into a CAN bus message (in line with the energy storage converter communication protocol), and the charging pile instruction is converted into an OCPP protocol control frame, so that the instruction can be correctly parsed and executed by the hardware equipment.

[0188] S320.5, based on the real-time regulation data fed back by the collaborative regulation module 330, if there is a deviation from the optimization instruction, trigger the parameter update of the multi-objective optimization model, and re-execute the steps S320.3-S320.4 to realize periodic rolling optimization.

[0189] As a further description of the embodiment, the process of realizing periodic rolling optimization in the embodiment is as follows:

[0190] First, receive the real-time running data of the equipment fed back by the collaborative regulation module 330 (such as the actual power of the energy storage, the actual load of the charging pile, and the latest SOC), and the sampling frequency is 1 second;

[0191] Then, evaluate the instruction execution effect by the deviation analysis algorithm: calculate the absolute deviation (such as the difference between the actual power of the energy storage and the instruction power) and the relative deviation (such as the deviation percentage) of the actual value and the instruction value; if the deviation exceeds the preset threshold (such as 15%) or the duration exceeds 3 sampling periods (i.e. 3 seconds), it is determined as an execution exception;

[0192] Next, trigger the model parameter update mechanism: re-collect the latest data (including abnormal feedback), adjust the constraint conditions (such as tightening the energy storage power limit) or the target weight (such as increasing the grid response target weight) in the optimization model, and re-execute the process of "constraint construction-optimization solution-instruction generation"; at the same time, the system records the abnormal events (such as the type of abnormality, the time of occurrence, and the adjustment strategy) to form the running log, which supports subsequent analysis and optimization;

[0193] Finally, set the rolling optimization cycle (such as 5 minutes): whether there is deviation, the system automatically repeats the optimization process every 5 minutes, combined with the latest prediction data and device status, dynamically adjust the control strategy, to ensure that the system is always running in the optimal state.

[0194] It should be noted that the multi-objective optimization module 320 in the embodiment is deployed on an industrial-grade edge server, relying on a multi-core processor and a real-time operating system to meet the computing power needs of complex algorithms, and through an Ethernet interface, device data of the communication adaptation unit 200 is acquired in real time, and a control instruction is issued to the energy storage converter through a CAN bus expansion card, thereby constructing a low-latency interactive channel of "data input-instruction output";

[0195] At the same time, for the running fault, the following hierarchical fault-tolerant mechanism is designed:

[0196] When the communication is interrupted, the local cache data (retaining the last 10 minutes of valid sequence) is called to maintain the optimization calculation, and the abnormality is automatically switched to a regularized strategy such as "fixed proportional allocation of photovoltaic power-charging load", and in extreme working conditions (such as the SOC of the energy storage approaching the safety boundary), a safety mode (limiting the charging and discharging power of the energy storage to 30% of the rated value, and forcing the charging pile to standby) is triggered, to ensure stable operation of the system;

[0197] In addition, the embodiment can also use the following "offline simulation + online testing" dual-track verification:

[0198] In the offline stage, based on historical data (such as annual photovoltaic output and load curve), scenes such as continuous rain and grid rapid peak shaving are simulated to verify the strategy effect of improving photovoltaic consumption rate and prolonging energy storage life;

[0199] In the online stage, real-time monitoring of grid instruction tracking delay (target ≤ 2 seconds), abnormal response time (target ≤ 1 minute) and other indicators is performed, and the algorithm running state is intuitively presented through system data dashboard, and the model parameters and constraint conditions are continuously iterated and optimized to ensure the effectiveness of the strategy in actual working conditions.

[0200] The grid interface unit 400 realizes secure communication with the dispatching master station based on the national SM2 and national SM4 algorithms, and establishes an end-to-end encryption channel through the longitudinal encryption module 410;

[0201] In the embodiment, the longitudinal encryption module 410 includes an encryption tunnel establishment submodule 411, a bidirectional identity authentication submodule 412, and a hardware acceleration submodule 413, wherein:

[0202] The encryption tunnel establishment submodule 411 establishes an end-to-end encryption channel based on an IPsec-VPN technology, supports the collaborative application of a national secret SM2 and a national secret SM4 algorithm (SM2 is used for key exchange, and SM4 is used for data encryption), adopts a tunnel mode to encapsulate network layer data, encrypts the header and the payload of an IP data packet at the same time, and ensures the confidentiality of a communication link;

[0203] As a further description of the embodiment, the embodiment realizes key exchange by using an IKEv2 protocol + a national secret SM2 algorithm:

[0204] A first stage (security association negotiation): devices exchange SM2 public key certificates with a dispatch master station, generate a session key through an elliptic curve Diffie-Hellman algorithm, negotiate encryption parameters (SM4-CBC mode, SM3 authentication algorithm), and complete a security policy handshake within 10 seconds;

[0205] A second stage (IPsec tunnel establishment): based on the session key, an ESP tunnel mode is used to encapsulate an IP data packet, the header and the payload of the data packet are encrypted at the same time (to prevent traffic analysis), and dual-link redundancy of a main optical fiber and a backup 4G private network is supported;

[0206] In addition, the data encapsulation mechanism described above includes:

[0207] For power grid interaction data (such as power regulation instructions and power quality monitoring data), an SM3 authentication header (integrity check) and an SM4 encrypted payload (confidentiality protection) are added through an ESP protocol, to realize “one-time transmission and double protection”.

[0208] The bidirectional identity authentication submodule 412 internally stores a device digital certificate based on an SM2 algorithm, performs bidirectional identity legality verification with a dispatch master station (a sender certificate contains a public key, and a receiver verifies a signature through an SM2 algorithm), and supports certificate validity checking (such as certificate expiration time and CA agency signature verification), to prevent illegal devices from accessing a communication network;

[0209] As a further description of the embodiment, the bidirectional identity authentication submodule 412 of the embodiment takes a national secret digital certificate as a core, and constructs a bidirectional trust mechanism of devices and a dispatch master station as follows:

[0210] A certificate secure carrier: a national secret certificate (containing device ID, SM2 public key, and valid period) issued by a power industry CA is internally stored in a hardware encryption module (HSM). The certificate and a matching private key are stored in the HSM. The private key only participates in signature operation inside the HSM hardware, to ensure that the private key is not leaked;

[0211] Authentication process: the dispatching master station generates a 64-bit random challenge value and sends it, the device calls the SM2 private key of the HSM, signs the "challenge value + timestamp (accurate to the moment) + monotonic sequence number", and returns the signature result together with the certificate;

[0212] The master station first verifies the validity of the certificate (checks the CA signature, validity period, and certificate revocation list CRL), and then verifies the signature through the device public key; after completion, it initiates a challenge in reverse, and the device performs the same verification logic;

[0213] If the authentication fails for three consecutive times, the device triggers local locking (which requires manual operation to unlock) to block illegal access attempts;

[0214] Certificate full-cycle management: 30 days before the certificate expires, the module automatically initiates a re-signing application to the CA; if the re-signing fails, the pre-configured backup certificate is used to maintain communication, which ensures the continuity of authentication.

[0215] The hardware acceleration submodule 413 integrates a special encryption chip to accelerate SM2 asymmetric encryption and SM4 symmetric encryption algorithms; supports pipeline processing of national encryption algorithms, realizes parallel processing of encryption and decryption operations and data transmission, and improves communication efficiency.

[0216] As a further description of the present embodiment, the hardware acceleration submodule 413 in the present embodiment is based on a national encryption special chip and realizes efficient operation of national encryption algorithms in the vertical encryption process as follows:

[0217] When the encryption tunnel establishment submodule 411 initiates IKE protocol negotiation, the built-in coprocessor in the chip independently handles SM2 asymmetric operations (including key exchange and signature verification), releases the main CPU resources, and ensures that the security policy handshake is completed within 10 seconds; after entering the business data interaction stage, the chip performs SM4 symmetric encryption / decryption operations in parallel with network data transmission through DMA direct memory access technology - in the ESP tunnel encapsulation process, data encryption and protocol encapsulation are completed synchronously, reducing transmission delay.

[0218] At the same time, the built-in temperature sensor and watchdog circuit in the chip: when the operating temperature exceeds the limit (such as 85°C) or the operation logic is abnormal, it automatically triggers a hardware reset to restore normal operation; and supports gigabit Ethernet full-bandwidth encryption, meeting the real-time transmission requirements of power "four remote" data (telemetry, remote signaling, remote control, and remote adjustment).

[0219] In addition, the hardware acceleration submodule 413 and the encryption tunnel establishment submodule 411 cooperate deeply: the SM2 key is derived synchronously during the IKE negotiation stage to support fast policy handshake; SM4 encryption and ESP tunnel encapsulation are processed in parallel during the data transmission stage to improve the overall communication efficiency of the vertical encryption module 410.

[0220] The human-computer interaction unit 500 is configured to provide visual interaction and remote control functions, construct a three-dimensional device state model by using a digital twin technology, and realize credible storage of charging data by using a blockchain technology.

[0221] In this embodiment, the human-computer interaction unit 500 includes a visual interaction module 510 and a data storage module 520.

[0222] The visual interaction module 510 is configured to construct a three-dimensional device state model by using a digital twin technology, to map running parameters (such as voltage, current, and SOC) of a photovoltaic device, an energy storage device, and a charging pile in real time, to support dynamic rendering of a device state (such as green in a normal state and red flickering in a fault state), and to provide an interactive operation interface (such as clicking a device model to call real-time data and issue a control instruction).

[0223] As a further description of this embodiment, the visual interaction module 510 in this embodiment subscribes to real-time data of the communication adaptation unit 200 by using an MQTT protocol, filters out abnormal values after being parsed into a JSON format by using a sliding window, and supplements a millisecond-level timestamp based on an NTP clock. Subsequently, CAD drawings or point cloud data of a device are imported to construct a geometric model (such as a photovoltaic panel and an energy storage battery), and a mapping relationship between running parameters and model attributes is established: an energy storage SOC is mapped to an electric quantity visualization of a battery model (0%→gray, 100%→green, supporting a gradual change); a photovoltaic power drives a panel model brightness to be dynamically adjusted; and a device fault code triggers a model state warning (red flickering represents a fault, and yellow constant represents a warning). A Three.js WebGL framework is used to develop a three-dimensional interface on a browser side, supporting PC and mobile terminal adaptive access, and interactive design includes: a parameter floating window (displaying voltage, current, etc.) is popped up by hovering a mouse; a single-click model expands a near-24-hour data curve; and a double-click model calls a remote control pop-up window (connecting an energy coordination unit 300 API, and an instruction is forwarded to a device by the communication adaptation unit).

[0224] The data storage module 520 realizes credible storage of charging data based on a blockchain technology, hashes values of charging order information (time, power, and cost) and device interaction logs (such as generating a data fingerprint by using a SHA-256 algorithm), supports automatic execution of a smart contract (such as triggering a cost settlement contract after charging is completed), and provides a blockchain data query interface.

[0225] In this embodiment, the visual interaction module 510 constructs a three-dimensional device state model, including the following steps.

[0226] S510.1, real-time running data of a device is obtained from the communication adaptation unit 200, is parsed into a unified format (such as a JSON) by using a protocol conversion interface, and abnormal data is filtered out and a timestamp is supplemented.

[0227] S510.2, based on CAD drawings or point cloud data to establish equipment three-dimensional geometric model, through digital twin mapping algorithm to bind the pretreated operation data and model parameters (such as mapping the color gradient attribute of energy storage SOC to the battery model);

[0228] S510.3, generate a three-dimensional visualization interface on the browser side using WebGL technology, support PC and mobile access; Set up interactive event response mechanism (such as mouse hover to display detailed parameters of the device, double-click to trigger remote control pop-up window).

[0229] Further, the data storage module 520 implements the charging data storage, including the following steps:

[0230] S520.1, combine the key data such as charging start and end time, device ID, transaction amount, and generate a unique data hash value through SHA-256 algorithm;

[0231] S520.2, connect the blockchain network node through the RPC interface, package the hash value and transaction information into a data block (including the previous block hash, timestamp, Merkle root);

[0232] S520.3, call the preset smart contract to verify the transaction legality (such as charging pile permission, account balance), and broadcast the block data to the blockchain network after verification to complete the data on-chain storage.

[0233] As a further description of the embodiment, the data storage module 520 in the embodiment extracts the charging order key data (start and end time, device ID, power, cost), concatenates it in the format of "device ID + timestamp + parameter", and generates a unique hash value through SHA-256 algorithm. Connect the alliance chain node (such as Hyperledger Fabric) through HTTP-RPC interface, package the hash value and transaction information into a block (block header includes previous block hash, storage timestamp, Merkle root; block body encapsulates charging data and device interaction log). Call the preset smart contract to automatically verify the transaction legality (such as charging pile identity certificate, user account balance), and after verification, confirm it through PBFT consensus algorithm, broadcast it to the alliance chain node to complete the on-chain storage. After on-chain storage, return the storage hash and block height, support data traceability (trace the whole life cycle by hash, tamper detection) and intelligent settlement (trigger the cost settlement contract at the end of charging, and link the third party payment platform to complete the electricity fee transfer).

[0234] In summary, the multiple units in the embodiment realize data interaction and collaboration through a layered architecture, specifically including:

[0235] The communication adapter unit 200, acting as the underlying hub, collects real-time operational data (such as voltage, current, and SOC) from photovoltaic, energy storage, and charging piles via industrial protocols such as Modbus and CAN. After protocol conversion and anomaly handling, the data is synchronously transmitted to the energy coordination unit 300 and the human-machine interaction unit 500 via a fiber optic ring network / wireless private network. Based on the aforementioned data and grid dispatch instructions (such as peak-shaving signals) forwarded by the grid interface unit 400, the energy coordination unit 300 generates energy storage charging and discharging and charging pile power allocation schemes through LSTM prediction and mixed integer programming. These schemes are then distributed to physical devices via the communication adapter unit 200, while the control results are fed back to the grid interface unit 400 (for master station interaction) and the human-machine interaction unit 500 (for status display). The grid interface unit 400 interacts bidirectionally with the dispatch master station through a national cryptographic encryption channel, uploading system operational data (such as photovoltaic absorption capacity and grid interaction power) and receiving instructions. This forms a closed-loop data link of "equipment data acquisition - central optimization decision-making - grid security interconnection - human-machine visualization control," enabling real-time linkage and information sharing among multiple units.

[0236] Those skilled in the art will understand that the process of implementing all or part of the steps of the above embodiments can be carried out by hardware or by a program instructing the relevant hardware.

[0237] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A protocol conversion and communication adaptation system for a light storage and grid-connected device, characterized in that, The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy storage. The application relates to a photovoltaic inverter, an energy storage converter, a charging pile and a power grid side device, and belongs to the field of photovoltaic power generation and energy S230.1, The edge computing node module (210) connects local multi-type devices through an industrial communication interface, collects device operation data at a preset period, and performs local preprocessing on the raw data; S230.2, The edge computing node module (210) converts the preprocessed device data from the original protocol format into a standardized data format unified by the system based on the built-in protocol mapping rule library, encapsulates the converted data into a communication data frame containing a priority, and distinguishes real-time control data from regular monitoring data to provide a priority scheduling basis for data transmission; S230.3, The edge computing node module (210) uploads the encapsulated standardized data frame to the central control module (220) through an industrial communication link, and performs global integration processing on the data uploaded by the multi-edge node through the central control module (220); S230.4, The central control module (220) generates device control instructions or rule update instructions based on the integrated global data and system control strategies, selects unicast or multicast transmission methods according to the instruction type and target device characteristics, and adds transmission priority labels to high-time-efficiency instructions to ensure real-time transmission of critical instructions; S230.5, After receiving the instructions issued by the central control module (220), the edge computing node module (210) converts the standardized instructions into a format recognizable by the target device based on the local protocol mapping rule library, sends the converted instructions to the corresponding local device, and collects the local device execution results in real time to form instruction execution feedback data frames temporarily stored in the edge node locally; S230.6, The edge computing node module (210) uploads the instruction execution feedback data frame to the central control module (220); the central control module (220) verifies the effectiveness of the instruction execution based on the feedback results, triggers an adaptive retransmission mechanism for instructions that fail to execute, generates alarm information for abnormal situations that fail multiple times, and records them to the system log to form a closed-loop management of device control; The energy coordination unit (300) is used for multi-objective optimization of photovoltaic output, energy storage SOC, charging load and grid instructions, integrates an LSTM time series prediction model and a mixed integer programming algorithm, adjusts the charging and discharging power of the energy storage through a CAN bus, and dynamically adjusts the charging power by using a virtual electric meter technology; The grid interface unit (400) realizes secure communication with the dispatching master station based on the national SM2 and SM4 algorithms, and establishes an end-to-end encryption channel through a vertical encryption module (410); The human-computer interaction unit (500) is used for providing visual interaction and remote control functions, adopts digital twin technology to build a three-dimensional device state model, and realizes credible storage of charging data by using a blockchain technology. 2.The system according to claim 1, wherein, The plug-in architecture module (110) includes a protocol analysis submodule (111), a data mapping submodule (112), and a plug-in management submodule (113), wherein: The protocol analysis submodule (111) internally integrates a state machine analysis engine, supports syntax analysis of binary encoding protocols and function code protocols in the field of industrial automation, The data mapping submodule (112) constructs a protocol data model mapping library based on knowledge graph technology, and dynamically maps device parameters of different protocols to a unified data format; The plug-in management submodule (113) realizes plug-and-play of industrial protocol plug-ins through a hot plug interface, supports plug-in running state monitoring and fault self-healing. 3.The system according to claim 1, wherein, The protocol conversion unit (100) automatically updates the protocol mapping rule library based on a machine learning algorithm, including the following steps: S120.1, protocol message feature extraction: Adopt the Transformer-XL network to the input protocol message sequence , encode, through the relative position coding mechanism handles long sequence context dependence relationship, formula is: ； wherein, , is a query weight matrix, a key-value weight matrix, used for feature mapping; , is a learnable position embedding vector, capturing position dependency; is an offset between the current position and the context position, representing the relative position relationship; is a position protocol feature mapping association degree of the context position ; S120.2, dynamic generation of variant protocol rules: A deep reinforcement learning framework is constructed, with the protocol parser as the agent and the device end as the environment for interactive training. The mapping rule generation strategy is dynamically adjusted through a reward function, which is defined as: ； wherein, is the accuracy of the analysis at the moment, measuring the correctness of the analysis; is the data throughput at the moment, measuring the efficiency of the analysis; is the number of analysis errors at the moment, measuring the stability of the analysis; is a weight coefficient and =1, for balancing different optimization objectives; is the reinforcement learning instant reward of the protocol analyzer; S120.3, global rule library iterative optimization: The edge nodes periodically synchronize the local rule base to the central control module (220), aggregate the multi-site rule data using a federated learning algorithm, with the aggregation formula being: ； Wherein, is the total number of edge nodes, representing the network size; is the training data volume of the th node, reflecting the data contribution of the node; is the total amount of global data, used for weighted average calculation; is the global protocol rule library aggregated across regions.

4. The system according to claim 1, wherein, The energy coordination unit (300) includes a prediction modeling module (310), a multi-objective optimization module (320), and a coordinated control module (330), wherein: The prediction modeling module (310) integrates a long-short term memory network time series prediction model, and predicts the photovoltaic power generation power, charging load demand, and energy storage remaining capacity in the future period based on the historical data of photovoltaic output, charging load, and energy storage SOC; The multi-objective optimization module (320) takes photovoltaic consumption, energy storage life, and grid response as optimization objectives, constructs a multi-objective optimization model combining energy storage SOC constraints, charging and discharging power constraints, and grid interaction power constraints, and solves the model using a mixed integer programming algorithm to generate a distribution scheme of energy storage charging and discharging power and charging power; The coordinated control module (330) issues energy storage charging and discharging control instructions through a CAN bus, dynamically allocates charging pile power using virtual meter technology, and collects the data after control to feed back to the multi-objective optimization module (320) to realize rolling optimization control. 5.The system of claim 4, wherein, The multi-objective optimization module (320) performs multi-objective optimization including the following steps: S320.1, collect real-time photovoltaic output, energy storage SOC, charging load, and grid instruction data of the communication adaptation unit (200), remove outliers using the 3σ rule, and repair missing data using linear interpolation; S320.2, call the long-short term memory network time series prediction model of the prediction modeling module (310) to learn the preprocessed historical data sequence and output the predicted values of photovoltaic power generation power, charging load, and energy storage remaining capacity in the future period; S320.3, set the energy storage SOC operating range, charging and discharging power limit, and grid interaction power threshold as constraint conditions, and construct a multi-objective optimization model with the maximum photovoltaic consumption, extended energy storage life, and improved grid response capability as objective functions; S320.4, solve the multi-objective optimization model using a mixed integer programming algorithm to generate executable instructions for energy storage charging and discharging power and charging pile power distribution, and mark the instruction priority; S320.5, based on the real-time control data fed back by the coordinated control module (330), if there is a deviation from the optimized instructions, trigger parameter update of the multi-objective optimization model, and re-execute steps S320.3-S320.4 to realize periodic rolling optimization. 6.The system of claim 1, wherein, The longitudinal encryption module (410) comprises an encryption tunnel establishment submodule (411), a bidirectional identity authentication submodule (412) and a hardware acceleration submodule (413), wherein: The encryption tunnel establishment submodule (411) constructs an end-to-end encryption channel based on the IPsec-VPN technology, supports the collaborative application of the national secret SM2 and national secret SM4 algorithms, adopts the tunnel mode to encapsulate the network layer data, encrypts the header and the payload of the IP data packet at the same time, and ensures the confidentiality of the communication link; The bidirectional identity authentication submodule (412) is built-in with a device digital certificate issued based on the SM2 algorithm, performs bidirectional identity legality verification with the dispatching master station, supports certificate validity check, and prevents illegal devices from accessing the communication network; The hardware acceleration submodule (413) integrates a special encryption chip, performs hardware acceleration on the SM2 asymmetric encryption and SM4 symmetric encryption algorithms, supports national secret algorithm pipeline processing, realizes parallel processing of encryption and decryption operations and data transmission, and realizes the improvement of communication efficiency.

7. The system according to claim 1, wherein the system further comprises a protocol conversion and communication adaptation system for the optical storage and charging grid-connected device, characterized in that, The man-machine interaction unit (500) comprises a visual interaction module (510) and a data storage module (520), wherein: The visual interaction module (510) constructs a three-dimensional device state model by using the digital twin technology, maps the operating parameters of the photovoltaic device, the energy storage device and the charging pile in real time, supports dynamic rendering of the device state, and provides an interactive operation interface; The data storage module (520) realizes trusted storage of charging data based on the blockchain technology, chains the hash values of the charging order information and the device interaction log, supports automatic execution of the smart contract, and provides a blockchain data query interface. 8.The system of claim 7, wherein, The visual interaction module (510) constructs a three-dimensional device state model, comprising the following steps: S510.1, obtain the real-time operating data of the device from the communication adaptation unit (200), parse the data into a unified format through a protocol conversion interface, eliminate abnormal data and supplement time stamps; S510.2, establish a three-dimensional geometric model of the device based on CAD drawings or point cloud data, and bind the preprocessed operating data and model parameters through a digital twin mapping algorithm; S510.3, generate a three-dimensional visual interface on the browser side by using the WebGL technology, support PC and mobile terminal access, and set an interactive event response mechanism.

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