Internet of Things-Based Energy Storage Device Management Method and System

Abstract

The present invention discloses a management method and system for energy storage devices based on the Internet of Things, which relates to the technical field of energy storage management. An initial topological graph mathematical model for processing concurrent block energy storage devices is constructed based on the Internet of Things and the blockchain network; a deep reinforcement learning strategy is generated for the optimized topological graph mathematical model. The present invention efficiently optimizes energy distribution by means of a directed acyclic graph, greatly reduces the operation steps without reducing the management decision effect, and deeply optimizes the decisions and states of the energy storage devices being managed. At the same time, it provides strong support for scenarios such as device status monitoring, scheduling decisions, and energy trading. The topological graph effectively reduces the complexity of concurrent block processing in the blockchain system, reduces redundant reference relationships, and thus simplifies the sorting logic. It is applicable to the shared energy storage power station scenario that needs to quickly process high-frequency energy storage services, effectively represents the interaction information between energy storage devices and the power grid using a multi-dimensional state vector, and performs precise control by combining discrete and continuous action instructions.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage management, and particularly to a method and system for managing energy storage devices based on the Internet of Things. Background Art

[0002] With the wide application of renewable energy sources (such as solar energy and wind energy), the role of energy storage technology in the power system has become increasingly important. Energy storage devices (such as battery energy storage systems, supercapacitors, etc.) can store excess electrical energy and release it during peak demand periods, helping to balance the grid load, improve the utilization rate of electrical energy, reduce electricity costs, and reduce carbon emissions. However, the management and monitoring of energy storage devices face complex challenges, including device status monitoring, battery life prediction, fault diagnosis, system optimization, and other issues.

[0003] Currently, the Chinese invention patent with the application number CN202410726747.9 discloses a method and system for managing energy storage electrical control devices based on digital twins, which relates to the technical field of energy storage electrical control. By constructing a deep integration of the digital twin model and real-time monitoring data, it can actively identify the abnormal state of electrical control devices before a fault occurs, effectively prevent accidents, and significantly enhance the safety management level of energy storage power station AC electrical control devices. However, the existing technology cannot achieve efficient optimization of energy distribution, and cannot greatly reduce the operation steps while deeply optimizing the decision-making and status of managing energy storage devices without reducing the management decision-making effect. Summary of the Invention

[0004] The technical problem solved by the present invention is that the existing technology cannot achieve efficient optimization of energy distribution, and cannot greatly reduce the operation steps while deeply optimizing the decision-making and status of managing energy storage devices without reducing the management decision-making effect.

[0005] To solve the above technical problem, the present invention provides the following technical solution: A method for managing energy storage devices based on the Internet of Things, comprising the following steps:

[0006] Step S1: Real-time collect the operating status and environmental data of the energy storage device through Internet of Things sensors, convert the collected device status and scheduling instructions into blockchain transactions, and broadcast them through the blockchain network;

[0007] Step S2: Build an initial topology graph mathematical model for processing concurrent block energy storage devices based on the Internet of Things and the blockchain network;

[0008] Step S3: Generate a deep reinforcement learning strategy for optimizing the topology graph mathematical model.

[0009] Preferably, the step S1 includes:

[0010] Real-time collect key data of energy storage devices and the surrounding environment through the Internet of Things gateway. The key data includes device status data, environmental data, and energy price data. The device status data includes the current battery level, charge and discharge status, device power, and charge and discharge efficiency. The environmental data includes temperature, humidity, air pressure, and weather conditions. The energy price data includes the electricity price in the energy market collected in real time;

[0011] Clean the key data through the edge computing node, remove noise and abnormal data, and perform formatting processing. Generate a charge and discharge scheduling instruction by combining the energy price, device power demand, and physical limitations of the energy storage device. The scheduling instruction includes start-stop control and power regulation;

[0012] Package the key data and the scheduling instruction into a queue data structure. The queue data structure includes fields such as timestamp, device ID, device signature, data content, and scheduling instruction;

[0013] Convert the queue data structure into a blockchain transaction, and broadcast the generated blockchain transaction to the slave chain nodes of the blockchain network through the Internet of Things gateway. Each slave chain node merges and packages the received transaction information into a micro-block;

[0014] Verify the blockchain transaction through the lightweight consensus algorithm PBFT variant.

[0015] Preferably, the step S2 includes:

[0016] Generate a number of concurrent blocks based on the blockchain network and the micro-block. The concurrent blocks include the timestamp and the key data of the energy storage device. When a blockchain transaction appears in the concurrent block, use the timestamp to mark the transaction time, and also allocate a space-time label in the micro-block of the current transaction. The space-time label includes the timestamp of the blockchain transaction, the geographical location of the energy storage device, and the device ID. Each slave chain node will sign the generated micro-block to generate multiple signatures;

[0017] After the transaction and packaging are completed, the micro-block is submitted to the slave chain node to complete data storage. The main chain node collects the micro-blocks from different slave chain nodes according to a preset period for data aggregation, and generates signatures for each micro-block. Construct a directed acyclic graph based on the standard value weights of the space-time labels calculated by standardization;

[0018] The blockchain main chain updates the global view according to a preset time period, triggering the next round of scheduling strategy optimization and block generation.

[0019] Preferably, the coordination mechanism of the master-slave blockchain structure includes:

[0020] When the number of legal signatures in the sub-chain reaches the threshold, the parent node of the initial topological graph mathematical model aggregates all the legal signatures corresponding to the sub-chain and broadcasts the aggregated legal signatures to the main chain;

[0021] The mathematical expression of the threshold is:

[0022] ;

[0023] where, is the threshold, is the number of faulty nodes;

[0024] After receiving the micro-blocks from each sub-chain, the main-chain node confirms the legality of the micro-blocks through the threshold signature mechanism and packs the confirmed legal and valid micro-blocks into main-chain blocks.

[0025] Preferably, based on the device ID, timestamp, and energy parameter hash value of each concurrent block, an initial topological graph mathematical model for the processing of the concurrent block energy storage device is constructed. The construction logic includes:

[0026] The standard value weight for normalizing the spatio-temporal label includes dynamically weighted summation of the spatio-temporal label;

[0027] Calculate the number of other nodes pointed to by each node in the directed acyclic graph as the out-degree. After calculating the out-degree of the directed acyclic graph, perform a redundant edge pruning operation on the directed acyclic graph, delete the redundant parts in the same connection, and dynamically select the node with the smallest out-degree as the priority processing node through the greedy algorithm for the directed acyclic graph after the redundant edge pruning operation to obtain the initial topological graph mathematical model;

[0028] Initializing the initial topological graph mathematical model includes: optimizing the computational complexity of micro-block sorting by minimizing the average out-degree of the topological nodes of the directed acyclic graph, and the main chain regularly aggregates sub-chain data to optimize the global scheduling view.

[0029] Preferably, the step S3 includes:

[0030] Record the historical transaction records and energy price trends in the blockchain, construct a multi-dimensional state vector in combination with the time label of the micro-block, and the action space constraint on the multi-dimensional state vector includes:

[0031] Generate discrete and continuous combined action instructions according to the physical limitations of the energy storage device and the grid interaction rules. The physical limitations include the upper limits of the charge and discharge power and the energy storage capacity of the energy storage device, and the grid interaction rules include the power purchase and sale power boundaries. The discrete action instructions include start-stop control, and the continuous action instructions include power regulation. Optimize the computational complexity of the initial topological graph mathematical model based on the multi-dimensional state vector to obtain the optimized topological graph mathematical model.

[0032] Preferably, the optimization process includes:

[0033] Collect a number of concurrent new blocks to be processed that appear within the current cycle, generate the node reference relationships between the concurrent new blocks to be processed through a random algorithm. If the generated new block node is on the main chain, randomly reference one or more new blocks generated by slave chain nodes;

[0034] If the generated new block node is on a slave chain, randomly reference new blocks generated by other slave chain nodes within this cycle;

[0035] Calculate the out-degree of each new block in the graph, initialize the mathematical model of the initial topology graph for adding new blocks. Based on the initialized mathematical model of the initial topology graph for adding new blocks, obtain the optimal directed acyclic graph through the longest out-path retention algorithm, record the block reference order of the optimal directed acyclic graph on the main chain, and send the order to each slave chain node. Each slave chain node performs a reference operation according to the received reference order and adds the new block back to the main chain;

[0036] The reference order is a path constructed by the directed acyclic graph after the micro-blocks are mapped through the graph nodes, and the reference order is also used to specify the parent block referenced by the new block.

[0037] Preferably, the step S3 includes:

[0038] Generating a deep reinforcement learning strategy for the optimized topology graph mathematical model includes performing state space superposition and reward function upgrade on the multi-dimensional state space;

[0039] The mathematical expression for performing state space superposition on the multi-dimensional state space is:

[0040] ;

[0041] Where is the state space vector at time t, is the current battery power level, is the power grid power at the current moment, is the temperature of the surrounding environment, is the change in electricity price, represents the change in electricity price from time t - 1 to time t.

[0042] Preferably, the reward function upgrade includes an upgrade of the multi-objective weighted design for the optimized topology graph mathematical model. The mathematical expression of the multi-objective weighted reward function is:

[0043] ；

[0044] Where 、 and are weight coefficients, R is the value of the overall reward or optimization objective function, is the cost saved, is the amount of renewable energy utilized, is the loss of the device life.

[0045] An Internet of Things-based energy storage device management system, which is used to execute an Internet of Things-based energy storage device management method, including constructing a hierarchical energy storage device management architecture, and dividing the hierarchical energy storage device management architecture into a perception layer, a blockchain layer, and a scheduling decision layer;

[0046] The perception layer is used to collect the operating status and environmental data of the energy storage device in real time through Internet of Things sensors, convert the collected device status and scheduling instructions into blockchain transactions, and broadcast them through the blockchain network;

[0047] The blockchain layer constructs an initial topology graph mathematical model for concurrent block energy storage device processing based on the Internet of Things and the blockchain network;

[0048] The scheduling decision layer generates a deep reinforcement learning strategy for optimizing the topology graph mathematical model.

[0049] Advantages of the present invention: The present invention uses a directed acyclic graph to efficiently optimize energy distribution, greatly reduces the operation steps without reducing the management decision effect, and deeply optimizes the decision and state of managing the energy storage device. With the decentralized feature of blockchain technology, it effectively optimizes the energy management process, and at the same time provides strong support for scenarios such as device status monitoring, scheduling decision-making, and energy trading. The topology graph effectively reduces the complexity of concurrent block processing in the blockchain system, reduces redundant reference relationships, thereby simplifies the sorting logic, is applicable to the shared energy storage power station scenario that needs to quickly process high-frequency energy storage services, uses a multi-dimensional state vector to effectively represent the interaction information between the energy storage device and the power grid, and combines discrete and continuous action instructions for precise control. Description of the Drawings

[0050] Figure 1 is a schematic diagram of the basic process of the Internet of Things-based energy storage device management method provided by an embodiment of the present invention. Detailed Embodiments

[0051] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments.

[0052] Refer to Figure 1, which is an embodiment of the present invention, provides an energy storage device management method based on the Internet of Things, including the following steps:

[0053] Step S1: Real-time collect the operating status and environmental data of the energy storage device through Internet of Things sensors, convert the collected device status and scheduling instructions into blockchain transactions, and broadcast them through the blockchain network;

[0054] Step S2: Build an initial topological graph mathematical model for processing concurrent block energy storage devices based on the Internet of Things and the blockchain network;

[0055] Step S3: Generate a deep reinforcement learning strategy for the optimized topological graph mathematical model.

[0056] Step S1 includes:

[0057] Real-time collect the key data of the energy storage device and the surrounding environment through the Internet of Things gateway. The key data includes device status data, environmental data, and energy price data. The device status data includes the current battery level, charge and discharge status, device power, and charge and discharge efficiency. The environmental data includes temperature, humidity, air pressure, and weather conditions. The energy price data includes the electricity price in the real-time collected energy market;

[0058] Clean the key data through the edge computing node, remove noise and abnormal data, and perform formatting processing. Generate charge and discharge scheduling instructions in combination with energy prices, device power requirements, and the physical limitations of the energy storage device. The scheduling instructions include start-stop control and power regulation;

[0059] Control the charge and discharge process of the energy storage device according to the scheduling strategy calculated by the deep reinforcement learning algorithm;

[0060] Pack the key data and scheduling instructions into a queue data structure. The queue data structure includes fields such as timestamp, device ID, device signature, data content, and scheduling instructions;

[0061] The timestamp is used to record the data collection time;

[0062] The device ID is the unique identifier for the energy storage device that collects the data;

[0063] To ensure the immutability of the data, use the device private key to sign to obtain the device signature;

[0064] The data content includes SOC, battery level, charge and discharge efficiency, load, electricity price, temperature and humidity, and weather;

[0065] Convert the data structure of the queue into blockchain transactions, and broadcast the generated blockchain transactions to the slave chain nodes of the blockchain network through the Internet of Things gateway. Each slave chain node merges and packs the received transaction information into micro-blocks. One micro-block contains data collected by multiple devices and scheduling instructions.

[0066] Verify the blockchain transactions through a lightweight consensus algorithm PBFT variant to ensure the authenticity and integrity of the data.

[0067] Step S2 includes:

[0068] Generate a number of concurrent blocks based on the blockchain network and micro-blocks. The concurrent blocks include timestamps and key data of energy storage devices. When blockchain transactions appear in the concurrent blocks, use the timestamps to mark the transaction times, and also allocate spatio-temporal labels in the micro-blocks of the current transactions. The spatio-temporal labels include the timestamps of blockchain transactions, the geographical locations of energy storage devices, and device IDs, to ensure that each data set is arranged in chronological order and can be marked by spatio-temporal characteristics. Each slave chain node will sign the generated micro-blocks to generate multiple signatures.

[0069] After the transactions and packing are completed, the micro-blocks are submitted to the slave chain nodes to complete data storage. The master chain node collects the micro-blocks from different slave chain nodes according to a preset period for data aggregation, and generates signatures for each micro-block, and constructs a directed acyclic graph based on the standard value weights of the spatio-temporal labels calculated by standardization.

[0070] The blockchain main chain updates the global view according to a preset time period, triggering the next round of scheduling strategy optimization and block generation.

[0071] Real-time collect the operating status and environmental data of energy storage devices through Internet of Things sensors, and convert these data into blockchain transactions for broadcasting, which can ensure the credibility, immutability, and transparency of the data. With the decentralized characteristics of blockchain technology, it effectively optimizes the energy management process, and at the same time provides strong support for scenarios such as device status monitoring, scheduling decision-making, and energy trading.

[0072] The coordination mechanism of the master-slave blockchain structure includes:

[0073] When the number of legal signatures in the slave chain reaches the threshold value, the parent node of the initial topological graph mathematical model aggregates all the legal signatures of the corresponding slave chains, and broadcasts the aggregated legal signatures to the main chain.

[0074] The mathematical expression of the threshold value is:

[0075] ;

[0076] Among them, is the threshold value, is the number of faulty nodes;

[0077] After the main-chain node receives the micro-blocks from each sub-chain, it confirms the legitimacy of the micro-blocks through the threshold signature mechanism, and packs the confirmed valid micro-blocks into main-chain blocks, ensuring that the data of each main-chain block is consistent globally and enabling efficient cross-community energy trading and scheduling decisions.

[0078] Based on the device IDs, timestamps, and energy parameter hash values of each concurrent block, construct an initial topological graph mathematical model for the processing of energy storage devices in concurrent blocks. The construction logic includes:

[0079] The standard value weights for standardizing the calculation of spatio-temporal tags include dynamically weighted summation of spatio-temporal tags. The dynamic weighting logic is, for example, that the weights of adjacent devices are higher and the weights of new data are higher;

[0080] Calculate the number of other nodes pointed to by each node in the directed acyclic graph as the out-degree. After calculating the out-degree of the directed acyclic graph, perform a redundant edge pruning operation on the directed acyclic graph, delete the redundant parts in the same connection, and dynamically select the node with the minimum out-degree as the priority processing node through the greedy algorithm. After redundant pruning, only the optimal reference relationships are retained in the directed acyclic graph, ensuring that each node has the fewest reference relationships, thereby improving the processing efficiency of the block and obtaining the initial topological graph mathematical model;

[0081] Initializing the initial topological graph mathematical model includes: optimizing the computational complexity of micro-block sorting by minimizing the average out-degree of the topological nodes in the directed acyclic graph. The main chain regularly aggregates sub-chain data, optimizes the global scheduling view, and ensures that there are no redundant paths in the topological graph, thereby optimizing the processing efficiency of concurrent blocks.

[0082] The greedy algorithm optimizes the structure of the graph by gradually connecting nodes with higher spatio-temporal weights, reducing the number of blocks referenced by each node, and thus minimizing the average out-degree.

[0083] Through the constructed directed acyclic graph and optimization algorithm, the main chain generates a global energy scheduling strategy, which takes into account factors such as the current state of the device, energy price fluctuations, and device life loss, so as to achieve the optimal allocation of energy among different regions and devices.

[0084] Through optimization techniques such as the master-slave blockchain structure, spatio-temporally weighted directed acyclic graph, and redundant edge pruning, this method can efficiently process concurrent blocks of energy storage devices, optimize block sorting and reduce computational complexity. At the same time, the combination of the threshold signature improved Byzantine fault tolerance algorithm and the lightweight consensus mechanism ensures global consistency and the security of cross-community energy trading.

[0085] Step S3 includes:

[0086] Record the historical transaction records and energy price trends in the blockchain, construct a multi-dimensional state vector by combining the time tags of micro-blocks, and the action space constraints on the multi-dimensional state vector include:

[0087] Generate discrete and continuous combined action instructions according to the physical limitations of energy storage devices and grid interaction rules. The physical limitations include the upper limits of charge and discharge power and the energy storage capacity of energy storage devices, and the grid interaction rules include the power purchase and sale power boundaries. The discrete action instructions include start-stop control, and the continuous action instructions include power regulation. Optimize the computational complexity of the initial topology graph mathematical model based on the multi-dimensional state vector to obtain the optimized topology graph mathematical model.

[0088] The optimization process includes:

[0089] Collect a number of concurrent new blocks to be processed that appear in the current cycle, generate the node reference relationship between the concurrent new blocks to be processed through a random algorithm. If the generated new block node is located on the main chain, randomly reference one or more new blocks generated by slave chain nodes;

[0090] If the generated new block node is located on the slave chain, randomly reference the new blocks generated by other slave chain nodes in this cycle;

[0091] Calculate the out-degree of each new block in the graph, initialize the initial topology graph mathematical model for adding new blocks, and based on the initialized initial topology graph mathematical model for adding new blocks, obtain the optimal directed acyclic graph through the longest out-path retention algorithm. Record the block reference order of the optimal directed acyclic graph on the main chain and send the order to each slave chain node. Each slave chain node performs reference operations according to the received reference order and adds the new block back to the main chain;

[0092] The reference order is a path constructed by the directed acyclic graph after the micro-blocks are mapped through the graph nodes. The reference order is also used to specify the parent block referenced by the new block.

[0093] Reduce the length of the dependency path between blocks, reduce the time complexity of topological sorting from O(k2) to close to O(k), and significantly improve the processing efficiency in high-concurrency scenarios.

[0094] This mathematical model effectively reduces the complexity of concurrent block processing in the blockchain system by constructing a directed acyclic graph with the minimum average out-degree. Its core idea is to reduce redundant reference relationships, thereby simplifying the sorting logic, and is applicable to the shared energy storage power station scenario that needs to quickly process high-frequency energy storage services.

[0095] Use the multi-dimensional state vector to effectively represent the interaction information between energy storage devices and the grid, and perform precise control by combining discrete and continuous action instructions.

[0096] Ensure the processing speed and efficiency by optimizing the reference order and computational complexity of concurrent blocks.

[0097] Improve the concurrent service processing speed of the shared energy storage power station, enabling better management of new blocks in the blockchain and enhancing the overall system performance.

[0098] Step S3 includes:

[0099] Generating a deep reinforcement learning strategy for the optimized topology graph mathematical model includes performing state space superposition and reward function upgrade on the multi-dimensional state space;

[0100] The mathematical expression for performing state space superposition on the multi-dimensional state space is:

[0101] ;

[0102] Among them, is the state space vector at time t, is the current battery charge level, is the grid power at the current moment, which can be the power input from the grid or the power taken from the grid, is the temperature of the surrounding environment, is the change in electricity price, represents the change in electricity price from time t - 1 to time t.

[0103] The formula combines different variables into an overall state vector through state space superposition to represent different dimensions and characteristics of the system;

[0104] State space superposition combines different dimensions to more comprehensively describe the current state of the system. In this case, the behavior of the system depends not only on a single factor but also takes into account multiple interacting factors. In this way, the mutual relationships between various factors in the system can be better handled, and more information can be provided for subsequent decision-making or control. For example, the energy storage device can decide whether to charge or discharge according to , interact with the grid according to , adjust the working mode of the device according to , and optimize the economic benefits according to .

[0105] Reward function upgrade includes the upgrade of multi-objective weighted design for the optimized topology graph mathematical model. The mathematical expression of the multi-objective weighted reward function is:

[0106] ；

[0107] Among them, 、 and are weight coefficients, which are respectively used to control the contribution of each objective to the final reward, R is the value of the overall reward or optimization objective function, which is used to reflect the effect of the optimized strategy, is the cost saved, which is used to represent reducing the energy purchase cost or waste by optimizing the interaction between the energy storage and the power grid, is the amount of renewable energy utilized, is the loss of the device lifespan, which is the performance degradation of the energy storage device or power grid hardware due to use, and is minimized as a penalty term.

[0108] This formula describes a multi-objective weighted reward function, which is used to optimize the strategy of the interaction between the energy storage device and the power grid in the topological graph mathematical model, aiming to balance the optimization of multiple objectives. The main objective of the reward function is to evaluate the pros and cons of the strategy according to different factors and optimize it through weighted design. This reward function helps to optimize the interaction strategy between the energy storage device and the power grid by comprehensively considering three objectives: cost saving, renewable energy utilization, and device lifespan degradation, and balancing the weights between these objectives by adjusting the weight coefficients, so as to improve the overall efficiency, economy, and sustainability of the system.

[0109] An energy storage device management system based on the Internet of Things, which is used to execute the energy storage device management method based on the Internet of Things, including constructing a hierarchical energy storage device management architecture, and dividing the hierarchical energy storage device management architecture into a perception layer, a blockchain layer, and a scheduling decision layer;

[0110] The perception layer is used to collect the operation status and environmental data of the energy storage device in real time through Internet of Things sensors, convert the collected device status and scheduling instructions into blockchain transactions, and broadcast them through the blockchain network;

[0111] The blockchain layer constructs an initial topological graph mathematical model for processing concurrent blocks of energy storage devices based on the Internet of Things and the blockchain network;

[0112] The scheduling decision layer generates a deep reinforcement learning strategy for the optimized topological graph mathematical model.

[0113] With the help of the decentralized feature of blockchain technology, the present invention effectively optimizes the energy management process, and at the same time provides strong support for scenarios such as device status monitoring, scheduling decision-making, and energy trading. The topological graph effectively reduces the complexity of processing concurrent blocks in the blockchain system, reduces redundant reference relationships, thereby simplifies the sorting logic, and is applicable to the shared energy storage power station scenario that needs to quickly process high-frequency energy storage services, effectively represents the interaction information between the energy storage device and the power grid using multi-dimensional state vectors, and combines discrete and continuous action instructions for precise control.

[0114] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 specified in the function of one block or multiple blocks.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An energy storage device management method based on the Internet of Things, characterized in that, It includes the following steps: Step S1: Real-time collect the operating status and environmental data of the energy storage device through Internet of Things sensors, convert the collected device status and scheduling instructions into blockchain transactions, and broadcast them through the blockchain network; Step S2: Build an initial topological graph mathematical model for concurrent block energy storage device processing based on the Internet of Things and blockchain network; Step S3: Generate a deep reinforcement learning strategy for the optimized topological graph mathematical model; The said Step S3 includes: Record the historical transaction records and energy price trends in the blockchain, construct a multi-dimensional state vector in combination with the time tags of micro-blocks, and perform action space constraints on the said multi-dimensional state vector, including: Generate a combined discrete and continuous action instruction according to the physical limitations of the energy storage device and the grid interaction rules. The said physical limitations include the upper limits of the charge and discharge power and the energy storage capacity of the energy storage device, and the grid interaction rules include the power purchase and sale power boundaries. The discrete action instruction includes start-stop control, and the continuous action instruction includes power regulation. Based on the said multi-dimensional state vector, optimize the computational complexity of the initial topological graph mathematical model to obtain the optimized topological graph mathematical model; The optimization process includes: Collect a number of concurrent new blocks to be processed that appear in the current period, generate the node reference relationship between the concurrent new blocks to be processed through a random algorithm. If the generated new block node is located on the main chain, randomly reference one or more new blocks generated by the slave chain nodes; If the generated new block node is located on the slave chain, randomly reference the new blocks generated by other slave chain nodes in this period; Calculate the out-degree of each new block in the graph, initialize the initial topological graph mathematical model with the new blocks added, and based on the initialized initial topological graph mathematical model with the new blocks added, obtain the optimal directed acyclic graph through the longest out-path retention algorithm, record the block reference order of the said optimal directed acyclic graph on the main chain, and send the order to each slave chain node. Each slave chain node performs a reference operation according to the received reference order and adds the new block back to the main chain; The said reference order is a path constructed by the directed acyclic graph after the micro-blocks are mapped through the graph nodes, and the reference order is also used to specify the parent block referenced by the new block.

2. The method for managing an energy storage device based on the Internet of Things according to claim 1, characterized in that The said Step S1 includes: Real-time collect the key data of the energy storage device and the surrounding environment through the Internet of Things gateway. The said key data includes device status data, environmental data, and energy price data. The device status data includes the current battery power, charge and discharge status, device power, and charge and discharge efficiency, the environmental data includes temperature, humidity, air pressure, and weather conditions, and the energy price data includes the electricity price in the real-time collected energy market; Clean the key data through the edge computing node, remove noise and abnormal data, and perform formatting processing. Generate a charge and discharge scheduling instruction in combination with the energy price, device power demand, and physical limitations of the energy storage device. The scheduling instruction includes start-stop control and power regulation; Package the key data and scheduling instructions into a queue data structure. The said queue data structure includes fields such as time stamp, device ID, device signature, data content, and scheduling instruction; Convert the data structure of the queue into a blockchain transaction, and broadcast the generated blockchain transaction to the slave chain nodes of the blockchain network through the IoT gateway. Each slave chain node merges and packages the received transaction information into a micro-block. Verify the blockchain transaction through a lightweight consensus algorithm PBFT variant.

3. The method for managing an energy storage device based on the Internet of Things according to claim 2, wherein, Step S2 includes: Generate a number of concurrent blocks based on the blockchain network and the micro-blocks. The concurrent blocks include a timestamp and key data of the energy storage device. When a blockchain transaction appears in the concurrent block, use the timestamp to mark the transaction time, and also allocate a spatio-temporal label in the micro-block of the current transaction. The spatio-temporal label includes the timestamp of the blockchain transaction, the geographical location of the energy storage device, and the device ID. Each slave chain node will sign the generated micro-block to generate multiple signatures. After the transaction and packaging are completed, the micro-block is submitted to the slave chain node to complete data storage. The master chain node collects the micro-blocks from different slave chain nodes according to a preset period for data aggregation, and generates signatures for each micro-block, and constructs a directed acyclic graph based on the standard value weights of the spatio-temporal labels calculated by standardization. The blockchain main chain updates the global view according to a preset time period, triggering the optimization of the next round of scheduling strategy and block generation.

4. The method for managing an energy storage device based on the Internet of Things according to claim 3, wherein The coordination mechanism of the master-slave blockchain structure includes: When the number of legal signatures in the slave chain reaches the threshold value, the parent node of the initial topological graph mathematical model aggregates all the legal signatures of the corresponding slave chains, and broadcasts the aggregated legal signatures to the master chain. The mathematical expression of the threshold value is: ； Among them, is the threshold value, is the number of faulty nodes; After the master chain node receives the micro-blocks from each slave chain, it confirms the legality of the micro-blocks through the threshold signature mechanism, and packages the confirmed valid micro-blocks into master chain blocks.

5. The method for managing an energy storage device based on the Internet of Things according to claim 4, wherein: Based on the device ID, timestamp, and energy parameter hash value of each concurrent block, construct an initial topological graph mathematical model for processing the energy storage device of the concurrent block. The construction logic includes: Standardizing the calculation of the standard value weight of the spatio-temporal label includes dynamically weighted summation of the spatio-temporal label. Calculate the number of other nodes pointed to by each node in the directed acyclic graph as the out-degree. After calculating the out-degree of the directed acyclic graph, perform a redundant edge pruning operation on the directed acyclic graph, delete the redundant part in the same connection, and dynamically select the node with the smallest out-degree in the directed acyclic graph after the redundant edge pruning operation as the priority processing node to obtain the initial topological graph mathematical model. Initializing the initial topological graph mathematical model includes: optimizing the computational complexity of micro-block sorting by minimizing the average out-degree of the topological nodes of the directed acyclic graph, and the master chain regularly aggregates slave chain data to optimize the global scheduling view.

6. The method for managing an energy storage device based on the Internet of Things according to claim 5, wherein, Step S3 includes: Generating a deep reinforcement learning strategy for the optimized topological graph mathematical model includes state space superposition and reward function upgrade for the multi-dimensional state space. The mathematical expression for state space superposition of the multi-dimensional state space is: ； Among them, is the state space vector at time t, is the current battery charge level, is the grid power at the current moment, is the temperature of the surrounding environment, is the change in electricity price, represents the change in electricity price from time t - 1 to time t.

7. The method for managing an energy storage device based on the Internet of Things according to claim 6, wherein: The upgrade of the reward function includes the upgrade of the multi-objective weighted design for the mathematical model of the optimized topology graph. The mathematical expression of the multi-objective weighted reward function is: ； Among them, 、 and are weight coefficients, R is the value of the overall reward or optimization objective function, is the cost saved, is the amount of renewable energy utilized, is the loss of the equipment life.

8. An energy storage device management system based on the Internet of Things, which is used to execute an energy storage device management method based on the Internet of Things, characterized in that, It includes constructing a hierarchical energy storage device management architecture, which is divided into a sensing layer, a blockchain layer, and a scheduling decision layer; The sensing layer is used to collect the operating status and environmental data of the energy storage device in real time through Internet of Things sensors, convert the collected device status and scheduling instructions into blockchain transactions, and broadcast them through the blockchain network; The blockchain layer constructs an initial topology graph mathematical model for concurrent block energy storage device processing based on the Internet of Things and the blockchain network; The scheduling decision layer generates a deep reinforcement learning strategy for the optimized topology graph mathematical model; Record the historical transaction records and energy price trends in the blockchain, construct a multi-dimensional state vector in combination with the time tags of micro-blocks. The action space constraints on the multi-dimensional state vector include: Generate a combined discrete and continuous action instruction according to the physical limitations of the energy storage device and the grid interaction rules. The physical limitations include the upper limit of the charge and discharge power of the energy storage device and the energy storage capacity, and the grid interaction rules include the power purchase and sale power boundaries. The discrete action instruction includes start-stop control, and the continuous action instruction includes power regulation. Optimize the computational complexity of the initial topology graph mathematical model based on the multi-dimensional state vector to obtain the optimized topology graph mathematical model; The optimization process includes: Collect a number of concurrent new blocks to be processed that appear in the current period, generate the node reference relationship between the concurrent new blocks to be processed through a random algorithm. If the generated new block node is located on the main chain, randomly reference one or more new blocks generated by slave chain nodes; If the generated new block node is located on the slave chain, randomly reference the new blocks generated by other slave chain nodes in this period; Calculate the out-degree of each new block in the graph, initialize the initial topology graph mathematical model with the added new block. Based on the initialized initial topology graph mathematical model with the added new block, obtain the optimal directed acyclic graph through the longest out-path retention algorithm, record the block reference order of the optimal directed acyclic graph on the main chain, and send the order to each slave chain node. Each slave chain node performs a reference operation according to the received reference order and adds the new block back to the main chain; The reference order is a path constructed by the directed acyclic graph after the micro-block is mapped through the graph nodes. The reference order is also used to specify the parent block referenced by the new block.

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