Energy storage equipment management method and system based on Internet of Things

Abstract

The invention discloses an energy storage equipment management method and system based on the Internet of Things, and relates to the technical field of energy storage management, and the method comprises the steps: constructing an initial topological graph mathematical model processed by concurrent block energy storage equipment based on the Internet of Things and a block chain network; according to the method, energy distribution is efficiently optimized by means of a directed acyclic graph, and under the condition that the management decision effect is not reduced, the operation steps are greatly reduced, and meanwhile, the decision and the state of the energy storage equipment are deeply optimized and managed; meanwhile, powerful support is provided for scenes such as equipment state monitoring, scheduling decision making and energy transaction, the complexity of concurrent block processing in the block chain system is effectively reduced through the topological graph, redundant reference relations are reduced, so that sorting logic is simplified, the method is suitable for shared energy storage power station scenes needing to rapidly process high-frequency energy storage services, and the efficiency of the shared energy storage power station scenes is improved. Multi-dimensional state vectors are utilized to effectively represent interaction information of energy storage equipment and a power grid, and accurate control is performed in combination with 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 (such as solar energy, 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 power 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, etc.

[0003] Currently, the Chinese invention patent with the application number CN202410726747.9 discloses a method and system for managing energy storage electric control devices based on digital twins, which relates to the technical field of energy storage electric control. By constructing a deep integration of a digital twin model and real-time monitoring data, it can actively identify the abnormal state of electric control devices before a fault occurs, effectively prevent accidents, and significantly enhance the safety management level of energy storage power station AC electric 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, including 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 processing concurrent block energy storage devices based on the Internet of Things and the blockchain network; Step S3: Generate a deep reinforcement learning strategy for the optimized topological graph mathematical model.

[0006] Preferably, the step S1 includes: Real-time collect the 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; Clean the key data through the edge computing node, remove noise and abnormal data, and perform formatting processing. Generate charge and discharge scheduling instructions by combining energy prices, device power requirements, and the physical limitations of energy storage devices. The scheduling instructions include start-stop control and power regulation; Package 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; 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; Verify the blockchain transaction through a lightweight consensus algorithm PBFT variant.

[0007] Preferably, the step S2 includes: 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 a blockchain transaction appears in the concurrent blocks, use the timestamp to mark the transaction time, and also allocate space-time tags in the micro-block of the current transaction. The space-time tags include 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 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, and constructs a directed acyclic graph based on the standard value weights of the space-time tags calculated by standardization; 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.

[0008] Preferably, 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 main chain; The mathematical expression of the threshold value is: ; Wherein, is the threshold value, is the number of faulty nodes; After the main-chain nodes receive the micro-blocks from each sub-chain, they confirm the legality of the micro-blocks through the threshold signature mechanism, and package the valid micro-blocks that are confirmed to be legal into main-chain blocks.

[0009] Preferably, based on the device ID, timestamp, and energy parameter hash value of each concurrent block, construct an initial topological graph mathematical model for the energy storage device processing of concurrent blocks. The construction logic includes: Normalize and calculate the standard value weight of the spatio-temporal label, including 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 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; 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. Preferably, the 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 label of the micro-block, and perform action space constraints on the multi-dimensional state vector, including: Generate a discrete and continuous combined action instruction according to the physical limitations of the energy storage device and the grid interaction rules. The physical limitations include the upper limits of the charging and discharging 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. Optimize the computational complexity of the initial topological graph mathematical model based on the multi-dimensional state vector to obtain an optimized topological graph mathematical model.

[0010] Preferably, the optimization process includes: Collect a number of concurrent new blocks to be processed that appear in the current period, generate a 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 the new blocks generated by one or more sub-chain nodes; If the generated new block node is located on a sub-chain, randomly reference the new blocks generated by other sub-chain nodes in this period; Calculate the out-degree of each new block in the computational graph, initialize the mathematical model of the initial topological graph for adding new blocks, and based on the initialized mathematical model of the initial topological 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; 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.

[0011] Preferably, the step S3 includes: Generating a deep reinforcement learning strategy for the optimized topological graph mathematical model includes performing state space superposition and reward function upgrade on the multi-dimensional state space; The mathematical expression for performing state space superposition on the multi-dimensional state space is: ; 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.

[0012] Preferably, the reward function upgrade includes an upgrade of the multi-objective weighted design for the optimized topological graph mathematical model, and the mathematical expression of the multi-objective weighted reward function is: ； 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.

[0013] An energy storage device management system based on the Internet of Things, which is used to execute a method for managing energy storage devices 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; The perception layer is used to collect the operation status and environmental data of energy storage devices 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 topological 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 topological graph mathematical model.

[0014] 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 energy storage devices. 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 topological graph effectively reduces the complexity of concurrent block processing 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. It uses a multi-dimensional state vector to effectively represent the interaction information between energy storage devices and the power grid, and combines discrete and continuous action instructions for precise control. Description of the Drawings

[0015] Figure 1 It is a schematic diagram of the basic process of the energy storage device management method based on the Internet of Things provided by an embodiment of the present invention. Detailed Embodiments

[0016] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them.

[0017] Referring to Figure 1 , an embodiment of the present invention provides an energy storage device management method based on the Internet of Things, including the following steps: Step S1: Collect the operation status and environmental data of energy storage devices 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; Step S2: Construct an initial topological graph mathematical model for concurrent block energy storage device processing based on the Internet of Things and the blockchain network; Step S3: Generate a deep reinforcement learning strategy for the optimized topological graph mathematical model.

[0018] Step S1 includes: Real-time collect the 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; 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 energy storage devices. The scheduling instructions include start-stop control and power regulation; Control the charge and discharge process of the energy storage device according to the scheduling strategy calculated by the deep reinforcement learning algorithm; Package 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; The timestamp is used to record the time of data collection; The device ID is the unique identifier for the energy storage device that collects data; To ensure the immutability of the data, use the device private key to sign and obtain the device signature; The data content includes SOC, battery level, charge and discharge efficiency, load, electricity price, temperature and humidity, and weather; 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. A micro-block contains the data and scheduling instructions collected by multiple devices; Verify the blockchain transaction through the lightweight consensus algorithm PBFT variant to ensure the authenticity and integrity of the data.

[0019] Step S2 includes: Generate a number of concurrent blocks based on the blockchain network and micro-blocks. The concurrent blocks include timestamps and the key data of energy storage devices. 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 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-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 main chain node collects the micro-blocks from different slave chain nodes according to a preset cycle 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 main chain of the blockchain updates the global view according to a preset time period, triggering the optimization of the next round of scheduling strategies and block generation.

[0020] By collecting the operating status and environmental data of energy storage devices in real time through Internet of Things sensors and converting this data into blockchain transactions for broadcasting, the credibility, immutability, and transparency of the data can be ensured. With the decentralized characteristics of blockchain technology, the energy management process can be effectively optimized, while providing strong support for scenarios such as device status monitoring, scheduling decisions, and energy transactions.

[0021] The coordination mechanism of the master-slave blockchain structure includes: When the number of legitimate signatures in the slave chain reaches the threshold value, the parent node of the initial topology graph mathematical model aggregates all the legitimate signatures corresponding to the slave chains and broadcasts the aggregated legitimate signatures to the main chain; The mathematical expression of the threshold value is: ; Among them, is the threshold value, is the number of faulty nodes; After receiving the micro-blocks from each slave chain, the main chain nodes confirm the legitimacy of the micro-blocks through the threshold signature mechanism and package the confirmed legitimate micro-blocks into main chain blocks, ensuring that the data of each main chain block is consistent globally and enabling effective cross-community energy transactions and scheduling decisions.

[0022] Based on the device IDs, timestamps, and energy parameter hash values of each concurrent block, an initial topology graph mathematical model for the processing of energy storage devices in concurrent blocks is constructed. The construction logic includes: The standard value weights for normalizing the spatio-temporal tags include dynamically weighted summation of the spatio-temporal tags. The dynamic weighting logic is, for example, that neighboring devices have higher weights and new data has higher weights; 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. After the redundant pruning, only the optimal reference relationships are retained in the directed acyclic graph to ensure that each node has the fewest reference relationships, thereby improving the processing efficiency of the blocks and obtaining the initial topology graph mathematical model; Initializing the initial topology 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 the data from the slave chains and optimizes the global scheduling view to ensure that there are no redundant paths in the topology graph, thereby optimizing the processing efficiency of concurrent blocks.

[0023] 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, thereby minimizing the average out-degree.

[0024] 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 devices, energy price fluctuations, and device life loss, so as to achieve the optimal allocation of energy among different regions and devices.

[0025] Through optimization techniques such as the master-slave blockchain structure, spatio-temporal 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 transactions.

[0026] 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 multi-dimensional state vector, including: 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 charging and discharging 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 topology graph mathematical model based on the multi-dimensional state vector to obtain the optimized topology graph mathematical model.

[0027] 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 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 blocks, and based on the initialized initial topology graph mathematical model with the added 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; 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.

[0028] Reduce the length of the dependency path between blocks, reducing the time complexity of topological sorting from O(k2) to nearly O(k), and significantly improving the processing efficiency in high-concurrency scenarios.

[0029] 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 it is applicable to the shared energy storage power station scenario that needs to quickly process high-frequency energy storage services.

[0030] Use a multi-dimensional state vector to effectively represent the interaction information between energy storage devices and the power grid, and combine discrete and continuous action instructions for precise control.

[0031] By optimizing the reference order and computational complexity of concurrent blocks, ensure the processing speed and efficiency.

[0032] Improve the concurrent service processing speed of the shared energy storage power station, and be able to better manage new blocks in the blockchain and improve the overall system performance.

[0033] 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 power of the power grid at the current moment, which can be the power input to the power grid or the power taken from the power 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.

[0034] The formula combines different variables into an overall state vector through state space superposition to represent different dimensions and characteristics of the system; 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 takes into account multiple interacting factors. In this way, the mutual relationship 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 power grid according to , and according to to adjust the working mode of the device, and at the same time according to to optimize the economic benefits.

[0035] The upgrade of the reward function includes the upgrade of the multi-objective weighted design for optimizing the mathematical model of the topology graph. The mathematical expression of the multi-objective weighted reward function is: ； where 、 and are the weight coefficients, which are used to control the contribution of each objective to the final reward respectively. 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 life, which is the performance degradation of the energy storage device or the power grid hardware caused by use, and is minimized as a penalty term.

[0036] This formula describes a multi-objective weighted reward function for optimizing the strategy of the interaction between the energy storage device and the power grid in the mathematical model of the topology graph, aiming to balance the optimization of multiple objectives. The main objective of the reward function is to evaluate the advantages and disadvantages 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 life 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.

[0037] 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 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 topological graph mathematical model for concurrent block processing of energy storage devices based on the Internet of Things and the blockchain network; The scheduling decision layer generates a deep reinforcement learning strategy for the optimized topological graph mathematical model.

[0038] With the decentralized characteristics of blockchain technology, the present invention effectively optimizes the energy management process and provides strong support for scenarios such as device status monitoring, scheduling decision-making, and energy trading. The topology map effectively reduces the complexity of concurrent block processing in the blockchain system, reduces redundant reference relationships, 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. The multi-dimensional state vector is used to effectively represent the interaction information between the energy storage device and the power grid, and precise control is carried out by combining discrete and continuous action instructions.

[0039] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. 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 (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions can also be stored in a computer-readable memory that can direct 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 realizes the functions specified in one process Figure 1 one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0040] 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: The following steps are involved: Step S1: collect the operating status and environmental data of the energy storage equipment in real time through the IoT sensors, convert the collected equipment status and dispatch instructions into blockchain transactions, and broadcast them through the blockchain network; Step S2: constructing a mathematical model of the initial topology graph 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 topology graph mathematical model.

2. The energy storage device management method based on the Internet of Things according to claim 1, characterized in that: The step S1 comprises: The key data of the energy storage device and the surrounding environment are collected in real time 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 power of the battery, the charge and discharge status, the device power and the charge and discharge efficiency. The environmental data includes temperature, humidity, air pressure and weather conditions. The energy price data includes the electricity price of the energy market collected in real time; The key data is cleaned through edge computing nodes to remove noise and abnormal data, and formatted. The charging and discharging scheduling instructions are generated based on energy prices, equipment power requirements, and physical limitations of energy storage equipment. The scheduling instructions include start-stop control and power regulation. Packing the key data and the scheduling instructions into a queue data structure, wherein the queue data structure includes fields for timestamp, device ID, device signature, data content, and scheduling instructions; Convert the data structure of the queue into a blockchain transaction, broadcast the generated blockchain transaction to the slave chain nodes of the blockchain network through the IoT gateway, and each slave chain node combines and packages the received transaction information into a micro-block; The blockchain transactions are verified by a lightweight consensus algorithm PBFT variant.

3. The energy storage device management method based on the Internet of Things as claimed in claim 2, characterized in that: The step S2 comprises: Generate several concurrent blocks based on the blockchain network and micro-blocks. The concurrent blocks include timestamps and key data of energy storage devices. When a blockchain transaction appears in the concurrent block, the transaction time is marked with a timestamp, and a time-space tag is also allocated in the micro-block of the current transaction. The time-space tag includes the timestamp of the blockchain transaction, the geographic 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 the data storage. The main chain node collects micro-blocks from different slave chain nodes according to the preset period to aggregate the data, and generates signatures for each micro-block. A directed acyclic graph is constructed based on the standard value weights of the space-time labels calculated by standardization. The blockchain main chain updates the global view according to the preset time period, triggering the next round of scheduling strategy optimization and block generation.

4. The method for managing energy storage equipment based on the Internet of Things according to claim 3, characterized in that: The coordination mechanism of the master-slave blockchain structure includes: When the number of legitimate signatures in the slave chain reaches the threshold value, the parent node of the mathematical model of the initial topology graph aggregates all legitimate signatures of the corresponding slave chains and broadcasts the aggregated legitimate signatures to the main chain; The mathematical expression of the threshold value is: ； in, is the threshold value, is the number of failed nodes; After receiving the micro-blocks from each slave chain, the main chain node confirms the legitimacy of the micro-blocks through the threshold signature mechanism, and packages the confirmed valid micro-blocks into the main chain blocks.

5. The energy storage device management method based on the Internet of Things as claimed in claim 4, characterized in that: Based on the device ID, timestamp and energy parameter hash value of each concurrent block, the mathematical model of the initial topology diagram of concurrent block energy storage device processing is constructed. The construction logic includes: The standardized calculation of the standard value weights of the spatiotemporal labels includes dynamically weighted summation of the spatiotemporal labels; Calculate the number of other nodes pointed to by each node in the directed acyclic graph as the out-degree, perform a redundant edge pruning operation on the directed acyclic graph after calculating the out-degree of 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 a greedy algorithm from the directed acyclic graph after the redundant edge pruning operation, and obtain an initial topology graph mathematical model; Initializing the mathematical model of the initial topology graph includes: optimizing the computational complexity of micro-block sorting by minimizing the average out-degree of directed acyclic graph topology nodes, the main chain periodically aggregating slave chain data, and optimizing the global scheduling view.

6. The method for managing energy storage equipment based on the Internet of Things according to claim 5, characterized in that: The step S3 comprises: Record historical transaction records and energy price trends in the blockchain, combine the time tags of micro-blocks to construct a multi-dimensional state vector, and constrain the action space of the multi-dimensional state vector, including: A combination of discrete and continuous action instructions are generated according to the physical limitations of the energy storage device and the grid interaction rules, wherein the physical limitations include the upper limit of the charging and discharging power and the energy storage capacity of the energy storage device, the grid interaction rules include the power boundary of purchasing and selling electricity, the discrete action instructions include start-stop control, and the continuous action instructions include power regulation. Based on the multidimensional state vector, the mathematical model of the initial topology map is optimized in computational complexity to obtain the mathematical model of the optimized topology map.

7. The energy storage device management method based on the Internet of Things according to claim 6, characterized in that: The optimization process includes: Collect several concurrent new blocks to be processed in the current cycle, generate node reference relationships between the concurrent new blocks to be processed through a random algorithm, and if the generated new block node is located in the main chain, randomly reference one or more new blocks generated from the chain node; If the generated new block node is located in the slave chain, randomly reference the new block generated by other slave chain nodes in this cycle; Calculate the out-degree of each new block in the graph, initialize the mathematical model of the initial topology graph with the new block added, obtain the optimal directed acyclic graph through the longest outgoing path retention algorithm based on the initialized mathematical model of the initial topology graph with the new block added, 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 the 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 a directed acyclic graph after the micro-blocks are mapped by the graph nodes. The reference order is also used to specify the parent block referenced by the new block.

8. The energy storage device management method based on the Internet of Things according to claim 7, characterized in that: The step S3 comprises: Generating a deep reinforcement learning strategy for the optimized topology mathematical model includes superimposing a state space and upgrading a reward function for a multi-dimensional state space; The mathematical expression for state space superposition of multidimensional state space is: ； in, is the state space vector at time t, is the current charge level of the battery, is the grid power at the current moment, is the ambient temperature, is the change in electricity price, Represents the change in electricity price from time t-1 to time t.

9. The energy storage device management method based on the Internet of Things according to claim 8, characterized in that: The reward function upgrade includes upgrading the multi-objective weighted design of the optimization topology mathematical model. The mathematical expression of the multi-objective weighted reward function is: ； in, 、 and is the weight coefficient, R is the overall reward or the value of the optimization objective function, To save costs, is the amount of renewable energy used, The loss of equipment life.

10. An energy storage device management system based on the Internet of Things, the system is used to execute an energy storage device management method based on the Internet of Things, characterized in that: It includes building a hierarchical energy storage device management architecture, dividing the hierarchical energy storage device management architecture into a perception layer, a blockchain layer, and a scheduling decision layer; The perception layer is used to collect the operating status and environmental data of energy storage equipment in real time through IoT sensors, convert the collected equipment status and scheduling instructions into blockchain transactions, and broadcast them through the blockchain network; The blockchain layer builds the mathematical model of the initial topology graph for concurrent block energy storage device processing based on the Internet of Things and blockchain network; The scheduling decision layer generates deep reinforcement learning strategies for the mathematical model of the optimized topology graph.

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