Power supply equipment cooperative scheduling method and system based on block chain consensus

Abstract

The invention relates to the field of power supply equipment scheduling, and discloses a block chain consensus-based power supply equipment cooperative scheduling method and system, and the method comprises the steps: querying an equipment operation state and real-time data, and generating an equipment cooperative cluster in combination with a power grid scheduling demand; analyzing a power adjustment instruction of the cluster scheduling task, obtaining an equipment execution index and calculating a response threshold value; determining a scheduling conflict point according to the response threshold value, and analyzing a task scale and an execution time limit to calculate a time offset; a consensus verification path is determined according to the offset, and cooperative nodes are identified; and finally, a cooperative power value is determined based on the nodes, and a cooperative scheduling result under the block chain is generated through equipment adaptation. According to the invention, the scheduling consistency of the power supply equipment in the block chain can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for collaborative scheduling of power supply equipment based on blockchain consensus, belonging to the field of power supply equipment scheduling. Background Art

[0002] Coordinated dispatch of power equipment refers to the process of achieving dynamic balance of regional power supply and demand, minimizing network losses, and ensuring system security and stability through real-time coordination of the operating status and output strategies of distributed power sources (such as photovoltaic, wind power, and energy storage devices) in a power system with the participation of multiple entities.

[0003] In the existing technology, the coordinated scheduling of power supply equipment mainly relies on centralized control systems or manual experience intervention, and lacks in-depth perception of the dynamic load of equipment and regional differences. Especially when the number of distributed devices increases, it is difficult for the scheduling center to process global data in real time, resulting in decision delays or resource conflicts; at the same time, the existing method relies on a single server, which is vulnerable to attacks and causes scheduling interruptions, and lacks transparent records, resulting in unfair scheduling or tampering risks, affecting the scheduling consistency of power supply equipment. Therefore, a power supply equipment coordinated scheduling method based on blockchain consensus is needed to improve the scheduling consistency of power supply equipment within the blockchain. Summary of the Invention

[0004] The present invention provides a method and system for collaborative scheduling of power supply equipment based on blockchain consensus, the main purpose of which is to improve the scheduling consistency of power supply equipment within the blockchain.

[0005] To achieve the above objectives, the present invention provides a method for collaborative scheduling of power supply equipment based on blockchain consensus, comprising: Query the device operating status corresponding to the power supply device in the target blockchain, collect real-time operating data corresponding to the device operating status, and generate a device collaboration cluster corresponding to the power supply device based on the real-time operating data and preset power grid scheduling requirements; Detecting a scheduling task in the device collaboration cluster, analyzing a power adjustment instruction corresponding to the scheduling task, querying a device execution index corresponding to the power adjustment instruction, and calculating a device response threshold corresponding to the device execution index; Determining, based on the device response threshold, a scheduling conflict point of the power supply device during the scheduling process, analyzing the task size and execution time limit corresponding to the scheduling conflict point, and calculating, based on the task size and execution time limit, a time offset of the power supply device for the execution process; Determining a consensus verification path corresponding to the scheduling instruction in the power supply device according to the time offset, and identifying a path cooperation node in the consensus verification path; Based on the path collaboration node, a collaborative power value corresponding to the power supply device is determined, and the collaborative power value is device adapted to generate a collaborative scheduling result corresponding to the power supply device with respect to the target blockchain.

[0006] Optionally, generating a device collaboration cluster corresponding to the power supply device based on the real-time operation data in combination with a preset power grid scheduling requirement includes: Extracting key equipment indicators from the real-time operation data; Analyze the collaboration potential value corresponding to the power supply equipment based on the key indicators of the equipment and the preset grid dispatch requirements; Mapping the device grouping range corresponding to the collaboration potential value; Analyze the scheduling compatibility characteristics corresponding to the devices within the device group range; Based on the scheduling compatibility feature, a device cooperation cluster corresponding to the power supply device is generated.

[0007] Optionally, analyzing the collaboration potential value corresponding to the power supply device based on the key indicators of the device in combination with preset grid scheduling requirements includes: Extracting dynamic / steady-state data from key indicators of the equipment; Based on the dynamic / steady-state data, analyzing the instantaneous performance index and continuous dispatching efficiency value of the power supply equipment to meet the preset grid dispatching requirements; Mapping the matching value range of the instantaneous performance index and the continuous dispatch effectiveness value to the grid dispatch demand; Analyzing the regulation margin characteristics corresponding to each power supply device within the matching value range; The cooperation potential value corresponding to the adjustment margin characteristic is quantified.

[0008] Optionally, querying a device execution index corresponding to the power adjustment instruction and calculating a device response threshold corresponding to the device execution index includes: Analyzing the power peak and valley values ​​corresponding to the power adjustment instructions; Based on the power peak and valley values, dividing the power adjustment interval corresponding to the power adjustment instruction; Determining, based on the power adjustment interval, response capability levels corresponding to different devices in the power supply device; analyzing execution performance data corresponding to the responsiveness level; Querying the device execution index corresponding to the execution performance data; The device response threshold corresponding to the device execution index can be calculated using the following formula: ; in, Indicates the device response threshold corresponding to the device execution index, represents the device time constant, Indicates the power adjustment range, Indicates the device execution index, represents the reference power, represents the system constraint factor, represents the constraint function, represents the efficiency reduction coefficient, Represents the conflict sensitivity coefficient.

[0009] Optionally, determining a scheduling conflict point of the power supply device in a scheduling process based on the device response threshold includes: Querying a threshold change trend corresponding to the device response threshold; determining key conflict characteristics in the scheduling process based on the threshold change trend; Based on the key conflict characteristics, locating potential conflicting units in the device cooperation cluster; Extracting a conflict identifier from the potential conflict unit; Based on the conflict identifier, a scheduling conflict point of the power supply device in a scheduling process is determined.

[0010] Optionally, locating potential conflicting units in the device collaboration cluster based on the key conflict characteristics includes: Querying a conflict distribution pattern corresponding to the key conflict characteristic; Determining a conflict hotspot area in the device collaboration cluster based on the conflict distribution pattern; Based on the conflict hotspot area, dividing the conflict candidate units in the cluster; Analyzing the unit conflict index corresponding to the conflict candidate unit; Based on the unit conflict index, potential conflicting units in the device cooperation cluster are located.

[0011] Optionally, the calculating, based on the task size and the execution time limit, a time offset of the power supply device for the execution process includes: Extracting the total amount of scheduling instructions corresponding to the task scale; Query the maximum execution time corresponding to the execution time limit; Based on the total amount of the scheduling instructions and the maximum execution time, identifying the task conflict period corresponding to the power supply device; Extracting the task demand peak value during the task conflict period; Based on the task demand peak, the time offset of the power supply device for the execution process can be calculated by the following formula: ; in, represents the time offset of the power supply device for the execution process, represents the total number of task conflict periods, represents the number index of the task conflict period, represents the peak value of task demand during the i-th task conflict period, Indicates the rated power of the power supply device. represents the device response threshold during the i-th task conflict period, represents the power attenuation factor, Indicates the number of scheduling instructions that the power supply can process per second. Indicates the device execution index.

[0012] Optionally, determining, according to the time offset, a consensus verification path corresponding to the scheduling instruction in the power supply device includes: Analyze the scheduling execution period corresponding to the time offset; Calculating a time period verification delay corresponding to the scheduling execution time period; Based on the verification delay in the time period, screening available verification channels corresponding to the network in the target blockchain; Marking efficient consensus nodes in the available verification channel; Based on the efficient consensus node, a consensus verification path corresponding to the scheduling instruction in the power supply device is determined.

[0013] Optionally, determining the cooperation power value corresponding to the power supply device based on the path cooperation node includes: Analyzing the scheduling response weight corresponding to the path cooperation node; Dividing the power supply device into power scheduling partitions in a preset power coordination domain based on the scheduling response weight; Locating the main power supply device in the power scheduling partition; Querying the collaborative adjustment amplitude corresponding to the main power supply device; Based on the collaborative adjustment amplitude, a collaborative power value corresponding to the power supply device is determined.

[0014] In order to solve the above problems, the present invention also provides a power supply equipment collaborative scheduling system based on blockchain consensus, the system comprising: A cluster generation module is used to query the device operating status corresponding to the power supply device in the target blockchain, collect real-time operating data corresponding to the device operating status, and generate a device collaboration cluster corresponding to the power supply device based on the real-time operating data and preset power grid scheduling requirements; a threshold calculation module, configured to detect a scheduling task in the device collaboration cluster, analyze a power adjustment instruction corresponding to the scheduling task, query a device execution index corresponding to the power adjustment instruction, and calculate a device response threshold corresponding to the device execution index; an offset calculation module, configured to determine, based on the device response threshold, a scheduling conflict point of the power supply device during the scheduling process, analyze the task size and execution time limit corresponding to the scheduling conflict point, and calculate, based on the task size and execution time limit, a time offset of the power supply device for the execution process; a node identification module, configured to determine, based on the time offset, a consensus verification path corresponding to the scheduling instruction in the power supply device, and identify a path cooperation node in the consensus verification path; A result generation module is used to determine the collaborative power value corresponding to the power supply device based on the path collaboration node, and perform device adaptation on the collaborative power value to generate a collaborative scheduling result corresponding to the power supply device with respect to the target blockchain.

[0015] Compared with the problems described in the background technology, the present invention queries the device operating status corresponding to the power supply device in the target blockchain and collects the real-time operating data corresponding to the device operating status. It can rely on the tamper-proof characteristics of the blockchain to ensure the authenticity and credibility of the data, and provide a reliable basis for subsequent scheduling decisions; at the same time, it can fully grasp the device dynamics, accurately match the grid scheduling needs, reduce the scheduling deviation caused by information asymmetry, and improve the accuracy and efficiency of the overall scheduling. The present invention detects the scheduling tasks in the device collaboration cluster and analyzes the power adjustment instructions corresponding to the scheduling tasks. It can accurately locate the specific scheduling goals that the cluster needs to execute and clarify the power adjustment direction and amplitude of each device; at the same time, it can identify the potential requirements and constraints in the task execution in advance, and provide a basis for the subsequent evaluation of the equipment execution capability. Furthermore, the present invention determines the scheduling conflict points of the power supply device in the scheduling process based on the device response threshold, and can accurately identify the contradictory links caused by the mismatch of response capabilities when the device executes the scheduling instructions, and provide a basis for the subsequent evaluation of the equipment execution capability. Early warning of potential scheduling risks; providing a basis for optimizing scheduling strategies, by adjusting instruction allocation or equipment coordination mode, avoiding conflicts that affect the stable operation of the power grid, and improving the reliability and efficiency of scheduling execution. Furthermore, the present invention determines the consensus verification path corresponding to the scheduling instruction in the power supply device according to the time offset, can accurately associate the device execution deviation with the scheduling instruction logic, and clarify the reliable transmission and verification direction of the instruction in the device collaboration; by adapting different offset conditions through differentiated paths, the consistency and accuracy of instruction execution are guaranteed, the scheduling conflict caused by time deviation is reduced, and the reliability and efficiency of cluster collaborative scheduling are improved. Finally, the present invention determines the collaborative power value corresponding to the power supply device based on the path collaboration node, and can accurately match the collaborative output level of the power supply device in combination with the instruction execution status verified by the node; through collaborative feedback between nodes, the device power allocation is dynamically optimized to ensure that the power adjustment is highly adapted to the scheduling requirements; at the same time, the collaborative efficiency of the device cluster is improved and the risk of power imbalance is reduced. Therefore, the embodiment of the present invention provides a power supply device collaborative scheduling method and system based on blockchain consensus, which can improve the scheduling consistency of power supply devices in the blockchain. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for collaborative scheduling of power supply equipment based on blockchain consensus provided by one embodiment of the present invention; Figure 2 A schematic diagram of a scheduling architecture in a method for collaborative scheduling of power supply equipment based on blockchain consensus provided by one embodiment of the present invention; Figure 3 A schematic diagram of a module for implementing a power supply equipment collaborative scheduling system based on blockchain consensus provided in one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] The embodiment of the present application provides a method for collaborative scheduling of power supply equipment based on blockchain consensus. The execution subject of the method for collaborative scheduling of power supply equipment based on blockchain consensus includes but is not limited to at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for collaborative scheduling of power supply equipment based on blockchain consensus can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0020] Reference Figure 1 FIG2 is a flow chart of a method for collaboratively scheduling power supply equipment based on blockchain consensus according to an embodiment of the present invention. In this embodiment, the method for collaboratively scheduling power supply equipment based on blockchain consensus includes: S1. Query the device operating status corresponding to the power supply device in the target blockchain, collect real-time operating data corresponding to the device operating status, and generate a device collaboration cluster corresponding to the power supply device based on the real-time operating data and preset power grid scheduling requirements.

[0021] By querying the device operating status corresponding to the power supply equipment in the target blockchain and collecting the real-time operating data corresponding to the device operating status, the present invention can rely on the tamper-proof characteristics of the blockchain to ensure the authenticity and credibility of the data, and provide a reliable basis for subsequent scheduling decisions; at the same time, it can fully grasp the device dynamics, accurately match the grid scheduling needs, reduce the scheduling deviation caused by information asymmetry, and improve the accuracy and efficiency of the overall scheduling.

[0022] The standard blockchain refers to a distributed ledger system built for the coordinated scheduling of power supply equipment. It stores information such as the historical operation records, scheduling instructions and execution results of power supply equipment, and the data cannot be tampered with and is traceable. All nodes involved in the scheduling (such as power stations, energy storage stations, etc.) can share and verify the data. For example, in a regional power grid blockchain, the output data of photovoltaic power station A from 9:00 to 15:00 every day for the past 30 days is recorded (such as 25MW output at 9:00 on X month XX day and 40MW output at 12:00). All collaborative nodes can query and verify the authenticity of this data; the power supply Equipment refers to various devices involved in power generation, storage or conversion, including distributed energy equipment (such as photovoltaic panels, wind turbines), energy storage equipment (such as lithium battery energy storage systems, lead-acid battery packs) and some controllable load equipment (such as adjustable industrial motors). It is the basis for power balance and stable operation of the power grid. For example, a community’s 100 250W photovoltaic panels (total installed capacity of 25kW), 2 500kWh lithium battery energy storage cabinets, and 3 industrial cooling pumps that can be adjusted within the range of 0-100kW are all power supply equipment in the blockchain; the equipment operating status refers to the power supply equipment at the current time. The working status and performance at the previous moment, including normal operation, overload, fault, standby and other states, reflects whether the equipment can participate in the dispatch as expected, and is the key basis for judging whether the equipment can execute the dispatch instruction. For example, a wind power equipment is currently in the "normal operation" state, and the output is maintained at 80-90MW when the wind speed is stable; while another energy storage device is in the "limited power operation" state because the battery temperature exceeds 45°C, and the maximum charge and discharge power is reduced from 200kW to 120kW, which cannot meet the full load dispatch demand; the real-time operation data refers to the real-time data collected by the power supply equipment during operation, reflecting its instantaneous working Quantitative information of the status, including parameters such as output power, load rate, temperature, voltage, and current, can dynamically reflect the real-time capabilities and status changes of the equipment. For example, the real-time data of a photovoltaic inverter shows: current output power 150kW, DC side voltage 600V, AC side current 220A, equipment temperature 38°C, and load rate 75%. These data are updated every 5 seconds, providing an immediate basis for the dispatch center to judge its regulation potential. Optionally, the query of the device operating status corresponding to the power supply device in the target blockchain can be implemented by calling the blockchain smart contract interface, such as using the Hyperledger Fabric SDK to initiate a transaction to query the device status information, and finally obtain the device operating status; the collection of real-time operating data corresponding to the device operating status can be implemented through the industrial Internet of Things sensor data collection protocol, such as using the OPC UA protocol to connect to the device edge node to read real-time parameters, and finally obtain real-time operating data.

[0023] Furthermore, the present invention generates a device collaboration cluster corresponding to the power supply equipment based on the real-time operation data combined with the preset power grid scheduling requirements, which can make the device grouping more in line with the actual operation capacity and the current needs of the power grid, avoiding resource mismatch; at the same time, it breaks down large-scale scheduling tasks into collaborative operations within the cluster, reduces the data processing pressure of a single node, and improves the response speed; and can also enhance the collaboration between devices through cluster management.

[0024] Among them, the preset grid dispatching demand refers to the dispatching instructions or indicators pre-established by the grid operator based on regional power supply and demand forecasts, safe operation standards and specific goals (such as energy conservation and emission reduction, peak leveling), which are used to guide the output adjustment and coordinated cooperation of power supply equipment and are an important basis for generating equipment collaborative clusters. For example, during the summer peak electricity consumption period (2:00 PM - 4:00 PM) in a certain region, the preset dispatching requirements are as follows: the total regional load must be stabilized at 800 MW - 850 MW, the total output of wind power and photovoltaic power must account for no less than 30%, and energy storage equipment must maintain a spare capacity of more than 200 MW to cope with sudden load fluctuations. The equipment collaboration cluster refers to a combination of equipment that can collaboratively perform specific dispatching tasks, selected from the equipment grouping range based on dispatching compatibility characteristics. The equipment within the group forms an overall dispatching capability through complementary advantages. For example, for the "supply and demand balance between 12:00 PM and 2:00 PM" task, a cluster consisting of two photovoltaic equipment (total output 150 MW), one energy storage equipment (spare capacity 50 MW), and one gas turbine (peak shaving range ±30 MW) can meet the region's real-time load demand of 180 MW through collaborative adjustment.

[0025] As an embodiment of the present invention, the device collaboration cluster corresponding to the power supply device is generated based on the real-time operation data in combination with the preset power grid scheduling requirements, including: extracting the key equipment indicators in the real-time operation data; parsing the collaboration potential value corresponding to the power supply device based on the key equipment indicators in combination with the preset power grid scheduling requirements; mapping the device group range corresponding to the collaboration potential value; analyzing the scheduling compatibility characteristics corresponding to the devices within the device group range; and generating the device collaboration cluster corresponding to the power supply device based on the scheduling compatibility characteristics.

[0026] Among them, the key indicators of the equipment refer to quantitative parameters extracted from real-time operation data that can reflect the core performance and operating status of the power supply equipment, including maximum output, regulation rate, current load rate, response delay, operating temperature, etc., which are the basis for evaluating the collaborative ability of the equipment. For example, the key indicators of a certain energy storage device are: maximum charging and discharging power 200kW, regulation rate 30kW / minute, current load rate 60%, response delay ≤2 seconds, and operating temperature 35°C. These indicators directly affect its collaborative potential with other equipment; the collaborative potential value refers to the final value obtained by quantifying the adjustment margin characteristics, integrating the immediate performance index and the continuous scheduling effectiveness, to measure the overall ability of the equipment to participate in collaborative scheduling. It is used to judge the adaptability of the equipment in the cluster. For example, a certain device has an immediate index of 80, a continuous effectiveness value of 90, an adjustment margin of ±40MW, and a collaborative potential value of 85 after comprehensive calculation; another device has an index of 75, an effectiveness value of 70, a margin of ±20MW, and a potential value of 65. The former is more suitable Cooperation is the core equipment of the cluster; the equipment grouping range refers to the boundary of the set of equipment that can be included in the same collaborative cluster based on the collaborative potential value, which is usually divided by potential value range to ensure that the equipment in the group has a similar collaborative basis. For example, equipment with a potential value of 80-100 points is classified into the "core collaborative group", which includes 3 energy storage devices (potential values ​​85, 92, and 88 points) and 2 gas turbines (potential values ​​90 and 86 points); equipment with a score of 60-79 is classified into the "auxiliary collaborative group" as supplementary force; the scheduling compatibility characteristics refer to the common or complementary characteristics of the equipment within the equipment grouping range in terms of scheduling response mode, operating parameter matching, task execution coordination, etc., which determine whether the equipment can efficiently cooperate to complete the scheduling task. For example, all equipment within a certain grouping range has the characteristics of "responding to power adjustment instructions within 5 seconds" and "output fluctuation range ≤5%", and the charging and discharging periods of the energy storage equipment complement the output peak of the photovoltaic equipment. These are all scheduling compatibility characteristics.

[0027] Furthermore, the extraction of key equipment indicators from the real-time operation data can be achieved through a time series feature extraction algorithm, such as: using a sliding window statistical method to calculate the equipment load rate and energy efficiency ratio, and finally obtaining the key equipment indicators; the mapping of the equipment grouping range corresponding to the collaboration potential value can be achieved through a clustering analysis method, such as: using the K-means algorithm to divide dynamic groups based on the similarity of equipment operation, and finally obtaining the equipment grouping range; the analysis of the scheduling compatibility characteristics corresponding to the equipment within the equipment grouping range can be achieved through an association rule mining algorithm, such as: applying the Apriori algorithm to identify the collaborative response pattern between devices, and finally obtaining the scheduling compatibility characteristics; the generation of the equipment collaboration cluster corresponding to the power supply equipment can be achieved through a distributed resource scheduling framework, such as: dynamically building a device virtual cluster based on the Kubernetes container orchestration engine, and finally obtaining a device collaboration cluster.

[0028] As another embodiment of the present invention, the collaboration potential value corresponding to the power supply equipment is analyzed based on the key indicators of the equipment in combination with the preset power grid scheduling requirements, including: extracting dynamic / steady-state data from the key indicators of the equipment; based on the dynamic / steady-state data, analyzing the instantaneous performance index and continuous scheduling effectiveness value of the power supply equipment that meet the preset power grid scheduling requirements; mapping the matching value range of the instantaneous performance index and the continuous scheduling effectiveness value to the power grid scheduling requirements; analyzing the adjustment margin characteristics corresponding to each power supply equipment within the matching value range; and quantifying the collaboration potential value corresponding to the adjustment margin characteristics.

[0029] Among them, the dynamic / steady-state data refer to two types of characteristic data distinguished from the key indicators of the equipment: dynamic data reflects the real-time response capability of the equipment as the working conditions change, such as power regulation rate, response delay, etc.; steady-state data reflects the inherent performance of the equipment during stable operation, such as rated power, maximum continuous output time, etc. For example, the dynamic data of a wind turbine is "power regulation rate 20MW / minute, response delay 3 seconds", and the steady-state data is "rated power 150MW, continuous full-load operation time ≥8 hours". The two together form the basis for evaluating the potential for collaboration; the instant performance index refers to a measure of the power supply equipment in scheduling, calculated based on dynamic data. A quantitative indicator of immediate response capability when a command is issued, reflecting whether the equipment can quickly meet the short-term needs of the power grid. For example, when the dispatch demand is "increase 20MW output within 10 seconds", a certain energy storage device can complete the adjustment within 8 seconds due to its adjustment rate of 5MW / second and response delay of 2 seconds, and the immediate performance index is 90; another photovoltaic device has an adjustment rate of 1MW / second and takes 20 seconds to complete, and the index is only 45; the continuous dispatch effectiveness value refers to an indicator based on steady-state data that reflects the ability of power supply equipment to stably perform dispatch tasks over a period of time, reflecting the level of support the equipment provides for the long-term needs of the power grid. For example, the dispatch requirement is "maintain 100M for 1 hour continuously." W output", a gas turbine with a rated power of 120MW and a continuous full-load operation capacity of ≥2 hours has a continuous dispatching efficiency of 95; a battery energy storage has a capacity limitation and can only operate at full load for 40 minutes, with an efficiency of 60, and requires frequent charging and discharging. The matching value range refers to the adaptation range between the equipment capacity and the grid dispatching demand mapped by combining the immediate performance index with the continuous dispatching efficiency, which is used to define whether the equipment has a collaborative basis. For example, when the dispatching demand is "quick response and continuous output for 2 hours", the matching value range is set to "immediate index ≥70, continuous efficiency ≥80". A certain equipment has an index of 85 and an efficiency of 88, which is in this range. The equipment with an index of 65 and an efficiency value of 90 is excluded due to insufficient immediate capacity. The regulation margin characteristic refers to the additional power adjustment space and flexibility characteristics that the equipment within the matching value range can bear under the current operating state, including the maximum increase / decrease output and the adjustment range. For example, the current output of a photovoltaic device is 80MW (rated 100MW), and the regulation margin characteristic is "output can be increased by 20MW, output can be reduced by 50MW, and the adjustment range is -50MW to +20MW"; the current output of a wind turbine is 120MW (rated 150MW), and the margin is "output can be increased by 30MW, output can be reduced by 80MW", reflecting the differences in regulation potential of different equipment.

[0030] Furthermore, the extraction of dynamic / steady-state data from the key indicators of the equipment can be achieved through a signal decomposition algorithm, such as: using the empirical mode decomposition (EMD) method to separate the transient and steady-state components of the equipment operation data, and finally obtaining the dynamic / steady-state data; the analysis of the instantaneous performance index of the power supply equipment to meet the preset grid scheduling requirements can be achieved through a real-time performance evaluation model, such as: constructing a comprehensive evaluation system of response speed and accuracy based on fuzzy logic, and finally obtaining the instantaneous performance index; the analysis of the continuous scheduling effectiveness of the power supply equipment to meet the preset grid scheduling requirements can be achieved through a time series prediction method, such as: using an LSTM neural network to predict the stable power supply capability of the equipment in the future scheduling cycle, and finally obtaining the continuous scheduling effectiveness. ; The mapping of the instantaneous performance index and the continuous scheduling effectiveness value to the matching value range of the power grid scheduling demand can be achieved through multi-dimensional space mapping technology, such as: applying the radial basis function (RBF) interpolation algorithm to construct a three-dimensional performance matching surface, and finally obtaining the matching value range; the analysis of the adjustment margin characteristics corresponding to each power supply device within the matching value range can be achieved through a sensitivity analysis method, such as: using the Sobol index method to quantify the degree of influence of equipment parameter fluctuations on the matching degree, and finally obtaining the adjustment margin characteristics; the quantification of the collaboration potential value corresponding to the adjustment margin characteristic can be achieved through a multi-objective optimization algorithm, such as: solving the equipment collaborative optimal solution set based on the NSGA-II non-dominated sorting genetic algorithm, and finally obtaining the collaboration potential value.

[0031] Specifically, to further understand the execution logic and data flow relationship of the power equipment coordinated scheduling system in this solution, please refer to Figure 2 The architecture diagram of the power equipment collaborative scheduling system (based on the target blockchain) in the Figure 2 As the core framework of the power equipment dispatching system, it clearly presents the complete chain from basic data support to cluster dispatching output: the target blockchain, as a distributed ledger system, stores equipment history records and dispatching instructions, which is the fundamental guarantee for data credibility and traceability. The data collection and status query module focuses on equipment operating status (normal / overload, etc.) and real-time operating data (output / temperature, etc.), providing a dynamic basis for dispatching decisions. The cluster generation core logic module integrates power equipment parameters (maximum output, regulation rate, etc.) based on grid dispatching requirements (such as midday load and wind and solar power ratio requirements) to build a coordinated dispatching equipment cluster. The relationship between the various modules in the architecture is essentially an abstract distillation of the blockchain-enabled power equipment dispatching logic. In actual scenarios, the complexity of data interaction (such as real-time data synchronization of multiple devices and blockchain node consensus mechanism) and the diversity of dispatching adaptation (dynamic reorganization of equipment clusters under different operating conditions) are far greater than what is presented in the diagram. This architecture only provides a concise display of the core logic, providing an intuitive reference for understanding the systematic approach of blockchain-based power equipment coordinated dispatching.

[0032] S2. Detecting a scheduling task in the device collaboration cluster, analyzing a power adjustment instruction corresponding to the scheduling task, querying a device execution index corresponding to the power adjustment instruction, and calculating a device response threshold corresponding to the device execution index.

[0033] By detecting the scheduling tasks in the device collaboration cluster and analyzing the power adjustment instructions corresponding to the scheduling tasks, the present invention can accurately locate the specific scheduling goals that the cluster needs to execute and clarify the power adjustment direction and amplitude of each device; at the same time, it can identify the potential requirements and constraints in task execution in advance, providing a basis for subsequent evaluation of device execution capabilities.

[0034] Among them, the dispatching task refers to the power dispatching target issued to the equipment collaborative cluster and needs to be completed within a specific time period. It is usually formulated based on the real-time supply and demand changes of the power grid, safe operation requirements or preset plans, and clarifies the specific responsibilities of the cluster such as power balance and load regulation. For example, the dispatching task received by a collaborative cluster from 10:00 to 11:00 is to make up for the 200MW power supply gap of the regional power grid, while controlling the total loss of equipment in the cluster to within 5%, and ensuring that the frequency is stable within the range of 50±0.2Hz to ensure the reliability of power supply during this period; the power adjustment instruction refers to the power adjustment instruction issued to the equipment collaborative cluster or a specific device in the cluster in order to complete the dispatching task. Adjustment commands issued by devices regarding power output or load, including key information such as the adjustment direction (increase / decrease), adjustment amount, and execution time limit, are the direct basis for device collaborative action. For example, for the above-mentioned scheduling task, the power adjustment command issued to the photovoltaic devices in the cluster is "increase the total output from the current 150MW to 180MW before 10:05," and the command issued to the energy storage device is "continuously release 30MW of power from 10:00 to 11:00, and feedback the actual output value every 10 minutes." Optionally, the detection of scheduling tasks in the device collaborative cluster can be implemented using a distributed task monitoring framework, such as using an Apache Kafka message queue to capture control commands issued by the power grid dispatching center in real time, ultimately obtaining scheduling tasks. The analysis of the power adjustment commands corresponding to the scheduling tasks can be implemented using a dynamic optimization algorithm, such as using a model predictive control (MPC) method to calculate the optimal power allocation plan for each device in the cluster, ultimately obtaining power adjustment commands.

[0035] Furthermore, the present invention can evaluate the actual ability of the device to execute instructions and clarify its adaptation boundary in the scheduling task by querying the device execution index corresponding to the power adjustment instruction and calculating the device response threshold corresponding to the device execution index; at the same time, it can predict in advance whether the device can complete the adjustment under the specified conditions, thereby ensuring the stability and efficiency of the scheduling process.

[0036] Among them, the device execution index refers to a quantitative indicator that is calculated based on the execution performance data and comprehensively measures the device's ability to execute power adjustment instructions. The higher the value, the better the execution effect. It is the core basis for judging whether the device can meet the instruction requirements. For example, combined with the adjustment rate, deviation, delay and other data, the execution index of a certain device is calculated to be 89 points (out of 100), indicating that it can complete the instruction efficiently; another device has a large deviation and the index is 62 points, requiring auxiliary equipment to cooperate in execution; the device response threshold refers to a key indicator that reflects the response capability boundary of the device when executing the power adjustment instruction. It comprehensively considers the device characteristics and scheduling requirements to quantify the time threshold allowed for the device to complete the power adjustment. For example, the calculated value is , which means that the device must complete the corresponding power adjustment within 10 seconds to meet the scheduling response requirements, and is used to judge the response speed of the device.

[0037] As an embodiment of the present invention, the querying of the device execution index corresponding to the power adjustment instruction includes: analyzing the power peak and valley values ​​corresponding to the power adjustment instruction; dividing the power adjustment interval corresponding to the power adjustment instruction based on the power peak and valley values; determining the response capability levels corresponding to different devices in the power supply device based on the power adjustment interval; analyzing the execution performance data corresponding to the response capability level; and querying the device execution index corresponding to the execution performance data.

[0038] Among them, the power peak and valley values ​​refer to the extreme power changes involved in the power adjustment instruction, including the maximum power value (peak value) and the minimum power value (valley value) during the adjustment process, reflecting the power fluctuation range and amplitude required by the instruction, and is the basis for dividing the adjustment interval. For example, a power adjustment instruction requires the equipment to increase from 50MW to 120MW and then decrease to 70MW within 1 hour, of which the peak value is 120MW and the valley value is 50MW. The difference of 70MW between the two reflects the power fluctuation intensity of the instruction; the power adjustment interval refers to the power adjustment stage range that the equipment needs to complete step by step based on the power peak and valley values. Each interval corresponds to a specific power change amplitude and time node, making complex adjustment tasks easier to execute and monitor. For example, for instructions with peak and valley values ​​of 120MW and 50MW, it can be divided into 3 intervals: 0-20 minutes from 50MW to 80MW, 20-40 minutes from 80MW to 120MW, and 40-60 minutes from 120MW to 70MW. Each interval clearly defines the stage The responsiveness level refers to the level of execution capability of different power supply devices within a specific power adjustment range. It is typically determined based on differences in the device's adjustment speed, accuracy, stability, and other factors, and reflects the device's adaptability to the tasks within the range. For example, in the range of "increasing from 80MW to 120MW within 20 minutes," energy storage device A is rated "Excellent" due to its adjustment rate of 2MW / minute and fluctuation of ≤2MW; wind turbine device B is rated "Medium" due to its adjustment rate of 1MW / minute and fluctuation of ±5MW. The execution performance data refers to the quantitative parameters generated by the device during actual power adjustment at the corresponding responsiveness level, including actual adjustment rate, power deviation, response delay, energy consumption, etc., and serves as the specific basis for evaluating the device's execution performance. For example, the execution performance data of a device at the "Excellent" response level are: average adjustment rate of 2.1MW / minute, maximum power deviation of 1.5MW, response delay of 1.2 seconds, and energy consumption of 0.05kWh per MW adjusted. These data directly reflect its execution quality.

[0039] Furthermore, the analysis of the power peak and valley values ​​corresponding to the power adjustment instruction can be achieved through an extreme value detection algorithm, such as: using a sliding window extreme value analysis method to identify the peak and valley characteristic points of the power curve, and ultimately obtaining the power peak and valley values; the division of the power adjustment interval corresponding to the power adjustment instruction can be achieved through a dynamic threshold segmentation method, such as: using the Jenks natural break point classification method to divide the power demand into multiple intervals, and ultimately obtaining the power adjustment interval; the determination of the response capability levels corresponding to different devices in the power supply equipment can be achieved through a multi-dimensional evaluation model, such as: constructing a comprehensive evaluation system based on entropy weight-TOPSIS to quantify the response speed and accuracy of the device, and ultimately obtaining the response capability level; the analysis of the execution performance data corresponding to the response capability level can be achieved through time series similarity calculation, such as: applying a dynamic time warping (DTW) algorithm to match the actual response curve with the instruction curve to obtain the execution performance data; the query of the device execution index corresponding to the execution performance data can be achieved through a normalized scoring method, such as: using Min-Max normalization processing to convert the multi-dimensional performance data into a comprehensive index in the range of 0-1, and ultimately obtaining the device execution index.

[0040] As another embodiment of the present invention, the device response threshold corresponding to the device execution index may be calculated using the following formula: ; in, Indicates the device response threshold corresponding to the device execution index (unit: s), Indicates the device time constant (unit: s), Indicates the power adjustment range (unit: W). Indicates the device execution index, Indicates the reference power (unit: W), represents the system constraint factor, represents the constraint function, represents the efficiency reduction coefficient, Represents the conflict sensitivity coefficient.

[0041] Specifically, the device time constant refers to the time characteristic of the device power regulation, for example, a certain energy storage device , indicating that its power change from the initial to the stable value of 63.2%, theoretically takes 2 seconds; the larger the value, the more obvious the equipment adjustment delay, like traditional thermal power equipment It may take tens of seconds, affecting the response speed. The power adjustment range refers to the power difference that the dispatch instruction requires the device to adjust, which is the absolute value of "target power - initial power". If the instruction requires the device to increase from 500W to 800W, then , which determines the amount of power that the device needs to mobilize. The larger the value, the higher the difficulty of adjustment and the higher the performance requirements for the device. The device execution index refers to a comprehensive quantitative value that measures the device's ability to execute power adjustment instructions. It is calculated based on the device's historical execution data and performance. For example, the device can complete multiple dispatches accurately and quickly. (The value range is 0-1, the closer to 1, the stronger the capability); a high index indicates that the device has good execution stability and efficiency, and is the core indicator for evaluating device adaptability; the reference power refers to the benchmark power value set by the system, which is used for normalization calculation to eliminate the differences between devices of different power levels. Assuming that the system reference power When the device adjusts power, this value is used as a reference, and the actual power adjustment is converted into a relative value for calculation, making the response thresholds of devices of different sizes comparable. The system constraint factor reflects the grid system's restrictions on device scheduling. It is a coefficient set based on factors such as grid capacity and safety margin. If the grid load rate is high and the backup capacity is small, K = 0.6 (range 0-1, with smaller values ​​indicating stronger constraints). It limits the "degrees of freedom" of device power adjustment. The stronger the constraint, the more precise the device's control over the adjustment process. The constraint function is the inverse hyperbolic tangent function, which is used in the formula to "compress" the input value range, mapping complex influences such as system constraints and device parameters to a reasonable range, making the calculation more stable. For example, the input value can be processed by the function to normalize the dispersed parameter influences to the range (-1, 1) (the actual calculation is combined with the coefficient and then adapted to the threshold logic), avoiding abnormal results caused by extreme values ​​and ensuring the rationality of the formula output. The efficiency attenuation coefficient refers to the degree of efficiency loss caused by changes in operating conditions (such as deviation from rated power or frequent adjustments) during the power adjustment process. If the device suffers significant efficiency loss under non-rated operating conditions, (The value range is 0-1, the closer to 1, the smaller the efficiency attenuation); for example, when the charge and discharge depth of the energy storage device is too large, The conflict sensitivity coefficient refers to the conflict risk between the power adjustment of the quantified equipment and other equipment and system constraints in the power grid. If the coordinated scheduling of equipment in the power grid is complex, power allocation conflicts are likely to occur. (A larger value indicates a higher conflict sensitivity.) When a device's power adjustment might occupy the adjustment space of other devices, this coefficient amplifies the impact of the conflict, prompting more cautious threshold calculation and avoiding scheduling conflicts.

[0042] S3. Based on the device response threshold, determine the scheduling conflict point of the power supply device in the scheduling process, analyze the task scale and execution time limit corresponding to the scheduling conflict point, and calculate the time offset of the power supply device for the execution process based on the task scale and the execution time limit.

[0043] The present invention determines the scheduling conflict points of the power supply equipment during the scheduling process based on the device response threshold, can accurately identify the conflicting links caused by the mismatch of response capabilities when the equipment executes the scheduling instructions, and provide early warning of potential scheduling risks; provide a basis for optimizing the scheduling strategy, and avoid conflicts that affect the stable operation of the power grid by adjusting the instruction allocation or the device coordination mode, thereby improving the reliability and efficiency of scheduling execution.

[0044] Among them, the scheduling conflict point refers to the specific conflict node that actually or will occur in the scheduling process, determined based on the conflict identifier. It clarifies the time when the conflict occurs, the equipment involved and the cause of the conflict. It is the direct object of scheduling optimization. For example, the scheduling conflict point located according to the identifier is "During the period of 14:00-14:10, the equipment WT-03 and PV-07 cannot complete the power adjustment requirement of the scheduling task 'Increase 25MW output before 14:05' because the response threshold exceeds 20 seconds", which requires targeted resolution.

[0045] As an embodiment of the present invention, determining the scheduling conflict point of the power supply device in the scheduling process based on the device response threshold includes: querying the threshold change trend corresponding to the device response threshold; determining the key conflict characteristics of the scheduling process based on the threshold change trend; locating the potential conflict unit in the device collaboration cluster based on the key conflict characteristics; extracting the conflict identifier in the potential conflict unit; and determining the scheduling conflict point of the power supply device in the scheduling process based on the conflict identifier.

[0046] Among them, the threshold change trend refers to the dynamic trend of the device response threshold with time, power adjustment amount or working conditions, reflecting the fluctuation of the device's response ability in different scheduling stages. For example, the response threshold of a certain energy storage device is 8 seconds in the initial stage. As the continuous scheduling time increases, it gradually rises to 12 seconds after 30 minutes and reaches 15 seconds after 1 hour, showing a gradual upward trend, indicating that the response ability of the device decreases after long-term operation, which may cause subsequent scheduling conflicts; the key conflict characteristics refer to the core features that may cause scheduling conflicts extracted based on the threshold change trend, including the probability of conflict occurrence, impact range, duration and severity, etc. For example, when the response thresholds of three devices in a collaborative cluster suddenly increase from 10 seconds to 25 seconds within 5 minutes, and the power adjustment of ±50MW needs to be completed during this period, its key conflict characteristics are "high probability conflict (80%), impact range covering the entire cluster, duration, etc." The duration is about 10 minutes, which needs to be handled with priority. The potential conflict unit refers to the equipment collaboration sub-unit that is screened out by the unit conflict index, has a high conflict risk, and requires special attention and intervention. For example, among the candidate units, units with an index ≥ 0.6 are determined to be potential conflict units. For example, unit B has an index of 0.82. Due to the delayed response of the equipment and the imbalance of power coordination, it is very likely to cause conflicts in scheduling and needs to be handled with priority. The conflict identifier refers to an information tag extracted from the potential conflict unit to uniquely mark the conflict characteristics, including the conflict type (such as response delay, insufficient power), the ID of the device involved, the associated scheduling task number and the risk level. For example, the identifier of a potential conflict unit is "Type: Response Delay; Device ID: WT-03, PV-07; Task Number: Scheduling-20230705-08; Risk Level: High". This identifier can be used to quickly locate and distinguish different conflicts.

[0047] Furthermore, the query of the threshold change trend corresponding to the device response threshold can be achieved through a time series prediction algorithm, such as: using the ARIMA model to analyze the periodic fluctuation law of historical threshold data, and finally obtaining the threshold change trend; the determination of the key conflict characteristics in the scheduling process can be achieved through a conflict feature extraction method, such as: applying the principal component analysis (PCA) algorithm to reduce the dimension and extract the mutually exclusive parameter combination in the scheduling instruction, and finally obtaining the key conflict characteristics; the positioning of the potential conflict unit in the device collaboration cluster can be achieved through a graph theory analysis method, such as: based on the community discovery algorithm to identify the device subgroup with high coupling degree in the cluster topology structure, and finally obtaining the potential conflict unit; the extraction of the conflict identifier in the potential conflict unit can be achieved through pattern recognition technology, such as: using the support vector machine (SVM) classifier to detect abnormal interaction features in the device operation log, and finally obtaining the conflict identifier; the determination of the scheduling conflict point of the power supply device in the scheduling process can be achieved through an event chain tracing method, such as: using a causal reasoning engine to analyze the instruction execution timing conflict in the multi-device collaboration process, and finally obtaining the scheduling conflict point.

[0048] As another embodiment of the present invention, locating the potential conflict units in the device collaboration cluster based on the key conflict characteristics includes: querying the conflict distribution pattern corresponding to the key conflict characteristics; determining the conflict hotspot area in the device collaboration cluster based on the conflict distribution pattern; dividing the conflict candidate units in the cluster based on the conflict hotspot area; analyzing the unit conflict index corresponding to the conflict candidate unit; and locating the potential conflict unit in the device collaboration cluster based on the unit conflict index.

[0049] Among them, the conflict distribution pattern refers to the distribution law of key conflict characteristics in the equipment collaboration cluster, covering the manifestation of conflicts in dimensions such as space and equipment association, and reflecting the characteristics of conflict propagation and aggregation. For example, in a certain cluster, conflicts caused by power fluctuations are distributed in a chain manner in the "wind power-energy storage" linkage area. The failure of three wind power equipment will trigger conflicts in two energy storage equipment in turn, forming a propagation path of "equipment A→equipment B→equipment C", thereby predicting the spread trend of conflicts; the conflict hotspot area refers to a local area in the cluster where conflicts occur frequently and have a large impact based on the conflict distribution pattern. It is the key range for concentrated conflict outbreaks. For example, the "photovoltaic array zone 3 + supporting inverter group" in the cluster triggered 5 warnings due to power adjustment conflicts within 1 hour, far exceeding the average frequency of 1 in other areas. This area is a conflict hotspot. It requires key monitoring; the conflict candidate unit refers to a subset of devices that may have conflict risks, which is obtained by dividing the cluster according to the conflict hotspot area. It is the basis for further screening potential conflict units. For example, in the hotspot area, "PV strings 3-1 to 3-5 + inverter 3-1" is divided into a candidate unit. This unit contains 5 groups of PV strings and 1 inverter. Due to the poor equipment coordination, it becomes a candidate for conflict investigation; the unit conflict index refers to an indicator that quantitatively evaluates the unit conflict risk by comprehensively considering parameters such as the device response threshold and power adjustment deviation of the conflict candidate unit. The higher the value, the greater the possibility of conflict. For example, for candidate unit A, the average device response threshold exceeds the standard by 15%, and the power adjustment deviation reaches 8%. The unit conflict index calculated by the formula is 0.75 (0-1, the closer to 1, the higher the risk), reflecting the degree of its conflict risk.

[0050] Furthermore, the query of the conflict distribution pattern corresponding to the key conflict characteristics can be achieved through a spatial density clustering algorithm, such as: applying the OPTICS algorithm to analyze the spatial clustering characteristics of conflict events, and finally obtaining the conflict distribution pattern; the determination of the conflict hotspot area in the device collaboration cluster can be achieved through a heat map rendering technology, such as: using the QGIS geographic information system tool to generate a conflict intensity heat distribution map, and finally obtaining the conflict hotspot area; the division of the conflict candidate units in the cluster can be achieved through a graph segmentation algorithm, such as: using a spectral clustering method to divide the device interaction network into highly cohesive subgraphs, and finally obtaining the conflict candidate units; the analysis of the unit conflict index corresponding to the conflict candidate unit can be achieved through a fuzzy comprehensive evaluation method, such as: constructing a conflict severity assessment model based on triangular fuzzy numbers, and finally obtaining the unit conflict index; the positioning of the potential conflict unit in the device collaboration cluster can be achieved through an abnormal pattern mining technology, such as: using a local outlier factor (LOF) algorithm to detect abnormal connection nodes in the device interaction network, and finally obtaining the potential conflict unit.

[0051] By analyzing the task scale and execution time limit corresponding to the scheduling conflict point, the present invention can clearly grasp the task magnitude and time requirements involved in the conflict, and provide a basis for evaluating the scope and urgency of the conflict; it can assist in optimizing resource allocation, match response capabilities according to task scale, and plan the handling rhythm in combination with the execution time limit, effectively reducing the interference of conflicts on power grid scheduling and improving the efficiency of solving scheduling problems.

[0052] Among them, the task scale refers to a quantitative indicator that measures the total amount and complexity of scheduling tasks involved in the scheduling conflict point, including the total power that needs to be adjusted, the number of equipment involved, the power change amplitude, etc., reflecting the impact range of the conflict on the power grid and the difficulty of handling. For example, the task scale corresponding to a certain scheduling conflict point is: a total power of 80MW needs to be adjusted in the regional power grid, involving 12 power supply equipment (including 5 photovoltaics, 4 energy storages, and 3 wind power). The maximum adjustment amount of a single device is 25MW, and multiple devices need to collaborate synchronously. The overall scale is large, and more resources need to be coordinated during processing; the execution time limit refers to the time range in which the task corresponding to the scheduling conflict point must be completed, including the task start time, end time, and allowed execution time. It is the key basis for judging the urgency of the task. For example, A certain scheduling conflict point requires "30MW power compensation to be completed between 14:00 and 14:10", with an execution time limit of 10 minutes, where 14:00 is the start time and 14:10 is the end time. If the equipment response delay exceeds 5 minutes, the task will not be completed on time, and resource allocation within this time period must be prioritized. Optionally, the analysis of the task scale corresponding to the scheduling conflict point can be achieved through a resource demand estimation algorithm, such as: using a Monte Carlo simulation method to calculate the computing resources and communication bandwidth required for the conflicting task, and finally obtaining the task scale; the analysis of the execution time limit corresponding to the scheduling conflict point can be achieved through a critical path analysis method, such as: using a PERT chart technique to identify the longest dependent path time in the scheduling task, and finally obtaining the execution time limit.

[0053] Furthermore, the present invention calculates the time offset of the power supply device for the execution process based on the task scale and the execution time limit, which can accurately quantify the degree of deviation between the actual execution progress of the equipment and the plan, and provide data support for evaluating the time impact of scheduling conflicts; it can help identify the lag or advance of the equipment in task execution, and provide a basis for subsequent optimization of scheduling rhythm and adjustment of coordination strategies.

[0054] The time offset is used to quantify the deviation between the actual time of the power supply equipment executing the scheduled task and the ideal / planned time. It is based on factors such as the peak demand during the task conflict period and the equipment response threshold, and reflects the lag or advance of the equipment execution progress. For example, the calculated , indicating that the actual execution time of the equipment is 5 seconds longer than the theoretical / planned time. It is a key indicator for judging the efficiency of scheduling execution and identifying the impact of conflicts.

[0055] As an embodiment of the present invention, the calculation of the time offset of the power supply device for the execution process based on the task scale and the execution time limit includes: extracting the total amount of scheduling instructions corresponding to the task scale; querying the maximum execution time corresponding to the execution time limit; identifying the task conflict period corresponding to the power supply device based on the total amount of scheduling instructions and the maximum execution time; extracting the task demand peak in the task conflict period; and calculating the time offset of the power supply device for the execution process based on the task demand peak.

[0056] Among them, the total amount of scheduling instructions refers to the total load or total adjustment of scheduling instructions that need to be executed by power supply equipment corresponding to the task scale, covering the absolute value sum of power adjustment, the number of instructions involved, etc., reflecting the overall workload of the task. For example, the total amount of scheduling instructions corresponding to the task scale of a certain scheduling conflict point is: total power adjustment amount 150MW (including 80MW increase instructions and 70MW decrease instructions), involving 20 specific equipment operation instructions, which need to be completed in coordination within the specified time limit. The larger the total amount, the higher the equipment execution pressure; the maximum execution time refers to the maximum time span allowed to complete the task within the execution time limit, that is, the maximum allowable time from the start to the end of the task, which is a key time parameter for measuring the urgency of the task. For example, if an execution time limit is set to "10:00 to 10:30", the maximum execution time is 30 minutes; if the task needs to be completed in a shorter time, such as "must be completed before 10:10", the maximum execution time is shortened to 10 minutes, which has a greater impact on the equipment execution efficiency. Put forward higher requirements; the task conflict period refers to the specific time period in which the power supply equipment is unable to complete the instructions on time due to insufficient execution capability based on the total amount of scheduling instructions and the maximum execution time. It is a concentrated manifestation of time conflict. For example, when the total amount of scheduling instructions is 200MW, the maximum execution time is 20 minutes, and the maximum adjustment rate of the equipment is 8MW / minute, in the 15-20 minute period, the cumulative adjustable amount of the equipment is 160MW, and the remaining 40MW cannot be completed. This period is the task conflict period; the task demand peak refers to the maximum instantaneous demand of the scheduling instruction for the power supply equipment during the task conflict period, which is usually manifested as the highest value of the power adjustment demand at a certain moment, reflecting the task pressure peak during the conflict period. For example, in the 15-20 minute task conflict period, the instantaneous power adjustment to be completed at 18 minutes is 50MW, far exceeding the 30-40MW at other times. The 50MW is the task demand peak, which is the key node that causes equipment execution delay.

[0057] Furthermore, the extraction of the total amount of scheduling instructions corresponding to the task scale can be achieved through an instruction stream analysis algorithm, such as: using a sliding window counting method to count the number of scheduling instructions per unit time, and finally obtaining the total amount of scheduling instructions; the query of the maximum execution time corresponding to the execution time limit can be achieved through an extreme value analysis method, such as: using a box plot statistical technique to identify the maximum outlier in the historical execution data, and finally obtaining the maximum execution time; the identification of the task conflict period corresponding to the power supply equipment can be achieved through time series pattern mining, such as: applying a K-means clustering algorithm to group the high overlapping periods of the equipment load curve, and finally obtaining the task conflict period; the extraction of the task demand peak in the task conflict period can be achieved through signal processing technology, such as: using a wavelet transform to decompose the equipment load signal and extract the high-frequency peak component, and finally obtaining the task demand peak; the calculation of the time offset of the power supply equipment for the execution process can be achieved by the following formula.

[0058] As another embodiment of the present invention, the time offset of the power supply device for the execution process can be calculated by the following formula: ; in, Indicates the time offset of the power supply device for the execution process (unit: s), represents the total number of task conflict periods, represents the number index of the task conflict period, represents the peak value of task demand during the i-th task conflict period (unit: W), Indicates the rated power of the power supply (unit: W). represents the device response threshold during the i-th task conflict period (unit: s), represents the power attenuation factor, Indicates the number of scheduling instructions that the power supply can process per second. Indicates the device execution index.

[0059] Specifically, the rated power refers to the maximum power that the power supply equipment can output stably and sustainably under the design standard working conditions. It is the basic parameter of equipment performance. For example, the rated power of a photovoltaic device is , which means that under ideal conditions such as light and temperature, it can output up to 5000 watts of electricity per hour; it is used as a benchmark in the formula to normalize the matching relationship between the peak value of task demand and the equipment capacity, and to measure the basic potential of the equipment to cope with the task; the power attenuation factor refers to the degree of attenuation of the output power below the rated power due to factors such as aging and environmental interference (such as excessive temperature and component loss) in actual operation of the equipment. It is a coefficient between 0 and 1. If the actual output power of the equipment is only 80% of the rated power due to long-term operation, dust accumulation, etc., then This factor is introduced into the formula to correct the impact of power loss caused by non-ideal device conditions on time offset, making the calculation more accurate for actual operating scenarios. Defined as max(0,x), it means that only when the power demand exceeds the rated power (i.e. > ) to consider positive deviations (to prevent negative values).

[0060] S4. Determine a consensus verification path corresponding to the scheduling instruction in the power supply device according to the time offset, and identify a path cooperation node in the consensus verification path.

[0061] The present invention determines the consensus verification path corresponding to the scheduling instruction in the power supply device based on the time offset, can accurately associate the device execution deviation with the scheduling instruction logic, and clarify the reliable transmission and verification direction of the instruction in device collaboration; through differentiated paths to adapt to different offset conditions, the consistency and accuracy of instruction execution are guaranteed, the scheduling conflicts caused by time deviation are reduced, and the reliability and efficiency of cluster collaborative scheduling are improved.

[0062] Among them, the consensus verification path refers to the complete verification link of the scheduling instruction from the initiator to the execution end, which is determined based on the efficient consensus node, including the channel selection of instruction transmission, the node verification order and the data feedback path, to ensure that the instruction is traceable and the verification is valid in the blockchain. For example, a consensus verification path is "scheduling center → channel A → node X verification → node Y secondary verification → power supply equipment". The total delay of this path is 3.5 seconds, and the verification is completed within the scheduling execution period to ensure the credibility of the instruction execution.

[0063] As an embodiment of the present invention, determining the consensus verification path corresponding to the scheduling instruction in the power supply device based on the time offset includes: parsing the scheduling execution period corresponding to the time offset; calculating the period verification delay corresponding to the scheduling execution period; based on the period verification delay, screening the available verification channels corresponding to the network in the target blockchain; marking the efficient consensus nodes in the available verification channels; and determining the consensus verification path corresponding to the scheduling instruction in the power supply device based on the efficient consensus nodes.

[0064] Among them, the scheduling execution period refers to the specific time interval in which the power supply equipment actually executes the scheduling instruction based on the time offset analysis, including the time when the instruction starts to execute, the time when the execution is completed, and the total time, reflecting the actual operation window corresponding to the time deviation between the equipment execution and the plan. For example, a certain instruction is planned to be executed from 8:00 to 8:10. Due to the time offset of +3 minutes, the actual scheduling execution period is 8:00 to 8:13. This period requires the synchronous completion of instruction transmission, device response and result feedback, which is the time benchmark for consensus verification; the period verification delay refers to the time delay generated when the target blockchain network verifies the scheduling instruction and execution result during the scheduling execution period, including data transmission time, node consensus time, etc., which is a key indicator for measuring verification efficiency. For example, in the scheduling execution period of 8:00-8:13, due to node communication congestion, a verification process takes 2 seconds for data transmission and 3 seconds for consensus calculation. The total period verification delay is 5 seconds, which needs to be controlled within The available verification channel refers to a communication channel selected from the target blockchain network based on the verification delay during the period, which can complete verification within the scheduled execution period and meets the delay requirements, supporting the secure transmission and verification interaction of instruction data. For example, three available verification channels are selected, with channel A having a delay of ≤4 seconds, channel B having a delay of ≤6 seconds, and channel C having a delay of ≤5 seconds. They all meet the requirement that the verification delay does not exceed 8 seconds during the scheduled execution period and can be used as alternative channels. The efficient consensus node refers to a blockchain node marked in the available verification channel that has the ability to quickly process verification tasks. Its characteristics include high computing power, low response delay, and high reliability, which can accelerate the consensus verification process. For example, in the available verification channel, node X has a computing power of 500Mhash / s and a verification response delay of 1.2 seconds, and node Y has a computing power of 450Mhash / s and a delay of 1.5 seconds. Both are marked as efficient consensus nodes and can participate in verification first.

[0065] Furthermore, the parsing of the scheduling execution period corresponding to the time offset can be achieved through a time window partitioning algorithm, such as: using the dynamic time warping (DTW) method to align the planned and actual execution time series, and ultimately obtaining the scheduling execution period; the calculation of the period verification delay corresponding to the scheduling execution period can be achieved through a queuing theory model, such as: using the M / M / 1 queue model to analyze the average waiting time of blockchain transaction verification, and ultimately obtaining the period verification delay; the screening of the available verification channels corresponding to the network in the target blockchain can be achieved through a graph traversal algorithm, such as: applying the Dijkstra shortest path algorithm to select low-latency communication links in the network topology, and ultimately obtaining available verification channels; the marking of efficient consensus nodes in the available verification channels can be achieved through a performance scoring method, such as: constructing a node reputation scoring system based on historical block speed and stability, and ultimately obtaining efficient consensus nodes; the determination of the consensus verification path corresponding to the scheduling instruction in the power supply device can be achieved through a path optimization algorithm, such as: using the ant colony optimization (ACO) method to search for the optimal message transmission path between verification nodes, and ultimately obtaining the consensus verification path.

[0066] By identifying the path collaboration nodes in the consensus verification path, the present invention can accurately identify the key nodes involved in the scheduling instruction verification and ensure the orderly progress of the verification process; by sorting out the node responsibilities and optimizing the collaboration between nodes, the verification efficiency and accuracy are improved to support the efficient execution of power grid scheduling instructions.

[0067] Among them, the path collaboration node refers to the blockchain node that participates in the scheduling instruction verification process, undertakes collaborative tasks such as data transmission and consensus calculation in the consensus verification path. They ensure the efficiency and accuracy of instruction verification through division of labor and cooperation (such as some nodes are responsible for preliminary verification and some are responsible for deep consensus). For example, a consensus verification path contains three types of nodes: 2 fast verification nodes with a computing power exceeding 500Mhash / s (first verification), 3 deep consensus nodes with a delay of ≤2s (second confirmation), and 1 data feedback node, which work together to complete instruction verification. Optionally, the identification of path collaboration nodes in the consensus verification path can be achieved through a community discovery algorithm, such as: using the Louvain modularity maximization method to detect high-collaboration node groups in the verification network, and finally obtaining path collaboration nodes.

[0068] S5. Based on the path collaboration node, determine the collaborative power value corresponding to the power supply device, and perform device adaptation on the collaborative power value to generate a collaborative scheduling result corresponding to the power supply device with respect to the target blockchain.

[0069] The present invention determines the collaborative power value corresponding to the power supply device based on the path collaboration node, and can accurately match the collaborative output level of the power supply device in combination with the instruction execution status verified by the node; through collaborative feedback between nodes, the device power distribution is dynamically optimized to ensure that power adjustment is highly adapted to scheduling requirements; at the same time, the collaborative efficiency of the device cluster is improved and the risk of power imbalance is reduced.

[0070] Among them, the collaborative power value refers to the total power or single device power that the power supply equipment cluster needs to collaboratively output based on the collaborative adjustment amplitude, combined with the scheduling response weight and power scheduling partition. It is the target value of equipment collaboration. For example, the adjustment amplitude of the main equipment in a certain partition is +50MW. After combining the weight distribution of each device, the collaborative power value of photovoltaic equipment is +20MW, energy storage is +10MW, and gas turbine is +20MW. The total collaborative power value is 50MW, which meets the scheduling instruction requirements.

[0071] As an embodiment of the present invention, determining the collaborative power value corresponding to the power supply device based on the path collaboration node includes: analyzing the scheduling response weight corresponding to the path collaboration node; dividing the power supply device into power scheduling partitions in a preset power coordination domain based on the scheduling response weight; locating the main power supply device in the power scheduling partition; querying the collaborative adjustment amplitude corresponding to the main power supply device; and determining the collaborative power value corresponding to the power supply device based on the collaborative adjustment amplitude.

[0072] Among them, the scheduling response weight refers to a quantitative value given based on the performance of the path collaboration node (such as verification efficiency and reliability) to reflect its influence in the execution of scheduling instructions. The higher the weight, the stronger the guiding role of the node in device collaboration. For example, a path collaboration node has a verification delay of ≤1 second and a success rate of 99%, and its scheduling response weight is 0.35; another node has a delay of 3 seconds and a success rate of 95%, and its weight is 0.2. The total weight is 1, which is used to allocate the influence ratio of each node on power adjustment; the preset power coordination domain refers to the power set by the system that the power supply equipment cluster can coordinately adjust. The range interval includes the upper and lower limits of the total power and the adjustment boundaries of each device, providing a constraint framework for power allocation. For example, the power coordination domain of a cluster is set to a total power of 200-500MW, of which the adjustment range of photovoltaic equipment is 50-150MW and that of energy storage equipment is 20-80MW, ensuring that the power adjustment does not exceed the system safety and equipment capacity range when the equipment cooperates; the power scheduling partition refers to the specific power adjustment sub-interval divided within the power coordination domain based on the scheduling response weight, and each power supply device is responsible for it. Each partition corresponds to a specific power adjustment task, for example, in the range of 200-500MW In the coordination domain, the node with a weight of 0.35 corresponds to the "350-500MW" partition (dominated by gas turbines), and the node with a weight of 0.2 corresponds to the "200-350MW" partition (coordinated by photovoltaics and energy storage). The equipment in the partition needs to bear the adjustment amount according to the weight ratio; the main power supply equipment refers to the equipment in the power scheduling partition that undertakes the main power adjustment task and plays a leading role in the total power change in the partition. It usually has the characteristics of a wide adjustment range and fast response speed. For example, in the "350-500MW" partition, a gas turbine with a rated power of 300MW and a regulation rate of The core main regulating device is responsible for 60% of the power adjustment in the zone, while the remaining devices play an auxiliary adjustment role. The collaborative adjustment amplitude refers to the maximum single power adjustment that the main regulating power device can complete in coordination with other devices within the power scheduling zone. It includes specific values ​​for increase and decrease, reflecting the driving capacity of the main regulating device. For example, the collaborative adjustment amplitude of a main regulating gas turbine is "+50MW (increase), -30MW (decrease)", which means that it can drive the devices in the zone to jointly increase or decrease the power by 50MW or 30MW, ensuring that the adjustment amplitude matches the system requirements.

[0073] Furthermore, the analysis of the dispatch response weight corresponding to the path cooperation node can be achieved through the entropy weight-TOPSIS comprehensive evaluation method, such as: constructing a multi-index decision matrix based on node response speed and stability, and finally obtaining the dispatch response weight; the division of the power scheduling partition of the power supply equipment in the preset power coordination domain can be achieved through a spectral clustering algorithm, such as: using the Normalized Cut method to perform partitioning based on the electrical distance of the equipment, and finally obtaining the power scheduling partition; the positioning of the main power supply equipment in the power scheduling partition can be achieved through the PageRank algorithm, such as: calculating the centrality score of each device in power regulation through power grid topology analysis, and finally obtaining the main power supply equipment; the query of the collaborative adjustment amplitude corresponding to the main power supply equipment can be achieved through a sensitivity analysis method, such as: using the adjoint variable method to calculate the marginal influence coefficient of the main equipment regulation on the partition voltage, and finally obtaining the collaborative adjustment amplitude; the determination of the collaborative power value corresponding to the power supply equipment can be achieved through a distributed optimization algorithm, such as: applying the alternating direction multiplier method (ADMM) to solve the optimal solution for the partition power balance, and finally obtaining the collaborative power value.

[0074] The present invention performs device adaptation on the collaborative power value to generate the collaborative scheduling result of the power supply device corresponding to the target blockchain, so that the power allocation can accurately match the performance of each device and the blockchain verification requirements, ensuring the feasibility of the scheduling instructions. By adapting and optimizing the device collaboration logic, the overall response efficiency and power regulation accuracy of the cluster are improved. At the same time, relying on the blockchain to realize the credible evidence and traceability of the scheduling results, the transparency and reliability of the scheduling process are enhanced.

[0075] Among them, the collaborative scheduling result refers to the specific scheduling plan generated after the collaborative power value is adapted, and the power supply equipment cluster reaches a consensus based on the target blockchain, including the power adjustment target, execution timing, blockchain verification node allocation and expected effect of each device, etc., which is the ultimate basis for the collaborative action of the equipment. For example, a result shows that the photovoltaic equipment needs to adjust the power from 100MW to 130MW within 5 minutes, and the energy storage equipment simultaneously releases 20MW. Node A (computing power 600Mhash / s) is responsible for real-time verification; the wind power equipment maintains 80MW output, and node B (delay ≤1.5s) records the execution data, and all adjustments must be completed and stored in the blockchain to ensure that the scheduling is traceable. Optionally, the device adaptation of the collaborative power value can be achieved through a dynamic matching algorithm, such as using the Hungarian algorithm to solve the optimal allocation plan for the actual output of the equipment and the collaborative demand, and finally obtaining the adapted power allocation result; the generation of the collaborative scheduling result corresponding to the power supply equipment on the target blockchain can be achieved through smart contract automatic execution technology, such as using Hyperledger The Fabric chain code writes the scheduling decision into the blockchain's tamper-proof ledger, ultimately obtaining the collaborative scheduling result.

[0076] Compared with the problems described in the background technology, the present invention queries the device operating status corresponding to the power supply device in the target blockchain and collects the real-time operating data corresponding to the device operating status. It can rely on the tamper-proof characteristics of the blockchain to ensure the authenticity and credibility of the data, and provide a reliable basis for subsequent scheduling decisions; at the same time, it can fully grasp the device dynamics, accurately match the grid scheduling needs, reduce the scheduling deviation caused by information asymmetry, and improve the accuracy and efficiency of the overall scheduling. The present invention detects the scheduling tasks in the device collaboration cluster and analyzes the power adjustment instructions corresponding to the scheduling tasks. It can accurately locate the specific scheduling goals that the cluster needs to execute and clarify the power adjustment direction and amplitude of each device; at the same time, it can identify the potential requirements and constraints in the task execution in advance, and provide a basis for the subsequent evaluation of the equipment execution capability. Furthermore, the present invention determines the scheduling conflict points of the power supply device in the scheduling process based on the device response threshold, and can accurately identify the contradictory links caused by the mismatch of response capabilities when the device executes the scheduling instructions, and provide a basis for the subsequent evaluation of the equipment execution capability. Early warning of potential scheduling risks; providing a basis for optimizing scheduling strategies, by adjusting instruction allocation or equipment coordination mode, avoiding conflicts that affect the stable operation of the power grid, and improving the reliability and efficiency of scheduling execution. Furthermore, the present invention determines the consensus verification path corresponding to the scheduling instruction in the power supply device according to the time offset, can accurately associate the device execution deviation with the scheduling instruction logic, and clarify the reliable transmission and verification direction of the instruction in the device collaboration; by adapting different offset conditions through differentiated paths, the consistency and accuracy of instruction execution are guaranteed, the scheduling conflict caused by time deviation is reduced, and the reliability and efficiency of cluster collaborative scheduling are improved. Finally, the present invention determines the collaborative power value corresponding to the power supply device based on the path collaboration node, and can accurately match the collaborative output level of the power supply device in combination with the instruction execution status verified by the node; through collaborative feedback between nodes, the device power allocation is dynamically optimized to ensure that the power adjustment is highly adapted to the scheduling requirements; at the same time, the collaborative efficiency of the device cluster is improved and the risk of power imbalance is reduced. Therefore, the embodiment of the present invention provides a power supply device collaborative scheduling method and system based on blockchain consensus, which can improve the scheduling consistency of power supply devices in the blockchain.

[0077] like Figure 3 The figure shows a functional module diagram of a power supply equipment collaborative scheduling system based on blockchain consensus according to the present invention.

[0078] The blockchain-based power supply equipment collaborative scheduling system 200 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the blockchain-based power supply equipment collaborative scheduling system can include a cluster generation module 201, a threshold calculation module 202, an offset calculation module 203, a node identification module 204, and a result generation module 205. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0079] In the embodiment of the present invention, the functions of each module / unit are as follows: The cluster generation module 201 is used to query the device operating status corresponding to the power supply device in the target blockchain, collect real-time operating data corresponding to the device operating status, and generate a device collaboration cluster corresponding to the power supply device based on the real-time operating data and preset power grid scheduling requirements; The threshold calculation module 202 is configured to detect a scheduling task in the device collaboration cluster, analyze a power adjustment instruction corresponding to the scheduling task, query a device execution index corresponding to the power adjustment instruction, and calculate a device response threshold corresponding to the device execution index; The offset calculation module 203 is configured to determine a scheduling conflict point of the power supply device during the scheduling process based on the device response threshold, analyze the task size and execution time limit corresponding to the scheduling conflict point, and calculate a time offset of the power supply device for the execution process based on the task size and the execution time limit; The node identification module 204 is configured to determine a consensus verification path corresponding to the scheduling instruction in the power supply device according to the time offset, and identify a path cooperation node in the consensus verification path; The result generation module 205 is used to determine the collaborative power value corresponding to the power supply device based on the path collaboration node, and perform device adaptation on the collaborative power value to generate a collaborative scheduling result corresponding to the power supply device with respect to the target blockchain.

[0080] In detail, the modules in the power equipment collaborative scheduling system 200 based on blockchain consensus in the embodiment of the present invention adopt the same Figure 1 The technical means described in the method for coordinated scheduling of power equipment based on blockchain consensus are the same and can produce the same technical effects, so I will not go into details here.

[0081] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Among the above multiple embodiments, each embodiment can be combined with each other or be independent, and deleting any one of them will not affect the technical implementation of other embodiments. Although the present invention is described in detail with reference to the preferred embodiments, ordinary technicians in this field should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for collaborative scheduling of power supply equipment based on blockchain consensus, characterized in that: The method comprises: Query the device operating status corresponding to the power supply device in the target blockchain, collect real-time operating data corresponding to the device operating status, and generate a device collaboration cluster corresponding to the power supply device based on the real-time operating data and preset power grid scheduling requirements; Detecting a scheduling task in the device collaboration cluster, analyzing a power adjustment instruction corresponding to the scheduling task, querying a device execution index corresponding to the power adjustment instruction, and calculating a device response threshold corresponding to the device execution index; Determining, based on the device response threshold, a scheduling conflict point of the power supply device during the scheduling process, analyzing the task size and execution time limit corresponding to the scheduling conflict point, and calculating, based on the task size and execution time limit, a time offset of the power supply device for the execution process; Determining a consensus verification path corresponding to the scheduling instruction in the power supply device according to the time offset, and identifying a path cooperation node in the consensus verification path; Based on the path collaboration node, a collaborative power value corresponding to the power supply device is determined, and the collaborative power value is device adapted to generate a collaborative scheduling result corresponding to the power supply device with respect to the target blockchain.

2. A method for collaborative scheduling of power supply equipment based on blockchain consensus as claimed in claim 1, characterized in that: The generating of a device collaboration cluster corresponding to the power supply device based on the real-time operation data and a preset power grid scheduling requirement includes: Extracting key equipment indicators from the real-time operation data; Analyze the collaboration potential value corresponding to the power supply equipment based on the key indicators of the equipment and the preset grid dispatch requirements; Mapping the device grouping range corresponding to the collaboration potential value; Analyze the scheduling compatibility characteristics corresponding to the devices within the device group range; Based on the scheduling compatibility feature, a device cooperation cluster corresponding to the power supply device is generated.

3. A method for collaborative scheduling of power supply equipment based on blockchain consensus as claimed in claim 2, characterized in that: The step of analyzing the collaboration potential value corresponding to the power supply device based on the key indicators of the device and a preset grid dispatching requirement includes: Extracting dynamic / steady-state data from key indicators of the equipment; Based on the dynamic / steady-state data, analyzing the instantaneous performance index and continuous dispatching efficiency value of the power supply equipment to meet the preset grid dispatching requirements; Mapping the matching value range of the instantaneous performance index and the continuous dispatch effectiveness value to the grid dispatch demand; Analyzing the regulation margin characteristics corresponding to each power supply device within the matching value range; The cooperation potential value corresponding to the adjustment margin characteristic is quantified.

4. The method for collaborative scheduling of power supply equipment based on blockchain consensus according to claim 1, characterized in that: The querying of the device execution index corresponding to the power adjustment instruction and calculating the device response threshold corresponding to the device execution index includes: Analyzing the power peak and valley values ​​corresponding to the power adjustment instructions; Based on the power peak and valley values, dividing the power adjustment interval corresponding to the power adjustment instruction; Determining, based on the power adjustment interval, response capability levels corresponding to different devices in the power supply device; analyzing execution performance data corresponding to the responsiveness level; Querying the device execution index corresponding to the execution performance data; The device response threshold corresponding to the device execution index is calculated using the following formula: ； in, Indicates the device response threshold corresponding to the device execution index, represents the device time constant, Indicates the power adjustment range, Indicates the device execution index, represents the reference power, represents the system constraint factor, represents the constraint function, represents the efficiency reduction coefficient, Represents the conflict sensitivity coefficient.

5. The method for collaborative scheduling of power supply equipment based on blockchain consensus according to claim 1, characterized in that: The determining, based on the device response threshold, a scheduling conflict point of the power supply device during the scheduling process includes: Querying a threshold change trend corresponding to the device response threshold; determining key conflict characteristics in the scheduling process based on the threshold change trend; Based on the key conflict characteristics, locating potential conflicting units in the device cooperation cluster; Extracting a conflict identifier from the potential conflict unit; Based on the conflict identifier, a scheduling conflict point of the power supply device in a scheduling process is determined.

6. A method for collaborative scheduling of power supply equipment based on blockchain consensus as claimed in claim 5, characterized in that: The locating the potential conflicting units in the device cooperation cluster based on the key conflict characteristics includes: Querying a conflict distribution pattern corresponding to the key conflict characteristic; Determining a conflict hotspot area in the device collaboration cluster based on the conflict distribution pattern; Based on the conflict hotspot area, dividing the conflict candidate units in the cluster; Analyzing the unit conflict index corresponding to the conflict candidate unit; Based on the unit conflict index, potential conflicting units in the device cooperation cluster are located.

7. The method for collaborative scheduling of power supply equipment based on blockchain consensus according to claim 1, characterized in that: The calculating, based on the task size and the execution time limit, a time offset of the power supply device for the execution process includes: Extracting the total amount of scheduling instructions corresponding to the task scale; Query the maximum execution time corresponding to the execution time limit; Based on the total amount of the scheduling instructions and the maximum execution time, identifying the task conflict period corresponding to the power supply device; Extracting the task demand peak value during the task conflict period; Based on the task demand peak, the time offset of the power supply device for the execution process is calculated by the following formula: ； in, represents the time offset of the power supply device for the execution process, represents the total number of task conflict periods, represents the number index of the task conflict period, represents the peak value of task demand during the i-th task conflict period, Indicates the rated power of the power supply device. represents the device response threshold during the i-th task conflict period, represents the power attenuation factor, Indicates the number of scheduling instructions that the power supply can process per second. Indicates the device execution index.

8. The method for collaborative scheduling of power supply equipment based on blockchain consensus according to claim 1, characterized in that: Determining, based on the time offset, a consensus verification path corresponding to the scheduling instruction in the power supply device includes: Analyze the scheduling execution period corresponding to the time offset; Calculating a time period verification delay corresponding to the scheduling execution time period; Based on the verification delay in the time period, screening available verification channels corresponding to the network in the target blockchain; Marking efficient consensus nodes in the available verification channel; Based on the efficient consensus node, a consensus verification path corresponding to the scheduling instruction in the power supply device is determined.

9. The method for collaborative scheduling of power supply equipment based on blockchain consensus according to claim 1, characterized in that: The determining, based on the path cooperation node, a cooperation power value corresponding to the power supply device includes: Analyzing the scheduling response weight corresponding to the path cooperation node; Dividing the power supply device into power scheduling partitions in a preset power coordination domain based on the scheduling response weight; Locating the main power supply device in the power scheduling partition; Querying the collaborative adjustment amplitude corresponding to the main power supply device; Based on the collaborative adjustment amplitude, a collaborative power value corresponding to the power supply device is determined.

10. A power supply equipment collaborative scheduling system based on blockchain consensus, characterized in that: The system comprises: A cluster generation module is used to query the device operating status corresponding to the power supply device in the target blockchain, collect real-time operating data corresponding to the device operating status, and generate a device collaboration cluster corresponding to the power supply device based on the real-time operating data and preset power grid scheduling requirements; a threshold calculation module, configured to detect a scheduling task in the device collaboration cluster, analyze a power adjustment instruction corresponding to the scheduling task, query a device execution index corresponding to the power adjustment instruction, and calculate a device response threshold corresponding to the device execution index; an offset calculation module, configured to determine, based on the device response threshold, a scheduling conflict point of the power supply device during the scheduling process, analyze the task size and execution time limit corresponding to the scheduling conflict point, and calculate, based on the task size and execution time limit, a time offset of the power supply device for the execution process; a node identification module, configured to determine, based on the time offset, a consensus verification path corresponding to the scheduling instruction in the power supply device, and identify a path cooperation node in the consensus verification path; A result generation module is used to determine the collaborative power value corresponding to the power supply device based on the path collaboration node, and perform device adaptation on the collaborative power value to generate a collaborative scheduling result corresponding to the power supply device with respect to the target blockchain.

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