Method and system for realizing intelligent power distribution of power supply equipment based on block chain

Abstract

The invention relates to the technical field of block chains, and discloses an intelligent power distribution method and system for realizing power supply equipment based on a block chain, and the method comprises the steps: constructing a block chain network of the power supply equipment, analyzing a dynamic mapping relation between input data and output data, and determining a discrete nonlinear function of the input data and the output data; determining a target function required by the power supply equipment, determining power distribution control parameters of the power supply equipment, and integrating a data driving control model of the power supply equipment; calculating an electrical distance between blockchain nodes, and dividing the blockchain network into a plurality of power distribution control areas; determining an area dynamic control mechanism of the multiple power distribution control areas, and determining a coordination control algorithm of the multiple power distribution control areas; and integrating the data driving control model, the regional dynamic control mechanism and the coordination control algorithm into a power distribution control module of the power supply equipment, and executing intelligent power distribution of the power supply equipment. The power distribution efficiency and stability of intelligent power distribution of the power supply equipment can be improved.

Description

Technical Field

[0001] The present invention relates to an intelligent power distribution method and system for power supply equipment based on blockchain, belonging to the technical field of blockchain. Background Art

[0002] Intelligent power distribution refers to the process of using advanced information technology, communication technology, control technology, and power electronics technology to optimize management, real-time monitoring, fault diagnosis, automatic control, and self-repair of the power distribution system. By monitoring and controlling power quality problems such as voltage fluctuations and harmonics in the power system, intelligent power distribution can ensure that power users obtain stable and high-quality electrical energy.

[0003] Traditional power distribution methods are achieved through discrete adjustment devices and continuous adjustment devices. Due to the uneven distribution of power system resources and different regulation time scales, coordination means are required to fully exert their respective regulation capabilities, resulting in large voltage fluctuations and poor voltage stability of power supply equipment. Therefore, there is an urgent need for a solution to improve the power distribution efficiency and stability of intelligent power distribution of power supply equipment. Summary of the Invention

[0004] The present invention provides an intelligent power distribution method and system for power supply equipment based on blockchain, and its main purpose is to improve the power distribution efficiency and stability of intelligent power distribution of power supply equipment.

[0005] To achieve the above object, an intelligent power distribution method for power supply equipment based on blockchain provided by the present invention includes: Constructing a blockchain network of the power supply equipment, using blockchain nodes in the blockchain network to collect input data and output data of each power supply equipment in real time, analyzing the dynamic mapping relationship between the input data and the output data, and based on the dynamic mapping relationship, determining a discrete non-linear function of the input data and the output data; Analyzing the power distribution requirements of the power supply equipment, determining an objective function required by the power supply equipment according to the power distribution requirements and the discrete non-linear function, and according to the objective function, using a preset control parameter analysis algorithm to determine the power distribution control parameters of the power supply equipment, and integrating a data-driven control model of the power supply equipment based on the power distribution control parameters; Collecting voltage and power data of the blockchain nodes, calculating the electrical distance between the blockchain nodes according to the voltage and power data, determining the control node density and control node distance of the blockchain nodes based on the electrical distance, and dividing the blockchain network into multiple power distribution control regions based on the control node density and the control node distance; Identify the power distribution losses in the multi-power distribution control area, determine the area dynamic control mechanism of the multi-power distribution control area based on the power distribution losses and the data-driven control model, collect the boundary voltage information of the boundary nodes in the multi-power distribution control area in real time, and determine the coordinated control algorithm of the multi-power distribution control area according to the boundary voltage information; Integrate the data-driven control model, the area dynamic control mechanism, and the coordinated control algorithm into the power distribution control module of the power supply device. Based on the power distribution control module, output the power distribution parameters of the power supply device, and perform intelligent power distribution of the power supply device based on the power distribution parameters.

[0006] Optionally, constructing the blockchain network of the power supply device includes: Clarify the network construction requirements of the power supply device, and determine the blockchain platform of the power supply device according to the network construction requirements; Construct the network topology of the blockchain platform and the power supply device, and identify the network characteristics of the network topology; Based on the network characteristics, determine the consensus mechanism and encryption algorithm of the blockchain platform; Identify the network nodes of the network topology, and configure the network devices of the network nodes; Integrate the network devices, the consensus mechanism, and the encryption algorithm into the network topology to obtain a blockchain network.

[0007] Optionally, analyzing the dynamic mapping relationship between the input data and the output data includes: Perform dynamic time series adjustment on the input data and the output data to obtain time series input data and time series output data; Unify the time series lengths of the time series input data and the time series output data, calculate the delay parameter between the time series input data and the time series output data, Based on the delay parameter and the time series length, use the following formula to calculate the cross-correlation between the time series input data and the time series output data: ; Where, represents the cross-correlation between the time series input data and the time series output data, represents the delay parameter, represents the time series length, represents the time point of the time series input data, represents the time point of the time series output data, the time point of the time series output data; Determine the dynamic mapping relationship between the timing input data and the timing output data according to the cross-correlation.

[0008] Optionally, determining the discrete non-linear function of the input data and the output data based on the dynamic mapping relationship includes: Discretize the input data and the output data to obtain discrete input data and discrete output data; Determine the non-linear function structure of the discrete input data and the discrete output data according to the dynamic mapping relationship; Determine the function parameters and loss function of the non-linear function structure; According to the function parameters and the loss function, initially fit the initial non-linear function of the discrete input data and the discrete output data; Check the goodness of fit of the initial non-linear function. When the goodness of fit is greater than the preset goodness-of-fit threshold, use the initial non-linear function as the discrete non-linear function of the discrete input data and the discrete output data.

[0009] Optionally, determining the objective function required by the power supply device according to the power distribution demand and the discrete non-linear function includes: Analyze the operation data of the power supply device, where the operation data includes: load data, device parameters, and grid status; Define the decision variables of the power supply device according to the operation data, and analyze the multi-objective requirements of the power distribution demand; Construct a sub-objective function of the target demand according to the decision variables and the discrete non-linear function; Determine the weight coefficients and constraint conditions of the sub-objective function; Integrate the sub-objective functions into an objective function according to the weight coefficients and the constraint conditions.

[0010] Optionally, determining the power distribution control parameters of the power supply device according to the objective function by using a preset control parameter analysis algorithm includes: Initialize the initial power distribution parameters of the power supply device according to the decision variables corresponding to the objective function; Based on the constraint conditions corresponding to the objective function, use the control parameter analysis algorithm to iterate the initial power distribution parameters to obtain iterative power distribution parameters; where the control parameter analysis algorithm includes: ; Wherein, represents the iterative power distribution parameter output at the th iteration, represents the Initial power distribution parameters of the next iteration Represents the step size of the control parameter analysis algorithm Represents the perturbation coefficient Represents the Perturbation term of the Represents the learning rate Represents the gradient of the objective function at the Next iteration According to the iterative power distribution parameters, the convergence value of the control parameter analysis algorithm is detected in real time When the convergence value meets the preset convergence threshold, the iterative power distribution parameters are used as the power distribution control parameters of the power supply device

[0011] Optionally, the calculating the electrical distance between the blockchain nodes according to the voltage and power data includes: Extracting the node active power and node reactive power of the blockchain nodes according to the voltage and power data Calculating the node equivalent impedance of the blockchain nodes Based on the node equivalent impedance, the node active power, and the node reactive power, the electrical distance between the blockchain nodes is calculated using the following formula ; Wherein, Represents the electrical distance between node and node in the blockchain nodes Represents the node active power of node in the blockchain nodes Represents the node active power of node in the blockchain nodes Represents the node reactive power of node in the blockchain nodes Represents the node reactive power of node in the blockchain nodes Represents the voltage of node in the blockchain nodes Represents the voltage of node in the blockchain nodes Represents the node equivalent impedance between node and node in the blockchain nodes

[0012] Optionally, the determining the regional dynamic control mechanism of the multi-power distribution control area based on the power distribution loss and the data-driven control model includes: Identifying the loss mode and loss factor of the power distribution loss Calculate the loss amount of the multi - distribution control area according to the loss mode and the loss factor; Determine the loss power source of the multi - distribution control area; Determine the resource control rules and the energy dispatching rules within the area of the multi - distribution control area according to the loss amount and the loss power source; Determine the area dynamic control mechanism of the multi - distribution control area according to the resource control rules and the energy dispatching rules within the area.

[0013] Optionally, the determining the coordinated control algorithm of the multi - distribution control area according to the boundary voltage information includes: Analyze the local control variables of the multi - distribution control area according to the boundary voltage information; Determine the local objective function of the multi - distribution control area according to the local control variables; Define the consistency target value of the multi - distribution control area; Construct the coordinated control algorithm of the multi - distribution control area according to the consistency target value, the local control variables and the local objective function, where the coordinated control algorithm includes: ; ; ; Wherein, represents the optimized local control variable corresponding to the th area in the multi - distribution control area, represents the independent variable function of the minimum value, represents the local objective function of the multi - distribution control area corresponding to the th area, represents the penalty coefficient, represents the local control variable of the multi - distribution control area corresponding to the th area, represents the th optimized consistency target value, represents the optimized dual variable corresponding to the th area in the multi - distribution control area, is the number of areas in the multi - distribution control area, represents the th optimized consistency target value, represents the optimized dual variable corresponding to the th area in the multi - distribution control area.

[0014] To solve the above problems, the present invention further provides an intelligent power distribution system for power supply equipment based on blockchain, and the system includes: A non-linear relationship analysis module, configured to construct a blockchain network of the power supply equipment, use blockchain nodes in the blockchain network to collect input data and output data of each power supply equipment in real time, analyze the dynamic mapping relationship between the input data and the output data, and determine a discrete non-linear function of the input data and the output data based on the dynamic mapping relationship; A data-driven control module, configured to analyze the power distribution requirements of the power supply equipment, determine an objective function required by the power supply equipment according to the power distribution requirements and the discrete non-linear function, determine power distribution control parameters of the power supply equipment by using a preset control parameter analysis algorithm according to the objective function, and integrate a data-driven control model of the power supply equipment based on the power distribution control parameters; A power distribution control area division module, configured to collect voltage and power data of the blockchain nodes, calculate the electrical distance between the blockchain nodes according to the voltage and power data, determine the control node density and control node distance of the blockchain nodes based on the electrical distance, and divide the blockchain network into multiple power distribution control areas based on the control node density and the control node distance; A regional coordinated control module, configured to identify power distribution losses in the multiple power distribution control areas, determine a regional dynamic control mechanism for the multiple power distribution control areas based on the power distribution losses and the data-driven control model, collect boundary voltage information of boundary nodes in the multiple power distribution control areas in real time, and determine a coordinated control algorithm for the multiple power distribution control areas according to the boundary voltage information; A power distribution control integration module, configured to integrate the data-driven control model, the regional dynamic control mechanism, and the coordinated control algorithm into a power distribution control module of the power supply equipment, output power distribution parameters of the power supply equipment based on the power distribution control module, and perform intelligent power distribution of the power supply equipment based on the power distribution parameters.

[0015] Compared with the problems described in the background art, the embodiments of the present invention can reduce the risk of single-point failure by constructing the blockchain network of the power supply device. Even if some nodes have problems, the entire network can still operate normally. Optionally, the embodiments of the present invention can better utilize the complex features in the data and improve the data utilization efficiency by determining the discrete non-linear function of the input data and the output data based on the dynamic mapping relationship. The embodiments of the present invention can optimize the output of the power supply device by determining the power distribution control parameters of the power supply device according to the objective function using a preset control parameter analysis algorithm, making the power transmission more efficient, reducing energy loss, and improving the overall performance of the power distribution system. The embodiments of the present invention can optimize within each region and improve the efficiency of power distribution and resource allocation by dividing the blockchain network into multiple power distribution control regions based on the control node density and the control node distance. The embodiments of the present invention can optimize within each region and improve the efficiency of power distribution and resource allocation by dividing the blockchain network into multiple power distribution control regions based on the control node density and the control node distance. Finally, the embodiments of the present invention can integrate the data-driven control model, the regional dynamic control mechanism, and the coordinated control algorithm into the power distribution control module of the power supply device to monitor and analyze the power grid status in real time, quickly respond to system changes, and maintain the stable operation of the power grid. At the same time, it can more effectively allocate energy, reduce energy waste, and improve energy utilization efficiency. Therefore, the intelligent power distribution method and system for power supply devices based on blockchain provided by the embodiments of the present invention can improve the power distribution efficiency and stability of intelligent power distribution of power supply devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flowchart of an intelligent power distribution method for a power supply device based on blockchain provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of a module for implementing the intelligent power distribution method for a power supply device based on blockchain provided by an embodiment of the present invention.

[0017] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0019] An embodiment of the present application provides an intelligent power distribution method for power supply devices based on blockchain. The execution entities of the intelligent power distribution method based on blockchain include, but are not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the intelligent power distribution method based on blockchain 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] Embodiment 1: Refer to Figure 1 As shown, it is a schematic flowchart of an intelligent power distribution method for power supply devices based on blockchain provided by an embodiment of the present invention. In this embodiment, the intelligent power distribution method based on blockchain includes: S1. Construct a blockchain network for the power supply devices, use the blockchain nodes in the blockchain network to collect the input data and output data of each power supply device in real time, analyze the dynamic mapping relationship between the input data and the output data, and based on the dynamic mapping relationship, determine the discrete non-linear function of the input data and the output data.

[0021] In the embodiment of the present invention, by constructing a blockchain network for the power supply devices, the risk of single-point failure can be reduced. Even if some nodes have problems, the entire network can still operate normally. Among them, the blockchain network refers to a distributed database system that ensures the security and immutability of data through a series of encryption technologies, and maintains the integrity and consistency of data through multiple nodes in the network.

[0022] As an embodiment of the present invention, the construction of the blockchain network for the power supply devices includes: Clarify the network construction requirements of the power supply devices, and determine the blockchain platform of the power supply devices according to the network construction requirements; Construct the network topology of the blockchain platform and the power supply devices, and identify the network characteristics of the network topology; Based on the network characteristics, determine the consensus mechanism and encryption algorithm of the blockchain platform; Identify the network nodes of the network topology, and configure the network devices of the network nodes; Integrate the network devices, the consensus mechanism, and the encryption algorithm into the network topology to obtain a blockchain network.

[0023] Among them, the network construction requirements refer to the specific goals and results to be achieved when constructing the blockchain network of power equipment, such as data security, decentralized management, etc. The blockchain platform refers to a set of software frameworks for constructing and managing blockchain networks, such as Ethereum, Hyperledger Fabric, Corda, etc. The network topology refers to the layout and connection method of each node in the blockchain network. The network characteristics refer to various technical attributes and functional features demonstrated by the blockchain network during the design and operation process. The consensus mechanism refers to the process and method for all participating nodes in the blockchain network to reach a consensus. The encryption algorithm refers to a series of mathematical and computational methods used to ensure data security and privacy in the blockchain network, such as elliptic curve encryption algorithm, Secure Hash Algorithm 256-bit, Elliptic Curve Diffie-Hellman key exchange algorithm, etc. The network node refers to an independent computing entity participating in data verification, storage, and transmission in the blockchain network. The network device refers to the hardware and software components used to achieve inter-node communication, data transmission, and storage in the blockchain network, such as routers, switches, storage devices, etc.

[0024] Optionally, the network topology of the blockchain platform and the power equipment can be constructed through network function virtualization. For example, virtualize the blockchain function of the blockchain platform and the device function of the power equipment through network function virtualization technology, and simulate the network topology of the blockchain platform and the power equipment according to the blockchain function and the device function.

[0025] In an embodiment of the present invention, by using the blockchain nodes in the blockchain network, the input data and output data of each power equipment can be collected in real time. Through the real-time collection and analysis of the data, it helps to achieve the self-management and decision-making of the equipment. Among them, the input data refers to the quantitative information of various resources and signals received or consumed by the power equipment during operation, such as operation strategy information such as the active and reactive power output of controllable equipment. The output data refers to a series of result data generated after the power equipment processes and converts the input data, such as the measurement information of state variables such as voltage, current, and power.

[0026] In an embodiment of the present invention, by analyzing the dynamic mapping relationship between the input data and the output data, the energy consumption pattern can be better understood, thereby implementing an effective energy management strategy. Among them, the dynamic mapping relationship refers to the real-time correspondence relationship between the input data and the output data of the power equipment.

[0027] As an embodiment of the present invention, the analysis of the dynamic mapping relationship between the input data and the output data includes: Perform dynamic time series adjustment on the input data and the output data to obtain time series input data and time series output data; Unify the timing lengths of the timing input data and the timing output data, and calculate the delay parameter between the timing input data and the timing output data. Based on the delay parameter and the timing length, use the following formula to calculate the cross-correlation between the timing input data and the timing output data: ; where represents the cross-correlation between the timing input data and the timing output data, represents the delay parameter, represents the timing length, represents the time point of the timing input data, represents the time point of the timing output data, the time point of the timing output data; Determine the dynamic mapping relationship between the timing input data and the timing output data according to the cross-correlation.

[0028] Among them, the timing input data refers to the input data that has been time-aligned with the output data after dynamic timing adjustment. The timing output data refers to the output data that has been time-aligned with the input data after dynamic timing adjustment. The timing length refers to the length of the time series data, that is, the number of time points in the dataset. The delay parameter refers to the amount of delay of the output data relative to the input data in time series analysis. The cross-correlation is a statistical index that measures the degree of mutual association between two time series at different time delays.

[0029] Optionally, the delay parameter between the timing input data and the timing output data can be calculated through a time series model, such as a vector autoregressive (VAR) model, a state space model, etc.

[0030] In the embodiment of the present invention, by determining the discrete non-linear function of the input data and the output data based on the dynamic mapping relationship, the complex features in the data can be better utilized, and the data utilization efficiency can be improved. Among them, the discrete non-linear function is a mathematical function that accepts discrete input values and generates discrete output values through non-linear relationships.

[0031] As an embodiment of the present invention, the determining the discrete non-linear function of the input data and the output data based on the dynamic mapping relationship includes: Discretize the input data and the output data to obtain discrete input data and discrete output data; Determine the non - linear function structure of the discrete input data and the discrete output data according to the dynamic mapping relationship; Determine the function parameters and loss function of the non - linear function structure; According to the function parameters and the loss function, preliminarily fit the initial non - linear function of the discrete input data and the discrete output data; Examine the goodness - of - fit of the initial non - linear function. When the goodness - of - fit is greater than the preset goodness - of - fit threshold, use the initial non - linear function as the discrete non - linear function of the discrete input data and the discrete output data.

[0032] Among them, the discrete input data refers to the input data after dividing the continuous input data into multiple intervals. The discrete output data refers to the output data after dividing the continuous output data into multiple intervals. The non - linear function structure refers to a mathematical model used to describe the complex relationship between input data and output data. The function parameters refer to the constants defined in the non - linear function. The loss function refers to a mathematical function used to quantify the difference between the predicted output and the true output of the non - linear function structure, and its optimization goal is to find the structural coefficients that minimize the loss. The initial non - linear function refers to the expression of the non - linear relationship between input and output assumed by preliminary data analysis. The goodness - of - fit refers to a measure used to quantify the explanatory ability of the determined discrete non - linear function for actual data, such as the coefficient of determination, mean square error, proportion of explained variance, etc. The preset goodness - of - fit threshold refers to a pre - set goodness - of - fit criterion used to judge whether the function fitting effect is acceptable.

[0033] Optionally, the function parameters of the non - linear function structure can be determined by non - linear least - squares methods, such as the quasi - Newton method, genetic algorithm, Gauss - Newton method, etc.

[0034] S2. Analyze the power distribution requirements of the power supply device. According to the power distribution requirements and the discrete non - linear function, determine the objective function required by the power supply device. According to the objective function, use a preset control parameter analysis algorithm to determine the power distribution control parameters of the power supply device. Based on the power distribution control parameters, integrate the data - driven control model of the power supply device.

[0035] In the embodiments of the present invention, by analyzing the power distribution requirements of the power supply device, the power distribution scheme of the power supply device can be optimized, energy loss can be reduced, and energy utilization efficiency can be improved. Among them, the power distribution requirements refer to a series of technical and performance requirements that the power supply device must meet when providing power for various loads.

[0036] Optionally, the power distribution requirements of the power supply device can be analyzed through big data analysis technology. By analyzing the power distribution data of the power supply device through big data analysis technology, the power distribution requirements of the power supply device can be mined from the power distribution data.

[0037] In the embodiment of the present invention, determining the objective function required by the power supply device according to the power distribution requirements and the discrete non-linear function can help the power supply device allocate energy more effectively, reduce energy waste, and improve the overall energy utilization efficiency. Among them, the objective function refers to a mathematical function that relates the power distribution control parameters of the power supply device to desired performance indicators (such as efficiency, cost, stability, etc.).

[0038] As an embodiment of the present invention, the determining the objective function required by the power supply device according to the power distribution requirements and the discrete non-linear function includes: Analyzing the operation data of the power supply device, where the operation data includes: load data, device parameters, and grid status; Defining decision variables of the power supply device according to the operation data, and analyzing the multi-objective requirements of the power distribution requirements; Constructing a sub-objective function of the objective requirements according to the decision variables and the discrete non-linear function; Determining the weight coefficients and constraint conditions of the sub-objective function; Integrating the sub-objective functions into an objective function according to the weight coefficients and the constraint conditions.

[0039] Among them, the operation data refers to various measured and recorded information related to the operation of the power supply device. The load data refers to data related to the load characteristics in the power system, such as load power, load current, load voltage, etc. The device parameters refer to the technical specifications and characteristics related to the performance and operation of the power supply device, such as the rated power, rated voltage, rated current, etc. of the device. The grid status refers to the operating conditions and performance indicators of each component in the power system at a specific time point, such as grid stability, frequency, grid load rate, etc. The decision variables refer to variables that can be adjusted in the decision-making process and are key factors affecting the power distribution effect. The multi-objective requirements refer to dividing the power distribution requirements into multiple independent objective requirements, such as efficiency requirements, economic requirements, stability requirements, etc. The sub-objective function refers to a single objective function decomposed in the multi-objective requirements to achieve the overall optimization goal. The weight coefficient refers to a numerical value used to allocate the relative importance of different sub-objective functions. The constraint conditions refer to a series of rules and restrictions that the decision variables must satisfy, such as resource limitations, technical limitations, safety standards, etc.

[0040] Optionally, the multi-objective requirements of the power distribution requirements can be analyzed by a multi-objective optimization algorithm, such as a genetic algorithm, multi-objective particle swarm optimization, multi-objective ant colony optimization, etc.

[0041] In the embodiment of the present invention, by using the preset control parameter analysis algorithm according to the objective function, the power distribution control parameters of the power supply device are determined, which can optimize the output of the power supply device, make the power transmission more efficient, reduce energy loss, and improve the overall performance of the power distribution system. Among them, the preset control parameter analysis algorithm refers to a series of calculation methods for analyzing and optimizing the power distribution control parameters of the power supply device. The power distribution control parameters refer to variables used to adjust and control the operation of the power distribution system, such as reactive power compensation, active power distribution, load management parameters, etc.

[0042] As an embodiment of the present invention, determining the power distribution control parameters of the power supply device by using the preset control parameter analysis algorithm according to the objective function includes: Initializing the initial power distribution parameters of the power supply device according to the decision variables corresponding to the objective function; Based on the constraint conditions corresponding to the objective function, using the control parameter analysis algorithm to iterate the initial power distribution parameters to obtain iterative power distribution parameters; where, the control parameter analysis algorithm includes: ; Wherein, represents the iterative power distribution parameter output at the -th iteration, represents the initial power distribution parameter of the -th iteration, represents the step size of the control parameter analysis algorithm, represents the perturbation coefficient, represents the perturbation term of the -th iteration, represents the learning rate, represents the gradient of the objective function at the -th iteration; According to the iterative power distribution parameters, the convergence value of the control parameter analysis algorithm is detected in real time; When the convergence value meets the preset convergence threshold, the iterative power distribution parameters are used as the power distribution control parameters of the power supply device.

[0043] Among them, the initial power distribution parameters refer to a set of initial values set for the power supply equipment before optimizing the power distribution control parameters. The iterative power distribution parameters refer to a set of new power distribution control parameters calculated by the control parameter analysis algorithm during the optimization process. The step size refers to a parameter in the control parameter analysis algorithm used to control the amplitude of parameter update in each iteration. The perturbation coefficient refers to a coefficient introduced to break the local optimal solution that the algorithm falls into and increase the exploration of the search process, which can be set to 0.01 in this application. The perturbation term refers to the perturbation vector introduced in the control parameter analysis algorithm to enhance the exploration in the search process and avoid falling into the local optimal solution. The gradient refers to the rate of change of the objective function with respect to each parameter.

[0044] In the embodiment of the present invention, by integrating the data-driven control model of the power supply equipment based on the power distribution control parameters, the energy distribution can be managed more effectively, energy waste can be reduced, and the energy utilization efficiency can be improved. Among them, the data-driven control model refers to a control model dynamically constructed through the input-output data collected in real time.

[0045] Optionally, the data-driven control model can be generated by constructing a high-fidelity digital twin of the power supply equipment, aggregating the edge node data of the high-fidelity digital twin through federated learning, calculating the model optimization parameters of the high-fidelity digital twin according to the edge node data, and generating the data-driven control model of the power supply equipment according to the model optimization parameters.

[0046] S3. Collect the voltage and power data of the blockchain nodes, calculate the electrical distance between the blockchain nodes according to the voltage and power data, determine the control node density and control node distance of the blockchain nodes based on the electrical distance, and divide the blockchain network into multiple power distribution control regions based on the control node density and the control node distance.

[0047] In the embodiment of the present invention, by collecting the voltage and power data of the blockchain nodes, the operating state of the power system can be monitored in real time, and abnormal changes in voltage and power can be quickly identified, thereby providing a data basis for subsequent cross-regional power distribution control. Among them, the voltage and power data refer to the data related to voltage and power collected by monitoring devices, such as phase voltage, line voltage, active power, reactive power, etc.

[0048] In the embodiment of the present invention, by calculating the electrical distance between the blockchain nodes according to the voltage and power data, it can be used as a basis for power dispatching and distribution, allocate power more reasonably, and reduce network losses. Among them, the electrical distance refers to the relative distance between two nodes in terms of electrical characteristics.

[0049] As an embodiment of the present invention, calculating the electrical distance between the blockchain nodes according to the voltage power data includes: Extracting the node active power and node reactive power of the blockchain nodes according to the voltage power data; Calculating the node equivalent impedance of the blockchain nodes; Based on the node equivalent impedance, the node active power, and the node reactive power, calculating the electrical distance between the blockchain nodes by using the following formula: ; Wherein, represents the electrical distance between node and node in the blockchain nodes, represents the node active power of node in the blockchain nodes, represents the node active power of node in the blockchain nodes, represents the node reactive power of node in the blockchain nodes, represents the node reactive power of node in the blockchain nodes, represents the voltage of node in the blockchain nodes, represents the voltage of node in the blockchain nodes, represents the node equivalent impedance between node and node in the blockchain nodes.

[0050] Wherein, the node active power refers to the power actually consumed by the node for performing useful work. The node reactive power refers to the electromagnetic energy exchanged or stored by the node. The node equivalent impedance refers to the impedance value that comprehensively represents the effects of all components such as resistors, inductors, and capacitors between two nodes and represents the total effect of these components.

[0051] In the embodiment of the present invention, by determining the control node density and control node distance of the blockchain nodes based on the electrical distance, the computing resources, storage resources, and bandwidth resources can be better allocated, and the resource utilization rate can be improved. Wherein, the control node density refers to the ratio and distribution of the number of control nodes to other nodes in a certain area.

[0052] Optionally, the control node density and control node distance can be determined by clustering analysis, such as K-means, DBSCAN, etc.

[0053] In the embodiments of the present invention, by dividing the blockchain network into multiple power distribution control regions based on the control node density and the control node distance, optimization can be carried out within each region, improving the efficiency of power distribution and resource allocation. Among them, the multiple power distribution control regions refer to dividing the entire blockchain network into several smaller and relatively independent control regions, and each region is responsible for managing and controlling the power distribution and related functions of a part of the blockchain nodes.

[0054] Optionally, the multiple power distribution control regions can be divided by a graph partitioning algorithm. For example, the blockchain network is mapped into a blockchain network graph, and the graph partitioning algorithm is used to divide the blockchain network graph into multiple power distribution control regions.

[0055] S4. Identify the power distribution loss of the multiple power distribution control regions, determine the regional dynamic control mechanism of the multiple power distribution control regions based on the power distribution loss and the data-driven control model, collect the boundary voltage information of the boundary nodes in the multiple power distribution control regions in real time, and determine the coordinated control algorithm of the multiple power distribution control regions according to the boundary voltage information.

[0056] In the embodiments of the present invention, by identifying the power distribution loss of the multiple power distribution control regions, the waste of energy is reduced, thereby reducing the power supply cost. Among them, the power distribution loss refers to the energy loss caused by factors such as resistance, inductance, and capacitance during the power transmission and distribution process.

[0057] In the embodiments of the present invention, by determining the regional dynamic control mechanism of the multiple power distribution control regions based on the power distribution loss and the data-driven control model, the resource allocation can be dynamically adjusted according to the load conditions of each region, achieving load balancing, maintaining the stable operation of the power grid, and reducing voltage fluctuations and frequency deviations. Among them, the regional dynamic control mechanism refers to a strategy and method for managing and optimizing the operation of multiple power distribution control regions in a power system.

[0058] As an embodiment of the present invention, determining the regional dynamic control mechanism of the multiple power distribution control regions based on the power distribution loss and the data-driven control model includes: Identifying the loss mode and loss factor of the power distribution loss; Calculating the loss amount of the multiple power distribution control regions according to the loss mode and the loss factor; Determining the power source of the loss power of the multiple power distribution control regions; Determining the resource control rule and the in-region energy scheduling rule of the multiple power distribution control regions according to the loss amount and the power source of the loss power; Determining the regional dynamic control mechanism of the multiple power distribution control regions according to the resource control rule and the in-region energy scheduling rule.

[0059] Among them, the loss mode refers to different types and ways that cause energy loss in the power system, such as resistance loss, reactance loss, harmonic loss, etc. The loss factor refers to a dimensionless parameter of energy loss caused by factors such as resistance, inductance, and capacitance in the power system, electronic components, or materials. The loss amount refers to the specific value of energy loss caused by various reasons (such as resistance, inductance, capacitance, etc.) in the power system. The loss power source refers to the specific location that causes energy loss in the power system. The resource control rule refers to a series of management measures formulated in the distribution system to optimize resource allocation, improve system efficiency, ensure power supply reliability, and meet user needs, including load management, voltage and frequency control, reactive power control, etc. The energy dispatching rule within the region refers to a series of strategies formulated in the multi-distribution control region to optimize energy supply, demand, storage, and distribution, such as power generation resource dispatching, reserve capacity management, etc.

[0060] Optionally, the loss amount of the multi-distribution control region can be calculated by a distributed computing method, such as Apache Hadoop, Apache Spark, etc.

[0061] By collecting the boundary voltage information of the boundary nodes in the multi-distribution control region in real time, the embodiment of the present invention can help the power dispatching center to perform power dispatching more effectively, optimize the grid operation mode, and improve the overall efficiency of the power system. Among them, the boundary voltage information refers to the relevant data of the node voltage at the boundary of the distribution control region, such as voltage amplitude, voltage phase angle, voltage fluctuation, etc.

[0062] By determining the coordinated control algorithm of the multi-distribution control region according to the boundary voltage information, the embodiment of the present invention can effectively maintain the voltage within the specified range, prevent voltage collapse and fluctuation, and improve the voltage stability. Among them, the coordinated control algorithm refers to a calculation method for managing and optimizing the power flow and voltage level of the multi-distribution control region.

[0063] As an embodiment of the present invention, determining the coordinated control algorithm of the multi-distribution control region according to the boundary voltage information includes: Analyzing the local control variables of the multi-distribution control region according to the boundary voltage information; Determining the local objective function of the multi-distribution control region according to the local control variables; Defining the consistency target value of the multi-distribution control region; Constructing the coordinated control algorithm of the multi-distribution control region according to the consistency target value, the local control variables, and the local objective function, where the coordinated control algorithm includes: ; ; ; Among them, represents the optimized local control variable corresponding to the th region in the multi - distribution control region, the independent variable function representing the minimum value, the local objective function corresponding to the th region in the multi - distribution control region, the penalty coefficient, represents the local control variable corresponding to the th region in the multi - distribution control region, the consistent objective value representing the th optimization, the dual variable corresponding to the th region in the multi - distribution control region, the number of regions in the multi - distribution control region, the consistent objective value representing the th optimization, the dual variable corresponding to the th region in the multi - distribution control region, the dual variable corresponding to the th region in the multi - distribution control region, the

[0064] Among them, the local control variable refers to the control variable maintained by each region in the multi - distribution control region, such as boundary voltage, reactive power output, etc. The local objective function refers to the mathematical function defined by each distribution control region to achieve its own optimization goal, such as minimizing the boundary voltage difference, minimizing the reactive power output, etc. The consistent objective value refers to the common objective value set to achieve the consistency and collaborative optimization of the entire multi - distribution control region. The penalty coefficient refers to the relationship coefficient that balances the objective function in the original optimization problem and the penalty term formed due to the introduction of the dual variable. The dual variable refers to the auxiliary variable used to handle the constraint conditions.

[0065] S5. Integrate the data - driven control model, the regional dynamic control mechanism, and the coordinated control algorithm into the distribution control module of the power supply equipment. Based on the distribution control module, output the distribution parameters of the power supply equipment, and based on the distribution parameters, perform the intelligent power distribution of the power supply equipment.

[0066] In the embodiments of the present invention, by integrating the data-driven control model, the regional dynamic control mechanism, and the coordinated control algorithm into the power distribution control module of the power supply device, the power grid status can be monitored and analyzed in real time, the system changes can be quickly responded to, and the stable operation of the power grid can be maintained. At the same time, energy can be more effectively allocated, energy waste can be reduced, and energy utilization efficiency can be improved. Among them, the power distribution control module refers to a hardware and software system integrating a data-driven control model, a regional dynamic control mechanism, and a coordinated control algorithm.

[0067] In the embodiments of the present invention, by outputting the power distribution parameters of the power supply device based on the power distribution control module, the stable operation of the power grid can be maintained, voltage fluctuations and frequency deviations can be reduced, and power supply quality can be improved. Among them, the power distribution parameters refer to a series of key indicators used to describe and control the operating state of power distribution equipment.

[0068] In the embodiments of the present invention, by performing intelligent power distribution of the power supply device based on the power distribution parameters, electric energy can be efficiently allocated according to actual needs, energy waste can be reduced, and it is ensured that the power supply quality meets the standards and specific load requirements.

[0069] Compared with the problems described in the background art, in the embodiments of the present invention, by constructing the blockchain network of the power supply device, the risk of single-point failure can be reduced. Even if some nodes have problems, the entire network can still operate normally; optionally, in the embodiments of the present invention, by determining the discrete non-linear function of the input data and the output data based on the dynamic mapping relationship, the complex features in the data can be better utilized, and the data utilization efficiency can be improved; in the embodiments of the present invention, by determining the power distribution control parameters of the power supply device according to the objective function and using the preset control parameter analysis algorithm, the output of the power supply device can be optimized, making the power transmission more efficient, reducing energy loss, and improving the overall performance of the power distribution system; in the embodiments of the present invention, by dividing the blockchain network into multiple power distribution control regions based on the control node density and the control node distance, optimization can be performed within each region to improve the efficiency of power distribution and resource allocation; in the embodiments of the present invention, by dividing the blockchain network into multiple power distribution control regions based on the control node density and the control node distance, optimization can be performed within each region to improve the efficiency of power distribution and resource allocation; finally, in the embodiments of the present invention, by integrating the data-driven control model, the regional dynamic control mechanism, and the coordinated control algorithm into the power distribution control module of the power supply device, the power grid status can be monitored and analyzed in real time, the system changes can be quickly responded to, and the stable operation of the power grid can be maintained. At the same time, energy can be more effectively allocated, energy waste can be reduced, and energy utilization efficiency can be improved. Therefore, the intelligent power distribution method and system for power supply devices based on blockchain provided by the embodiments of the present invention can improve the power distribution efficiency and stability of intelligent power distribution of power supply devices.

[0070] Embodiment 2: As Figure 2 shown, it is a functional module diagram of an intelligent power distribution system for power supply equipment based on blockchain in the present invention.

[0071] The intelligent power distribution system 200 for power supply equipment based on blockchain in the present invention can be installed in an electronic device. According to the functions achieved, the intelligent power distribution system for power supply equipment based on blockchain may include a non-linear relationship analysis module 201, a data-driven control module 202, a power distribution control area division module 203, a regional coordination control module 204, and a power distribution control integration module 205. The modules in the present invention may also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0072] In the embodiments of the present invention, the functions of each module / unit are as follows: The non-linear relationship analysis module 201 is used to construct the blockchain network of the power supply equipment, utilize the blockchain nodes in the blockchain network to collect the input data and output data of each power supply equipment in real time, analyze the dynamic mapping relationship between the input data and the output data, and determine the discrete non-linear function of the input data and the output data based on the dynamic mapping relationship; The data-driven control module 202 is used to analyze the power distribution requirements of the power supply equipment, determine the target function required by the power supply equipment according to the power distribution requirements and the discrete non-linear function, determine the power distribution control parameters of the power supply equipment by using a preset control parameter analysis algorithm according to the target function, and integrate the data-driven control model of the power supply equipment based on the power distribution control parameters; The power distribution control area division module 203 is used to collect the voltage and power data of the blockchain nodes, calculate the electrical distance between the blockchain nodes according to the voltage and power data, determine the control node density and control node distance of the blockchain nodes based on the electrical distance, and divide the blockchain network into multiple power distribution control areas based on the control node density and the control node distance; The regional coordination control module 204 is used to identify the power distribution losses in the multiple power distribution control areas, determine the regional dynamic control mechanism of the multiple power distribution control areas based on the power distribution losses and the data-driven control model, collect the boundary voltage information of the boundary nodes in the multiple power distribution control areas in real time, and determine the coordination control algorithm of the multiple power distribution control areas according to the boundary voltage information; The distribution control integration module 205 is configured to integrate the data-driven control model, the regional dynamic control mechanism, and the coordinated control algorithm into the distribution control module of the power supply device, output the distribution parameters of the power supply device based on the distribution control module, and perform intelligent power distribution of the power supply device based on the distribution parameters.

[0073] Specifically, each module in the intelligent power distribution system 200 for power supply devices implemented based on blockchain in the embodiments of the present invention adopts the same technical means as those in the Figure 1 intelligent power distribution method for power supply devices implemented based on blockchain described above, and can achieve the same technical effects, which will not be elaborated here.

[0074] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent power distribution method for power supply equipment based on blockchain, characterized in that The method includes: Constructing a blockchain network for the power supply device, using blockchain nodes in the blockchain network to collect input data and output data of each power supply device in real time, analyzing the dynamic mapping relationship between the input data and the output data, and determining a discrete non-linear function of the input data and the output data based on the dynamic mapping relationship; Analyzing the power distribution requirements of the power supply device, determining an objective function required by the power supply device according to the power distribution requirements and the discrete non-linear function, determining power distribution control parameters of the power supply device using a preset control parameter analysis algorithm according to the objective function, and integrating a data-driven control model of the power supply device based on the power distribution control parameters; Collecting voltage and power data of the blockchain nodes, calculating the electrical distance between the blockchain nodes according to the voltage and power data, determining the control node density and control node distance of the blockchain nodes based on the electrical distance, and dividing the blockchain network into multiple power distribution control regions based on the control node density and the control node distance; Identifying the power distribution loss of the multiple power distribution control regions, determining a regional dynamic control mechanism of the multiple power distribution control regions based on the power distribution loss and the data-driven control model, collecting boundary voltage information of boundary nodes in the multiple power distribution control regions in real time, and determining a coordinated control algorithm of the multiple power distribution control regions according to the boundary voltage information; Integrating the data-driven control model, the regional dynamic control mechanism, and the coordinated control algorithm into a power distribution control module of the power supply device, outputting power distribution parameters of the power supply device based on the power distribution control module, and performing intelligent power distribution of the power supply device based on the power distribution parameters.

2. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 1, characterized in that The constructing of the blockchain network for the power supply device includes: Clarifying the network construction requirements of the power supply device and determining the blockchain platform of the power supply device according to the network construction requirements; Constructing a network topology of the blockchain platform and the power supply device and identifying network characteristics of the network topology; Determining a consensus mechanism and an encryption algorithm of the blockchain platform based on the network characteristics; Identifying network nodes of the network topology and configuring network devices of the network nodes; Integrating the network devices, the consensus mechanism, and the encryption algorithm into the network topology to obtain a blockchain network.

3. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 1, wherein, The analyzing of the dynamic mapping relationship between the input data and the output data includes: Performing dynamic time series adjustment on the input data and the output data to obtain time series input data and time series output data; Unifying the time series lengths of the time series input data and the time series output data and calculating a time delay parameter between the time series input data and the time series output data; Based on the time delay parameter and the time series length, calculating the cross-correlation between the time series input data and the time series output data using the following formula: ； Among them, represents the cross-correlation between the timing input data and the timing output data, represents the delay parameter, represents the timing length, represents the time point of the timing input data, represents the time point of the timing output data, the time point of the timing output data; Determining the dynamic mapping relationship between the time series input data and the time series output data according to the cross-correlation.

4. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 1, characterized in that, Determining the discrete non - linear function of the input data and the output data based on the dynamic mapping relationship includes: Discretizing the input data and the output data to obtain discrete input data and discrete output data; Determining the non - linear function structure of the discrete input data and the discrete output data according to the dynamic mapping relationship; Determining the function parameters and loss function of the non - linear function structure; Preliminarily fitting the initial non - linear function of the discrete input data and the discrete output data according to the function parameters and the loss function; Testing the goodness of fit of the initial non - linear function. When the goodness of fit is greater than the preset goodness - of - fit threshold, taking the initial non - linear function as the discrete non - linear function of the discrete input data and the discrete output data.

5. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 1, wherein, Determining the objective function required by the power supply device according to the power distribution demand and the discrete non - linear function includes: Analyzing the operation data of the power supply device, where the operation data includes: load data, device parameters, and grid status; Defining the decision variables of the power supply device according to the operation data and analyzing the multi - objective requirements of the power distribution demand; Constructing a sub - objective function of the target demand according to the decision variables and the discrete non - linear function; Determining the weight coefficients and constraint conditions of the sub - objective function; Integrating the sub - objective functions into an objective function according to the weight coefficients and the constraint conditions.

6. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 1, wherein Determining the power distribution control parameters of the power supply device according to the objective function by using a preset control parameter analysis algorithm includes: Initializing the initial power distribution parameters of the power supply device according to the decision variables corresponding to the objective function; Based on the constraint conditions corresponding to the objective function, using the control parameter analysis algorithm to iterate the initial power distribution parameters to obtain iterative power distribution parameters; where the control parameter analysis algorithm includes: ； Among them, represents the iterative power distribution parameters output in the -th iteration, represents the initial power distribution parameters in the -th iteration, represents the step size of the control parameter analysis algorithm, represents the perturbation coefficient, represents the perturbation term in the -th iteration, represents the learning rate, represents the gradient of the objective function at the -th iteration; Real - time detecting the convergence value of the control parameter analysis algorithm according to the iterative power distribution parameters; When the convergence value meets the preset convergence threshold, taking the iterative power distribution parameters as the power distribution control parameters of the power supply device.

7. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 6, wherein, Calculating the electrical distance between the blockchain nodes according to the voltage - power data includes: Extracting the node active power and node reactive power of the blockchain nodes according to the voltage - power data; Calculating the node equivalent impedance of the blockchain nodes; Based on the node equivalent impedance, the node active power, and the node reactive power, calculating the electrical distance between the blockchain nodes by using the following formula: ； Among them, represents the electrical distance between node and node in the blockchain node. represents the active power of node in the blockchain node. represents the active power of node in the blockchain node. represents the reactive power of node in the blockchain node. represents the reactive power of node in the blockchain node. represents the voltage of node in the blockchain node. represents the voltage of node in the blockchain node. represents the equivalent impedance between node and node in the blockchain node.

8. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 1, wherein Determining the regional dynamic control mechanism of the multi - power distribution control area based on the power distribution loss and the data - driven control model includes: Identifying the loss mode and loss factor of the power distribution loss; Calculating the loss amount of the multi - power distribution control area according to the loss mode and the loss factor; Determining the power source of the loss power in the multi - power distribution control area; Determining the resource control rules and the energy scheduling rules within the area of the multi - power distribution control area according to the loss amount and the power source of the loss power; Determine the regional dynamic control mechanism of the multi - distribution control region according to the resource control rules and the energy dispatching rules within the region.

9. The intelligent power distribution method for power supply equipment implemented based on blockchain according to claim 1, characterized in that The determining of the coordinated control algorithm of the multi - distribution control region according to the boundary voltage information includes: Analyze the local control variables of the multi - distribution control region according to the boundary voltage information; Determine the local objective function of the multi - distribution control region according to the local control variables; Define the consistency target value of the multi - distribution control region; Construct the coordinated control algorithm of the multi - distribution control region according to the consistency target value, the local control variables and the local objective function, wherein the coordinated control algorithm includes: ； ； ； Among them, represents the th optimized local control variable in the independent variable function representing the minimum value, represents the local objective function of the th area corresponding to the multi - distribution control area, represents the th optimized consistency target value, represents the th optimized dual variable in the area corresponding to the multi - distribution control area, represents the th optimized consistency target value, represents the th optimized dual variable in the area corresponding to the multi - distribution control area.

10. An intelligent power distribution system for power supply equipment implemented based on blockchain, characterized in that, The system includes: A non - linear relationship analysis module, configured to construct a blockchain network of the power supply devices, use the blockchain nodes in the blockchain network to collect the input data and output data of each power supply device in real time, analyze the dynamic mapping relationship between the input data and the output data, and determine the discrete non - linear function of the input data and the output data based on the dynamic mapping relationship; A data - driven control module, configured to analyze the power distribution requirements of the power supply devices, determine the target function required by the power supply devices according to the power distribution requirements and the discrete non - linear function, determine the power distribution control parameters of the power supply devices according to the target function by using a preset control parameter analysis algorithm, and integrate the data - driven control model of the power supply devices based on the power distribution control parameters; A power distribution control region division module, configured to collect the voltage - power data of the blockchain nodes, calculate the electrical distance between the blockchain nodes according to the voltage - power data, determine the control node density and control node distance of the blockchain nodes based on the electrical distance, and divide the blockchain network into multi - distribution control regions based on the control node density and the control node distance; A regional coordinated control module, configured to identify the power distribution loss of the multi - distribution control region, determine the regional dynamic control mechanism of the multi - distribution control region based on the power distribution loss and the data - driven control model, collect the boundary voltage information of the boundary nodes in the multi - distribution control region in real time, and determine the coordinated control algorithm of the multi - distribution control region according to the boundary voltage information; A power distribution control integration module, configured to integrate the data - driven control model, the regional dynamic control mechanism and the coordinated control algorithm into the power distribution control module of the power supply device, output the power distribution parameters of the power supply device based on the power distribution control module, and perform intelligent power distribution of the power supply device based on the power distribution parameters.

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