Blockchain-based power system resource intelligent configuration method and system

Abstract

The application provides a blockchain-based power system resource intelligent configuration method and system, relates to the technical field of power system resource configuration, and comprises the following steps: obtaining device resources and demand data to construct a topology layer, calculating a matching weight to identify a combination relationship to form an extended matching set, detecting conflicts through a time-space index and utilizing a game propagation diagram to eliminate the conflicts to generate a correlation graph, verifying electrical partitioning and distributing to a blockchain verification, and finally executing resource configuration through a smart contract. The application can realize accurate and efficient configuration of power resources, improve system operation reliability, and reduce configuration costs.

Description

Technical Field

[0001] This invention relates to the field of power system resource allocation technology, and in particular to a blockchain-based intelligent allocation method and system for power system resources. Background Technology

[0002] With the rapid development of the energy internet and smart grids, power system resource allocation faces new challenges of diversification, distribution, and dynamism. Power system resources include generation resources, energy storage resources, and demand-side response resources. The efficient allocation of these resources in time and space is crucial to ensuring the economic efficiency and stability of the power system. Traditional power resource allocation mainly relies on centralized dispatch systems to meet load demands through unified planning and dispatch.

[0003] In recent years, with the large-scale integration of renewable energy, the rapid growth of distributed resources, and the advancement of electricity marketization, the power system resource allocation model is shifting from centralized to distributed. Blockchain technology, with its decentralized, immutable, and traceable characteristics, provides a new approach to the intelligent allocation of power system resources. Blockchain can ensure fair and just electricity transactions and achieve efficient resource allocation, making it an important technological means for power system innovation. Summary of the Invention

[0004] This invention provides a blockchain-based intelligent allocation method and system for power system resources, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a blockchain-based intelligent allocation method for power system resources, comprising:

[0006] Acquire power resource data and transaction demand data of various equipment in the power system, and construct resource nodes and demand nodes;

[0007] The resource nodes are connected by collaborative edges to form a resource supply topology layer, and the demand nodes are connected by dependency edges to form a demand intent topology layer. Based on the supply and demand matching degree and spatiotemporal constraints, the inter-layer mapping weights are calculated, the resource demand combination relationship of multi-hop transmission is identified, and an extended matching relationship set is generated.

[0008] Using time interval trees and spatial partition indexes, resource competition conflicts in the spatiotemporal dimension of the matching relationship set are detected. Based on the urgency of demand and the scarcity of resources, a game propagation graph is constructed to iteratively propagate and resolve conflicts, generating a resource spatiotemporal association graph.

[0009] Based on the aforementioned resource spatiotemporal correlation map, electrical decoupling zones are divided and the power balance between resource adjustment and load changes is verified. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and a resource allocation scheme is generated through two rounds of consensus voting.

[0010] The resource allocation scheme is encapsulated into a smart contract and written into the blockchain, and the resource allocation execution action is automatically triggered through the smart contract.

[0011] The resource nodes are connected by collaborative edges to form a resource supply topology layer, and the demand nodes are connected by dependency edges to form a demand intent topology layer. Based on the supply-demand matching degree and spatiotemporal constraints, the inter-layer mapping weights are calculated to identify multi-hop resource demand combination relationships and generate an extended matching relationship set, including:

[0012] From the power resource data, the output capacity and geographical location of each resource node are extracted. The product of the capacity complementarity and the distance attenuation factor between any two resource nodes is calculated as the coordination weight. Coordination edges are established between resource nodes whose coordination weight is greater than the coordination threshold to obtain the resource supply topology layer.

[0013] Based on transaction demand data, the time window and resource type of each demand node are extracted, the overlap rate of the time window and the similarity of the resource type with the preceding demand node are calculated, and the dependency weight is obtained by weighted summation. Dependency edges are established between demand nodes with dependency weights greater than the dependency threshold to obtain the demand intent topology layer.

[0014] The degree of fit between the power supply curve of the resource node and the power load curve of the demand node is calculated to obtain the supply and demand matching degree. Combined with the constraints of geographical coverage overlap and time response coincidence, the inter-layer mapping weight is obtained through adaptive weighted fusion.

[0015] The starting demand node is identified from the demand intent topology layer. Multi-hop traversal is performed along the dependency edges. Resource nodes are selected by combining the inter-layer mapping weights and the collaborative edges. The matching relationship between each demand node and the resource node sequence is recorded to generate the resource demand matching relationship. The multi-hop traversal paths are summarized to form a matching relationship set.

[0016] Using a time interval tree and spatial partition index, resource competition conflicts in the spatiotemporal dimension of the matching relationship set are detected. Based on demand urgency and resource scarcity, a game-theoretic propagation graph is constructed for iterative propagation to resolve conflicts, generating a resource spatiotemporal correlation graph, including:

[0017] The temporal features of each matching relationship in the extended matching relationship set are mapped to a time interval tree and the interval nodes it occupies are marked. The spatial features are mapped to a spatial partition index and the partition units it occupies are marked. By querying the interval nodes that overlap and the partition units that overlap, the matching relationship pairs with spatiotemporal competition are identified and the conflict relationship set is constructed.

[0018] For each matching relationship in the set of conflict relationships, the initial game weight is calculated and a game propagation graph is constructed based on the urgency of demand and the scarcity of resources. By tracing the conflict propagation path, indirect conflict relationships with multiple levels of transmission are identified. The initial game weights of indirect conflict relationships are reduced and corrected to generate a game weight distribution after path correction.

[0019] An iterative game process is performed in the game propagation graph. In each iteration, the conflicting nodes calculate their game payoffs based on their game weights and the game weights of their adjacent conflicting nodes. Nodes whose game payoffs are lower than the exit threshold are marked as ineffective and their occupied resources are released. The released resources are then distributed in reverse to nodes with high game payoffs according to the propagation path. The iteration terminates when the game payoffs of all nodes are stable or the maximum number of iterations is reached. The remaining matching relationships are then used to construct a resource spatiotemporal correlation graph.

[0020] For each matching relationship in the set of conflict relationships, based on the urgency of demand and the scarcity of resources, initial game weights are calculated and a game propagation graph is constructed. Indirect conflict relationships involving multiple levels of transmission are identified by tracing the conflict propagation path, including:

[0021] For each matching relationship in the set of conflicting relationships, the time-decrease parameter of its demand node is extracted as the demand urgency, and the supply-demand ratio parameter of its resource node is extracted as the resource scarcity. The number of conflicting edges between each matching relationship and other matching relationships in the set of conflicting relationships is counted. The competition intensity correction coefficient is calculated based on the number of conflicting edges, and multiplied by the weighted sum of the demand urgency and the resource scarcity to obtain the initial game weight.

[0022] A game propagation graph is constructed using each matching relationship in the set of conflict relationships as a node and the conflict relationships as edges. The initial game weight is assigned to the corresponding node as the node state. Starting from any node in the game propagation graph, a traversal is performed, and the sequence of nodes traversed is recorded to form a propagation path set. For propagation paths with a path length greater than the direct conflict threshold, the node carrying the most edge connections is identified as the path target node, and its betweenness centrality metric is calculated. When the betweenness centrality metric exceeds the centrality threshold, the relationship between the path start point and the path end point is marked as a multi-level indirect conflict relationship.

[0023] Based on the aforementioned resource spatiotemporal correlation graph, electrical decoupling zones are divided and the power balance between resource adjustments and load changes is verified. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and a resource allocation scheme is generated through a two-round consensus voting process, including:

[0024] Based on the resource spatiotemporal correlation map, the electrical coupling strength between resource nodes and demand nodes is calculated and multiple electrical decoupling partitions are divided. The power generation adjustment of resource nodes and the load change of demand nodes are extracted and power balance verification is performed. The transmission capacity limit of the inter-partition interconnection branch is identified. Electrical decoupling partitions that fail the power balance verification are marked as partitions to be adjusted, and new electrical decoupling partitions are re-divided for power balance verification.

[0025] The resource allocation operation within the partition that has passed the power balance verification is encapsulated as a sub-decision unit. The power flow increment generated by the sub-decision unit on the interconnection branch is calculated using the DC power flow model and used as the power grid impact parameter. The sub-decision unit and the impact parameter are then distributed to the blockchain node.

[0026] Each blockchain node verifies whether the power flow increment meets the transmission capacity limit and generates a partition verification message. The first round of consensus voting is conducted through the Byzantine fault tolerance protocol. Sub-decision units with more votes than the fault tolerance threshold are marked as partition feasible units, and the corresponding impact parameters are extracted. Sub-decision units that fail to pass the vote are marked as units to be adjusted and returned to the game propagation graph for re-resolution.

[0027] For the feasible partition units, the power flow increments on the same connection branch are superimposed to obtain the power flow distribution of the entire network. A second round of consensus voting is initiated to verify whether all transmission capacity limits are met. If the verification fails, some feasible partition units are removed in descending order of impact parameters and then re-verified. The feasible partition units that have passed the verification are integrated to generate a resource allocation scheme.

[0028] Based on the resource spatiotemporal correlation map, the electrical coupling strength between resource nodes and demand nodes is calculated, and multiple electrical decoupling partitions are divided. The generation adjustment of resource nodes and the load change of demand nodes are extracted for power balance verification. The transmission capacity limitations of inter-partition interconnection branches are identified, including:

[0029] The geographical location and supply-demand matching relationship of each node are extracted from the resource spatiotemporal correlation graph. The electrical distance between each resource node and each demand node is calculated based on the geographical location. The electrical coupling strength between nodes is calculated by combining the power transmission path in the supply-demand matching relationship.

[0030] Based on the electrical coupling strength, the resource nodes and demand nodes are clustered and decomposed to obtain multiple electrical decoupling partitions. The branches connecting different electrical decoupling partitions are identified and marked as connection branches. The rated transmission power of the connection branches is extracted as the transmission capacity limit.

[0031] Within each electrical decoupling zone, node pairing combinations are determined based on supply and demand matching relationships to obtain resource allocation operations. Resource generation adjustment and demand load changes are extracted, power deviation of node pairing combinations is calculated and accumulated to obtain zone power imbalance, and zone power balance verification equations are established.

[0032] When the power imbalance of the partition is less than the balance error threshold, the set of tie branches corresponding to the electrically decoupled partition that has passed the power balance verification is extracted, and its transmission capacity limit is bound to the partition identifier and stored as a partition constraint mapping table, which serves as the input condition for the sub-decision unit.

[0033] The resource allocation scheme is encapsulated into a smart contract and written to the blockchain. The resource allocation execution action is automatically triggered through the smart contract, including:

[0034] Based on the resource allocation scheme, a multi-level triggering structure containing a contract lifecycle state machine is constructed. The multi-level triggering structure includes a resource scheduling instruction sequence, execution status feedback signals, and exception handling rules. The multi-level triggering structure is bound to the state machine triggering conditions to form an event-driven smart contract.

[0035] The smart contract is broadcast to the blockchain nodes. Based on the node timing dependency relationship in the resource allocation scheme, a call chain graph between contracts is established. A hierarchical and progressive verification mechanism is used to verify the contract executability of the call chain graph. After the verification is passed, the smart contract is written into the blockchain.

[0036] The system monitors the trigger conditions defined in the smart contract. When the trigger conditions are met, the resource scheduling instruction sequence is converted into resource configuration execution actions according to the hierarchical order of the call chain diagram, and the execution result status is written back to the blockchain. When the execution fails or there is no response after a timeout, the exception handling rules of the smart contract are triggered, and an exception record is generated and written to the blockchain.

[0037] A second aspect of this invention provides a blockchain-based intelligent allocation system for power system resources, comprising:

[0038] The first unit is used to acquire power resource data and transaction demand data of various equipment in the power system, and to construct resource nodes and demand nodes.

[0039] The second unit is used to connect the resource nodes through collaborative edges to form a resource supply topology layer, and connect the demand nodes through dependency edges to form a demand intention topology layer. Based on the supply and demand matching degree and spatiotemporal constraints, the inter-layer mapping weight is calculated, the resource demand combination relationship of multi-hop transmission is identified, and an extended matching relationship set is generated.

[0040] The third unit is used to detect resource competition conflicts in the spatiotemporal dimension of the matching relationship set by using time interval trees and spatial partition indexes, and to construct a game propagation graph based on the urgency of demand and the scarcity of resources to iteratively propagate and resolve conflicts, thereby generating a resource spatiotemporal association graph.

[0041] The fourth unit is used to divide electrical decoupling zones based on the resource spatiotemporal correlation map and verify the power balance between resource adjustment and load change. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and resource allocation schemes are generated through two rounds of consensus voting.

[0042] The fifth unit is used to encapsulate the resource allocation scheme into a smart contract and write it into the blockchain, and automatically trigger the resource configuration execution action through the smart contract.

[0043] A third aspect of the present invention,

[0044] An electronic device is provided, comprising:

[0045] processor;

[0046] Memory used to store processor-executable instructions;

[0047] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0048] Fourth aspect of the embodiments of the present invention,

[0049] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0050] The beneficial effects of this application are as follows:

[0051] By constructing a two-layer network structure consisting of a resource supply topology layer and a demand intention topology layer, the accurate mapping of power resources and demand and the identification of multi-hop transmission relationships are realized, improving the accuracy and globality of resource allocation and avoiding the local optimization problem in traditional methods.

[0052] Based on the distributed verification mechanism of electrical decoupling partitioning and blockchain nodes, the reliability and security of the resource allocation scheme are ensured through a two-round consensus voting method, effectively solving the problem of collaborative decision-making in large-scale power systems.

[0053] By encapsulating resource allocation schemes into smart contracts and writing them into the blockchain, the automated execution of resource allocation and the immutable transaction records are realized, which improves the transparency and traceability of system operation and reduces transaction costs and the risk of human intervention. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the intelligent allocation method for power system resources based on blockchain, as described in an embodiment of the present invention.

[0055] Figure 2 This is a flowchart illustrating the method for generating resource allocation schemes by integrating feasible partitioning units according to an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0058] Figure 1 This is a flowchart illustrating the blockchain-based intelligent allocation method for power system resources according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0059] Acquire power resource data and transaction demand data of various equipment in the power system, and construct resource nodes and demand nodes;

[0060] The resource nodes are connected by collaborative edges to form a resource supply topology layer, and the demand nodes are connected by dependency edges to form a demand intent topology layer. Based on the supply and demand matching degree and spatiotemporal constraints, the inter-layer mapping weights are calculated, the resource demand combination relationship of multi-hop transmission is identified, and an extended matching relationship set is generated.

[0061] Using time interval trees and spatial partition indexes, resource competition conflicts in the spatiotemporal dimension of the matching relationship set are detected. Based on the urgency of demand and the scarcity of resources, a game propagation graph is constructed to iteratively propagate and resolve conflicts, generating a resource spatiotemporal association graph.

[0062] Based on the aforementioned resource spatiotemporal correlation map, electrical decoupling zones are divided and the power balance between resource adjustment and load changes is verified. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and a resource allocation scheme is generated through two rounds of consensus voting.

[0063] The resource allocation scheme is encapsulated into a smart contract and written into the blockchain, and the resource allocation execution action is automatically triggered through the smart contract.

[0064] In one optional implementation, the resource nodes are connected via collaborative edges to form a resource supply topology layer, and the demand nodes are connected via dependency edges to form a demand intent topology layer. Based on the supply-demand matching degree and spatiotemporal constraints, inter-layer mapping weights are calculated to identify multi-hop resource demand combination relationships, and an extended matching relationship set is generated, including:

[0065] From the power resource data, the output capacity and geographical location of each resource node are extracted. The product of the capacity complementarity and the distance attenuation factor between any two resource nodes is calculated as the coordination weight. Coordination edges are established between resource nodes whose coordination weight is greater than the coordination threshold to obtain the resource supply topology layer.

[0066] Based on transaction demand data, the time window and resource type of each demand node are extracted, the overlap rate of the time window and the similarity of the resource type with the preceding demand node are calculated, and the dependency weight is obtained by weighted summation. Dependency edges are established between demand nodes with dependency weights greater than the dependency threshold to obtain the demand intent topology layer.

[0067] The degree of fit between the power supply curve of the resource node and the power load curve of the demand node is calculated to obtain the supply and demand matching degree. Combined with the constraints of geographical coverage overlap and time response coincidence, the inter-layer mapping weight is obtained through adaptive weighted fusion.

[0068] The starting demand node is identified from the demand intent topology layer. Multi-hop traversal is performed along the dependency edges. Resource nodes are selected by combining the inter-layer mapping weights and the collaborative edges. The matching relationship between each demand node and the resource node sequence is recorded to generate the resource demand matching relationship. The multi-hop traversal paths are summarized to form a matching relationship set.

[0069] In this specific embodiment, it is necessary to obtain power resource data or transaction demand data for each power device, divide each power device into resource nodes and demand nodes based on the data, and extract the output capacity and geographical location information of each resource node. For each resource node R i Record its maximum output capacity C i (t) curve of change with time t and geographic coordinates (x) i y i For example, a photovoltaic power station has a higher output capacity during the day and zero at night; a hydropower station has a higher output during the high-water season, for any two resource nodes R. i and R jCalculate the capacity complementarity Comp(i,j) between the two nodes. This complementarity represents the complementarity of the output capacity of the two nodes in the time dimension, which can be obtained by calculating the negative correlation between the output capacity curves of the two nodes. Simultaneously, calculate the distance decay factor Dist(i,j) between the two nodes. This factor decreases with increasing geographical distance, typically using an exponential decay model. The product of the capacity complementarity and the distance decay factor is used as the collaborative weight W(i,j) = Comp(i,j) × Dist(i,j). When W(i,j) is greater than a preset collaborative threshold θ... r At that time, in resource node R i and R j Establish collaborative edges between them to construct a complete resource supply topology layer. In practical applications, the collaborative threshold can be dynamically adjusted according to the grid stability requirements, and is usually between 0.6 and 0.8.

[0070] Construct a demand intent topology layer based on transaction demand data, for each demand node D i Record the time window of its demand [t] si , t ei and the required set of resource types T i Calculate any two demand nodes D i and D j The time window overlap rate, Overlap(i,j), represents the proportion of the time windows of two demand nodes overlapping within the total time. Simultaneously, the resource type similarity, Sim(i,j), is calculated, which is the number of intersection elements of the resource types required by the two demand nodes divided by the number of union elements. The dependency weight, Dep(i,j), is obtained by weighted summing of the time window overlap rate and the resource type similarity, as follows: Dep(i,j) = α × Overlap(i,j) + β × Sim(i,j), where α and β are weighting coefficients and α + β = 1. When the dependency weight Dep(i,j) is greater than a preset dependency threshold θ... d At demand node D i and D j Dependency edges are established between them to complete the construction of the demand intention topology layer. In practice, the weight coefficients α and β can be adjusted according to the supply and demand characteristics of the electricity market. Typically, α is 0.4 to 0.6 and β is 0.4 to 0.6.

[0071] After constructing the two topology layers, calculate the inter-layer mapping weights between resource nodes and demand nodes. For resource node R... i and demand node D j Calculate the supply and demand matching degree Match(R) i D jThis represents the degree of fit between the power supply curve of a resource node and the power load curve of a demand node. Curve similarity calculation methods such as cosine similarity or Pearson correlation coefficient can be used, along with calculating the geographical overlap (Geo(R)). i D j The overlap between the power supply range of resource nodes and the geographical location of demand nodes is calculated as Time(R), which is the degree of overlap in the time response. i D j ), representing the degree of matching between the available power supply periods of resource nodes and the power consumption periods of demand nodes, and obtaining the inter-layer mapping weight Map(R) through adaptive weighted fusion. i D j )=w1×Match(R i D j )+w2×Geo(R i D j )+w3×Time(R i D j ), where w1, w2, and w3 are adaptive weight coefficients that are dynamically adjusted based on historical matching success rates, satisfying w1+w2+w3=1.

[0072] Based on the constructed two-layer topology and inter-layer mapping weights, the resource demand combination relationship of multi-hop transmission is identified. In the demand intent topology layer, all nodes with an in-degree of zero are identified as the initial demand node set S. For each initial demand node s∈S, a depth-first search algorithm is executed to perform multi-hop traversal along the dependency edges. During the traversal, for each visited demand node D... i Based on inter-layer mapping weights, the most suitable set of resource nodes is selected. The selection process considers the collaborative edge relationships between resource nodes, prioritizing combinations of resource nodes with collaborative relationships to improve overall power supply efficiency. Specifically, for demand node D... i Select the one that satisfies Map(R) j D i )>θ m Resource node R j , where θ m This is the mapping threshold. If multiple resource nodes meet the conditions, their collaborative relationships with the selected resource nodes are further considered, and the node with the highest collaborative weight is selected. The matching relationship between each demand node and its corresponding resource node sequence is recorded, and all matching relationships on the multi-hop traversal path are summarized to form the final extended matching relationship set.

[0073] In electricity trading market applications, this method can effectively identify complex resource demand combinations. For example, when an industrial park requires additional power support during peak electricity consumption periods, the system can automatically identify and combine nearby photovoltaic power stations, wind farms, and energy storage facilities to form an optimized power supply scheme that meets electricity demand while ensuring stable grid operation. Through multi-hop transmission of resource demand combinations, it can handle cascading demand situations, such as scenarios where one demand triggers another demand, requiring more power resources, thereby achieving precise resource allocation and efficient utilization.

[0074] In one optional implementation, a time interval tree and spatial partition index are used to detect resource competition conflicts in the spatiotemporal dimension of the matching relationship set. Based on the urgency of demand and the scarcity of resources, a game-theoretic propagation graph is constructed for iterative propagation to resolve conflicts, generating a resource spatiotemporal association graph, including:

[0075] The temporal features of each matching relationship in the extended matching relationship set are mapped to a time interval tree and the interval nodes it occupies are marked. The spatial features are mapped to a spatial partition index and the partition units it occupies are marked. By querying the interval nodes that overlap and the partition units that overlap, the matching relationship pairs with spatiotemporal competition are identified and the conflict relationship set is constructed.

[0076] For each matching relationship in the set of conflict relationships, the initial game weight is calculated and a game propagation graph is constructed based on the urgency of demand and the scarcity of resources. By tracing the conflict propagation path, indirect conflict relationships with multiple levels of transmission are identified. The initial game weights of indirect conflict relationships are reduced and corrected to generate a game weight distribution after path correction.

[0077] An iterative game process is performed in the game propagation graph. In each iteration, the conflicting nodes calculate their game payoffs based on their game weights and the game weights of their adjacent conflicting nodes. Nodes whose game payoffs are lower than the exit threshold are marked as ineffective and their occupied resources are released. The released resources are then distributed in reverse to nodes with high game payoffs according to the propagation path. The iteration terminates when the game payoffs of all nodes are stable or the maximum number of iterations is reached. The remaining matching relationships are then used to construct a resource spatiotemporal correlation graph.

[0078] In this specific embodiment, a time interval tree and a spatial partition index need to be constructed for spatiotemporal conflict detection. The time interval tree adopts a balanced tree structure, where each node represents a time interval, supporting interval overlap queries. For each matching relationship in the extended matching relationship set, its time features (start time and end time) are mapped to the time interval tree, and the interval nodes it occupies are marked. The time feature can be represented as [t] start , t endThe [] represents the start and end times of resource occupancy. Simultaneously, spatial features are mapped to a spatial partitioning index, using a quadtree or grid index structure to divide the space. The spatial extent of each matching relationship is marked in the corresponding partitioning unit. Spatial features can be geographic coordinates (x, y) or polygonal regions.

[0079] Spatiotemporal conflict detection is achieved through query operations. The time interval tree is queried to obtain the set T of matching relationships that overlap in time. For each matching relationship in T, the spatial range of the matching relationship is queried in the spatial partition index to see if it overlaps with other matching relationships. If two matching relationships overlap in both time and spatial dimensions, they are identified as a pair of competing matching relationships and added to the set C of conflicting relationships.

[0080] For a set of conflicting relationships, game weights are calculated based on demand urgency and resource scarcity. Demand urgency can be calculated using time urgency indicators, such as the difference between the deadline and the current time. Resource scarcity is determined by the ratio of the total demand to the available supply of the resource. For each matching relationship r, the initial game weight w(r) is calculated as a weighted sum of demand urgency and resource scarcity: w(r) = α·urgency(r) + β·scarcity(r), where α and β are weighting coefficients.

[0081] Construct a game propagation graph G, where nodes represent matching relationships and edges represent conflict relationships. Use depth-first search to trace the conflict propagation path and identify indirect conflict relationships. For example, if relationships A and B conflict, and B and C conflict, then A and C have an indirect conflict. For indirect conflict relationships with a propagation path length of d, the initial game weights are adjusted by decreasing: w'(r) = w(r)·γ d , where γ is the attenuation coefficient (0<γ<1), can generate the game weight distribution after path correction, reflecting the transmission attenuation characteristics of the impact of conflict.

[0082] In the game propagation graph, an iterative game process is performed to resolve conflicts. In each iteration, each conflict node r calculates its game payoff s(r), taking into account its own weight and the weights of adjacent nodes: s(r)=w'(r)-λ·∑w'(n), where n is the node that directly conflicts with r and λ is the influence factor. If the game payoff s(r) of node r is lower than the preset exit threshold θ, it is marked as in a failed state and the resources it occupies are released.

[0083] The released resources are distributed in reverse order of the propagation path to nodes with higher game payoffs. Specifically, when node r exits the game, the nodes in the set N(r) that had conflicts with it recalculate their game payoffs and are sorted in descending order of payoff, with the released resources allocated first to nodes with higher payoffs. Resource allocation must ensure that the new allocation does not introduce new conflicts.

[0084] The iterative process continues until the termination condition is met: the rate of change of the game payoff of all nodes is lower than the stability threshold ε, or the maximum number of iterations M is reached, the state is retained as a valid matching relationship, and a spatiotemporal correlation graph of resources is constructed. In the graph, the nodes are resource and demand entities, the edges are valid matching relationships, and the edge attributes include information such as time window, spatial location, and matching strength.

[0085] In practical applications, such as power system resource allocation scenarios, generator sets can be regarded as resources, electricity load as demand, and the matching relationship as the allocation scheme between power generation resources and load. When multiple loads overlap during peak electricity consumption periods, this method can effectively detect and resolve resource conflicts, improve power supply efficiency, and quickly identify load areas with capacity conflicts in the conflict detection stage. In the game propagation stage, the game weights are calculated based on load priority and power generation resource availability. In the iterative game stage, the power allocation scheme is dynamically adjusted, and finally, the optimal power dispatch map is generated.

[0086] The above methods can effectively detect and resolve resource competition conflicts in the spatiotemporal dimensions in complex multi-resource scheduling environments, improve resource utilization efficiency, and meet more high-priority needs.

[0087] In one optional implementation, for each matching relationship in the set of conflict relationships, an initial game weight is calculated based on the urgency of demand and the scarcity of resources, and a game propagation graph is constructed. Indirect conflict relationships with multiple levels of transmission are identified by tracing the conflict propagation path, including:

[0088] For each matching relationship in the set of conflicting relationships, the time-decrease parameter of its demand node is extracted as the demand urgency, and the supply-demand ratio parameter of its resource node is extracted as the resource scarcity. The number of conflicting edges between each matching relationship and other matching relationships in the set of conflicting relationships is counted. The competition intensity correction coefficient is calculated based on the number of conflicting edges, and multiplied by the weighted sum of the demand urgency and the resource scarcity to obtain the initial game weight.

[0089] A game propagation graph is constructed using each matching relationship in the set of conflict relationships as a node and the conflict relationships as edges. The initial game weight is assigned to the corresponding node as the node state. Starting from any node in the game propagation graph, a traversal is performed, and the sequence of nodes traversed is recorded to form a propagation path set. For propagation paths with a path length greater than the direct conflict threshold, the node carrying the most edge connections is identified as the path target node, and its betweenness centrality metric is calculated. When the betweenness centrality metric exceeds the centrality threshold, the relationship between the path start point and the path end point is marked as a multi-level indirect conflict relationship.

[0090] In this specific embodiment, the basic attribute parameters of the matching relationships need to be extracted one by one from the set of conflict relationships. For each matching relationship, which consists of a demand node and a resource node, the time decay parameter of the demand node is read. This parameter is obtained by analyzing the time sensitivity characteristics of the power load and reflects the degree of value loss of power demand over time. For the power demand of critical facilities such as hospitals, the time decay parameter is set to a high value close to 1.0, indicating that power supply delay will have serious consequences. The time decay parameter of industrial users is usually set between 0.6 and 0.8, while the parameter value of general residential electricity is about 0.3 to 0.5.

[0091] The supply-demand ratio parameter of resource nodes is extracted synchronously as a scarcity indicator. This parameter is calculated by dividing the available capacity of the current generator unit by the total regional power demand. When the supply-demand ratio parameter is lower than 0.6, it indicates that the power resources are in a relatively tight state; when the parameter value is lower than 0.3, it indicates that the resources are extremely scarce. By monitoring the operating status of each power generation unit and load forecast data in real time, the supply-demand ratio parameter is dynamically updated to ensure that the parameter reflects the current resource status.

[0092] A conflict edge statistics mechanism is established, which traverses the entire set of conflict relationships and counts the number of direct conflicts between each matching relationship and other matching relationships. The identification of conflict edges is based on the principle of resource competition. When two matching relationships need to compete for the same power generation capacity or transmission channel, a conflict edge is established between them. For example, a gas turbine can supply power to both a chemical industrial park and a data center, so there is a conflict edge between these two matching relationships. During the statistics process, an adjacency matrix is ​​used to store conflict relationships. A value of 1 in the matrix element indicates that there is a conflict, and a value of 0 indicates that there is no conflict.

[0093] Based on the statistically obtained number of conflict edges, the competition intensity correction coefficient is calculated using the logarithmic normalization method. First, the maximum number of conflict edges for any matching relationship in the conflict relationship set is determined as the normalization benchmark. The number of conflict edges for the current matching relationship is incremented by 1, the logarithm is taken, and then divided by the logarithm of the maximum number of conflict edges plus 1 to obtain the normalized competition intensity correction coefficient. The logarithmic transformation can effectively compress the numerical range, avoid extreme conflict situations from having too much impact on subsequent calculations, and at the same time maintain the distinguishability between different conflict intensities.

[0094] The initial game weights are calculated by weighting demand urgency and resource scarcity. The weighting coefficient for demand urgency is usually set to 0.6 to reflect the importance of demand-side factors. Since a smaller supply-demand ratio indicates greater resource scarcity, the reciprocal of the supply-demand ratio is used to accurately reflect the degree of scarcity. The weighted result is multiplied by the competition intensity correction coefficient to obtain the initial game weights of the matching relationship. These weights comprehensively reflect the importance and urgency of the matching relationship in the allocation of electricity resources.

[0095] The data structure for constructing the game propagation graph is stored in the form of an adjacency list. Each node corresponds to a matching relationship. The node contains the matching relationship identifier and the initial game weight information. The adjacency list records the list of other nodes that have conflicting relationships with the current node, and also stores the edge weight information. After the graph is constructed, the node connectivity is verified to ensure that the main matching relationships can be connected through conflicting edges to form a network structure.

[0096] A depth-first traversal algorithm is implemented, randomly selecting a node in the graph as the starting point for traversal. A visit state array is maintained to mark the visit status of each node in the current path, preventing the formation of circular paths. During the traversal, the sequence of nodes visited is recorded in detail. When a leaf node is reached or all adjacent nodes have been visited, the current path is added to the propagation path set. Through a backtracking mechanism, all possible path combinations are systematically explored to form a complete propagation path set.

[0097] Each path in the propagation path set was length-filtered and analyzed. On test datasets of multiple typical power grid dispatching scenarios, the accuracy and false negative rates of indirect conflict identification were statistically analyzed for path lengths of 1, 2, 3, 4, and 5. Experimental results show that when the path length threshold is set to 3, the identification accuracy reaches 87.6%, with the lowest combined false positive and false negative rates of 12.1% and 13.2%, respectively, demonstrating optimal overall performance. Therefore, a direct conflict threshold of 3 is set. Those skilled in the art can also adaptively adjust this threshold within the range of 2 to 5 according to specific power grid scale and accuracy requirements. With a direct conflict threshold of 3, paths with lengths greater than this threshold are selected as indirect conflict candidates. For paths meeting the criteria, the number of edge connections for each node in the entire game propagation graph is counted. By comparing the connectivity of each node, the node carrying the most edge connections is identified as the target node of that path. The target node is typically a key hub in the power system, playing a crucial role in resource allocation control.

[0098] The Brandes algorithm is used to calculate the betweenness centrality metric of the target node. This algorithm efficiently calculates betweenness centrality through two traversals: the first traversal establishes a shortest path tree from the source node to all other nodes, and the second traversal accumulates the betweenness centrality contribution of each node. During the calculation, the number of all shortest paths passing through the target node is counted and divided by the total number of shortest paths between corresponding node pairs to obtain the contribution of that node to the betweenness centrality of the target node. When the betweenness centrality metric of the target node exceeds the set centrality threshold of 0.15, the centrality threshold can be adjusted within the range of 0.10 to 0.20 according to the actual situation. Indirect conflict relationship marking is then performed, adding new relationship entries to the original conflict relationship set. The relationship between the start and end points of the propagation path is marked as a multi-level indirect conflict relationship, and the propagation path information and target node identifier are recorded. This marking mechanism can reveal the potential cascading effects in the power system and provide a more comprehensive view of conflict relationships for scheduling decisions.

[0099] Figure 2 This is a flowchart illustrating the method for generating resource allocation schemes by integrating feasible partition units according to an embodiment of the present invention. In one optional implementation, based on the resource spatiotemporal correlation graph, electrical decoupling partitions are divided and the power balance between resource adjustments and load changes is verified. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and a resource allocation scheme is generated through a two-round consensus voting process, including:

[0100] Based on the resource spatiotemporal correlation map, the electrical coupling strength between resource nodes and demand nodes is calculated and multiple electrical decoupling partitions are divided. The power generation adjustment of resource nodes and the load change of demand nodes are extracted and power balance verification is performed. The transmission capacity limit of the inter-partition interconnection branch is identified. Electrical decoupling partitions that fail the power balance verification are marked as partitions to be adjusted, and new electrical decoupling partitions are re-divided for power balance verification.

[0101] The resource allocation operation within the partition that has passed the power balance verification is encapsulated as a sub-decision unit. The power flow increment generated by the sub-decision unit on the interconnection branch is calculated using the DC power flow model and used as the power grid impact parameter. The sub-decision unit and the impact parameter are then distributed to the blockchain node.

[0102] Each blockchain node verifies whether the power flow increment meets the transmission capacity limit and generates a partition verification message. The first round of consensus voting is conducted through the Byzantine fault tolerance protocol. Sub-decision units with more votes than the fault tolerance threshold are marked as partition feasible units, and the corresponding impact parameters are extracted. Sub-decision units that fail to pass the vote are marked as units to be adjusted and returned to the game propagation graph for re-resolution.

[0103] For the feasible partition units, the power flow increments on the same connection branch are superimposed to obtain the power flow distribution of the entire network. A second round of consensus voting is initiated to verify whether all transmission capacity limits are met. If the verification fails, some feasible partition units are removed in descending order of impact parameters and then re-verified. The feasible partition units that have passed the verification are integrated to generate a resource allocation scheme.

[0104] In this specific embodiment, it is necessary to divide the electrical decoupling partition based on the resource spatiotemporal correlation map and verify the power balance between resource adjustment and load change. The resource spatiotemporal correlation map includes the spatiotemporal distribution and correlation of various resource nodes (such as generator sets, adjustable loads, energy storage devices, etc.) and demand nodes (such as electricity loads) in the power system.

[0105] Specifically, electrical decoupling partitions are defined by calculating the electrical coupling strength between resource nodes and demand nodes. The electrical coupling strength can be calculated using the electrical distance between nodes and the power transmission path. When the coupling strength exceeds a preset threshold (e.g., 0.05 per unit), significant electrical correlation is considered to exist between nodes. Based on the electrical coupling strength, a clustering algorithm is used to divide the system into multiple electrical decoupling partitions. In practical applications, a provincial power grid is divided into five to ten electrical decoupling partitions. Electrical coupling between nodes within each partition is relatively strong, while electrical coupling between partitions is relatively weak.

[0106] After the partitioning is completed, the power generation adjustment of resource nodes and the load change of demand nodes in each partition are extracted to verify whether the power balance condition is met in the partition: the sum of the power generation adjustment of all resource nodes in the partition should be equal to the sum of the load change of all demand nodes in the partition, with a deviation of less than the preset error (such as 0.5%) allowed. At the same time, the interconnection branches between partitions are identified and their transmission capacity limit values ​​are recorded.

[0107] For electrical decoupling partitions that fail the power balance verification, the resource nodes and demand nodes involved are released, and the partitions are re-decoupled. After the partitioning is completed, the power generation adjustment of resource nodes and the load change of demand nodes in the new electrical decoupling partitions are extracted to verify whether the power balance conditions are met. If the power balance conditions are met, the corresponding tie branches are extracted and their transmission capacity limits are recorded. If there are still partitions that fail, the partitioning continues until the maximum number of iterations is reached, and an anomaly report is sent to the device terminal.

[0108] The resource allocation operation within a partition that has passed the power balance verification is encapsulated into an independent sub-decision unit. Each sub-decision unit contains the adjustment plan for each resource node within the partition and the corresponding load change information, namely the partition identifier, the list of generator sets involved, the list of load nodes, and the corresponding power adjustment amount. The sub-decision unit is encapsulated in a standardized data format to facilitate transmission and processing in the blockchain network. At the same time, a unique identifier is assigned to each sub-decision unit to facilitate subsequent tracking and management.

[0109] The DC power flow model is used to calculate the power flow impact of sub-decision units on tie branches. The DC power flow model simplifies the computational complexity through linearization and is suitable for fast power flow analysis. For each sub-decision unit, its power adjustment is taken as the injected power change, and the power flow increment generated by this change on each tie branch is calculated. The calculation of the power flow increment is based on the network's transmission distribution factor matrix, which reflects the sensitivity of node power injection to branch power flow.

[0110] The sub-decision units and their corresponding power grid impact parameters are distributed to various nodes in the blockchain network. After receiving the data from the sub-decision units, the blockchain nodes verify the data integrity and format correctness, read the power flow increment data generated by the sub-decision units, compare it with the transmission capacity limit of the interconnection branches stored locally, and determine whether the power flow increment is within the allowable range.

[0111] Each blockchain node generates a partition verification message based on the verification result. The verification message includes the sub-decision unit identifier, the verification result, and the node signature information. When the power flow increment meets the transmission capacity limit, a "pass" verification message is generated; when the capacity limit is exceeded, a "reject" verification message is generated, and the specific value of exceeding the limit is recorded. The verification message is broadcast to other nodes through the blockchain network.

[0112] The first round of consensus voting mechanism based on the Byzantine fault tolerance protocol is initiated. Each node collects verification messages from other nodes, counts the number of "pass" votes for each sub-decision unit, and sets the fault tolerance threshold to 2 / 3 of the total number of participating voting nodes. When the number of "pass" votes for a sub-decision unit exceeds the threshold, it is marked as a feasible unit for partitioning. Sub-decision units that do not reach the threshold are marked as units to be adjusted, and their corresponding resource allocation requests are returned to the game propagation graph for re-resolution.

[0113] For sub-decision units marked as feasible units in a region, their corresponding power flow increment impact parameters are extracted. On the same tie branch, the power flow increments from different feasible units in different regions are algebraically superimposed to calculate the total power flow change of that branch. By traversing all tie branches, a complete power flow distribution map of the entire network is constructed, reflecting the power grid operation status under the combined effect of all feasible units in different regions. For example, if on the tie line from region A to region B, the sub-decision unit of region A contributes 50MW of power flow increment, and the sub-decision unit of region C contributes 30MW of power flow increment, then the total power flow increment on this tie line is 80MW.

[0114] Based on the overall network power flow distribution, blockchain nodes initiate a second round of consensus voting to verify whether all connection branches meet the transmission capacity limit. Each blockchain node checks whether the total power flow of each connection branch exceeds its transmission capacity limit, generates a network-wide verification message, and participates in the voting. If the verification fails, some feasible units in the partition are removed in descending order of the impact parameters, with priority given to removing units with larger power flow increments. The overall network power flow distribution is recalculated and verified again. This process is repeated until the overall network power flow distribution meets all constraints.

[0115] The feasible units of each zone that have passed final verification are integrated to generate a complete power resource allocation scheme. This scheme includes the generation dispatch plan, load allocation strategy, and power flow distribution of interconnecting branches for each zone. The allocation scheme is output in a standardized format, including timestamps, scheme identifiers, and detailed execution instructions, which are then implemented by the power grid dispatch center.

[0116] In a specific application scenario, a provincial power grid is divided into six electrically decoupled zones. The photovoltaic power station in the third zone experiences a 200MW reduction in power generation due to cloud cover changes, requiring the use of resources from other zones for balancing. This method calculates a scheme to increase the power generation of thermal power units in the first and fifth zones by 100MW each. After two rounds of consensus verification, it is confirmed that the scheme meets all tie-line transmission capacity limits, and finally, a resource scheduling instruction is formed.

[0117] In one optional implementation, based on the resource spatiotemporal correlation map, the electrical coupling strength between resource nodes and demand nodes is calculated and multiple electrical decoupling partitions are divided. The generation adjustment of resource nodes and the load change of demand nodes are extracted, power balance verification is performed, and the transmission capacity limitations of inter-partition interconnection branches are identified, including:

[0118] The geographical location and supply-demand matching relationship of each node are extracted from the resource spatiotemporal correlation graph. The electrical distance between each resource node and each demand node is calculated based on the geographical location. The electrical coupling strength between nodes is calculated by combining the power transmission path in the supply-demand matching relationship.

[0119] Based on the electrical coupling strength, the resource nodes and demand nodes are clustered and decomposed to obtain multiple electrical decoupling partitions. The branches connecting different electrical decoupling partitions are identified and marked as connection branches. The rated transmission power of the connection branches is extracted as the transmission capacity limit.

[0120] Within each electrical decoupling zone, node pairing combinations are determined based on supply and demand matching relationships to obtain resource allocation operations. Resource generation adjustment and demand load changes are extracted, power deviation of node pairing combinations is calculated and accumulated to obtain zone power imbalance, and zone power balance verification equations are established.

[0121] When the power imbalance of the partition is less than the balance error threshold, the set of tie branches corresponding to the electrically decoupled partition that has passed the power balance verification is extracted, and its transmission capacity limit is bound to the partition identifier and stored as a partition constraint mapping table, which serves as the input condition for the sub-decision unit.

[0122] In this specific embodiment, in the partitioning and power balance verification method based on electrical coupling strength, it is necessary to extract the geographical location information and supply-demand matching relationship of resource nodes and demand nodes from the resource spatiotemporal correlation map. The resource spatiotemporal correlation map is a data structure that represents the spatiotemporal distribution characteristics of resource nodes and demand nodes in a power system, including information such as node geographical coordinates, line topology, and impedance parameters.

[0123] Based on the extracted geographical location information, the electrical distance between each resource node and the demand node is calculated. Electrical distance is different from geographical distance. It reflects the electrical "nearness" relationship between nodes in the power system and can be calculated through the grid impedance matrix or power flow sensitivity factor. Specifically, for resource nodes and demand nodes, the electrical distance can be calculated through the equivalent impedance between nodes. This impedance reflects the electrical path characteristics of power transmission from one node to another. The smaller the electrical distance, the closer the two nodes are electrically, and the higher the power transmission efficiency.

[0124] By combining the power transmission path in the supply and demand matching relationship, the electrical coupling strength between nodes is calculated. The supply and demand matching relationship represents the power allocation relationship between resource nodes and demand nodes, including the actual power flow path and the magnitude of transmission power. The electrical coupling strength can be quantified by multiplying the inverse of the electrical distance by the transmission power, which represents the tightness of the electrical connection between two nodes. The greater the coupling strength, the more frequent the power exchange between nodes and the more significant the mutual influence.

[0125] Based on the calculated electrical coupling strength, spectral clustering is used to cluster and decompose resource nodes and demand nodes. During the clustering process, the electrical coupling strength is used as a similarity measure between nodes, so that nodes with high coupling strength tend to be assigned to the same partition. By setting an appropriate clustering threshold, multiple electrically decoupled partitions are obtained. The electrical coupling between nodes in each partition is strong, while the coupling between partitions is weak.

[0126] Identify the branches connecting different electrical decoupling zones and mark them as tie branches. Tie branches are transmission lines that cross the boundaries of different zones and play an important role in multi-zone coordinated control. Extract the rated transmission power of each tie branch as the transmission capacity limit for power exchange between zones. These limits ensure that physical transmission capacity constraints are not violated during the zone-independent decision-making process.

[0127] Within each electrical decoupling zone, node pairing combinations are further determined based on the previously established supply and demand matching relationships. Each node pairing combination represents the supply and demand matching relationship between a specific resource node and a demand node. These pairing relationships form the basis of resource allocation operations. For each pairing combination, the resource generation adjustment amount and the demand load change amount are extracted. The generation adjustment amount represents the increase or decrease in the generation of the resource node relative to the baseline operating condition, and the load change amount represents the load fluctuation of the demand node relative to the baseline operating condition.

[0128] Calculate the power deviation for each node pairing, which is the difference between the resource generation adjustment and the demand load change. Sum the power deviations of all node pairings within the partition to obtain the partition power imbalance. Ideally, power balance should be achieved within the partition, meaning the total generation adjustment should equal the total load change. Based on the partition power imbalance, establish a partition power balance verification equation.

[0129] When the power imbalance of a zone is less than the preset balance error threshold, the zone is considered to have passed the power balance verification. The balance error threshold is a non-negative tolerance parameter used to accommodate rounding errors and model simplification deviations during the calculation process. The reasonable value of this parameter is closely related to the system capacity, voltage level, and safety constraints of the power system. The specific determination method is as follows: For the dispatching scenario of the transmission network with a voltage level of 220kV and above, the system capacity is usually in the gigawatt (GW) range. It is recommended to set the balance error threshold to 0.01% to 0.05% of the rated capacity of the system, corresponding to a typical value range of 0.1MW to 5MW. For the 110kV distribution network scenario, the system capacity is usually in the hundred megawatt (MW) range. The recommended value range is 0.01MW to 0.5MW. In a specific embodiment of the present invention, a provincial 500kV main grid (system capacity approximately 8000MW) is used as the verification object. The balance error threshold is set to 1MW (i.e., 0.0125% of the system capacity). At this point, the residual convergence determination of the power flow balance equation is consistent with the actual dispatch safety constraints, and the number of calculation iterations and accuracy both meet the engineering requirements. Those skilled in the art should understand that the final value of the balance error threshold must not exceed the power imbalance safety limit specified in the relevant power grid dispatch regulations to ensure the executability of dispatch instructions and system stability. The set of tie branches corresponding to the electrical decoupling zones that have passed the power balance verification is extracted, and their transmission capacity limits are bound to the zone identifiers and stored as a zone constraint mapping table.

[0130] A partition constraint mapping table is a data structure that records partition identifiers, the set of connecting branches at the partition boundary, and their transmission capacity limits. This mapping table serves as the input condition for sub-decision units, providing boundary constraints for subsequent distributed optimization decisions. In distributed decision-making, each partition can independently formulate a scheduling plan based on these constraints, while ensuring that power exchange between partitions does not exceed the transmission capacity of the connecting branches.

[0131] In practical applications, the power grid's operating status changes dynamically, requiring periodic updates to the resource spatiotemporal correlation map and re-execution of partitioning and power balance verification. This dynamic adjustment mechanism ensures that the partitioning always reflects the actual operating characteristics of the power grid, improving the adaptability and effectiveness of distributed decision-making.

[0132] In one optional implementation, the resource allocation scheme is encapsulated as a smart contract and written to the blockchain. The smart contract automatically triggers resource configuration execution actions, including:

[0133] Based on the resource allocation scheme, a multi-level triggering structure containing a contract lifecycle state machine is constructed. The multi-level triggering structure includes a resource scheduling instruction sequence, execution status feedback signals, and exception handling rules. The multi-level triggering structure is bound to the state machine triggering conditions to form an event-driven smart contract.

[0134] The smart contract is broadcast to the blockchain nodes. Based on the node timing dependency relationship in the resource allocation scheme, a call chain graph between contracts is established. A hierarchical and progressive verification mechanism is used to verify the contract executability of the call chain graph. After the verification is passed, the smart contract is written into the blockchain.

[0135] The system monitors the trigger conditions defined in the smart contract. When the trigger conditions are met, the resource scheduling instruction sequence is converted into resource configuration execution actions according to the hierarchical order of the call chain diagram, and the execution result status is written back to the blockchain. When the execution fails or there is no response after a timeout, the exception handling rules of the smart contract are triggered, and an exception record is generated and written to the blockchain.

[0136] In this specific embodiment, a multi-level triggering structure including a contract lifecycle state machine is constructed according to a pre-defined resource allocation scheme. This multi-level triggering structure consists of three core components: a resource scheduling instruction sequence, execution status feedback signals, and exception handling rules. The resource scheduling instruction sequence defines the specific operation steps for resource allocation, such as calculating the resource allocation amount, determining the allocation priority, and setting the resource usage period. The execution status feedback signals are used to monitor the resource allocation execution status in real time, including successful allocation signals, resource usage status signals, and resource release signals. The exception handling rules pre-set handling mechanisms for possible abnormal situations, such as a priority adjustment mechanism for insufficient resources, a retry mechanism for network latency timeouts, and a rollback mechanism for malicious operations.

[0137] When constructing a multi-level trigger structure, a finite state machine model is used to define the lifecycle of the contract, including initialization, waiting for trigger, execution, completion, and exception states. Each state corresponds to specific contract behaviors and transition conditions. For example, when a user resource request event is detected, the contract state changes from "waiting for trigger" to "execution"; when the resource is successfully allocated, the state changes to "complete"; if an exception occurs, it transitions to the "exception" state and starts the corresponding processing flow. By binding the multi-level trigger structure with the state machine trigger conditions, an event-driven smart contract is formed, enabling precise control over the resource allocation process.

[0138] Once constructed, the smart contract is broadcast to nodes in the blockchain network. The broadcast uses a distributed consensus protocol to ensure that all nodes obtain the same contract content. Based on the node timing dependencies defined in the resource allocation scheme, a call chain diagram between contracts is established. This chain diagram clearly shows the order and dependencies of contract execution, such as the resource check contract must be executed before the resource allocation contract, and the resource allocation contract must be executed before the resource usage right confirmation contract.

[0139] For the established call chain graph, a layered and progressive verification mechanism is adopted to verify the executability of the contract. The verification process includes three levels: syntax verification, logic verification, and resource verification. Syntax verification ensures that the contract code conforms to the smart contract language specification; logic verification checks whether there are circular dependencies or deadlock risks in the call relationships between contracts; and resource verification assesses whether the resources required for contract execution are available. After the verification is passed, the smart contract is written into the blockchain through a consensus algorithm, becoming an immutable distributed ledger record.

[0140] Continuously monitor the trigger conditions defined in the smart contract. The trigger conditions can be time-triggered (such as daily timed resource allocation), event-triggered (such as user submitting resource requests), or state-triggered (such as available resources reaching a threshold). When the trigger conditions are met, convert the resource scheduling instruction sequence into specific resource configuration execution actions according to the hierarchical order of the call chain diagram.

[0141] The conversion process is completed by parsing the opcodes and parameters in the scheduling instruction sequence and calling the corresponding resource management interface. For example, when the "allocate computing resources" instruction is parsed, the cloud platform API is called to create a virtual machine instance; when the "allocate storage resources" instruction is parsed, the storage service is called to allocate the specified storage space. During the execution process, a status log is generated in real time to record the execution status and results of each step. After the execution is completed, the final execution result status is constructed as a blockchain transaction and written back to the blockchain through the consensus mechanism to ensure the immutability and traceability of the result.

[0142] During execution, if an execution failure or timeout occurs, the predefined exception handling rules in the smart contract will be automatically triggered. The exception handling rules will take corresponding measures according to the exception type, such as retry mechanism, rollback operation or alternative solution. At the same time, an exception record containing exception details, including the exception occurrence time, exception type, scope of impact and handling measures, will be generated and written into the blockchain as a blockchain transaction to form a complete audit chain.

[0143] The above methods enable the transformation and automated execution of resource allocation schemes into smart contracts, ensuring the transparency, efficiency, and security of the resource allocation process. They also provide complete execution records and exception handling mechanisms, providing solid technical support for resource management.

[0144] This invention relates to a blockchain-based intelligent allocation system for power system resources, comprising:

[0145] The first unit is used to acquire power resource data and transaction demand data of various equipment in the power system, and to construct resource nodes and demand nodes.

[0146] The second unit is used to connect the resource nodes through collaborative edges to form a resource supply topology layer, and connect the demand nodes through dependency edges to form a demand intention topology layer. Based on the supply and demand matching degree and spatiotemporal constraints, the inter-layer mapping weight is calculated, the resource demand combination relationship of multi-hop transmission is identified, and an extended matching relationship set is generated.

[0147] The third unit is used to detect resource competition conflicts in the spatiotemporal dimension of the matching relationship set by using time interval trees and spatial partition indexes, and to construct a game propagation graph based on the urgency of demand and the scarcity of resources to iteratively propagate and resolve conflicts, thereby generating a resource spatiotemporal association graph.

[0148] The fourth unit is used to divide electrical decoupling zones based on the resource spatiotemporal correlation map and verify the power balance between resource adjustment and load change. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and resource allocation schemes are generated through two rounds of consensus voting.

[0149] The fifth unit is used to encapsulate the resource allocation scheme into a smart contract and write it into the blockchain, and automatically trigger the resource configuration execution action through the smart contract.

[0150] A third aspect of the present invention provides an electronic device, comprising:

[0151] processor;

[0152] Memory used to store processor-executable instructions;

[0153] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0154] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0155] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0156] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain-based intelligent allocation method for power system resources, characterized in that, include: Acquire power resource data and transaction demand data of various equipment in the power system, and construct resource nodes and demand nodes; The resource nodes are connected by collaborative edges to form a resource supply topology layer, and the demand nodes are connected by dependency edges to form a demand intent topology layer. Based on the supply and demand matching degree and spatiotemporal constraints, the inter-layer mapping weights are calculated, the resource demand combination relationship of multi-hop transmission is identified, and an extended matching relationship set is generated. Using time interval trees and spatial partition indexes, resource competition conflicts in the spatiotemporal dimension of the matching relationship set are detected. Based on the urgency of demand and the scarcity of resources, a game propagation graph is constructed to iteratively propagate and resolve conflicts, generating a resource spatiotemporal association graph. Based on the aforementioned resource spatiotemporal correlation graph, electrical decoupling zones are divided and the power balance between resource adjustments and load changes is verified. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and a resource allocation scheme is generated through a two-round consensus voting process, including: Based on the resource spatiotemporal correlation map, the electrical coupling strength between resource nodes and demand nodes is calculated and multiple electrical decoupling partitions are divided. The power generation adjustment of resource nodes and the load change of demand nodes are extracted and power balance verification is performed. The transmission capacity limit of the inter-partition interconnection branch is identified. Electrical decoupling partitions that fail the power balance verification are marked as partitions to be adjusted, and new electrical decoupling partitions are re-divided for power balance verification. The resource allocation operation within the partition that has passed the power balance verification is encapsulated as a sub-decision unit. The power flow increment generated by the sub-decision unit on the interconnection branch is calculated using the DC power flow model and used as the power grid impact parameter. The sub-decision unit and the impact parameter are then distributed to the blockchain node. Each blockchain node verifies whether the power flow increment meets the transmission capacity limit and generates a partition verification message. The first round of consensus voting is conducted through the Byzantine fault tolerance protocol. Sub-decision units with more votes than the fault tolerance threshold are marked as partition feasible units, and the corresponding impact parameters are extracted. Sub-decision units that fail to pass the vote are marked as units to be adjusted and returned to the game propagation graph for re-resolution. For the feasible partition units, the power flow increments on the same contact branch are superimposed to obtain the power flow distribution of the entire network. A second round of consensus voting is initiated to verify whether all transmission capacity limits are met. If the verification fails, some feasible partition units are removed in descending order of impact parameters and then re-verified. The feasible partition units that have passed the verification are integrated to generate a resource allocation scheme. The resource allocation scheme is encapsulated into a smart contract and written into the blockchain, and the resource allocation execution action is automatically triggered through the smart contract.

2. The method according to claim 1, characterized in that, The resource nodes are connected by collaborative edges to form a resource supply topology layer, and the demand nodes are connected by dependency edges to form a demand intent topology layer. Based on the supply-demand matching degree and spatiotemporal constraints, the inter-layer mapping weights are calculated to identify multi-hop resource demand combination relationships and generate an extended matching relationship set, including: From the power resource data, the output capacity and geographical location of each resource node are extracted. The product of the capacity complementarity and the distance attenuation factor between any two resource nodes is calculated as the coordination weight. Coordination edges are established between resource nodes whose coordination weight is greater than the coordination threshold to obtain the resource supply topology layer. Based on transaction demand data, the time window and resource type of each demand node are extracted, the overlap rate of the time window and the similarity of the resource type with the preceding demand node are calculated, and the dependency weight is obtained by weighted summation. Dependency edges are established between demand nodes with dependency weights greater than the dependency threshold to obtain the demand intent topology layer. The degree of fit between the power supply curve of the resource node and the power load curve of the demand node is calculated to obtain the supply and demand matching degree. Combined with the constraints of geographical coverage overlap and time response coincidence, the inter-layer mapping weight is obtained through adaptive weighted fusion. The starting demand node is identified from the demand intent topology layer. Multi-hop traversal is performed along the dependency edges. Resource nodes are selected by combining the inter-layer mapping weights and the collaborative edges. The matching relationship between each demand node and the resource node sequence is recorded to generate the resource demand matching relationship. The multi-hop traversal paths are summarized to form a matching relationship set.

3. The method according to claim 1, characterized in that, Using a time interval tree and spatial partition index, resource competition conflicts in the spatiotemporal dimension of the matching relationship set are detected. Based on demand urgency and resource scarcity, a game-theoretic propagation graph is constructed for iterative propagation to resolve conflicts, generating a resource spatiotemporal correlation graph, including: The temporal features of each matching relationship in the extended matching relationship set are mapped to a time interval tree and the interval nodes it occupies are marked. The spatial features are mapped to a spatial partition index and the partition units it occupies are marked. By querying the interval nodes that overlap and the partition units that overlap, the matching relationship pairs with spatiotemporal competition are identified and the conflict relationship set is constructed. For each matching relationship in the set of conflict relationships, the initial game weight is calculated and a game propagation graph is constructed based on the urgency of demand and the scarcity of resources. By tracing the conflict propagation path, indirect conflict relationships with multiple levels of transmission are identified. The initial game weights of indirect conflict relationships are reduced and corrected to generate a game weight distribution after path correction. An iterative game process is performed in the game propagation graph. In each iteration, the conflicting nodes calculate their game payoffs based on their game weights and the game weights of their adjacent conflicting nodes. Nodes whose game payoffs are lower than the exit threshold are marked as ineffective and their occupied resources are released. The released resources are then distributed in reverse to nodes with high game payoffs according to the propagation path. The iteration terminates when the game payoffs of all nodes are stable or the maximum number of iterations is reached. The remaining matching relationships are then used to construct a resource spatiotemporal correlation graph.

4. The method according to claim 3, characterized in that, For each matching relationship in the set of conflict relationships, based on the urgency of demand and the scarcity of resources, initial game weights are calculated and a game propagation graph is constructed. Indirect conflict relationships involving multiple levels of transmission are identified by tracing the conflict propagation path, including: For each matching relationship in the set of conflicting relationships, the time-decrease parameter of its demand node is extracted as the demand urgency, and the supply-demand ratio parameter of its resource node is extracted as the resource scarcity. The number of conflicting edges between each matching relationship and other matching relationships in the set of conflicting relationships is counted. The competition intensity correction coefficient is calculated based on the number of conflicting edges, and multiplied by the weighted sum of the demand urgency and the resource scarcity to obtain the initial game weight. A game propagation graph is constructed using each matching relationship in the set of conflict relationships as a node and the conflict relationships as edges. The initial game weight is assigned to the corresponding node as the node state. Starting from any node in the game propagation graph, a traversal is performed, and the sequence of nodes traversed is recorded to form a propagation path set. For propagation paths with a path length greater than the direct conflict threshold, the node carrying the most edge connections is identified as the path target node, and its betweenness centrality metric is calculated. When the betweenness centrality metric exceeds the centrality threshold, the relationship between the path start point and the path end point is marked as a multi-level indirect conflict relationship.

5. The method according to claim 1, characterized in that, Based on the resource spatiotemporal correlation map, the electrical coupling strength between resource nodes and demand nodes is calculated, and multiple electrical decoupling partitions are divided. The generation adjustment of resource nodes and the load change of demand nodes are extracted for power balance verification. The transmission capacity limitations of inter-partition interconnection branches are identified, including: The geographical location and supply-demand matching relationship of each node are extracted from the resource spatiotemporal correlation graph. The electrical distance between each resource node and each demand node is calculated based on the geographical location. The electrical coupling strength between nodes is calculated by combining the power transmission path in the supply-demand matching relationship. Based on the electrical coupling strength, the resource nodes and demand nodes are clustered and decomposed to obtain multiple electrical decoupling partitions. Branches connecting different electrical decoupling partitions are identified and marked as connection branches. The rated transmission power of the connection branches is extracted as the transmission capacity limit. Within each electrical decoupling zone, node pairing combinations are determined based on supply and demand matching relationships to obtain resource allocation operations. Resource generation adjustment and demand load changes are extracted, power deviation of node pairing combinations is calculated and accumulated to obtain zone power imbalance, and zone power balance verification equations are established. When the power imbalance of the partition is less than the balance error threshold, the set of tie branches corresponding to the electrically decoupled partition that has passed the power balance verification is extracted, and its transmission capacity limit is bound to the partition identifier and stored as a partition constraint mapping table, which serves as the input condition for the sub-decision unit.

6. The method according to claim 1, characterized in that, The resource allocation scheme is encapsulated into a smart contract and written to the blockchain. The resource allocation execution action is automatically triggered through the smart contract, including: Based on the resource allocation scheme, a multi-level triggering structure containing a contract lifecycle state machine is constructed. The multi-level triggering structure includes a resource scheduling instruction sequence, execution status feedback signals, and exception handling rules. The multi-level triggering structure is bound to the state machine triggering conditions to form an event-driven smart contract. The smart contract is broadcast to the blockchain nodes. Based on the node timing dependency relationship in the resource allocation scheme, a call chain graph between contracts is established. A hierarchical and progressive verification mechanism is used to verify the contract executability of the call chain graph. After the verification is passed, the smart contract is written into the blockchain. The system monitors the trigger conditions defined in the smart contract. When the trigger conditions are met, the resource scheduling instruction sequence is converted into resource configuration execution actions according to the hierarchical order of the call chain diagram, and the execution result status is written back to the blockchain. When the execution fails or there is no response after a timeout, the exception handling rules of the smart contract are triggered, and an exception record is generated and written to the blockchain.

7. A blockchain-based intelligent allocation system for power system resources, used to implement the method as described in any one of claims 1-6, characterized in that, include: The first unit is used to acquire power resource data and transaction demand data of various equipment in the power system, and to construct resource nodes and demand nodes. The second unit is used to connect the resource nodes through collaborative edges to form a resource supply topology layer, and connect the demand nodes through dependency edges to form a demand intention topology layer. Based on the supply and demand matching degree and spatiotemporal constraints, the inter-layer mapping weight is calculated, the resource demand combination relationship of multi-hop transmission is identified, and an extended matching relationship set is generated. The third unit is used to detect resource competition conflicts in the spatiotemporal dimension of the matching relationship set by using time interval trees and spatial partition indexes, and to construct a game propagation graph based on the urgency of demand and the scarcity of resources to iteratively propagate and resolve conflicts, thereby generating a resource spatiotemporal association graph. The fourth unit is used to divide electrical decoupling zones based on the resource spatiotemporal correlation map and verify the power balance between resource adjustment and load change. Feasible operations are encapsulated into sub-decision units, distributed to blockchain nodes to verify power flow constraints, and resource allocation schemes are generated through two rounds of consensus voting. The fifth unit is used to encapsulate the resource allocation scheme into a smart contract and write it into the blockchain, and automatically trigger the resource configuration execution action through the smart contract.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

Images