Micro-grid resource visual topological index generation method and system

Abstract

The invention discloses a micro-grid resource visual topological index generation method and system. The method comprises the following steps: receiving geographic boundary coordinate data of a micro-grid system to establish a micro-grid physical region coordinate system; a hexagonal honeycomb grid structure is constructed in the microgrid physical region coordinate system; generating a cellular grid hierarchical hash code according to the cellular grid; constructing a skip list chain of the network topology structure and a skip list chain access path cache table based on the hash code; and finally, executing cross-level jump retrieval by utilizing the cache table and generating a micro-grid resource visual topological index. According to the method, by combining the hexagonal honeycomb grid and the skip list chain structure, efficient cross-level retrieval is achieved, the speed and accuracy of equipment positioning in a complex micro-grid system are remarkably improved, and the fault processing efficiency and the operation and maintenance response speed are powerfully guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and more specifically, to a method and system for generating a visualized topology index for microgrid resources. Background Art

[0002] With the rapid development of smart grid technology, the scale and complexity of microgrid systems are increasing. This is particularly true in application scenarios such as industrial park microgrids, urban distribution clusters, and community integrated energy systems. These scenarios place higher demands on efficient management and rapid retrieval of microgrid resources. Microgrid systems typically employ a multi-tiered business model, encompassing microgrid clusters, units, elements, and devices. This architecture facilitates modular management and functional partitioning of the system.

[0003] The Chinese patent application with publication number CN119621722A discloses a memory topology indexing system for power grid data. The system realizes efficient management and retrieval of power grid data through the collaborative work of the central processing unit module, login module, warehousing module, storage module, query response module, encoding module, index management module and generation module. The system introduces a metadata extraction process, which can better manage the compatibility of various data assets. The Chinese patent with authorization announcement number CN108323232B proposes a method for maintaining the index and chain topology structure between multi-level blockchain systems. The top-level blockchain records the seed node to index the lower-level blockchain, and uses a distributed monitoring cluster to regularly detect the availability of the seed node list, thereby realizing rapid indexing and topology structure maintenance between multi-level blockchains.

[0004] However, the existing technology still has significant technical problems when dealing with device retrieval in multi-level microgrid systems. When an abnormality occurs in a device node, the traditional hierarchical retrieval mechanism is for the operation and maintenance personnel to query step by step in the multi-level structure such as "microgrid cluster → unit → element → device". When faced with a system with a large hierarchical depth, the retrieval complexity increases exponentially with the number of levels. For example, in an industrial park microgrid, when an abnormality occurs in a certain type of device node, the operation and maintenance personnel need to query step by step from the microgrid cluster layer to the device layer. This process is not only time-consuming, but also may cause fault handling delays in emergency situations. The existing technology fails to fully utilize the spatial distribution characteristics of the equipment and lacks an efficient cross-level jump mechanism, which makes the retrieval path lengthy and cannot meet the real-time requirements of rapid fault location. In addition, when dealing with multi-level business models, the traditional method fails to effectively combine the spatial location correlation of the equipment, resulting in low retrieval efficiency and difficulty in achieving rapid positioning of equipment in complex microgrid systems. Summary of the Invention

[0005] The present invention is applicable to various microgrid systems with multi-level business models, such as industrial park microgrids, urban distribution clusters, and community integrated energy systems. In these scenarios, microgrid systems typically have complex hierarchical structures with widespread and numerous device nodes. When an anomaly occurs in a certain device node, operations and maintenance personnel need to quickly locate the faulty device to reduce downtime and improve system reliability. To overcome the aforementioned shortcomings of the prior art, the present invention provides a method and system for generating a visual topological index for microgrid resources. By receiving geographic boundary coordinate data for the microgrid system, the system establishes a physical coordinate system for the microgrid and constructs a hexagonal honeycomb grid structure within this coordinate system. Based on the honeycomb grid structure, a honeycomb grid-level hash code is generated, and a skip table chain and a skip table chain access path cache table are constructed for the network topology structure, enabling cross-level hop retrieval. Combining the hexagonal honeycomb grid with the skip table chain structure, the present invention fully utilizes the spatial distribution characteristics of devices, significantly shortening cross-level retrieval time and improving spatial positioning accuracy. This system allows operations and maintenance personnel to quickly locate specific nodes after an equipment anomaly alarm, significantly improving fault handling efficiency and operation and maintenance response speed for complex microgrid systems.

[0006] To achieve the above object, the present invention provides the following technical solutions: The method for generating a visual topological index of microgrid resources includes: Receive geographic boundary coordinate data of the microgrid system and establish a physical area coordinate system of the microgrid; and establish a hexagonal honeycomb grid structure based on the physical area coordinate system of the microgrid; Based on the hexagonal honeycomb grid structure, a honeycomb grid level hash code is generated; based on the honeycomb grid level hash code, a skip list chain of the network topology structure is generated; based on the skip list chain of the network topology structure, a skip list chain access path cache table is constructed; Based on the skip table chain access path cache table, cross-level skip retrieval is performed to generate a visual topology index of microgrid resources.

[0007] Furthermore, the geographical boundary coordinate data of the microgrid system at least includes the longitude and latitude coordinates of all device nodes in the microgrid system; The Z-axis of the microgrid physical area coordinate system vertically upward represents the service level depth, and the origin is the geographical center coordinate of the microgrid system, which is obtained based on the latitude and longitude coordinates of all device nodes.

[0008] Furthermore, the business hierarchy depth is 4 layers, namely, the controllable device layer, the microgrid element layer, the microgrid unit layer, and the microgrid cluster layer; the business hierarchy depth is represented by a discrete mapping on the Z axis, where the controllable device layer corresponds to Z=D, the microgrid element layer corresponds to Z=C, the microgrid unit layer corresponds to Z=B, and the microgrid cluster layer corresponds to Z=A.

[0009] Furthermore, the hexagonal honeycomb grid structure is established in the XY plane of the microgrid physical area coordinate system, and the side length of the hexagonal honeycomb grid is adaptively adjusted based on the average distribution density of the device nodes in the microgrid system; The method for generating a honeycomb grid level hash code based on a hexagonal honeycomb grid structure includes: The center coordinates of each hexagonal cellular grid are calculated, and the center coordinates of each hexagonal cellular grid are encoded using the Hilbert space-filling curve to generate a cellular grid-level hash code with a fixed length of L. The first M bits of the cellular grid-level hash code are defined as a spatial hash prefix, and the last LM bits are used as a service-level identifier. The spatial hash prefix is ​​used to identify the hexagonal cellular grid position.

[0010] Furthermore, the method for generating a jump table chain of a network topology structure based on a cellular grid-level hash code includes: Based on the cellular grid-level hash code, the device node service level attribution is identified; Based on the identification results of the business level of the device node, a bidirectional skip table pointer is injected and redundant paths are pruned to form a skip table chain of the network topology structure.

[0011] Furthermore, the bidirectional jump table pointer includes an upward jump pointer and a downward jump pointer. The pointing relationship of the upward jump pointer is from the lower-level business layer node to the upper-level business layer node. The lower-level business layer node includes the controllable device layer node, the microgrid element layer node and the microgrid unit layer node. Whenever an upward jump pointer is established from the lower-level business layer node to the upper-level business layer node, the upper-level business layer node automatically obtains a downward jump pointer pointing to the lower-level business layer node.

[0012] Furthermore, the method for performing redundant path pruning includes: Calculate the physical distance between the device node and a higher-level node that is not directly above the device node. If the physical distance is less than a preset distance threshold, create a direct jump pointer from the device node to the higher-level node that is not directly above the device node. The higher-level node that is not directly above the device node refers to a microgrid unit layer node and a microgrid cluster layer node.

[0013] Furthermore, the method for constructing a skip list chain access path cache table based on the skip list chain of the network topology structure includes: Injecting the spatial hash prefix of the cellular grid level hash code into the skip table pointer of the skip table chain, the skip table pointer includes a bidirectional skip table pointer and a direct skip pointer; Pre-aggregate multiple jump table pointers within the same cellular grid to generate a jump table pointer cluster head node; the jump table pointer cluster head node has an upward jump pointer and a direct jump pointer; Based on the generated skip list pointer cluster head node, a skip list chain access path cache table is generated.

[0014] Furthermore, the method of performing cross-level jump retrieval based on the jump table chain access path cache table to generate a microgrid resource visualization topology index includes: Receiving a search request instruction and converting the search request instruction into a multi-constraint combination search condition; Based on the multi-constraint combination retrieval conditions and the skip list chain access path cache table, a topology heat map guided retrieval path optimization strategy is constructed; Based on the topological heat map-guided search path optimization strategy and multiple constraint combination search conditions, cross-level jump search is performed; Based on the execution results of cross-level jump retrieval, the retrieval results are aggregated and sorted to generate a visual topological index of microgrid resources.

[0015] A microgrid resource visualization topology index generation system is used to implement the above-mentioned microgrid resource visualization topology index generation method, and the system includes: Grid construction module: used to receive the geographical boundary coordinate data of the microgrid system and establish the microgrid physical area coordinate system; based on the microgrid physical area coordinate system, establish a hexagonal honeycomb grid structure; Skip-list construction module: Generates a cellular grid-level hash code based on the hexagonal cellular grid structure; generates a skip-list of network topology structures based on the cellular grid-level hash code; Path cache module: Based on the skip table chain of the network topology, it builds a skip table chain access path cache table; Topology index generation module: Based on the skip table chain access path cache table, it performs cross-level jump retrieval and generates a visual topology index for microgrid resources.

[0016] An electronic device includes a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit, wherein the central processing unit implements the above-mentioned method for generating a microgrid resource visualization topology index when executing the computer program.

[0017] A computer-readable storage medium is characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed, the above-mentioned microgrid resource visualization topology index generation method is implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes efficient indexing of the spatial location of microgrid devices by constructing a hexagonal honeycomb grid structure and combining it with Hilbert curve coding. At the same time, it uses a jump table chain and a direct jump pointer mechanism to break the level-by-level traversal limitation of traditional hierarchical retrieval and form a fast cross-level jump path. Based on the pre-calculation optimization of the access path cache table, the path discovery in the retrieval process is converted into a fast search, which effectively avoids the problem of retrieval path explosion under the multi-level business model. This solution greatly shortens the cross-level retrieval time and significantly improves the accuracy of spatial positioning. It supports operation and maintenance personnel to quickly locate specific nodes when equipment is abnormal, realizes efficient retrieval of equipment and rapid fault location in complex microgrid systems, and provides core technical support for improving the operation and maintenance efficiency of multi-level microgrid systems such as industrial parks and urban distribution network clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of a method for generating a visual topology index of microgrid resources in the present invention; Figure 2 A flow chart of a method for generating a jump table chain of a network topology structure according to the present invention; Figure 3 A schematic diagram of the pointing relationship of the bidirectional jump table pointer of the present invention; Figure 4 Schematic diagram of the pointing relationship of the direct jump pointer of the present invention; Figure 5 A flow chart of a method for generating a visual topology index of microgrid resources according to the present invention; Figure 6 This is a functional module diagram of the microgrid resource visualization topology index generation system in the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Example 1:

[0023] See also Figure 1As shown, this embodiment provides a method for generating a visualized topology index of microgrid resources, including: Step S100: Receive geographic boundary coordinate data of the microgrid system, establish a microgrid physical area coordinate system, establish a hexagonal cellular grid structure based on the microgrid physical area coordinate system, generate a cellular grid hierarchical hash code based on the hexagonal cellular grid structure, generate a hop list chain of a network topology structure based on the cellular grid hierarchical hash code, and construct a hop list chain access path cache table based on the hop list chain of the network topology structure. Furthermore, step S100 includes: Step S110, receiving geographic boundary coordinate data of the microgrid system, establishing a microgrid physical area coordinate system, establishing a hexagonal honeycomb grid structure based on the microgrid physical area coordinate system, and generating a honeycomb grid level hash code based on the hexagonal honeycomb grid structure; Specifically, the geographic boundary coordinate data of the microgrid system includes the northeast corner coordinate point, the southwest corner coordinate point, and the longitude and latitude coordinates of all device nodes in the microgrid system. The process of establishing the coordinate system of the microgrid physical area includes two steps: coordinate transformation and axial definition. The coordinate transformation uses the Mercator projection method to convert the longitude and latitude coordinates in the geographic coordinate system into the XY coordinates in the plane rectangular coordinate system. The axial definition follows the standard geographic coordinate convention, with the X-axis pointing to the geographic east direction, the Y-axis pointing to the geographic north direction, and the Z-axis pointing vertically upward to represent the depth of the business hierarchy. The business hierarchy depth is 4 layers, namely the controllable device layer, the microgrid element layer, the microgrid unit layer, and the microgrid cluster layer. The business hierarchy depth on the Z axis is represented by a discrete mapping, including Z=D for the controllable device layer, Z=C for the microgrid element layer, Z=B for the microgrid unit layer, and Z=A for the microgrid cluster layer. The coordinate system of the microgrid's physical area is based on the geographic center of gravity of the microgrid system. The geographic center of gravity coordinates are calculated using a centroid algorithm, using the longitude and latitude coordinates of all device nodes as mass points. The longitude coordinate of the geographic center of gravity is equal to the arithmetic mean of the longitude coordinates of all device nodes, and the latitude coordinate of the geographic center of gravity is equal to the arithmetic mean of the latitude coordinates of all device nodes. The centroid algorithm ensures that the origin of the coordinate system is located at the geometric center of the device node distribution, resulting in a relatively balanced spatial distribution in all directions and avoiding spatial bias caused by arbitrarily selecting the origin.

[0024] The hexagonal honeycomb grid structure is established within the XY plane of the microgrid's physical coordinate system. The side lengths of the hexagonal honeycomb grid are adaptively adjusted based on the average distribution density of device nodes within the microgrid system. The center coordinates of each hexagonal honeycomb grid are calculated using a standard hexagonal tessellation pattern. The average distribution density of device nodes is calculated as the total number of device nodes divided by the total area covered by the microgrid system.

[0025] Please refer to Table 1. The method for adaptively adjusting the side length of the hexagonal honeycomb grid based on the average distribution density of device nodes in the microgrid system includes: The side length of the hexagonal honeycomb grid is set to k, and the average distribution density of device nodes is set to ρ. If ρ>ρ1, the device distribution is dense, and the k value needs to be reduced to improve spatial resolution. If ρ<ρ2, the device distribution is sparse, and the k value needs to be increased to reduce the number of grid cells. If ρ2≤ρ≤ρ1, the k value remains unchanged. ρ2 is the low-density threshold, and ρ1 is the high-density threshold. ρ1 and ρ2 are determined by analyzing the device distribution statistics of typical microgrid systems. The high-density threshold ρ1 corresponds to urban microgrid environments with dense device distribution, while the low-density threshold ρ2 corresponds to suburban or rural microgrid environments with sparse device distribution.

[0026] Table 1 Adaptive adjustment method of the side length of the hexagonal honeycomb grid

[0027] Based on a hexagonal cellular grid structure, a method for generating a cellular grid hierarchical hash code includes: encoding the center coordinates of each hexagonal cellular grid using a Hilbert space-filling curve to generate a cellular grid hierarchical hash code with a fixed length of L; defining the first M bits of the cellular grid hierarchical hash code as a spatial hash prefix, using the spatial hash prefix as a spatial location identifier to identify the hexagonal cellular grid location, and using the last LM bits as a service level identifier to achieve a hash aggregation effect of spatially adjacent devices.

[0028] The construction of business-level identifiers utilizes a direct binary mapping method based on the Z-axis coordinate values. As shown in Table 2, the identifier D at the controllable device layer corresponds to binary code 11, the identifier C at the microgrid element layer corresponds to binary code 10, the identifier B at the microgrid unit layer corresponds to binary code 01, and the identifier A at the microgrid cluster layer corresponds to binary code 00. This encoding method ensures that different business levels have unique binary identifiers, enabling the complete representation of the four business levels through a two-bit binary code. The final format of the cellular grid-level hash code is a concatenation of the spatial hash prefix and the business-level identifier, forming a fixed-length complete hash code. This hash code simultaneously incorporates the device's spatial location information and business-level information, providing a unified indexing foundation for subsequent multi-dimensional retrieval.

[0029] Table 2 Representation of business level identifiers

[0030] For example, the spatial hash prefix of a cellular grid is a 16-bit binary code 1011010010101101 (corresponding to a spatial location identifier), and the service level is the microgrid element layer (identifier C, binary code 10). The complete hash code is 10110100101010110110, where the first 16 bits are the spatial hash prefix and the last 2 bits are the service level identifier. The code also represents "the microgrid element layer node located in the spatial area 1011010010101101".

[0031] Step S110 establishes a microgrid physical area coordinate system with the geographic center of gravity as the origin. This optimizes the spatial distribution of device nodes for optimal balance compared to traditional methods that select arbitrary origins. The selection of the geographic center of gravity gives the coordinate system natural spatial symmetry, resulting in a more even distribution of device nodes across all quadrants and avoiding spatial index imbalance caused by excessive concentration of device nodes in a single quadrant. The hexagonal honeycomb grid structure fundamentally improves spatial connectivity compared to the traditional square grid. Each cell in the hexagonal honeycomb grid has six adjacent cells, compared to the four adjacent cells in a square grid. This provides a richer set of spatial connection paths and reduces path breakage during cross-grid searches. The hexagonal geometry is closer to a circle, with significantly higher circularity than a square, resulting in a more accurate representation of the radiation influence range of device nodes and reducing spatial coverage errors caused by irregular grid shapes. The adaptive adjustment mechanism for the average distribution density of device nodes allows the grid granularity to dynamically adapt to the device distribution characteristics of different regions, providing higher spatial resolution in densely populated areas and reducing unnecessary grid divisions in sparsely populated areas, thereby optimizing storage space utilization. The Hilbert space-filling curve encoding technology achieves a high-fidelity mapping of two-dimensional spatial coordinates to one-dimensional hash sequences. The spatial locality preservation property of the Hilbert curve ensures that adjacent grids in two-dimensional space also maintain an adjacent relationship in the one-dimensional hash sequence. This property enables prefix matching-based spatial retrieval operations to quickly locate the target area. The hash codes of adjacent grids maintain numerical continuity, reducing random access operations in the hash table and improving the efficiency of hash-based spatial searches. The hierarchical structure design of the cellular grid-level hash code realizes the organic integration of spatial location information and service-level information, realizes precise indexing of grid locations through spatial hash prefixes, and realizes direct representation of device service attributes through service-level identifiers, effectively improving the information carrying density of the hash code.

[0032] The synergy between the geometric characteristics of the hexagonal cellular grid and the Hilbert curve encoding produces a spatial hash aggregation effect. Due to the high connectivity of the hexagonal grid and the locality-preserving properties of the Hilbert curve, device nodes within the same cellular grid form a natural storage cluster in the hash table. This aggregation effect enables batch retrieval operations to exploit the spatial locality principle of memory, significantly improving cache hit rates and memory access efficiency. The combined encoding method of business-level identifiers and spatial hash prefixes enables accelerated optimization of cross-level association retrieval. Association searches for nodes at different business levels within the same spatial area can be quickly achieved through partial matching of hash prefixes, providing an efficient indexing foundation for subsequent skip-list chain construction.

[0033] Step S120, generating a jump list chain of the network topology structure based on the cellular grid level hash code; See also Figure 2 As shown, further, step S120 includes: Step S121, identifying the service level of the device node based on the cellular grid level hash code; The business-level attribution of device nodes utilizes a dual mechanism of device type identifiers and control ownership. Device type identifiers are determined by analyzing technical characteristics such as the device's hardware model, communication protocol, and control interface. These include standardized codes such as PV_INV for photovoltaic inverters, ESS_UNIT for energy storage units, EV_CHARGER for charging stations, WIND_GEN for wind turbines, and LOAD_CTRL for load controllers. Each device type identifier has a predefined mapping to a specific layer within the four-layer business model. This mapping is determined based on the device's functional characteristics and control capabilities. For example, devices with direct physical control capabilities are assigned to the controllable device layer, while units capable of coordinated multi-device control belong to the microgrid element layer. Control ownership is determined by parsing the microgrid system's control topology configuration file, which is stored in a structured data format and contains key information such as the device node's parent control unit identifier, a list of subordinate controlled devices, communication interface type, control protocol version, and control authority scope. The four-layer business model is a hierarchical model consisting of "microgrid cluster → unit → element → device."

[0034] The hierarchical attribution of the four-layer service model follows a bottom-up recursive analysis principle. The analysis begins by identifying all physical devices in the system with direct control capabilities. Direct control capability refers to the ability of a device to receive control commands and perform corresponding physical actions, such as PV inverters receiving power adjustment commands, energy storage systems receiving charge and discharge commands, and load devices receiving start and stop commands. These devices are classified as the controllable device layer, corresponding to Z-axis coordinate value D. The microgrid element layer contains logical units that coordinate and control multiple controllable devices. For example, the photovoltaic generation element includes multiple photovoltaic inverters and their controllers, and the energy storage element includes multiple energy storage units and their management systems. These element units have local optimization and coordinated control capabilities, corresponding to Z-axis coordinate value C. The microgrid unit layer contains functional subsystems composed of multiple microgrid elements, such as independent microgrid units containing photovoltaic generation elements, energy storage elements, and load elements. These units have independent energy management and optimized scheduling functions, capable of achieving local supply and demand balance, corresponding to Z-axis coordinate value B. The microgrid cluster layer includes an overall management system composed of multiple microgrid units, which has global optimization scheduling and coordinated control capabilities, and can realize resource allocation and energy exchange across units, corresponding to the Z-axis coordinate value A.

[0035] Hierarchical attribution identification utilizes topological sorting methods from graph theory, reading all device node information and control relationships from the configuration file to construct a directed acyclic graph of control relationships. Nodes in the graph represent devices or control units, and edges indicate the direction of control relationships, from the controller to the controlled. The algorithm determines a node's position within the control hierarchy by calculating its in-degree and out-degree. A node with an in-degree of 0 represents a top-level control unit that is not controlled by other nodes, while a node with an out-degree of 0 represents a bottom-level physical device that does not control other nodes. Based on the in-degree and out-degree analysis results, combined with the node's device type identifier, each node's hierarchical attribution within the four-layer business model is determined.

[0036] Step S122 : Based on the identification result of the service level of the device node, a bidirectional skip table pointer is injected and redundant paths are pruned to form a skip table chain of the network topology structure.

[0037] The bidirectional jump table pointer injection process includes the construction of the upward jump pointer and the downward jump pointer. Figure 3As shown, there are four discrete service levels on the Z axis of the microgrid's physical coordinate system. Each service level has a corresponding node. The upward jump pointer points from lower-level service level nodes (referred to as lower-level nodes, such as controllable device level nodes, microgrid element level nodes, and microgrid unit level nodes) to upper-level service level nodes (referred to as upper-level nodes, such as microgrid element level nodes, microgrid unit level nodes, and microgrid cluster level nodes). For example, a controllable device level node (Z = D, such as a photovoltaic inverter) is linked to a microgrid element level node (Z = C, such as a photovoltaic power generation element); a microgrid element level node (Z = C, such as an energy storage element) is linked to a microgrid unit level node (Z = B, such as an independent microgrid unit). The upward jump pointer is constructed using a spatial proximity-priority search strategy. For example, for each controllable device level node, the nearest microgrid element level node is searched within its local cell and adjacent cells. An upward jump pointer is then established from the device node to the element level node. The search range is determined based on the neighbor relationship of the cellular grid, including the current grid and its six directly adjacent grids, and extended to the secondary neighbor grids when necessary to ensure that a suitable upper node can be found. When there are multiple candidate upper nodes of equal distance, the node with a more direct control relationship and lower communication delay is preferred as the jump target. The downward jump pointer is the reverse index of the upward jump pointer and is automatically generated when the upward jump pointer is established. Figure 3 As shown, whenever an upward jump pointer is established from a lower-level service level node to an upper-level service level node, the upper-level service level node automatically obtains a downward jump pointer pointing to the lower-level service level node, forming a bidirectional jump link.

[0038] The redundant path pruning mechanism creates cross-level direct jump pointers by analyzing the physical distance and business association strength between nodes to optimize search paths. Physical distance determination is constrained by a preset distance threshold d, determined based on the geometric characteristics of the hexagonal cellular grid and the typical control range of the microgrid system. When the physical distance between a device node and its indirect upper-level node is less than the distance threshold d, indicating sufficient spatial proximity between the two, a direct jump pointer is created from the device node to the indirect upper-level node, skipping intermediate nodes to reduce search path length. For controllable device-layer nodes, their direct upper-level nodes are microgrid element-layer nodes, while their indirect upper-level nodes are microgrid unit-layer nodes and microgrid cluster-layer nodes. The creation of direct jump pointers adheres to the principle of business logic rationality. Direct jump pointers are only established when a clear business association exists between the device node and its upper-level node, avoiding jump relationships that do not conform to business logic.

[0039] For example, see Figure 4As shown in the figure, there are four discrete business levels on the Z axis of the microgrid physical area coordinate system, namely the controllable device layer, microgrid element layer, microgrid unit layer, and microgrid cluster layer. Each business level has a corresponding node, and a direct jump pointer from the controllable device layer node to the microgrid unit layer node and a direct jump pointer from the microgrid element layer node to the microgrid cluster layer node are created.

[0040] The jump table chain uses a multi-level index data structure. Each device node maintains a jump pointer array, which contains different types of pointers, such as upward jump pointers, downward jump pointers, and direct jump pointers. The jump pointer data structure includes attribute fields such as the target node unique identifier, target node type identifier, physical distance value, jump type identifier, and weight coefficient. Jump types are divided into layer-by-layer jumps and direct jumps. Layer-by-layer jumps follow a strict hierarchical progression, while direct jumps allow users to directly reach the target layer by skipping intermediate layers. The weight coefficient is calculated based on factors such as physical distance, service connection strength, and communication quality, and is used for subsequent path selection optimization.

[0041] A dual-determination mechanism for identifying the service-level attribution of device nodes improves the accuracy of service-level classification. Device type identifiers provide basic classification capabilities based on hardware characteristics, while control attribution relationship analysis provides precise location capabilities based on business logic. The combination of these two ensures accurate and consistent attribution in complex microgrid environments. The introduction of control attribution relationships enables the system to handle complex control topology scenarios such as cross-control, multi-level control, and dynamic control, avoiding the misjudgment issues that can arise from classification based solely on device type. The construction of bidirectional skip table pointers enables bidirectional optimization and increased flexibility of cross-level search paths. Upward skip pointers enable rapid location from lower-level devices to higher-level management units, meeting bottom-up information flow requirements such as device status reporting and fault alarm upload. Downward skip pointers enable rapid traversal from a management unit to subordinate devices, meeting top-down control flow requirements such as control command issuance and device status query. The bidirectional skip mechanism enables search operations to select the most appropriate traversal direction based on specific needs, avoiding the circuitous paths that can result from one-way searches. A redundant path pruning mechanism significantly optimizes search paths through the intelligent creation of direct skip pointers. In densely populated areas, nodes at multiple levels may overlap significantly. Traditional layer-by-layer hopping generates numerous redundant intermediate access steps. The introduction of direct jump pointers allows the system to skip intermediate levels and reach the target node directly, significantly shortening the search path. A dynamic adjustment mechanism based on preset distance thresholds allows the pruning strategy to adapt to microgrids of varying sizes and densities, achieving optimal path optimization while ensuring search integrity. The creation of direct jump pointers significantly reduces the number of intermediate node accesses during the search process, lowering memory access overhead and improving overall search response speed.

[0042] The synergy between the skip list chain structure and the cellular grid-level hash code improves spatial search speed. Because the target node selection of the skip pointer follows the spatial nearest neighbor principle, related nodes exhibit a natural clustered distribution in the hash code space. This clustering effect enables batch searches based on spatial hash prefixes to exploit the principle of spatial locality, improving search efficiency. The similarity of hash code prefixes between nodes in adjacent hierarchies provides a fast entry point for cross-hierarchical association analysis, allowing the system to quickly locate sets of related nodes through partial hash matching. The symmetric design of the bidirectional skip list pointers enables adaptive fault tolerance of the search path. When a search path becomes inaccessible due to node failures, communication interruptions, or other reasons, the system automatically constructs an alternative path using reverse skip pointers or alternative direct skip pointers, ensuring the continuity and reliability of the search operation. This adaptive fault tolerance mechanism enables the system to maintain normal search functionality even in the face of partial node failures, significantly enhancing the system's robustness and availability.

[0043] Step S130 : constructing a skip list access path cache table based on the skip list of the network topology.

[0044] Furthermore, step S130 includes: Step S131: injecting the spatial hash prefix of the cell grid level hash code into the skip table pointer of the skip table chain, where the skip table pointer refers to a bidirectional skip table pointer and a direct skip pointer; The spatial hash prefix injection process embeds the first M bits of spatial information of the cellular grid level hash code into the skip table pointer structure to achieve a direct association between spatial position and jump relationship. The specific implementation of spatial hash prefix injection adopts the pointer attribute extension mechanism to add a spatial hash prefix field to the data structure of each bidirectional skip table pointer and direct jump pointer. This field stores the first M bits of the cellular grid level hash code of the device node to which the pointer belongs. The injection format of the spatial hash prefix adopts a unified encoding specification. The record format of the skip table pointer is "source node ID→target node ID@spatial hash prefix", where the source node ID represents the unique identifier of the jump starting node, the target node ID represents the unique identifier of the jump target node, the @ symbol is used as a separator, and the spatial hash prefix represents the spatial area identifier where the jump relationship is located. For example, for photovoltaic inverter device PV_001 located in a cell with hash code 101101001010111, its jump table pointer to microgrid unit node Unit_07 is recorded as "PV_001→Unit_07@1011010010101," where "1011010010101" is a 13-bit spatial hash prefix, indicating that the jump occurs within cell number 5333. This encoding format allows the system to directly obtain spatial location information by parsing the skip table pointer record format, eliminating the need for additional location queries. The length of the spatial hash prefix, M, is selected based on the spatial resolution requirements and storage efficiency of the microgrid system. When M is 16 bits, it can support the unique identification of 65,536 cell grids, suitable for large microgrids covering hundreds of square kilometers. When M is 12 bits, it can support the unique identification of 4,096 cell grids, suitable for medium-sized microgrids covering tens of square kilometers.

[0045] Step S132, pre-aggregating multiple skip table pointers in the same cellular grid to generate a skip table pointer cluster head node; The skip table pointer pre-aggregation process solves the inefficient problem of decentralized management of multiple skip table pointers within the same cellular grid by introducing a cluster head node mechanism. The skip table pointer cluster head node is used to uniformly manage all skip table pointers within the same cellular grid, reducing the traversal overhead during pointer retrieval. The skip table pointer index is a data structure maintained internally by the cluster head node. It uses an ordered array or hash table to store skip table pointer references to all device nodes within the cellular grid. The index structure supports fast search based on device node identifiers and batch retrieval based on device types. The creation trigger condition of the cluster head node is based on a preset node number threshold N. th , the node number threshold N th Based on the balance between management overhead and retrieval efficiency, when N th A value that is too small will result in the creation and management overhead of a large number of cluster head nodes. thIf the value is too large, the efficiency advantage of pre-aggregation will be lost. th The preferred value range of is 8 to 32 device nodes, which is determined by analyzing the device distribution characteristics of microgrid systems of different sizes and the retrieval performance test results.

[0046] The data structure of a cluster head node contains key fields such as a cell mesh identifier, a device node count, a hop table pointer index array, an upward hop pointer, and a direct hop pointer. The cell mesh identifier stores the complete hash code of the cell mesh managed by the cluster head node. The device node count records the total number of device nodes within the current mesh. The hop table pointer index array stores references to all internal hop table pointers in lexicographic order by device node identifier. The cluster head node itself also possesses an upward hop pointer and a direct hop pointer. These pointers point to the upper-level service-level node containing the cell mesh, enabling the cluster head node to participate in cross-level hop searches on behalf of the entire cell mesh. The cluster head node's upward hop pointer utilizes a representative node selection strategy. By analyzing the service ownership relationships of all device nodes within the cell mesh, it selects the highest-level node with a control range covering the entire cell mesh as the hop target, ensuring that the cluster head node's hop relationships accurately reflect the service ownership of the entire cell mesh.

[0047] Step S133: Generate a skip list chain access path cache table based on the generated skip list pointer cluster head node.

[0048] The skip-list chain access path cache is generated through path pre-calculation and priority sorting. The specific path pre-calculation process involves traversing all generated skip-list pointer cluster head nodes. Based on each cluster head node's upward hop pointer and direct hop pointer, a breadth-first search algorithm is used to calculate all possible access paths from the cluster head node to each upper-level service layer node. The path search terminates when the target service layer is reached or the search depth exceeds a preset limit. The search depth limit is based on the number of layers in the four-layer service model and is typically set to 4, corresponding to a complete hop path from the controllable device layer to the microgrid cluster layer. The path length is calculated for each access path. Path length is defined as the weighted sum of the number of hops from the starting node to the target node and the physical distance; the physical distance is the three-dimensional Euclidean distance between the starting and target nodes. The number of hops is calculated by counting the number of hop pointers in the access path. Each hop pointer corresponds to a hop operation, with both layer-by-layer hops and direct hops counting as one hop step. Based on the calculated access path length, the access paths for each cluster head node are prioritized to generate the skip-list chain access path cache.

[0049] The skip-list access path cache uses a key-value pair storage structure. The key is composed of the string concatenation of the start node identifier and the target level identifier, using underscores as delimiters. The value is a sequence of access paths sorted in ascending order by path length. The key design supports dual indexing based on the start position and the target level, allowing the system to quickly locate the cache path corresponding to a specific search requirement.

[0050] In step S130, the injection of a spatial hash prefix improves the spatial indexing capabilities of skiplist pointers. By directly embedding the spatial location information of the cellular grid into the skiplist pointer structure, the system achieves fast pointer location based on spatial regions. When a search request involves a specific spatial region, the system can directly filter out the relevant skiplist pointers through spatial hash prefix matching, avoiding a full traversal of the entire skiplist chain. Skiplist pointer pre-aggregation, through the cluster head node mechanism, enables centralized management of homogeneous skiplist pointers. When a cellular grid contains a large number of device nodes, traditional decentralized pointer management methods result in a large number of random memory accesses during the search process. The introduction of cluster head nodes organizes all skiplist pointers within the same grid into a continuous data structure, leveraging the spatial locality of memory access to improve cache hit rates. The cluster head node's inherent upward jump capability enables cross-level searches at the grid granularity. When the search target is an entire spatial region rather than a specific device, the system can achieve fast region-level jumps directly through the cluster head node, avoiding individual accesses to devices within the grid. The construction of a skiplist chain access path cache table enables pre-calculation optimization and intelligent selection of search paths. By precalculating and caching all possible access paths, the system transforms the path discovery problem during retrieval into a path search problem, reducing retrieval response time. The key-value pair storage structure of the skip-link access path cache supports precise matching based on the starting location and target level, avoiding inefficient searches of irrelevant paths. The orderly arrangement of the path sequences within the cache ensures that the system prioritizes the shortest access paths.

[0051] Step S200 : Based on the skip table chain access path cache table, a cross-level skip search is performed to generate a microgrid resource visualization topology index.

[0052] See also Figure 5 As shown, further, step S200 includes: Step S210: receiving a search request instruction and converting the search request instruction into a multi-constraint combination search condition; Furthermore, step S210 includes: Step S211 , performing semantic analysis and entity recognition on the received search request instruction to obtain a device type identifier, a service level identifier, and a cellular grid level hash code prefix; The semantic analysis and entity recognition process uses natural language processing technology to convert the user's natural language search request into structured query conditions that the system can understand. The method for performing semantic analysis and entity recognition includes: performing word segmentation processing on the received search request instruction to extract device type keywords and spatial location keywords; matching the device type keywords with the device type dictionary to obtain the corresponding device type identifier and business level identifier; matching the spatial location keywords with the spatial location dictionary to obtain the corresponding cellular grid level hash code prefix. When the matching success rate is greater than the preset matching threshold, the subsequent steps are continued; when the matching success rate is less than the preset matching threshold, a prompt message is returned to the user indicating that the search conditions are unclear, requiring the user to re-enter the search request instruction.

[0053] Named entity recognition (NER) technology is used to extract device type keywords. A pre-trained device entity recognition model is used to identify relevant terms such as device name, device type, and device attributes in retrieval requests. The device entity recognition model is built based on a conditional random field algorithm. The model's training data consists of a large amount of annotated microgrid device description text, covering major device types such as photovoltaic inverters, energy storage units, charging stations, wind turbines, and load controllers, as well as their aliases, abbreviations, and model variants. The device type dictionary is constructed using a hierarchical classification structure: the top level represents major device categories such as power generation equipment, energy storage equipment, and load equipment; the middle level represents device subcategories such as photovoltaic power generation equipment and wind power generation equipment; and the bottom level represents specific device models and specifications. Each device entry in the device type dictionary includes attribute information such as the standard name, alias list, device type identifier, business level identifier, and technical parameter range. Device identification based on partial and fuzzy matching is supported.

[0054] Spatial location keywords are identified using geographic information extraction technology. A spatial entity recognition algorithm extracts spatially relevant expressions such as area names, coordinate information, and distance descriptions from search request instructions. The spatial entity recognition algorithm combines regular expression matching with semantic role labeling. Regular expression matching is responsible for identifying standardized expressions such as coordinate formats, distance units, and directional vocabulary, while semantic role labeling is responsible for identifying complex spatial descriptions such as "within area A," "near unit B," and "within a radius R from point C." The spatial location dictionary pre-stores all geographic area division information within the microgrid system, including spatial range definitions at different levels, such as administrative regions, functional regions, and equipment distribution areas. Each area entry contains attribute information such as the area name, boundary coordinates, center coordinates, coverage area, and the corresponding set of cellular grid-level hash code prefixes.

[0055] The matching success rate is calculated using a weighted average method, comprehensively considering three dimensions: device type match, spatial location match, and semantic completeness. Device type match is based on the similarity between the identified device keyword and the device type dictionary entry. This similarity is calculated using a combination of edit distance and semantic vector similarity. Edit distance reflects the similarity of lexical form, while semantic vector similarity reflects the relevance of lexical meaning. Spatial location match is calculated based on the matching of the identified spatial keyword with the spatial location dictionary entry. Full matches are awarded full marks, while partial matches are assigned a score based on the degree of match. Semantic completeness is assessed based on the completeness of the grammatical structure and semantic roles of the search request. Complete subject-verb-object structures and clear semantic role relationships receive higher scores. The matching threshold is determined based on the system's search accuracy requirements and user experience considerations. A too low threshold can lead to misinterpreted search requests being incorrectly executed, while a too high threshold can lead to frequent rejection of legitimate search requests. The preset matching threshold is determined by analyzing the matching results of a large number of user search requests and system execution results, with a preferred range of 0.7 to 0.8.

[0056] Step S212 : Based on the device type identifier, the service level identifier and the cellular grid level hash code prefix, a multi-constraint combination search condition including a space constraint condition, a device type constraint condition and a service level constraint condition is generated.

[0057] Multiple constraint-combined search conditions convert semantic analysis results into standardized query expressions within the system. Spatial constraints limit the spatial scope of the search using a set of cell-level hash code prefixes, which includes the spatial hash prefixes corresponding to all cells that intersect the user-specified area. Device type constraints limit the device type scope of the search using a set of device type identifiers, which includes all type identifiers matching the user-specified device. Business-level constraints limit the level scope of the search using a set of business-level identifiers, which includes all identifiers corresponding to the user-specified level. Constraint combination strategies are expressed using Boolean logic expressions, supporting the use of logical operators such as AND, OR, and NOT. When a search request involves multiple spatial areas, spatial constraints use OR logic to combine, indicating that search results must be within any of the specified areas. When a search request involves multiple device types, device type constraints use OR logic to combine, indicating that search results must match any of the specified device types. When a search request involves a specific business level, business-level constraints use AND logic to combine, indicating that search results must strictly meet the level requirements. The priority of constraints is based on constraint selectivity. Constraints with high selectivity are executed first to quickly narrow the search scope. The selectivity is calculated based on historical statistical data, reflecting the proportion of data that can be filtered by each constraint.

[0058] For example, for a search request instruction “find all energy storage unit fault nodes in Park A”, the semantic analysis process is as follows: Keyword extraction: Park A, energy storage unit, fault node; Device type matching: Energy storage unit → device type identifier ESS_UNIT, business level is controllable device layer (D); Spatial location matching: Park A → Spatial hash prefix H2B5 (pre-stored in the spatial location dictionary); Generation constraints: spatial hash prefix H2B5 AND device type ESS_UNIT AND service level D.

[0059] The application of semantic analysis and entity recognition technology in step S210 enables automated understanding and conversion of natural language search requests. By employing a hybrid word segmentation algorithm based on a dictionary and statistical models, the system achieves dual recognition capabilities for microgrid-specific terminology and general expressions. This allows it to accurately identify specialized device names such as photovoltaic inverters and energy storage units, while also correctly processing general descriptive terms such as "nearby" and "fault within range." The introduction of named entity recognition technology enables the system to precisely extract key information from complex natural language expressions, avoiding the semantic ambiguity and contextual misunderstandings that can occur with traditional keyword matching methods. The hierarchical construction of a device type dictionary and a spatial location dictionary enables standardized management of search terms and efficient matching. This hierarchical classification structure provides a unified mapping framework for multi-level representations of devices and spaces. Users can search using expressions of varying granularity, such as broad categories, subcategories, or specific models, and the system accurately recognizes and converts them into corresponding identifiers. The dictionary's support for aliases and variants addresses matching failures caused by varying user expression habits. Whether a user uses expressions such as "photovoltaic panel solar panel" or "PV module," the system recognizes them as the same device type. Geographic information extraction enables the system to process various spatial expressions such as coordinates, area names, distance descriptions, etc., providing users with flexible and diverse spatial retrieval methods.

[0060] The comprehensive evaluation mechanism of the matching success rate realizes the quantitative control of the retrieval quality and the guarantee of the system reliability. By comprehensively considering the three dimensions of device type matching, spatial location matching and semantic integrity, the system can comprehensively evaluate the understanding quality of the retrieval request instructions, avoiding the one-sided judgment that may be caused by single-dimensional evaluation. The structured representation of multiple constraint combination retrieval conditions realizes the precise expression and efficient processing of complex retrieval requirements. By converting natural language retrieval request instructions into standardized constraint conditions covering the three dimensions of space, device type and business level, the system provides clear guidance and constraints for subsequent retrieval execution. Boolean logic expression enables the system to handle complex combination query requirements, and users can express precise retrieval intentions through logical operators such as AND, OR, and NOT.

[0061] The collaboration between semantic analysis and cellular grid hash coding enables automatic association of spatial semantics. When users use directional descriptions such as "eastern area" and "central area", the system can automatically infer related adjacent areas using the spatial continuity characteristics of the cellular grid, expanding the search scope to provide more complete search results. This spatial semantic association capability enables the system to process implicit spatial requirements in user expressions. When users query for equipment in a certain area, the system can intelligently include related equipment in areas with fuzzy boundaries, improving the completeness and practicality of the search results. The automatic association of spatial semantics also provides semantic support for fault impact analysis and equipment linkage control. The system can automatically identify possible impact ranges and associated equipment based on natural language descriptions.

[0062] The integration of entity recognition and the business hierarchy model enables intelligent adaptation of search granularity. When a user's search request includes business entities at different levels, the system automatically identifies the target granularity and adjusts the search strategy and result presentation accordingly. For example, when a user queries for "microgrid unit," the system automatically locates the microgrid unit level for search; when a user queries for a specific device, the system automatically drills down to the device level for precise search results. This granularity adaptation capability enables the system to provide search results with the appropriate level of detail based on the user's actual needs, avoiding information overload or insufficient information. The synergy between the multiple constraint combination mechanism and the skip list chain structure enables intelligent predictive capabilities for path retrieval. The system can pre-determine the most likely search path and target node based on the combined characteristics of the constraints, completing path planning and resource preparation before the search is executed. This predictive capability significantly reduces trial-and-error and backtracking during the search process, improving search response speed and system resource utilization efficiency.

[0063] Step S220, constructing a topology heat map guided search path optimization strategy based on the multi-constraint combined search conditions and the skip list chain access path cache table; A topology heat map-guided retrieval path optimization strategy is constructed based on multiple constraint combination retrieval conditions and skip list chain access path cache table. Through statistical analysis of historical access data and dynamic optimization of access frequency, the problem of unstable retrieval efficiency caused by blind path selection in traditional retrieval systems is solved.

[0064] Furthermore, step S220 includes: Step S221: Count historical retrieval data and generate a node access frequency heat map based on the access frequencies of device nodes and skip list pointers. The node access frequency heat map includes high-frequency access nodes, medium-frequency access nodes, and low-frequency access nodes. Specifically, the node access frequency heat map generation process realizes the quantitative analysis of the system's historical retrieval behavior through time window statistics and frequency stratification mechanism. The setting of the statistical time window is based on the stability requirements of the operating cycle and access pattern of the microgrid system. If the time window is too short, the randomness of the access frequency calculation will be increased, and if the time window is too long, the system's sensitivity to changes in access patterns will be reduced. Preferably, the time window range is 5 to 10 days, which can capture the periodic operating mode of the microgrid system while maintaining the ability to respond to changes in access trends. The access frequency is obtained by dividing the number of times each device node is retrieved within the statistical time window by the length of the time window. The number of times each device node is retrieved is obtained through system log records. Based on the numerical distribution of access frequency, all device nodes are divided into high-frequency access nodes (F i >F high ), intermediate frequency access node (F low ≤F i ≤F high ) and low-frequency access nodes (F i <F low ) three levels, among which F i is the access frequency of the i-th time window, F high is the high-frequency access threshold, F low is the low-frequency access threshold. high and F low It is determined by calculating the cumulative distribution function of the access frequency of all device nodes. high Set to the 85th percentile of the access frequency distribution to ensure that high-frequency access nodes account for about 15% of the total number of devices. low It is set to the 35th percentile of the access frequency distribution to ensure that low-frequency access nodes account for about 35% of the total number of devices, and medium-frequency access nodes account for the remaining 50%. This hierarchical ratio is based on a variant of the Pareto principle and reflects the reality that a few key devices in the microgrid system bear most of the access load.

[0065] The visualization of the node access frequency heat map is mapped using the HSV color space. High-frequency nodes are assigned a hue of 0 degrees, corresponding to red; medium-frequency nodes are assigned a hue of 60 degrees, corresponding to yellow; and low-frequency nodes are assigned a hue of 120 degrees, corresponding to green. Saturation and brightness are fine-tuned based on the specific access frequency values ​​to achieve fine-grained differentiation within the same frequency level. The heat map's update frequency is synchronized with the statistical time window. Whenever the time window slides, the access frequency is recalculated and the heat map display is updated, providing operations personnel with real-time monitoring of system access status.

[0066] Step S222: Prioritize the skip table pointers of the frequently visited nodes, pre-cache the corresponding search paths, and re-order the priorities of the skip table pointers. The method for priority improvement processing includes: for high-frequency access nodes, moving forward the storage position of their upward jump pointer and direct jump pointer in the skip list chain access path cache table so that they are matched first during the retrieval process; creating a dedicated fast access channel for high-frequency access nodes, pre-allocating a fixed storage space in the memory, and storing the optimal access path from the node to each upper-level business level node. The specific implementation of moving the storage position forward adopts the reorganization strategy of the skip list chain access path cache table, and migrates the skip list pointer of the high-frequency access node from the original storage position to the front-end area of ​​the cache table. The access speed of the front-end area is significantly better than that of the back-end area due to the high cache hit rate. The dedicated fast access channel is implemented using memory pool technology, and a continuous memory space is pre-allocated for each high-frequency access node to store its complete access path information. The size of the memory pool is dynamically adjusted according to the number of high-frequency nodes and the average path complexity.

[0067] The method of pre-caching the retrieval path includes: recalculating the path lengths of all possible paths from the frequently accessed nodes to each target level based on the access paths recorded in the skip list chain access path cache table, and introducing the access frequency correction factor -γ×F into the path length calculation. i , add -γ×F to the original calculation formula i Where γ is the frequency correction factor, reflecting the degree of influence of access frequency on path selection priority. The value range of γ is 0.1 to 0.5, which is determined by analyzing the retrieval performance under different correction factors. When the value of γ is too small, the correction effect is not obvious. When the value of γ is too large, it may cause low-frequency but important nodes to be excessively ignored. Based on the corrected path length calculated by the access frequency correction factor, as well as the results of the storage position forward shift and pre-caching processing, the priority of the skip table pointers is reordered.

[0068] Step S223: Based on the optimized skip list pointer priority sorting, the skip list chain access path cache table is updated to form a topology heat map guided search path optimization strategy; The method for updating the skip list chain access path cache table includes: traversing all cache items in the skip list chain access path cache table, and reordering the access paths containing frequently accessed nodes based on the corrected path lengths; updating the sorted access path sequence to the corresponding cache table item to ensure that the optimal path to the frequently accessed node is at the first place in the sequence.

[0069] The topology heatmap-guided search path optimization strategy adopts a hierarchical search architecture, which includes two levels: pre-cached path search and regular path search. Specifically, when the system receives a search request instruction, it first searches for matching search targets from the pre-cached paths of frequently accessed nodes. When no matching targets exist in the pre-cached paths, it searches from the updated skip-list access path cache table in priority order. Through this hierarchical search strategy, the system can maximize the improvement of search efficiency while ensuring search integrity. Pre-cached path search prioritizes searches for dedicated fast channels maintained for frequently accessed nodes, with a search time complexity of O(1) constant time, which is suitable for frequently accessed hotspot devices in the system. Regular path search uses the updated skip-list access path cache table for searches for medium and low-frequency access nodes, with a search time complexity of O(logn) logarithmic time, where n is the number of cache entries. The automatic switching mechanism of hierarchical search automatically selects the appropriate search level based on the access frequency attribute of the search target, without the need for manual user intervention.

[0070] A three-tiered access frequency grading mechanism provides the foundation for differentiated optimization strategies. High-frequency access nodes receive priority resource allocation and path optimization, while medium-frequency access nodes maintain standard search quality of service. Low-frequency access nodes adopt a resource-efficient search strategy. Prioritizing high-frequency access nodes enables intelligent allocation of search resources and performance optimization. By moving the skip table pointers of high-frequency access nodes forward to the high-speed access area of ​​the cache table, the system reduces search latency and memory access overhead for these nodes. Dedicated fast access channels provide independent search paths for high-frequency access nodes, avoiding resource competition with regular nodes and ensuring fast search response times for critical services. The synergy between access frequency heat maps and cellular grid hash codes enables automatic discovery of spatial access patterns. Due to the spatial continuity of cellular grids and the locality-preserving properties of hash codes, the system can automatically identify spatial access hotspots and access propagation paths by analyzing the access frequency distribution of adjacent grids. This spatial pattern discovery capability provides data support for capacity planning and device deployment optimization in microgrid systems. The system can predict which areas are likely to become future access hotspots and proactively allocate resources and optimize performance. The deep integration of priority boosting and the skip table chain structure achieves dynamic balancing of search loads. When the load on certain frequently accessed nodes becomes excessive, the system automatically redirects some traffic to alternative paths through path reordering and caching adjustments, preventing system performance degradation caused by single-point overload. This dynamic balancing mechanism also enhances the system's fault tolerance. If the primary search path is interrupted by a fault, the system can quickly switch to the alternative path to maintain normal search service.

[0071] Step S230 , performing a cross-level jump search based on the search path optimization strategy guided by the topology heat map and the multi-constraint combination search conditions; Specifically, a topology heatmap-guided search path optimization strategy and multi-constraint combination search conditions are used to perform cross-level jump search, and a phased progressive strategy is used to achieve efficient microgrid resource positioning. The execution process includes three core stages: constraint pre-filtering, path selection decision-making, and jump execution.

[0072] During the constraint pre-filtering phase, spatial constraints are prioritized to rapidly narrow the search scope. The system uses the grid-level hash code prefix for the first round of filtering, extracting all cache entries whose spatial hash prefixes match the constraints from the skip list access path cache. Spatial hash prefix matching utilizes a prefix tree data structure, with each node representing a binary bit of the hash code. The matching process begins at the root node and performs a depth-first traversal of the spatial hash prefix in the constraints, collecting all device node identifiers corresponding to fully matched prefixes to form a set of candidate nodes. Device type constraint filtering is implemented using a device type identifier mapping table. The system traverses the set of candidate nodes, querying the device type identifier of each candidate node. Candidate nodes are retained if their device type identifiers intersect with the set of device type identifiers in the constraints. Service-level constraint filtering extracts the service-level identifier after the grid-level hash code of each candidate node, matching it against the set of service-level identifiers in the constraints for verification.

[0073] The path selection decision stage performs intelligent path planning based on the heat map guided optimization strategy. The system first checks whether the target node candidate set contains high-frequency access nodes. If a high-frequency access node is included, it directly accesses the dedicated fast access channel of the node to obtain the pre-cached retrieval path. The dedicated fast access channel uses memory pool technology to allocate continuous memory space for each high-frequency access node, storing the complete path information from the node to each upper-level business level node. The path information format is a path node sequence, and each path node contains attributes such as node identifier, node type, jump type, and physical coordinates. When the target node candidate set does not contain a high-frequency access node, the system retrieves the path in priority order from the updated skip list chain access path cache table. By constructing a composite key of the starting node identifier and the target business level identifier, the system locates the corresponding path sequence in the skip list chain access path cache table and selects the path with the shortest path length as the execution path.

[0074] The jump execution phase achieves cross-level access through step-by-step jumps and state maintenance. The system maintains retrieval state variables including the current access node, target level, remaining jump steps, access path records and other information. The retrieval execution starts from the starting node and performs jump operations in sequence according to the jump pointer in the selected execution path. Each jump operation includes jump pointer parsing to obtain the target node identifier and jump type, target node access to read the complete node information including device type, business level, physical coordinates, control status and other attributes, node information extraction selectively extracts relevant information according to retrieval requirements, and state updates include switching the current access node, decrementing the remaining jump steps, and appending access path records. The system introduces a dynamic path adjustment mechanism to handle abnormal situations. When a node is unreachable or the communication times out, it automatically tries an alternative jump pointer. When the alternative jump pointer does not exist, it starts the path replanning program and recalculates the feasible retrieval path based on the current access status and the remaining retrieval target.

[0075] Step S230 achieves a coordinated balance between retrieval accuracy and efficiency through a phased progressive retrieval strategy. The constraint pre-filtering mechanism decomposes the multidimensional constraints into three independent filtering steps: space, device type, and business level. Each step uses a specially optimized data structure. Spatial filtering uses a prefix tree to achieve efficient hash prefix matching, device type filtering uses a hash table to achieve rapid identifier lookup, and business level filtering uses bit operations to achieve parallel verification of hierarchical identifiers, reducing retrieval time complexity. The heat map-guided path selection decision mechanism realizes the intelligent allocation of retrieval load. The dedicated fast access channel for high-frequency access nodes provides a path acquisition capability with constant time complexity, reducing retrieval response time and rationally allocating system resources. Through continuous monitoring of retrieval paths and access patterns, the system accumulates a large amount of retrieval behavior data and can automatically discover the implicit associations and spatial distribution patterns between devices, which is helpful for the optimized design and preventive maintenance of the microgrid system. The retrieval path reuse rate analysis reveals the key devices and key paths in the system.

[0076] Step S240 : Based on the execution results of the cross-level jump search, the search results are aggregated and sorted to generate a microgrid resource visualization topology index.

[0077] Furthermore, step S240 includes: Step S241: Collect all node information accessed during the cross-level jump search process, and build a hierarchical association graph based on the business hierarchical relationship of the nodes; Node information is collected by extracting the complete node access sequence from the access path record during the search execution. The access path record uses a stack data structure to maintain a complete jump trajectory from the starting node to the final target node. Each access node's information includes core attributes such as the node's unique identifier, device type identifier, service level identifier, physical coordinates, jump in-degree, and jump out-degree. The jump in-degree indicates how many other nodes point to the node via jump pointers during the search process, and the jump out-degree indicates how many other nodes the node points to via jump pointers. The system uses an association query mechanism to expand the collection of associated node information. This not only collects information about directly accessed nodes, but also queries related nodes based on control ownership relationships, communication connection relationships, and spatial proximity relationships. Control ownership relationships are obtained by parsing the microgrid system control topology configuration file, recording the parent control unit and subordinate controlled devices of each device node to form a tree-like control hierarchy. Communication connection relationships are obtained from the network communication protocol stack connection status information, including direct communication connections and communication quality parameters. Spatial proximity relationships are determined by cellular grid neighbor relationships, including spatial relationships at different levels, such as device nodes within the same grid and device nodes in adjacent grids.

[0078] The hierarchical association graph is constructed using a directed graph data structure. Nodes in the graph represent the devices or control units accessed during the search process, and edges represent the business hierarchical relationships between nodes. Business hierarchical relationships follow the principle of a four-layer business model with progressive hierarchies, including four types: superior control relationships, subordinate controlled relationships, peer coordination relationships, and cross-level direct connections. Graph node attributes are represented using multi-dimensional feature vectors encompassing four dimensions: spatial location features, business function features, connection topology features, and operational status features. Graph edge attributes use weighted edges to represent relationship strength, with edge weights calculated based on factors such as control frequency, communication quality, response time, and coordination effectiveness.

[0079] Step S242, calculating the spatial aggregation degree and topological connectivity index of the search results based on the hierarchical association graph and physical coordinate information; The spatial aggregation degree is calculated using the density-based DBSCAN clustering algorithm. The core parameters include the neighborhood radius Ep, which is set to twice the length of the hexagonal grid side, and the minimum number of neighbors min. pts , set to 3 to 5 devices. The DBSCAN algorithm traverses all device nodes in the search results and calculates the number of neighbor nodes in the neighborhood of each node Ep. When the number of node neighbors is greater than or equal to min pts When , it is marked as a core node and cluster expansion is started, and all density-reachable nodes are added to the same cluster through depth-first search.

[0080] The quantitative calculation of spatial aggregation degree uses two indicators: cluster density and cluster separation. Cluster density is measured by calculating the average distance between nodes within the cluster. The calculation formula is C tigh =1 / (1+d avg ), where d avg is the average Euclidean distance between nodes within the cluster, d avg It is obtained by averaging the distance between any two nodes in the cluster, C tigh The clustering density ranges from 0 to 1. The larger the value, the tighter the clustering. The cluster separation is measured by calculating the average distance between the centers of gravity of different clusters. The calculation formula is C sepa= d sep / d max , where d sep is the average distance between cluster centers, d max is the maximum diameter of the search result coverage area, C sepa It is the degree of cluster separation, ranging from 0 to 1. The larger the value, the higher the degree of separation between clusters.

[0081] Topological connectivity metrics are calculated using graph theory connectivity analysis methods, including four key indicators: the number of connected components, average path length, network diameter, and clustering coefficient. The number of connected components is calculated by traversing the hierarchical association graph using a depth-first search algorithm, counting the number of disconnected subgraphs in the graph. The number of connected components reflects the degree of topological segmentation of the search results. The average path length is obtained by calculating the average of the shortest path lengths between any two nodes in the graph. The Floyd-Warshall algorithm is used to calculate the shortest paths between all pairs of nodes. The average path length reflects the transmission efficiency and response speed of the network. The network diameter is defined as the maximum value of the shortest path length between any two nodes in the graph, reflecting the maximum transmission delay of the network. The clustering coefficient is obtained by calculating the local clustering coefficient of each node and averaging it. The local clustering coefficient of a node is defined as the ratio of the actual number of connections between the node's neighboring nodes to the possible number of connections, reflecting the local connectivity density of the network.

[0082] Step S243 : Based on the spatial aggregation degree and topological connectivity index, the search results are intelligently sorted and grouped to generate a microgrid resource visualization topology index.

[0083] Intelligent sorting uses a multi-indicator comprehensive scoring mechanism, combining spatial aggregation and topological connectivity indicators through weighted summation to form a comprehensive score. Weight allocation is determined based on the importance of the indicators. The weights of topological connectivity-related indicators are higher than those of spatial aggregation-related indicators. The weight setting reflects the important impact of topological connectivity on system stability and control efficiency in the microgrid system. The calculation formula for the comprehensive score is:

[0084] Where Com is the number of connected components, is the average path length, is the network diameter, is the clustering coefficient, is the weight of clustering density, is the weight of cluster separation, is the weight of the number of connected components, is the weight of the average path length, is the weight of the network diameter, is the weight of the clustering coefficient, satisfying The specific values ​​of each weight coefficient are determined by historical data analysis and system performance testing based on factors such as the scale of the microgrid system, equipment distribution characteristics, and control complexity. .

[0085] The search results are grouped using a hierarchical grouping strategy. First, a first-level grouping is performed based on business-level identifiers, organizing the search results into four business levels: controllable device level, microgrid element level, microgrid unit level, and microgrid cluster level. Second, a second-level grouping is performed based on spatial clustering results. Within each business level, device nodes belonging to the same spatial cluster are grouped together according to the DBSCAN clustering results. Finally, a third-level grouping is performed based on topological connectivity. Within each spatial cluster, topologically connected device nodes are grouped together according to the graph-theoretic connectivity components. The grouping results are represented in a tree-like hierarchical structure, with the root node representing the complete search result set, the first-level child nodes representing business-level groups, the second-level child nodes representing spatial cluster groups, and the third-level child nodes representing topological connectivity groups.

[0086] The visualization topology index for microgrid resources is generated using a multi-layer index structure, consisting of three levels: spatial index, business index, and topological index. The spatial index is constructed based on the cellular grid-level hash code. Each index item contains information such as the grid identifier, the list of devices within the grid, and spatial aggregation indicators. The business index is constructed based on the business-level identifier. Each index item contains information such as the level identifier, the list of devices within the level, and the level association relationship. The topological index is constructed based on graph-theoretic connectivity. Each index item contains information such as the connected component identifier, the list of devices within the component, and the connectivity indicator. The index structure is implemented using a B+ tree data structure, supporting range and prefix queries. The index key is a composite key design, containing identification information from multiple dimensions, such as the spatial hash prefix, the business-level identifier, and the connected component identifier.

[0087] Step S240 achieves orderly organization and efficient access to microgrid resource information through structured retrieval result processing. The construction of a hierarchical association graph transforms discrete retrieval nodes into an organic whole with clear business hierarchical relationships. The directed graph data structure accurately reflects the control hierarchy and management relationship of the microgrid system, providing a structured foundation for subsequent topological analysis and association queries. The node attribute representation method of the multi-dimensional feature vector achieves a comprehensive characterization of device information. The spatial location feature supports distance-based similarity calculation, the business function feature supports function-based device classification, the connection topology feature supports importance-based device sorting, and the operating status feature supports health-based device screening. The quantitative calculation of spatial aggregation degree and topological connectivity indicators provides an objective standard for the quality assessment of retrieval results. The density-based characteristics of the DBSCAN clustering algorithm enable the system to automatically identify device clustering areas of arbitrary shapes, avoiding the limitations of the traditional K-means algorithm on spherical clustering. The clustering results are closer to the actual device distribution characteristics of the microgrid system. The introduction of clustering density and separation indicators enables quantitative evaluation of spatial distribution quality. Clustering with high density indicates that the spatial distribution of equipment is concentrated, which is conducive to centralized management. Clustering with high separation indicates that the clear division of different functional areas is conducive to zoning control.

[0088] A comprehensive analysis of topological connectivity metrics reveals the structural characteristics and performance bottlenecks of the microgrid system. The number of connected components reflects the degree of system segmentation. Fewer connected components indicate good system integrity, while more connected components indicate the presence of isolated subsystems requiring attention. Calculation of average path length and network diameter provides a quantitative basis for evaluating control latency and response speed. A shorter average path length indicates efficient information transfer, while a smaller network diameter indicates strong system responsiveness. Analysis of the clustering coefficient reveals the system's level of redundant connectivity. A higher clustering coefficient indicates strong fault tolerance, while a lower clustering coefficient suggests the need for additional backup connections. Through a comprehensive analysis of spatial aggregation and topological connectivity, the system automatically identifies functional areas and critical paths within the microgrid system, providing users with expanded information beyond the initial search requirements. The discovery of spatial clusters enables the system to automatically recommend related devices within the same area, providing complete device information within the area even if the user does not explicitly query for these devices. Topological connectivity analysis enables the system to automatically identify dependencies and impact paths between devices. When a device fails, the system automatically recommends potentially affected associated devices, providing decision support for fault handling and emergency response. The establishment of a multi-layer index structure realizes the organic integration of multi-dimensional retrieval capabilities. Users can simultaneously conduct complex queries based on multiple dimensions such as spatial location, business functions, topological relationships, etc., and the relevance and completeness of the retrieval results are significantly improved.

[0089] Example 2:

[0090] This embodiment provides a system for generating a microgrid resource visualization topology index based on embodiment 1. Figure 6 Shown, including: Grid construction module: used to receive the geographical boundary coordinate data of the microgrid system and establish the microgrid physical area coordinate system; based on the microgrid physical area coordinate system, establish a hexagonal honeycomb grid structure; Skip-list construction module: Generates a cellular grid-level hash code based on the hexagonal cellular grid structure; generates a skip-list of network topology structures based on the cellular grid-level hash code; Path cache module: Based on the skip table chain of the network topology, it builds a skip table chain access path cache table; Topology index generation module: Based on the skip table chain access path cache table, it performs cross-level jump retrieval and generates a visual topology index for microgrid resources.

[0091] In the skip list chain construction module, the specific process of generating the skip list chain of the network topology structure based on the cellular grid level hash code includes: Step S121, identifying the service level of the device node based on the cellular grid level hash code; Step S122 : Based on the identification result of the service level of the device node, a bidirectional skip table pointer is injected and redundant paths are pruned to form a skip table chain of the network topology structure.

[0092] In the path cache module, the specific process of constructing the skip list chain access path cache table based on the network topology structure includes: Step S131: injecting the spatial hash prefix of the cell grid level hash code into the skip table pointer of the skip table chain, where the skip table pointer refers to a bidirectional skip table pointer and a direct skip pointer; Step S132, pre-aggregating multiple skip table pointers in the same cellular grid to generate a skip table pointer cluster head node; Step S133: Generate a skip list chain access path cache table based on the generated skip list pointer cluster head node.

[0093] Example 3:

[0094] This embodiment discloses an electronic device that may include one or more processors and one or more memories. The memories may store computer-readable code that, when executed by the one or more processors, may execute the method for generating a microgrid resource visualization topology index as described above.

[0095] The method or system according to the embodiments of the present application can also be implemented using the architecture of an electronic device. The electronic device may include a bus, one or more CPUs, read-only memory (ROM), random access memory (RAM), a communication port connected to a network, input / output components, a hard disk, etc. A storage device in the electronic device, such as a ROM or hard disk, can store the method for generating a microgrid resource visualization topology index provided in this application. The method for generating a microgrid resource visualization topology index may, for example, include: receiving geographic boundary coordinate data of the microgrid system and establishing a microgrid physical area coordinate system; establishing a hexagonal cellular grid structure based on the microgrid physical area coordinate system; generating a cellular grid hierarchical hash code based on the hexagonal cellular grid structure; generating a hop list chain of the network topology structure based on the hop list chain of the network topology; constructing a hop list chain access path cache table based on the hop list chain access path cache table; performing a cross-level hop search based on the hop list chain access path cache table to generate a microgrid resource visualization topology index.

[0096] Furthermore, the electronic device may further include a user interface. Of course, the architecture disclosed in the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0097] Example 4:

[0098] This embodiment discloses a computer-readable storage medium storing computer-readable instructions. When executed by a processor, the computer-readable instructions can execute the method for generating a microgrid resource visualization topology index according to the embodiments of this application. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, or flash memory.

[0099] In addition, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions capable of being executed by a processor to execute instructions corresponding to the method steps provided herein, such as: receiving geographic boundary coordinate data of a microgrid system and establishing a microgrid physical area coordinate system; establishing a hexagonal cellular grid structure based on the microgrid physical area coordinate system; generating a cellular grid hierarchical hash code based on the hexagonal cellular grid structure; generating a hop list chain for a network topology structure based on the cellular grid hierarchical hash code; constructing a hop list chain access path cache table based on the network topology structure; and performing cross-level hop retrieval based on the hop list chain access path cache table to generate a microgrid resource visualization topology index. When executed by a central processing unit (CPU), the computer program performs the above-described functions defined in the method of the present application.

[0100] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing the methods according to the present application.

[0101] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0102] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for generating a visual topology index of microgrid resources, characterized in that: The method comprises: Receive geographic boundary coordinate data of the microgrid system and establish a physical area coordinate system of the microgrid; and establish a hexagonal honeycomb grid structure based on the physical area coordinate system of the microgrid; Based on the hexagonal honeycomb grid structure, a honeycomb grid level hash code is generated; based on the honeycomb grid level hash code, a skip list chain of the network topology structure is generated; based on the skip list chain of the network topology structure, a skip list chain access path cache table is constructed; Based on the skip table chain access path cache table, cross-level skip retrieval is performed to generate a visual topology index of microgrid resources.

2. The method for generating a microgrid resource visualization topology index according to claim 1, characterized in that: The geographical boundary coordinate data of the microgrid system at least includes the latitude and longitude coordinates of all device nodes in the microgrid system; The Z-axis of the microgrid physical area coordinate system vertically upward represents the service level depth, and the origin is the geographical center coordinate of the microgrid system, which is obtained based on the latitude and longitude coordinates of all device nodes.

3. The method for generating a microgrid resource visualization topology index according to claim 2, characterized in that: The business hierarchy depth is 4 layers, namely, the controllable device layer, the microgrid element layer, the microgrid unit layer, and the microgrid cluster layer. The business hierarchy depth is represented by a discrete mapping on the Z axis, where the controllable device layer corresponds to Z=D, the microgrid element layer corresponds to Z=C, the microgrid unit layer corresponds to Z=B, and the microgrid cluster layer corresponds to Z=A.

4. The method for generating a microgrid resource visualization topology index according to claim 3, characterized in that: The hexagonal honeycomb grid structure is established in the XY plane of the microgrid physical area coordinate system, and the side length of the hexagonal honeycomb grid is adaptively adjusted based on the average distribution density of device nodes in the microgrid system; The method for generating a honeycomb grid level hash code based on a hexagonal honeycomb grid structure includes: Calculate the center coordinates of each hexagonal honeycomb grid, encode the center coordinates of each hexagonal honeycomb grid using the Hilbert space-filling curve, and generate a honeycomb grid-level hash code with a fixed length of L; The first M bits of the cellular grid level hash code are defined as a spatial hash prefix, and the last LM bits are used as a service level identifier; the spatial hash prefix is ​​used to identify the hexagonal cellular grid location.

5. The method for generating a microgrid resource visualization topology index according to claim 4, characterized in that: The method for generating a jump table chain of a network topology structure based on a cellular grid level hash code includes: Based on the cellular grid-level hash code, the device node service level attribution is identified; Based on the identification results of the business level of the device node, a bidirectional skip table pointer is injected and redundant paths are pruned to form a skip table chain of the network topology structure.

6. The method for generating a microgrid resource visualization topology index according to claim 5, characterized in that: The bidirectional jump table pointer includes an upward jump pointer and a downward jump pointer, and the upward jump pointer points from the lower business layer node to the upper business layer node, and the lower business layer node includes the controllable device layer node, the microgrid element layer node and the microgrid unit layer node; Whenever an upward jump pointer is established from a lower service level node to an upper service level node, the upper service level node automatically obtains a downward jump pointer pointing to the lower service level node.

7. The method for generating a microgrid resource visualization topology index according to claim 6, characterized in that: The method for performing redundant path pruning includes: Calculate the physical distance between the device node and a higher-level node that is not directly above the device node. If the physical distance is less than a preset distance threshold, create a direct jump pointer from the device node to the higher-level node that is not directly above the device node. The higher-level node that is not directly above the device node refers to a microgrid unit layer node and a microgrid cluster layer node.

8. The method for generating a microgrid resource visualization topology index according to claim 7, characterized in that: The method for constructing a skip list chain access path cache table based on a network topology structure includes: Injecting the spatial hash prefix of the cellular grid level hash code into the skip table pointer of the skip table chain, the skip table pointer includes a bidirectional skip table pointer and a direct skip pointer; Pre-aggregate multiple jump table pointers within the same cellular grid to generate a jump table pointer cluster head node; the jump table pointer cluster head node has an upward jump pointer and a direct jump pointer; Based on the generated skip list pointer cluster head node, a skip list chain access path cache table is generated.

9. The method for generating a microgrid resource visualization topology index according to claim 8, characterized in that: The method for performing cross-level jump retrieval based on the jump table chain access path cache table to generate a microgrid resource visualization topology index includes: Receiving a search request instruction and converting the search request instruction into a multi-constraint combination search condition; Based on the multi-constraint combination retrieval conditions and the skip list chain access path cache table, a topology heat map guided retrieval path optimization strategy is constructed; Based on the topological heat map-guided search path optimization strategy and multiple constraint combination search conditions, cross-level jump search is performed; Based on the execution results of cross-level jump retrieval, the retrieval results are aggregated and sorted to generate a visual topological index of microgrid resources.

10. A microgrid resource visualization topology index generation system, which is used to implement the microgrid resource visualization topology index generation method according to any one of claims 1 to 9, characterized in that: The system comprises: Grid construction module: used to receive the geographical boundary coordinate data of the microgrid system and establish the microgrid physical area coordinate system; based on the microgrid physical area coordinate system, establish a hexagonal honeycomb grid structure; Skip-list construction module: Generates a cellular grid-level hash code based on the hexagonal cellular grid structure; generates a skip-list of network topology structures based on the cellular grid-level hash code; Path cache module: Based on the skip table chain of the network topology, it builds a skip table chain access path cache table; Topology index generation module: Based on the skip table chain access path cache table, it performs cross-level jump retrieval and generates a visual topology index for microgrid resources.

11. An electronic device comprising a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit, wherein: When the central processing unit executes the computer program, the method for generating a microgrid resource visualization topology index according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed, implements the method for generating a microgrid resource visualization topology index according to any one of claims 1 to 9.

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