Microgrid resource visualized topology index generation method and system

Abstract

The application discloses a microgrid resource visualized topology index generation method and system, the method comprises the following steps: receiving geographical boundary coordinate data of a microgrid system to establish a microgrid physical area coordinate system; constructing a hexagonal honeycomb grid structure in the microgrid physical area coordinate system; generating a honeycomb grid level hash code according to the honeycomb grid; constructing a skip list chain of a network topology structure and a skip list chain access path cache table based on the hash code; finally, performing cross-level jump retrieval by using the cache table and generating a microgrid resource visualized topology index; by combining the hexagonal honeycomb grid and the skip list chain structure, efficient cross-level retrieval is realized, the speed and accuracy of device positioning in a complex microgrid system are significantly improved, and the fault processing efficiency and operation and maintenance response speed are effectively ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid, more particularly, the present application relates to a microgrid resource visualization topology index generation method and system. BACKGROUND

[0002] With the rapid development of smart grid technology, the scale and complexity of microgrid systems are increasing, especially in industrial park microgrids, urban distribution network clusters, community integrated energy systems and other application scenarios, higher requirements for efficient management and rapid retrieval of microgrid resources are proposed. Microgrid systems usually use a multi-level business model, including microgrid clusters, units, elements and devices, etc. This architecture helps to achieve modular management and functional partitioning of the system.

[0003] Chinese patent application No. CN119621722A discloses a memory topology index system for power grid data, which realizes efficient management and retrieval of power grid data through the cooperative work of a central processor module, a login module, a warehousing module, a storage module, a query response module, an encoding module, an index management module and a generation module. The system introduces a metadata extraction process, which can better manage the compatibility of various data assets. Chinese patent No. CN108323232B discloses a method for maintaining index and chain topology structure between multi-level blockchains, which records seed node index lower blockchains through top-level blockchains, and uses a distributed monitoring cluster to regularly detect the availability of 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 in handling device retrieval of multi-level microgrid systems. When a device node is abnormal, the traditional hierarchical retrieval mechanism is for the operation and maintenance personnel to query in the multi-level structure of "microgrid cluster→unit→element→device", etc. In the face of systems with a large number of levels, the retrieval complexity increases exponentially with the number of levels. For example, in an industrial park microgrid, when a certain type of device node is abnormal, the operation and maintenance personnel need to query from the microgrid cluster layer to the device layer, which not only takes a long time, but also may cause delay in fault handling in emergency situations. The existing technology fails to fully utilize the spatial distribution characteristics of devices, lacks an efficient cross-level jumping mechanism, making the retrieval path long and unable to meet the real-time requirements of rapid fault positioning. In addition, the traditional method fails to effectively combine the spatial position correlation of devices when handling multi-level business models, resulting in low retrieval efficiency and difficulty in rapid positioning of devices in complex microgrid systems. SUMMARY

[0005] The application is suitable for various micro-grid systems containing multi-level business models, such as industrial park micro-grid, urban distribution network cluster, community integrated energy system, etc. In these scenarios, the micro-grid system usually has a complex hierarchical structure, and the device nodes are widely distributed and numerous. When an abnormality occurs in a certain type of device node, the operation and maintenance personnel need to quickly locate the faulty device to reduce downtime and improve system reliability. In order to overcome the above-mentioned defects of the prior art, the application provides a micro-grid resource visualized topology index generation method and system, which receives geographic boundary coordinate data of a micro-grid system, establishes a micro-grid physical region coordinate system, and constructs a hexagonal honeycomb grid structure in the coordinate system. Based on the honeycomb grid structure, a honeycomb grid level hash code is generated, a skip list chain of the network topology structure and a skip list chain access path cache table are constructed, and cross-level jump retrieval is realized. The application combines hexagonal honeycomb grid and skip list chain structure, fully utilizes the spatial distribution characteristics of devices, significantly shortens the retrieval time across multiple levels, improves the spatial positioning accuracy, supports the operation and maintenance personnel to locate specific nodes within a short time after device abnormal alarm, and significantly improves the fault handling efficiency and operation and maintenance response speed of complex micro-grid systems.

[0006] To achieve the above object, the application provides the following technical scheme:

[0007] The micro-grid resource visualized topology index generation method comprises:

[0008] Receiving geographic boundary coordinate data of a micro-grid system, establishing a micro-grid physical region coordinate system; based on the micro-grid physical region coordinate system, establishing a hexagonal honeycomb grid structure;

[0009] Based on the hexagonal honeycomb grid structure, generating a honeycomb grid level hash code; based on the honeycomb grid level hash code, generating a skip list chain of the network topology structure; based on the skip list chain of the network topology structure, constructing a skip list chain access path cache table;

[0010] Based on the skip list chain access path cache table, performing cross-level jump retrieval to generate a micro-grid resource visualized topology index.

[0011] Further, the geographic boundary coordinate data of the micro-grid system at least includes the longitude and latitude coordinates of all device nodes in the micro-grid system;

[0012] The Z-axis of the micro-grid physical region coordinate system is vertically upward to represent the business level depth, and the origin is the geographic center coordinate of the micro-grid system, which is obtained according to the longitude and latitude coordinates of all device nodes.

[0013] Further, the service level depth is 4 layers, respectively, the controllable device layer, the micro-grid element layer, the micro-grid unit layer and the micro-grid cluster layer; the service level depth is discretely mapped in the Z axis, wherein the controllable device layer corresponds to Z=D, the micro-grid element layer corresponds to Z=C, the micro-grid unit layer corresponds to Z=B, and the micro-grid cluster layer corresponds to Z=A.

[0014] Further, the hexagonal honeycomb grid structure is established in the X-Y plane of the micro-grid 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 micro-grid system.

[0015] The method for generating the honeycomb grid level hash code based on the hexagonal honeycomb grid structure comprises:

[0016] The center coordinates of each hexagonal honeycomb grid are calculated, the center coordinates of each hexagonal honeycomb grid are encoded by using the Hilbert space-filling curve to generate a honeycomb grid level hash code with a fixed length L; the first M bits of the honeycomb grid level hash code define a space hash prefix, and the last L-M bits serve as a service level identifier; the space hash prefix is used to identify the position of the hexagonal honeycomb grid.

[0017] Further, the method for generating the skip list chain of the network topology structure based on the honeycomb grid level hash code comprises:

[0018] Based on the honeycomb grid level hash code, the service level attribution of the device nodes is identified.

[0019] Based on the identification result of the service level attribution of the device nodes, the bidirectional skip pointers are injected, and the redundant path is pruned to form the skip list chain of the network topology structure.

[0020] Further, the bidirectional skip pointers comprise upward jump pointers and downward jump pointers, the pointing relationship of the upward jump pointers is from the lower service level nodes to the upper service level nodes, and the lower service level nodes comprise the controllable device layer nodes, the micro-grid element layer nodes and the micro-grid unit layer nodes; 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.

[0021] Further, the method for pruning the redundant path comprises:

[0022] The physical distance between the device node and the higher node of the non-direct superior of the device node is calculated, and if the physical distance is less than a preset distance threshold, a direct connection jump pointer from the device node to the higher node of the non-direct superior is created; the higher node of the non-direct superior of the device node refers to the micro-grid unit layer node and the micro-grid cluster layer node.

[0023] Further, the method for constructing the skip list chain access path cache table based on the network topology structure comprises:

[0024] injecting a spatial hash prefix of the honeycomb grid level hash code into a skip list pointer of the skip list chain, the skip list pointer comprising a bidirectional skip list pointer and a direct connection jump pointer;

[0025] performing pre-aggregation processing on multiple skip list pointers in the same honeycomb grid to generate a skip list pointer cluster head node; the skip list pointer cluster head node has an upward jump pointer and a direct connection jump pointer;

[0026] generating a skip list chain access path cache table based on the generated skip list pointer cluster head node.

[0027] Further, the method for executing cross-level jump retrieval based on the skip list chain access path cache table to generate a microgrid resource visual topology index comprises:

[0028] receiving a retrieval request instruction and converting the retrieval request instruction into a multiple constraint combination retrieval condition;

[0029] constructing a topology heat map guided retrieval path optimization strategy based on the multiple constraint combination retrieval condition and the skip list chain access path cache table;

[0030] executing cross-level jump retrieval based on the topology heat map guided retrieval path optimization strategy and the multiple constraint combination retrieval condition;

[0031] performing retrieval result aggregation and sorting based on the execution result of the cross-level jump retrieval to generate a microgrid resource visual topology index.

[0032] A microgrid resource visual topology index generation system for implementing the microgrid resource visual topology index generation method described above, the system comprising:

[0033] a grid construction module for receiving geographic boundary coordinate data of a microgrid system, establishing a microgrid physical region coordinate system, and establishing a hexagonal honeycomb grid structure based on the microgrid physical region coordinate system;

[0034] a skip list chain construction module for generating a honeycomb grid level hash code based on the hexagonal honeycomb grid structure, and generating a network topology structure skip list chain based on the honeycomb grid level hash code;

[0035] a path cache module for constructing a skip list chain access path cache table based on the network topology structure skip list chain;

[0036] a topology index generation module for executing cross-level jump retrieval based on the skip list chain access path cache table to generate a microgrid resource visual topology index.

[0037] An electronic device comprises 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 micro-grid resource visualized topology index generation method when executing the computer program.

[0038] A computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program implements the micro-grid resource visualized topology index generation method when executed.

[0039] Compared with the prior art, the micro-grid resource visualized topology index generation method has the following beneficial effects:

[0040] The micro-grid device space position is efficiently indexed by constructing a hexagonal honeycomb grid structure and combining Hilbert curve coding, and meanwhile, the traditional hierarchical retrieval is broken through the level-by-level traversal limitation by using skip list chain and direct connection jump pointer mechanism to form a cross-level fast jump path. Based on the pre-computation optimization of the access path cache table, the path discovery in the retrieval process is converted into fast lookup, effectively avoiding the retrieval path explosion problem under the multi-level service model. The cross-level retrieval time is greatly shortened, the space positioning accuracy is significantly improved, the operation and maintenance personnel can quickly locate the specific node when the device is abnormal, the efficient retrieval and fast fault positioning of the device in the complex micro-grid system are realized, and core technical support is provided for the operation and maintenance efficiency improvement of the multi-level micro-grid system in industrial parks, urban distribution network clusters and the like. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0042] Figure 1 The method flowchart of the micro-grid resource visualized topology index generation method in the present application;

[0043] Figure 2 The method flowchart of the skip list chain of the network topology structure in the present application;

[0044] Figure 3 The pointing relationship diagram of the bidirectional skip list pointer in the present application;

[0045] Figure 4 The pointing relationship diagram of the direct connection jump pointer in the present application;

[0046] Figure 5 The method flowchart of the micro-grid resource visualized topology index generation method in the present application;

[0047] Figure 6 The figure of functional modules of the microgrid resource visualized topology index generation system in the application is shown. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0049] Embodiment 1

[0050] Please refer to Figure 1 The embodiment provides a microgrid resource visualized topology index generation method, which comprises the following steps:

[0051] In step S100, geographic boundary coordinate data of a microgrid system is received, a microgrid physical region coordinate system is established, a hexagonal honeycomb grid structure is established based on the microgrid physical region coordinate system, a honeycomb grid hierarchical hash code is generated based on the hexagonal honeycomb grid structure, a skip list chain of a network topology structure is generated based on the honeycomb grid hierarchical hash code, and a skip list chain access path cache table is constructed based on the skip list chain of the network topology structure.

[0052] Further, step S100 comprises the following steps:

[0053] In step S110, geographic boundary coordinate data of a microgrid system is received, a microgrid physical region coordinate system is established, a hexagonal honeycomb grid structure is established based on the microgrid physical region coordinate system, and a honeycomb grid hierarchical hash code is generated based on the hexagonal honeycomb grid structure.

[0054] 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 establishment process of the microgrid physical area coordinate system includes two links of coordinate transformation and axis definition. The coordinate transformation adopts the Mercator projection method to convert the longitude and latitude coordinates in the geographic coordinate system into the X-Y coordinates in the plane rectangular coordinate system. The axis definition follows the standard geographic coordinate convention, the X-axis points to the geographic east direction, the Y-axis points to the geographic north direction, and the Z-axis is vertically upward to represent the business level depth. The business level depth is 4 layers, which are the controllable device layer, the microgrid element layer, the microgrid unit layer, and the microgrid cluster layer. The representation of the business level depth in the Z-axis adopts 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 microgrid physical area coordinate system takes the geographic center coordinate of the microgrid system as the origin, and the geographic center coordinate is calculated by the centroid algorithm. The longitude coordinate of the geographic center is equal to the arithmetic average of the longitude coordinates of all device nodes, and the latitude coordinate of the geographic center is equal to the arithmetic average 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, making the spatial distribution in each direction relatively balanced and avoiding the spatial bias problem caused by arbitrary selection of the origin.

[0055] The hexagonal honeycomb grid structure is established in the X-Y 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 center coordinates of each hexagonal honeycomb grid are calculated according to the standard hexagonal close packing mode. The calculation formula of the average distribution density of the device nodes is the total number of device nodes divided by the total area covered by the microgrid system.

[0056] Please refer to Table 1, the method of adaptively adjusting the side length of the hexagonal honeycomb grid based on the average distribution density of the device nodes in the microgrid system includes:

[0057] Set the side length of the hexagonal honeycomb grid as k, and the average distribution density of the device nodes as p. If p>p1, it indicates that the device distribution is dense, and the value of k needs to be reduced to improve the spatial resolution; if p<p2, it indicates that the device distribution is sparse, and the value of k needs to be increased to reduce the number of grids; if p2≤p≤p1, the value of k remains unchanged; wherein p2 is the low density threshold, and p1 is the high density threshold. p1 and p2 are determined by analyzing the device distribution statistical data of typical microgrid systems. The high density threshold p1 corresponds to the urban microgrid environment with dense device distribution, and the low density threshold p2 corresponds to the suburban or rural microgrid environment with sparse device distribution.

[0058] Table 1 Adaptive adjustment method of side length of hexagonal honeycomb grid

[0059]

[0060] Based on the hexagonal honeycomb grid structure, the method for generating the honeycomb grid hierarchical hash code comprises: encoding the center coordinates of each hexagonal honeycomb grid by using the Hilbert space-filling curve to generate a honeycomb grid hierarchical hash code with a fixed length L; the first M bits of the honeycomb grid hierarchical hash code define a spatial hash prefix, and the spatial hash prefix is used as a spatial position identifier to identify the hexagonal honeycomb grid position, and the last L-M bits are used as a service hierarchical identifier to realize the hash aggregation effect of spatial adjacent devices.

[0061] The construction of the service hierarchical identifier adopts a direct binary mapping method of the Z-axis coordinate value, as shown in Table 2, wherein the identifier D of the device layer corresponds to the binary code 11, the identifier C of the micro-network element layer corresponds to the binary code 10, the identifier B of the micro-network unit layer corresponds to the binary code 01, and the identifier A of the micro-network cluster layer corresponds to the binary code 00. This coding mode ensures that different service hierarchies have unique binary identifiers, and realizes the complete representation of four service hierarchies through 2-bit binary coding. The final format of the honeycomb grid hierarchical hash code is the splicing combination of the spatial hash prefix and the service hierarchical identifier, forming a complete hash code with a fixed length, which contains the spatial position information and the service hierarchical information of the device, and provides a unified index basis for subsequent multi-dimensional retrieval.

[0062] Table 2: Representation method of service hierarchical identifier

[0063]

[0064] For example, the spatial hash prefix of a certain honeycomb grid is a 16-bit binary code 1011010010101101 (corresponding to the spatial position identifier), and the service hierarchy is the micro-network element layer (identifier C, binary code 10), and the complete hash code is 101101001010110110, wherein the first 16 bits are the spatial hash prefix, and the last 2 bits are the service hierarchical identifier. This code simultaneously represents "the micro-network element layer node located in the spatial area 1011010010101101".

[0065] Step S110 realizes the balance optimization of the spatial distribution of device nodes by establishing a micro-network physical area coordinate system with the geographical center as the origin. Compared with the traditional arbitrary origin selection method, the selection of the geographical center origin makes the coordinate system have natural spatial symmetry, and the distribution of device nodes in each quadrant is more uniform, avoiding the problem of spatial index imbalance caused by excessive concentration of device nodes in a certain quadrant. The hexagonal honeycomb grid structure realizes the fundamental improvement of spatial connectivity compared with the traditional square grid. Each grid of the hexagonal honeycomb grid has 6 adjacent grids, compared with 4 adjacent grids of the square grid, which provides more abundant spatial connection paths and reduces the path breaking phenomenon during cross-grid retrieval. The geometric shape of the hexagon is closer to a circle, and its circularity is significantly higher than that of a square, so that the representation of the device node radiation influence range is more accurate, and the spatial coverage error caused by irregular grid shape is reduced. The adaptive adjustment mechanism of the average distribution density of the device nodes enables the grid granularity to dynamically match the device distribution characteristics of different areas, providing higher spatial resolution in device dense areas and reducing unnecessary grid division in device sparse areas, thereby optimizing the utilization efficiency of storage space. The Hilbert space-filling curve coding technology realizes high-fidelity mapping of two-dimensional space coordinates to one-dimensional hash sequence. The spatial locality preservation property of the Hilbert curve ensures that adjacent grids in two-dimensional space also maintain an adjacent relationship in one-dimensional hash sequence. This property enables the spatial retrieval operation based on prefix matching to quickly locate the target area. The hash codes of adjacent grids remain continuous in numerical value, reducing random access operations in the hash table and improving the efficiency of hash-based spatial search.

[0066] The spatial hash aggregation effect is generated by the coordination of the geometric properties of the hexagonal honeycomb grid and the Hilbert curve coding. Due to the high connectivity of the hexagonal grid and the locality preservation property of the Hilbert curve, device nodes within the same honeycomb grid form a natural storage cluster in the hash table. This aggregation effect enables batch retrieval operations to utilize the spatial locality principle of memory, significantly improving cache hit rate and memory access efficiency. The combined coding method of the business level identifier and the spatial hash prefix realizes the acceleration optimization of cross-level association retrieval. The association lookup of different business level nodes within the same spatial area can be quickly realized through partial matching of the hash prefix, providing an efficient index basis for subsequent skip list construction.

[0067] Step S120 generates a skip list chain of the network topology based on the honeycomb grid level hash code.

[0068] Please refer toFigure 2 As shown, further, step S120 comprises:

[0069] Step S121, based on the cellular grid level hash code, performing device node service level attribution recognition;

[0070] The device node service level attribution recognition adopts a dual decision mechanism of device type identifier and control attribution, the device type identifier is determined by analyzing the technical characteristics of the device's hardware model, communication protocol, control interface, etc., including the standardized coding of PV_INV corresponding to photovoltaic inverters, ESS_UNIT corresponding to energy storage units, EV_CHARGER corresponding to charging piles, WIND_GEN corresponding to wind turbines, LOAD_CTRL corresponding to load controllers, etc. Each device type identifier has a predefined mapping relationship with a specific level in the four-layer service model, which is determined based on the functional characteristics and control capabilities of the device, for example, devices with direct physical control capabilities are attributed to controllable device layer, and units with multi-device coordinated control capabilities are attributed to microgrid element layer. The control attribution is obtained by analyzing the control topology configuration file of the microgrid system, the configuration file is stored in a structured data format, containing key information such as the identification of the superior control unit of the device node, the list of subordinate controlled devices, the communication interface type, the control protocol version, the control authority range, etc. The four-layer service model refers to the "microgrid cluster→unit→element→device" hierarchical model.

[0071] The hierarchical attribution determination of the four-layer service model follows the principle of recursive analysis from bottom to top, and the analysis process starts from identifying all physical devices with direct control capabilities in the system. Direct control capability refers to the ability of a device to receive control instructions and perform corresponding physical actions, such as photovoltaic inverters receiving power regulation instructions, energy storage systems receiving charge and discharge instructions, and load devices receiving start and stop instructions, etc. These devices are attributed to the controllable device layer, corresponding to the Z-axis coordinate value D. The microgrid element layer contains logical units that coordinate the control of multiple controllable devices, such as photovoltaic power generation elements containing multiple photovoltaic inverters and their controllers, and energy storage elements containing multiple energy storage units and their management systems. These element units have local optimization and coordinated control capabilities, corresponding to the Z-axis coordinate value C. The microgrid unit layer contains functional subsystems composed of multiple microgrid elements, such as independent microgrid units containing photovoltaic power generation elements, energy storage elements, and load elements. These units have independent energy management and optimization scheduling functions, and can achieve local supply and demand balance, corresponding to the Z-axis coordinate value B. The microgrid cluster layer contains a whole management system composed of multiple microgrid units, with global optimization scheduling and coordinated control capabilities, and can realize cross-unit resource allocation and energy exchange, corresponding to the Z-axis coordinate value A.

[0072] The hierarchical attribution recognition adopts a topological sorting method in graph theory, reads all device node information and control relationship from the configuration file, and constructs a directed acyclic graph of control relationship. The nodes in the graph represent devices or control units, and the edges represent the direction of the control relationship, i.e. from the control party to the controlled party. The algorithm determines the position of the node in the control hierarchy by calculating the in-degree and out-degree of the node. The node with an in-degree of 0 represents the top-level control unit that is not controlled by other nodes, and the node with an out-degree of 0 represents the bottom-level physical device that does not control other nodes. Based on the analysis results of the in-degree and out-degree, combined with the device type identifier of the node, the hierarchical attribution of each node in the four-layer service model is determined.

[0073] In step S122, based on the recognition results of the service hierarchical attribution of the device nodes, bidirectional skip list pointers are injected, and redundant path pruning is performed to form a skip list chain of the network topology structure.

[0074] The bidirectional skip list pointer injection process includes the construction of upward jump pointers and downward jump pointers. Please refer to Figure 3 As shown in the figure, there are 4 discrete service levels on the Z-axis of the microgrid physical area coordinate system, and each service level has corresponding nodes. The pointing relationship of the upward jump pointer is from the lower service level node (which can be called the subordinate node, such as the controllable device layer node, the microgrid element layer node, and the microgrid unit layer node) to the upper service level node (which can also be called the superior node, such as the microgrid element layer node, the microgrid unit layer node, and the microgrid cluster layer node). For example, the controllable device layer node (Z=D, such as a photovoltaic inverter) → the microgrid element layer node (Z=C, such as a photovoltaic power generation element); the microgrid element layer node (Z=C, such as an energy storage element) → the microgrid unit layer node (Z=B, such as an independent microgrid unit). The construction of the upward jump pointer adopts a search strategy with spatial proximity priority. For example, for each controllable device layer node, search for the nearest microgrid element layer node within the range of the cell grid and adjacent grids where it is located, and establish an upward jump pointer from the device node to the element layer node. The determination of the search range is based on the neighbor relationship of the cell grid, including the current grid and its 6 directly adjacent grids, and if necessary, it can be extended to the second neighbor grid to ensure that a suitable superior node can be found. When there are multiple equally distant candidate superior nodes, the node with 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 at the same time as the establishment of the upward jump pointer. Please refer to Figure 3 As shown in the figure, whenever an upward jump pointer from a lower service level node to an upper service level node is established, the upper service level node automatically obtains a downward jump pointer pointing to the lower service level node, forming a bidirectional jump link.

[0075] The redundant path pruning mechanism creates direct connection jump pointers across the hierarchy to optimize the retrieval path by analyzing the physical distance and business association strength between nodes. The physical distance determination is constrained by a preset distance threshold d, which is determined based on the geometric characteristics of the hexagonal cellular grid and the typical control range of the micro-grid system. When the physical distance between a device node and a high-level node that is not its direct superior is less than the distance threshold d, it indicates that the two are sufficiently proximate in space, and a direct connection jump pointer is created from the device node to the high-level node that is not its direct superior, skipping the intermediate hierarchy nodes to reduce the retrieval path length. For controllable device layer nodes, the high-level node of its direct superior refers to the micro-grid element layer node, and the high-level node of its non-direct superior refers to the micro-grid unit layer node and the micro-grid cluster layer node. The creation of direct connection jump pointers follows the principle of business logic rationality, and only when there is a clear business association relationship between the device node and the superior node, a direct connection jump pointer is established to avoid creating jump relationships that do not conform to business logic.

[0076] For example, referring to Figure 4 As shown, there are discrete 4 business levels on the Z-axis of the micro-grid physical area coordinate system, which are controllable device layer, micro-grid element layer, micro-grid unit layer, and micro-grid cluster layer, each of which has corresponding nodes, and direct connection jump pointers are created from controllable device layer nodes to micro-grid unit layer nodes and from micro-grid element layer nodes to micro-grid cluster layer nodes.

[0077] The skip list chain adopts a multi-level index data structure, and each device node maintains a jump pointer array, which includes upward jump pointers, downward jump pointers, and direct connection jump pointers of different types. The data structure of the jump pointer includes attributes such as target node unique identifier, target node type identifier, physical distance value, jump type identifier, and weight coefficient. The jump type is divided into layer-by-layer jump and direct connection jump, the layer-by-layer jump follows a strict hierarchical progressive relationship, and the direct connection jump allows direct access to the target level across the intermediate level. The weight coefficient is calculated based on physical distance, business association strength, communication quality, etc., and is used for subsequent path selection optimization.

[0078] The dual decision mechanism of device node service level attribution recognition improves the accuracy of service level classification. The device type identifier provides basic classification capability based on hardware features, and the control attribution analysis provides accurate positioning capability based on service logic. The combination of the two ensures the accuracy and consistency of the level attribution in complex micro-network environments. The introduction of the control attribution enables the system to handle complex control topology scenarios such as cross-control, multi-level control, and dynamic control, avoiding misjudgment problems that may occur based solely on device type classification. The construction of bidirectional skip list pointers realizes the bidirectional optimization and flexibility improvement of cross-level search paths. The upward jump pointer supports fast positioning from bottom-level devices to upper-level management units, meeting the needs of device state reporting, fault alarm uploading, and other bottom-up information flow requirements. The downward jump pointer supports fast traversal of subordinate devices from the management unit, meeting the needs of control instruction issuance, device state query, and other top-down control flow requirements. The bidirectional jump mechanism enables the search operation to choose the most suitable traversal direction according to specific needs, avoiding the path detour problem that may occur with one-way search. The redundant path pruning mechanism realizes significant optimization of search paths through the intelligent creation of direct jump pointers. In device-dense areas, multiple levels of nodes may overlap spatially, and the traditional layer-by-layer jump method will generate a large number of redundant intermediate access steps. The introduction of direct jump pointers enables the system to skip intermediate levels and directly reach the target node, significantly shortening the search path length. The dynamic adjustment mechanism of the preset distance threshold enables the pruning strategy to adapt to micro-network systems of different sizes and densities, achieving optimal path optimization effect while ensuring search integrity. The creation of direct jump pointers significantly reduces the number of intermediate node accesses during the search process, reduces memory access overhead, and improves overall search response speed.

[0079] The coordination of skip list chain structure and honeycomb grid level hash code can improve the speed of spatial search. Since the target node selection of the jump pointer follows the principle of spatial nearest neighbor, the associated nodes exhibit a natural clustering distribution characteristic in the hash code space. This clustering effect enables batch search operations based on spatial hash prefixes to utilize the principle of spatial locality, achieving improved search efficiency. The similarity of hash code prefixes of adjacent level nodes provides a quick entry for cross-level association analysis, and the system can quickly locate the relevant node set through partial hash matching. The symmetry design of bidirectional jump list pointers realizes the adaptive fault tolerance capability of search paths. When a search path cannot be normally accessed due to node failure, communication interruption, or other reasons, the system can automatically use the reverse jump pointer or the alternative direct jump pointer to build an alternative path, ensuring the continuity and reliability of the search operation. This adaptive fault tolerance mechanism enables the system to maintain normal search functions even in the face of partial node failures, significantly enhancing the robustness and usability of the system.

[0080] Step S130, based on the skip list chain of the network topology structure, a skip list access path cache table is constructed.

[0081] Further, step S130 includes:

[0082] Step S131, the spatial hash prefix of the cellular grid hierarchical hash code is injected into the skip list pointer of the skip list chain, and the skip list pointer refers to the bidirectional skip list pointer and the direct connection jump pointer;

[0083] The spatial hash prefix injection process realizes the direct association of spatial position and jump relationship by embedding the first M bits of the cellular grid hierarchical hash code into the skip list pointer structure. The specific implementation of the spatial hash prefix injection adopts the pointer attribute expansion mechanism, and a spatial hash prefix field is added to the data structure of each bidirectional skip list pointer and direct connection jump pointer, which stores the first M bits of the cellular grid hierarchical hash code of the device node to which the pointer belongs. The injection format of the spatial hash prefix adopts a unified coding specification, and the record format of the skip list pointer is "source node ID→target node ID@spatial hash prefix", wherein 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 of the jump relationship. For example, the photovoltaic inverter device PV_001 located in the cellular grid with a hash code of 101101001010111 jumps to the microgrid unit layer node Unit_07, and the skip list pointer record is "PV_001→Unit_07@1011010010101", wherein "1011010010101" is a 13-bit spatial hash prefix, indicating that the jump relationship occurs in the 5333th cellular grid. The design of this coding format enables the system to directly obtain spatial position information by analyzing the record format of the skip list pointer without additional position query operations. The length M of the spatial hash prefix is selected based on the spatial resolution requirement and storage efficiency of the microgrid system. When M is 16 bits, 65536 unique identifiers of cellular grids can be supported, which is suitable for large microgrid systems with a coverage area of hundreds of square kilometers; when M is 12 bits, 4096 unique identifiers of cellular grids can be supported, which is suitable for medium-sized microgrid systems with a coverage area of tens of square kilometers.

[0084] Step S132, a plurality of skip list pointers in the same cellular grid are pre-aggregated to generate a skip list pointer cluster head node;

[0085] The skip list pointer pre-aggregation process solves the low efficiency problem of scattered management of multiple skip list pointers in the same cellular grid by introducing a cluster head node mechanism. The skip list pointer cluster head node is used to uniformly manage all skip list pointers in the same cellular grid, reducing the traversal overhead when searching for pointers. The skip list pointer index is a data structure maintained internally by the cluster head node, which stores the skip list pointer references to all device nodes in the cellular grid in the form of an ordered array or hash table. The index structure supports fast lookup based on device node identifiers and batch retrieval based on device types. The creation trigger condition of the cluster head node is based on the preset node number threshold N th , the node number threshold N th is determined based on the balance between management overhead and retrieval efficiency. When N th is too small, it will lead to a large number of cluster head node creation and management overhead, and when N th is too large, it will lose the efficiency advantage of pre-aggregation. The preferred value range of N th is 8 to 32 device nodes, which is determined by analyzing the device distribution characteristics and retrieval performance test results of micro-network systems of different sizes.

[0086] The data structure of the cluster head node includes cellular grid identifier, device node count, skip list pointer index array, upward jump pointer and direct connection jump pointer. The cellular grid identifier stores the complete hash code of the cellular grid managed by the cluster head node, the device node count records the total number of device nodes in the current grid, and the skip list pointer index array stores all internal skip list pointer references in the dictionary order of device node identifiers. The cluster head node itself also has upward jump pointers and direct connection jump pointers, which point to the upper business level nodes containing the cellular grid, so that the cluster head node can participate in cross-level jump retrieval on behalf of the entire cellular grid. The upward jump pointer of the cluster head node adopts a representative node selection strategy, which selects the highest level node whose control range covers the entire grid as the jump target by analyzing the business affiliation of all device nodes in the cellular grid, ensuring that the jump relationship of the cluster head node accurately reflects the business affiliation of the entire grid.

[0087] In step S133, based on the generated skip list pointer cluster head node, a skip list chain access path cache table is generated.

[0088] The skip list chain access path cache table is obtained through path precomputation and priority sorting. The specific process of path precomputation is as follows: all generated skip list pointer cluster head nodes are traversed, based on the upward jump pointer and the direct connection jump pointer of each cluster head node, the breadth-first search algorithm is used to calculate all possible access paths from the cluster head node to each upper layer service level node. The termination condition of path search is to reach the target service level or the search depth exceeds the preset limit. The search depth limit is set based on the number of layers of the four-layer service model, and is usually 4, corresponding to the complete jump path from the controllable device layer to the micro network cluster layer. The path length of each access path is calculated, and the path length is defined as the weighted sum of the jump step number and the physical distance from the starting node to the target node. The physical distance refers to the three-dimensional Euclidean distance between the starting node and the target node. The jump step number is obtained by counting the number of jump pointers contained in the access path. Each jump pointer corresponds to one jump operation, and each layer jump and direct connection jump are counted as one jump step. Based on the calculated access path length, the access paths of each cluster head node are prioritized to generate the skip list chain access path cache table.

[0089] The skip list chain access path cache table adopts a key-value pair storage structure. The key is composed of the string splicing of the starting node identifier and the target level identifier, using an underscore as a separator, and the value is the access path sequence arranged in ascending order of path length. The design of the key supports double indexing based on the starting position and the target level, so that the system can quickly locate the cache path corresponding to a specific retrieval requirement.

[0090] The injection of the spatial hash prefix improves the spatial indexing capability of the skip list pointer. By embedding the spatial location information of the cellular grid directly into the skip list pointer structure, the system obtains the fast pointer positioning capability based on the spatial region. When the retrieval request involves a specific spatial region, the system can directly filter out the relevant skip list pointer through spatial hash prefix matching, avoiding the full traversal of the entire skip list chain. The pre-aggregation processing of the skip list pointer realizes the intensive management of homogeneous skip list pointers through the cluster head node mechanism. When a large number of device nodes are contained in the cellular grid, the traditional decentralized pointer management method will cause a large number of random memory accesses in the retrieval process. The introduction of the cluster head node organizes all the skip list pointers in the same grid into a continuous data structure, and uses the spatial locality principle of memory access to improve the cache hit rate. The upward jumping capability of the cluster head node itself enables the system to perform cross-level retrieval at the grid granularity. When the retrieval target is the entire spatial region rather than a specific device, the system can directly realize fast jumping at the regional level through the cluster head node, avoiding the one-by-one access to the devices inside the grid. The construction of the skip list chain access path cache table realizes the pre-computation optimization and intelligent selection of the retrieval path. By pre-computing and caching all possible access paths, the system converts the path discovery problem in the retrieval process into a path lookup problem, reducing the retrieval response time. The key-value pair storage structure of the skip list chain access path cache table supports accurate matching based on the starting position and target level, avoiding invalid lookup of irrelevant paths, and the ordered arrangement of the path sequence in the cache table ensures that the system can preferentially select the shortest access path.

[0091] At step S200, based on the skip list chain access path cache table, cross-level jumping retrieval is performed to generate a micro-grid resource visualized topology index.

[0092] Referring to Figure 5 Further, step S200 includes:

[0093] At step S210, a retrieval request instruction is received, and the retrieval request instruction is converted into a multi-constraint combined retrieval condition.

[0094] Further, step S210 includes:

[0095] At step S211, semantic analysis and entity recognition are performed on the received retrieval request instruction to obtain a device type identifier, a service level identifier, and a cellular grid level hash code prefix.

[0096] The semantic analysis and entity recognition process converts the user's natural language search request into a structured query condition that the system can understand using natural language processing technology. The method of semantic analysis and entity recognition includes: performing word segmentation processing on the received search request instruction, extracting 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, continue to execute the subsequent steps; when the matching success rate is less than the preset matching threshold, return a prompt message to the user that the search condition is not clear, and ask the user to re-enter the search request instruction.

[0097] The extraction of device type keywords uses named entity recognition technology to identify device names, device types, device attributes, and other related words in the search request instruction through a pre-trained device entity recognition model. The device entity recognition model is based on a conditional random field algorithm, and the training data of the model contains a large number of annotated micro-grid device description texts, covering photovoltaic inverters, energy storage units, charging piles, wind turbines, load controllers, and other main device types and their aliases, abbreviations, and model variations. The device type dictionary is constructed using a hierarchical classification structure, with the top layer being device categories such as power generation equipment, energy storage equipment, and load equipment, the middle layer being device subcategories such as photovoltaic power generation equipment and wind power generation equipment, and the bottom layer being specific device models and specifications. Each device entry in the device type dictionary contains standard names, alias lists, device type identifiers, business level identifiers, technical parameter ranges, and other attribute information, supporting device recognition based on partial matching and fuzzy matching.

[0098] The recognition of spatial location keywords uses geographic information extraction technology to extract spatial-related expressions such as region names, coordinate information, and distance descriptions from the search request instruction using a spatial entity recognition algorithm. The spatial entity recognition algorithm combines regular expression matching and semantic role labeling technology, with regular expression matching responsible for identifying coordinate formats, distance units, and direction words, and semantic role labeling responsible for identifying complex spatial descriptions such as "within region A", "near unit B", and "distance C point radius R range". The spatial location dictionary pre-stores all geographic region division information within the micro-grid system, including administrative regions, functional regions, and device distribution regions at different levels of spatial range definitions, and each region entry contains region names, boundary coordinates, center coordinates, coverage area, and corresponding cellular grid level hash code prefix sets.

[0099] The matching success rate is calculated by using a weighted average method, which comprehensively considers three dimensions of device type matching degree, spatial position matching degree and semantic integrity. The device type matching degree is calculated based on the similarity between the recognized device keywords and the device type dictionary entries. The similarity is calculated by using a combination of edit distance and semantic vector similarity. The edit distance reflects the degree of similarity in vocabulary form, and the semantic vector similarity reflects the degree of correlation in vocabulary meaning. The spatial position matching degree is calculated based on the matching of the recognized spatial keywords and the spatial position dictionary entries. The keywords that are completely matched are given full marks, and the keywords that are partially matched are given corresponding scores according to the matching degree. The semantic integrity is evaluated based on the completeness of the syntax structure and the semantic role of the search request instruction. A complete subject-predicate-object structure and an explicit semantic role relationship obtain a higher score. The determination of the matching threshold is based on the search accuracy requirement of the system and the user experience. A too low threshold will cause the misinterpreted search request instruction to be executed incorrectly, and a too high threshold will cause the reasonable search request instruction to be frequently rejected. The preset matching threshold is determined by analyzing a large number of user search request instructions and the matching conditions of the system execution results. The preferred value range is 0.7 to 0.8.

[0100] In step S212, a multi-constraint combined search condition including a spatial constraint condition, a device type constraint condition and a service level constraint condition is generated based on the device type identifier, the service level identifier and the cellular grid level hash code prefix.

[0101] The multiple constraint combination retrieval condition converts the semantic analysis result into a standardized query expression inside the system. The spatial constraint condition limits the spatial range of the retrieval through a set of cell grid level hash code prefixes, which contains all spatial hash prefixes corresponding to the cell grids intersecting the user-specified area. The device type constraint condition limits the device type range of the retrieval through a set of device type identifiers, which contains all type identifiers matching the user-specified device. The service level constraint condition limits the level range of the retrieval through a set of service level identifiers, which contains all identifiers corresponding to the user-specified level. The combination strategy of the constraint conditions is expressed by a Boolean logic expression, supporting the combined use of AND, OR, NOT, and other logical operators. When the retrieval request involves multiple spatial areas, the spatial constraint condition uses OR logical combination, indicating that the retrieval result needs to be located within any specified area. When the retrieval request involves multiple device types, the device type constraint condition uses OR logical combination, indicating that the retrieval result needs to match any specified device type. When the retrieval request involves a specific service level, the service level constraint condition uses AND logical combination, indicating that the retrieval result needs to strictly meet the level requirement. The priority order of the constraint conditions is based on the constraint selectivity, and the constraint condition with high selectivity is executed first to quickly narrow down the retrieval range. The selectivity calculation is based on historical statistical data, reflecting the proportion of data that each constraint condition can filter.

[0102] For example, the retrieval request instruction "Find all energy storage unit fault nodes in A park" is analyzed as follows:

[0103] Key word extraction: A park, energy storage unit, fault node;

[0104] Device type matching: energy storage unit → device type identifier ESS_UNIT, service level is controllable device level (D);

[0105] Spatial position matching: A park → spatial hash prefix H2B5 (pre-stored in the spatial position dictionary);

[0106] Constraint condition generation: spatial hash prefix H2B5 AND device type ESS_UNIT AND service level D.

[0107] The application of semantic analysis and entity recognition technology in step S210 realizes the automatic understanding and conversion of natural language retrieval requests. By adopting a hybrid word segmentation algorithm based on a dictionary and a statistical model, the system has dual recognition capabilities for micro-grid professional terms and general expressions, and can accurately recognize professional device names such as photovoltaic inverters and energy storage units, and correctly process general descriptive words such as "nearby" and "range fault". The introduction of named entity recognition technology enables the system to accurately extract key information from complex natural language expressions, avoiding semantic ambiguity and context misunderstanding problems that may occur in traditional keyword matching methods. The hierarchical construction of the device type dictionary and the spatial location dictionary realizes the standardized management and efficient matching of retrieval terms. The hierarchical classification structure provides a unified mapping framework for multi-level expressions of devices and spaces. Users can use different granularity expressions such as device categories, subcategories, or specific models for retrieval, and the system can accurately identify and convert them into corresponding identifiers. The alias and variant support function of the dictionary solves the problem of matching failure caused by differences in user expression habits. Whether the user uses "photovoltaic panel solar cell panel" or "PV module" and other different expressions, the system can identify them as the same device type. The geographic information extraction enables the system to handle multiple spatial expression forms such as coordinates, region names, and distance descriptions, providing users with flexible and diverse spatial retrieval methods.

[0108] The comprehensive evaluation mechanism of matching success rate realizes the quantitative control of retrieval quality and the guarantee of system reliability. By considering the equipment type matching degree, spatial location matching degree and semantic integrity in three dimensions, the system can comprehensively evaluate the understanding quality of the retrieval request instruction, avoiding one-sided judgment caused by single dimension evaluation. The structured representation of multiple constraint combination retrieval conditions realizes the accurate expression and efficient processing of complex retrieval requirements. By converting the natural language retrieval request instruction into standardized constraint conditions containing 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 accurate retrieval intentions through AND, OR, NOT, and other logical operators.

[0109] The semantic analysis cooperates with the honeycomb grid hash coding to automatically associate spatial semantics. When the user uses directional descriptions such as "eastern region", "central region", etc., the system can automatically infer the related adjacent regions by using the spatial continuity characteristics of the honeycomb grid, expand the search range, and provide more complete search results. This spatial semantic association capability enables the system to handle the implicit spatial requirements in the user's expression, and when the user queries a device in a certain region, the system can intelligently include related devices in the boundary fuzzy region, improving the completeness and practicality of the search results. The automatic association of spatial semantics also provides semantic support for fault impact analysis and device linkage control. The system can automatically identify the possible impact range and associated devices based on natural language descriptions.

[0110] The combination of entity recognition and business hierarchy model realizes intelligent adaptation of search granularity. When the user's search request instruction contains business entities of different levels, the system can automatically identify the target granularity of the search and adjust the search strategy and result presentation mode accordingly. For example, when the user queries "micro-network unit", the system automatically locates to the micro-network unit level for searching; when the user queries a specific device, the system automatically goes deep into the device level for accurate searching. This granularity self-adaptation capability enables the system to provide search results with appropriate detail level according to the user's actual needs, avoiding the problems of information overload or information deficiency. The combination of the multi-constraint combination mechanism and the skip list chain structure enables the path search to have intelligent prediction capability. The system can predict the most likely search path and target node according to the combination characteristics of the constraint conditions, and complete path planning and resource preparation before search execution. This prediction capability significantly reduces the trial-and-error and backtracking operations in the search process, improving the search response speed and system resource utilization efficiency.

[0111] Step S220, based on the multi-constraint combination search condition and the skip list chain access path cache table, constructing a topology heat map guided search path optimization strategy;

[0112] Based on the multi-constraint combination search condition and the skip list chain access path cache table, constructing a topology heat map guided search path optimization strategy, through statistical analysis of historical access data and dynamic optimization of access frequency, solving the problem of unstable search efficiency caused by blind path selection in traditional search systems.

[0113] Further, step S220 includes:

[0114] Step S221, statistical historical search data, based on the access frequency of device nodes and skip list pointers, generating a node access frequency heat map, the node access frequency heat map containing high-frequency access nodes, medium-frequency access nodes and low-frequency access nodes;

[0115] Specifically, the node access frequency heat map generation process realizes quantitative analysis of the system historical search behavior through a time window statistics and a frequency stratification mechanism. The setting of the statistical time window is based on the running period of the microgrid system and the stability requirement of the access mode. A too short time window will lead to an increase in randomness of the access frequency calculation, and a too long time window will reduce the sensitivity of the system to changes in the access mode. Preferably, the time window range is 5 to 10 days, which can capture the periodic operation mode of the microgrid system while maintaining the response capability to changes in the access trend. The access frequency is obtained by dividing the number of searches of each device node in the statistical time window by the length of the time window. The number of searches of each device node is obtained through system log records. Based on the numerical distribution of the access frequency, all device nodes are divided into three levels of high-frequency access nodes (F i >F high ), medium-frequency access nodes (F low ≤F i ≤F high ) and low-frequency access nodes (F i <F low ), wherein F i is the access frequency of the i-th time window, F high is the high-frequency access threshold, and F low is the low-frequency access threshold. F high and F low are determined by calculating the cumulative distribution function of the access frequency of all device nodes. F high is set to the 85th percentile of the access frequency distribution, ensuring that the high-frequency access nodes account for about 15% of the total number of devices, and F low is set to the 35th percentile of the access frequency distribution, ensuring that the low-frequency access nodes account for about 35% of the total number of devices, and the medium-frequency access nodes account for the remaining 50%. This stratification ratio is based on a variation of the Pareto principle, reflecting the reality that a small number of key devices in the microgrid system bear most of the access load.

[0116] The visual representation of the node access frequency heat map is mapped using the HSV color space, with the hue value of the high-frequency access nodes set to 0 degrees corresponding to red, the hue value of the medium-frequency access nodes set to 60 degrees corresponding to yellow, and the hue value of the low-frequency access nodes set to 120 degrees corresponding to green. The saturation and brightness are fine-tuned according to the specific access frequency value to achieve fine-grained differentiation within the same frequency level. The update frequency of the heat map is synchronized with the statistical time window, and the access frequency is recalculated and the heat map display is updated every time the time window slides, providing real-time system access state monitoring for operation and maintenance personnel.

[0117] In step S222, the priority of the skip list pointer of the high-frequency access node is promoted, and the corresponding search path is pre-cached. The skip list pointer is re-ordered in priority;

[0118] The priority promotion processing method includes: for high-frequency access nodes, moving their up-jump pointers and direct-jump pointers in the memory location of the skip list chain access path cache table to the front, so that they are preferentially matched in the retrieval process; creating a dedicated fast access channel for high-frequency access nodes, pre-allocating fixed storage space in memory to store the optimal access path from the node to each upper-level service level node. The specific implementation of the memory location movement adopts the reorganization strategy of the skip list chain access path cache table, and migrates the skip list pointers of the high-frequency access nodes from the original storage location 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 the 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.

[0119] The pre-cached retrieval path method includes: based on the access paths recorded in the skip list chain access path cache table, recalculating the path length for all possible paths of high-frequency access nodes to each target level, and introducing an access frequency correction factor term -γ×F i On the basis of the original calculation formula, -γ×F i is added, where γ is the frequency correction coefficient, reflecting the influence degree of access frequency on the priority of path selection, and the value range of γ is 0.1 to 0.5. This range is determined by analyzing the retrieval performance under different correction coefficients. When γ is too small, the correction effect is not obvious, and when γ is too large, low-frequency but important nodes may be ignored. Based on the corrected path length calculated by the access frequency correction factor, and the results of the storage location movement and the pre-caching processing, the priority of the skip list pointer is reordered.

[0120] Step S223, based on the optimized skip list pointer priority sorting, updating the skip list chain access path cache table to form a topology heat map guided retrieval path optimization strategy;

[0121] 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 for the access path containing high-frequency access nodes, reordering based on the corrected path length; updating the reordered access path sequence to the corresponding cache table item to ensure that the optimal path of the high-frequency access node is located at the beginning of the sequence.

[0122] The execution of the search path optimization strategy guided by the topology heat map adopts a hierarchical search architecture, which includes two levels of pre-cache path search and regular path search. Specifically, when the system receives a search request instruction, it first searches for a matching search target in the pre-cache path of the high-frequency access node; when there is no matching target in the pre-cache path, it searches in 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 the integrity of the search. The pre-cache path search uses the dedicated fast channel maintained by the high-frequency access node for priority search, with a search time complexity of O(1) constant time, suitable for frequently accessed hot devices in the system. The regular path search uses the updated skip list access path cache table for the medium and low-frequency access nodes, with a search time complexity of O(logn) logarithmic time, where n is the number of cache table entries. The automatic switching mechanism of hierarchical search automatically selects the appropriate search level according to the access frequency attribute of the search target, without the need for manual intervention.

[0123] The three-level hierarchical mechanism of access frequency provides a differentiated optimization strategy basis for the system, with high-frequency access nodes having priority resource allocation and path optimization, medium-frequency access nodes maintaining standard search service quality, and low-frequency access nodes using resource-saving search strategies. The priority elevation process of high-frequency access nodes achieves intelligent allocation of search resources and performance optimization. By moving the skip list pointers of high-frequency access nodes to the high-speed access area of the cache table, the system reduces the search delay and memory access overhead of these nodes. The establishment of a dedicated fast access channel provides an independent search path for high-frequency access nodes, avoiding resource competition with ordinary nodes and ensuring the search response speed of critical services. The synergy of the access frequency heat map and the honeycomb grid hash coding enables automatic discovery of spatial access patterns. Due to the spatial continuity of the honeycomb grid and the locality preservation characteristics of the hash coding, the system can automatically identify spatial access hot regions 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 of the micro-network system, enabling the system to predict which areas may become future access hotspots and perform resource configuration and performance optimization in advance. The priority elevation process combined with the depth of the skip list structure achieves dynamic load balancing. When the load of some high-frequency access nodes is too high, the system automatically directs part of the access traffic to alternative paths through path rearrangement and cache strategy adjustment, avoiding system performance degradation caused by single-point overload. This dynamic balancing mechanism also enhances the fault tolerance of the system, enabling it to quickly switch to alternative paths to maintain normal search services when the main search path is interrupted due to failure.

[0124] Step S230, based on the topology heat map guided search path optimization strategy and multiple constraint combined search conditions, perform cross-level jump search;

[0125] Specifically, based on the topology heat map guided search path optimization strategy and multiple constraint combined search conditions, perform cross-level jump search, and adopt a phased progressive strategy to achieve efficient micro-network resource positioning. The execution process includes three core stages of constraint condition pre-filtering, path selection decision and jump execution.

[0126] The constraint condition pre-filtering stage narrows down the search range quickly through spatial constraint condition priority screening. The system uses the honeycomb grid level hash code prefix to perform the first round of filtering, and extracts all cache items matching the spatial hash prefix and constraint conditions from the skip list chain access path cache table. Spatial hash prefix matching uses a prefix tree data structure, where each node represents a binary bit of the hash code. The matching process starts from the root node and performs depth-first traversal bit by bit according to the spatial hash prefix in the constraint condition, collecting all device node identifiers corresponding to the completely matched prefix to form a candidate node set. Device type constraint condition filtering is implemented through a device type identifier mapping table. The system iterates through the candidate node set, queries the device type identifier of each candidate node, and retains the candidate node when the device type identifier intersects with the device type identifier set in the constraint condition. The service level constraint condition filtering extracts the L-M bit service level identifier of each candidate node's honeycomb grid level hash code, and matches it with the service level identifier set in the constraint condition for verification.

[0127] 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 a high-frequency access node. When it contains a high-frequency access node, it directly accesses the dedicated fast access channel of that node to obtain the pre-cached search 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 that node to each upper-level service level node. The path information format is a path node sequence, each path node containing node identifier, node type, jump type, physical coordinates, etc. When the target node candidate set does not contain a high-frequency access node, the system retrieves the path from the updated skip list chain access path cache table in priority order, locates the corresponding path sequence in the skip list chain access path cache table by constructing a combination key of the starting node identifier and the target service level identifier, and selects the shortest path as the execution path.

[0128] The jump execution stage realizes cross-level access through step-by-step jumping and state maintenance. The system maintains retrieval state variables including current access node, target level, remaining jump steps, access path record and the like. Retrieval execution starts from the starting node, and jump operations are sequentially performed according to jump pointers in the selected execution path. Each jump operation includes jump pointer analysis to obtain a target node identifier and a jump type, target node access to read node complete information including device type, service level, physical coordinates, control state and the like, node information extraction to selectively extract relevant information according to retrieval requirements, and state update including current access node switching, remaining jump step decrement, access path record appending and the like. The system introduces a dynamic path adjustment mechanism to handle abnormal situations. When a node is unreachable or communication times out, the system automatically attempts an alternative jump pointer. When the alternative jump pointer does not exist, the system starts a path re-planning program to recalculate a feasible retrieval path based on the current access state and the remaining retrieval target.

[0129] Step S230 realizes a coordinated balance between retrieval precision and efficiency through a phased progressive retrieval strategy. The constraint condition pre-filtering mechanism decomposes multi-dimensional constraint conditions into three independent filtering steps of space, device type and service level. Each step uses a specially optimized data structure. Space filtering uses a prefix tree to realize efficient hash prefix matching. Device type filtering uses a hash table to realize fast identifier lookup. Service level filtering uses bit operation to realize parallel verification of level identifiers, reducing retrieval time complexity. The heat map guided path selection decision mechanism realizes intelligent allocation of retrieval load. The dedicated fast access channel of high-frequency access nodes provides constant time complexity path acquisition capability, reducing retrieval response time and reasonably configuring system resources. Through continuous monitoring of retrieval paths and access patterns, the system accumulates a large amount of retrieval behavior data, which can automatically discover implicit association relationships and spatial distribution rules between devices, helping to optimize the design and preventive maintenance of micro-grid systems. Retrieval path reuse rate analysis reveals key devices and key paths in the system.

[0130] Step S240, based on the execution result of the cross-level jump retrieval, performs retrieval result aggregation and sorting to generate a micro-grid resource visualized topology index.

[0131] Further, step S240 includes:

[0132] Step S241, collect all node information accessed in the cross-level jump retrieval process, and construct a level association graph based on the service level relationship of the nodes;

[0133] The node information collection extracts a complete node access sequence from the access path record of the search execution state, and the access path record maintains a complete jump trajectory from a starting node to a final target node in a stack data structure. Each access node information includes a node unique identifier, a device type identifier, a service level identifier, a physical coordinate, a jump in-degree, and a jump out-degree, and the like core attributes, wherein the jump in-degree indicates how many other nodes point to the node through a jump pointer in the search process, and the jump out-degree indicates how many other nodes the node points to through a jump pointer. The system extends the collection of associated node information through an association query mechanism, not only collecting directly accessed node information, but also extending the query of related nodes through control attribution, communication connection relationship, and spatial proximity relationship. The control attribution is obtained by analyzing the micro network system control topology configuration file, recording the superior control unit and subordinate controlled device of each device node to form a tree-shaped control hierarchy. The communication connection relationship is obtained through the network communication protocol stack connection state information, including direct communication connection, communication quality parameters, and the like information. The spatial proximity relationship is determined through the cellular grid neighbor relationship, including device nodes within the same grid, device nodes of adjacent grids, and the like different level spatial associations.

[0134] The hierarchical association graph construction adopts a directed graph data structure, wherein the nodes in the graph represent the devices or control units accessed in the search process, and the edges in the graph represent the service level relationships between the nodes. The service level relationship follows the four-layer service model level progression principle, including superior control relationship, subordinate controlled relationship, peer coordination relationship, and cross-level direct connection relationship. The graph node attributes are represented by a multi-dimensional feature vector, including spatial position features, service function features, connection topology features, and running state features. The graph edge attributes are represented by weighted edges to represent the relationship strength, and the edge weight is obtained by comprehensively calculating the control frequency, communication quality, response time, coordination effect, and the like factors.

[0135] In step S242, based on the hierarchical association graph and the physical coordinate information, the spatial aggregation degree and the topological connectivity index of the search result are calculated.

[0136] The spatial aggregation degree calculation adopts a DBSCAN clustering algorithm based on density, and the core parameters include a neighborhood radius Ep, which is set to 2 times the side length of the hexagonal grid, and a minimum neighbor number min pts , which is set to 3 to 5 devices. The DBSCAN algorithm traverses all device nodes in the search result, calculates the number of neighbor nodes within the Ep neighborhood of each node, and when the number of node neighbors is greater than or equal to min pts , marks it as a core node and starts clustering expansion, and adds all density reachable nodes to the same cluster through depth-first search.

[0137] The spatial aggregation degree quantification calculation adopts two indexes of cluster tightness and cluster separation. The cluster tightness is measured by calculating the average distance between nodes in the cluster, and the calculation formula is C tigh =1 / (1+d avg ), wherein d avg is the average Euclidean distance between nodes in the cluster, d avg is obtained by averaging the distance between any two nodes in the cluster, C tigh is the cluster tightness, and the value range is 0 to 1, and the larger the value is, the tighter the cluster is. The cluster separation is measured by calculating the average distance between cluster centers, and the calculation formula is C sepa= d sep / d max , wherein d sep is the average distance between cluster centers, d max is the maximum diameter of the search result coverage area, and C sepa is the cluster separation, and the value range is 0 to 1, and the larger the value is, the higher the separation degree between clusters is.

[0138] The topological connectivity index calculation adopts the graph theory connectivity analysis method, including four key indexes of connected component number, average path length, network diameter and clustering coefficient. The connected component number is obtained by traversing the hierarchical association graph through the depth-first search algorithm, and the number of disconnected subgraphs in the graph is counted. The connected component number reflects the topological segmentation degree of the search result. The average path length is obtained by calculating the average value of the shortest path length between any two nodes in the graph. The Floyd-Warshall algorithm is used to calculate the shortest path between all node pairs. 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, and reflects the maximum transmission delay of the network. The clustering coefficient is obtained by calculating the local clustering coefficient of each node and averaging the values. The local clustering coefficient of a node is defined as the ratio of the actual number of connections between neighbor nodes to the possible number of connections, and reflects the local connectivity density of the network.

[0139] In step S243, based on the spatial aggregation degree and the topological connectivity index, the search result is intelligently sorted and grouped to generate a microgrid resource visual topology index.

[0140] The intelligent sorting adopts a multi-index comprehensive scoring mechanism, and the spatial aggregation degree and the topological connectivity index are combined into a comprehensive score by weighted summation. The weight distribution is determined based on the importance of the indexes, and the weight of the topological connectivity related indexes is higher than that of the spatial aggregation degree related indexes. The weight setting reflects the important influence of the topological connectivity on the system stability and control efficiency in the microgrid system. The calculation formula of the comprehensive score Score is:

[0141]

[0142] 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 tightness, is the weight of clustering 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 according to factors such as the scale of the microgrid system, device distribution characteristics, control complexity, and the like. Exemplary .

[0143] The retrieval result grouping adopts a hierarchical grouping strategy. First, a first-level grouping is performed based on the business level identifier, and the retrieval results are organized according to four business levels of the controllable device layer, the microgrid element layer, the microgrid unit layer, and the microgrid cluster layer. Then, a second-level grouping is performed based on the spatial clustering result, and the device nodes belonging to the same spatial cluster are grouped together within each business level according to the DBSCAN clustering result. Finally, a third-level grouping is performed based on the topological connectivity, and the topologically connected device nodes are grouped together within each spatial cluster according to the graph theory connected component. The grouping result is represented by a tree-like hierarchical structure, with the root node representing the complete retrieval result set, the first-level child nodes representing the business level grouping, the second-level child nodes representing the spatial clustering grouping, and the third-level child nodes representing the topological connectivity grouping.

[0144] The generation of the microgrid resource visualized topology index adopts a multi-layer index structure, including a spatial index layer, a business index layer, and a topology index layer. The spatial index layer is constructed based on the honeycomb grid level hash code, and each index item contains the grid identifier, the list of devices within the grid, and the spatial aggregation indicators. The business index layer is constructed based on the business level identifier, and each index item contains the level identifier, the list of devices within the level, and the level association relationship. The topology index layer is constructed based on the graph theory connectivity, and each index item contains the connected component identifier, the list of devices within the component, and the connectivity indicators. The index structure is implemented by a B+ tree data structure, supporting range queries and prefix queries, and the index key is designed as a composite key, containing identification information of multiple dimensions such as the spatial hash prefix, the business level identifier, and the connected component identifier.

[0145] Step S240 realizes the orderly organization and efficient access of micro-grid resource information through structured search result processing. The construction of the hierarchical association graph converts discrete search nodes into an organic whole with clear business hierarchy, accurately reflects the control levels and management relationships of the micro-grid system through the directed graph data structure, and provides a structured foundation for subsequent topology analysis and association query. The node attribute representation method of the multi-dimensional feature vector realizes the comprehensive characterization of device information, the spatial location feature supports distance-based similarity calculation, the business function feature supports device classification based on function, the connection topology feature supports device sorting based on importance, and the running state feature supports device screening based on health degree. The quantitative calculation of spatial aggregation degree and topological connectivity provides an objective standard for the quality evaluation of search results. The density-based characteristics of the DBSCAN clustering algorithm enable the system to automatically identify device aggregation areas of any shape, avoiding the limitation of the traditional K-means algorithm on spherical clustering, and the clustering results are closer to the actual device distribution characteristics of the micro-grid system. The introduction of clustering tightness and separation degree indexes realizes the quantitative evaluation of spatial distribution quality, and a high tightness of clustering indicates that the concentrated spatial distribution of devices is conducive to centralized management, and a high separation degree of clustering distribution indicates that the clear division of different functional areas is conducive to partition control.

[0146] The comprehensive analysis of the topological connectivity index reveals the structural characteristics and performance bottlenecks of the micro-grid system. The number of connected components reflects the degree of system segmentation, fewer connected components indicate good system integrity, and more connected components suggest isolated subsystems that need attention. The calculation of average path length and network diameter provides a quantitative basis for evaluating control delay and response speed, a shorter average path length indicates high information transmission efficiency, and a smaller network diameter indicates strong system response capability. The analysis of the clustering coefficient reveals the degree of redundant connection of the system, a higher clustering coefficient indicates strong fault tolerance capability of the system, and a lower clustering coefficient suggests the need to increase backup connections. Through the comprehensive analysis of spatial aggregation and topological connectivity, the system can automatically identify functional areas and key paths in the micro-grid system, providing users with extended information beyond the original search requirements. The discovery of spatial clustering enables the system to automatically recommend related devices within the same area, even if the user does not explicitly query these devices, the complete device information within the area can be obtained. The analysis of topological connectivity enables the system to automatically identify the dependency relationship and influence path between devices, and when a device fails, the system can automatically recommend related devices that may be affected, providing decision support for fault handling and emergency response. The establishment of the multi-layer index structure realizes the organic integration of multi-dimensional search capability, users can perform complex queries based on multiple dimensions such as spatial location, business function, and topological relationship, and the relevance and completeness of the search results are significantly improved.

[0147] Example 2:

[0148] The embodiment is based on the embodiment 1, and provides a micro-grid resource visualized topology index generation system, as shown in the formula (I), which comprises: Figure 6

[0149] A grid construction module is configured to receive geographical boundary coordinate data of the micro-grid system, establish a micro-grid physical region coordinate system, and establish a hexagonal honeycomb grid structure based on the micro-grid physical region coordinate system.

[0150] A skip list chain construction module is configured to generate a honeycomb grid level hash code based on the hexagonal honeycomb grid structure, and generate a skip list chain of the network topology structure based on the honeycomb grid level hash code.

[0151] A path cache module is configured to construct a skip list chain access path cache table based on the skip list chain of the network topology structure.

[0152] A topology index generation module is configured to perform cross-level skip retrieval based on the skip list chain access path cache table, and generate a micro-grid resource visualized topology index.

[0153] In the skip list chain construction module, the specific process of generating the skip list chain of the network topology structure based on the honeycomb grid level hash code comprises:

[0154] Step S121, performing device node service level attribution identification based on the honeycomb grid level hash code.

[0155] Step S122, injecting bidirectional skip list pointers based on the identification result of the device node service level attribution, and performing redundant path pruning to form the skip list chain of the network topology structure.

[0156] In the path cache module, the specific process of constructing the skip list chain access path cache table based on the skip list chain of the network topology structure comprises:

[0157] Step S131, injecting a space hash prefix of the honeycomb grid level hash code into a skip list pointer of the skip list chain, and the skip list pointer refers to a bidirectional skip list pointer and a direct connection skip pointer.

[0158] Step S132, performing pre-aggregation processing on a plurality of skip list pointers in the same honeycomb grid to generate a skip list pointer cluster head node.

[0159] Step S133, generating a skip list chain access path cache table based on the generated skip list pointer cluster head node.

[0160] Embodiment 3

[0161] ​The embodiment discloses an electronic device which can include one or more processors and one or more memories. The memory stores computer readable code which, when executed by the one or more processors, can perform the microgrid resource visualized topology index generation method as described above.

[0162] The method or system according to the embodiments of the present application can also be implemented by means of an architecture of an electronic device. The electronic device can include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, an input / output component, a hard disk, etc. The storage device in the electronic device, such as the ROM or the hard disk, can store the microgrid resource visualized topology index generation method provided by the present application. The microgrid resource visualized topology index generation method can for example include: receiving geographic boundary coordinate data of a microgrid system, establishing a microgrid physical region coordinate system; based on the microgrid physical region coordinate system, establishing a hexagonal honeycomb grid structure; based on the hexagonal honeycomb grid structure, generating a honeycomb grid level hash code; based on the honeycomb grid level hash code, generating a skip list chain of a network topology structure; based on the skip list chain of the network topology structure, constructing a skip list chain access path cache table; based on the skip list chain access path cache table, performing cross-level jump retrieval, and generating a microgrid resource visualized topology index.

[0163] Further, the electronic device can also include a user interface. Of course, the architecture disclosed by the present application is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed by the present application can be omitted according to actual needs.

[0164] Embodiment 4:

[0165] The embodiment discloses a computer readable storage medium, which stores computer readable instructions. When the computer readable instructions are executed by a processor, the microgrid resource visualized topology index generation method according to the embodiments of the present application can be executed. The storage medium includes but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory can include, for example, random access memory (RAM) and cache memory, etc. The non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0166] In addition, according to the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions executable by a processor to perform instructions corresponding to the method steps provided by the present application, for example: receiving geographic boundary coordinate data of a micro network system, establishing a micro network physical region coordinate system; based on the micro network physical region coordinate system, establishing a hexagonal honeycomb grid structure; based on the hexagonal honeycomb grid structure, generating a honeycomb grid hierarchical hash code; based on the honeycomb grid hierarchical hash code, generating a skip list chain of a network topology structure; based on the skip list chain of the network topology structure, constructing a skip list chain access path cache table; based on the skip list chain access path cache table, performing cross-hierarchical jump retrieval, and generating a micro network resource visualized topology index. When this computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are performed.

[0167] The method and system, device of the present application can be implemented in many ways. For example, the method and system, device of the present application can be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present application are not limited to the above specifically described order, unless otherwise specifically described. In addition, in some embodiments, the present application can also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Thus, the present application also covers the recording medium storing the programs for executing the method according to the present application.

[0168] In addition, the part of the above technical solutions provided in the embodiments of the present application that is consistent with the implementation principle of the corresponding technical solutions in the prior art is not described in detail to avoid excessive repetition.

[0169] The specific embodiments described above further illustrate the objects, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for generating a visual topology index for microgrid resources, characterized in that, The method comprises: receiving geographic boundary coordinate data of the microgrid system, establishing a microgrid physical region coordinate system; based on the microgrid physical region coordinate system, establishing a hexagonal honeycomb grid structure; based on the hexagonal honeycomb grid structure, generating a honeycomb grid hierarchical hash code; based on the honeycomb grid hierarchical hash code, generating a skip list chain of the network topology structure; based on the skip list chain of the network topology structure, constructing a skip list chain access path cache table; based on the skip list chain access path cache table, performing cross-level jump retrieval to generate a microgrid resource visualized topology index; The cross-level jump retrieval is specifically: adopting a phased progressive strategy to realize efficient microgrid resource positioning, and the execution process includes three core stages of constraint condition pre-filtering, path selection decision and jump execution, The constraint condition pre-filtering stage narrows down the search range quickly through spatial constraint condition priority screening; the path selection decision stage performs intelligent path planning based on a heat map guided optimization strategy; and the jump execution stage realizes cross-level access through step-by-step jump and state maintenance.

2. The microgrid resource visualized topology index generation method according to claim 1, characterized in that, The geographic 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 region coordinate system represents the business level depth vertically upward, and the origin is the geographic center coordinate of the microgrid system, which is obtained according to the longitude and latitude coordinates of all device nodes.

3. The microgrid resource visualized topology index generating method according to claim 2, characterized in that, The business level depth is 4 layers, which are controllable device layer, microgrid element layer, microgrid unit layer and microgrid cluster layer; the business level depth is represented by discretization mapping on the Z-axis, wherein 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 microgrid resource visualized topology index generating method according to claim 3, characterized in that, The hexagonal honeycomb grid structure is established in the X-Y plane of the microgrid physical region 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 hierarchical hash code based on the hexagonal honeycomb grid structure comprises: calculating the center coordinates of each hexagonal honeycomb grid, encoding the center coordinates of each hexagonal honeycomb grid using Hilbert space-filling curves to generate a honeycomb grid hierarchical hash code with a fixed length L; The first M bits of the honeycomb grid hierarchical hash code define a space hash prefix, and the last L-M bits serve as a business level identifier; the space hash prefix is used to identify the position of the hexagonal honeycomb grid.

5. The microgrid resource visualized topology index generation method of claim 4, wherein, The method for generating a skip list chain of the network topology structure based on the honeycomb grid hierarchical hash code comprises: based on the honeycomb grid hierarchical hash code, identifying the business level attribution of the device nodes; based on the identification result of the business level attribution of the device nodes, injecting bidirectional skip list pointers and performing redundant path pruning to form a skip list chain of the network topology structure.

6. The microgrid resource visualized topology index generating method according to claim 5, characterized in that, The bidirectional skip list pointers include upward jump pointers and downward jump pointers, and the pointing relationship of the upward jump pointers is from lower business level nodes to upper business level nodes, including controllable device layer nodes, microgrid element layer nodes and microgrid unit layer nodes; Whenever an upward jump pointer is established from a lower level service hierarchy node to a higher level service hierarchy node, the higher level service hierarchy node automatically obtains a downward jump pointer to the lower level service hierarchy node.

7. The microgrid resource visualized topology index generating method according to claim 6, characterized in that, The method for performing the redundant path pruning comprises: The physical distance between the device node and the high-level node of the non-direct superior of the device node is calculated, and if the physical distance is less than a preset distance threshold, a direct connection jump pointer from the device node to the high-level node of the non-direct superior is created. The high-level node of the non-direct superior of the device node refers to the microgrid unit layer node and the microgrid cluster layer node.

8. The microgrid resource visualized topology index generating method according to claim 7, characterized in that, The method for constructing the jump list chain access path cache table based on the network topology structure comprises: The spatial hash prefix of the honeycomb grid level hash code is injected into the jump list pointer of the jump list chain, and the jump list pointer comprises a bidirectional jump list pointer and a direct connection jump pointer; The multiple jump list pointers in the same honeycomb grid are pre-aggregated to generate a jump list pointer cluster head node; the jump list pointer cluster head node has an upward jump pointer and a direct connection jump pointer; Based on the generated jump list pointer cluster head node, a jump list chain access path cache table is generated.

9. The microgrid resource visualized topology index generating method according to claim 8, characterized in that, The method for performing cross-level jump retrieval based on the jump list chain access path cache table to generate a microgrid resource visualized topology index comprises: A retrieval request instruction is received, and the retrieval request instruction is converted into a multiple constraint combination retrieval condition; Based on the multiple constraint combination retrieval condition and the jump list chain access path cache table, a topology heat map guided retrieval path optimization strategy is constructed; Based on the topology heat map guided retrieval path optimization strategy and the multiple constraint combination retrieval condition, cross-level jump retrieval is performed; Based on the execution result of the cross-level jump retrieval, retrieval result aggregation and sorting are performed to generate a microgrid resource visualized topology index.

10. A microgrid resource visualized topology index generation system for implementing the microgrid resource visualized topology index generation method of any one of claims 1-9, characterized in that, The system comprises: A grid construction module: configured to receive geographic boundary coordinate data of a microgrid system, establish a microgrid physical region coordinate system, and establish a hexagonal honeycomb grid structure based on the microgrid physical region coordinate system; A jump list chain construction module: configured to generate a honeycomb grid level hash code based on the hexagonal honeycomb grid structure, and generate a network topology structure jump list chain based on the honeycomb grid level hash code; A path cache module: configured to construct a jump list chain access path cache table based on the network topology structure jump list chain; A topology index generation module: configured to perform cross-level jump retrieval based on the jump list chain access path cache table to generate a microgrid resource visualized topology index.

11. An electronic device comprising a memory, a central processing unit, and a computer program stored on the memory and executable on the central processing unit, characterized in that, The central processor executes the computer program to implement the microgrid resource visualized topology index generation method in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores the computer program, and the computer program is executed to implement the microgrid resource visualized topology index generation method in any one of claims 1-9.

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