A dynamic node mapping method and system based on a nine-square relative relationship map

Abstract

The present application belongs to the technical field of data visualization and knowledge graph display, and discloses a dynamic node mapping method and system based on a nine-square kinship graph. The kinship nodes in the initial adjacent subgraph are distributed to the coordinate matrix through the nine-square space mapping engine, the center node switching is realized based on the sliding gesture, the coordinate projection transformation is performed by using the topological deflection angle, and the nodes beyond the visible range are mapped to the hidden layer and the virtual connection is established. The asynchronous topological correction module is also included to complete the graph disconnected boundary, the differential rendering of node data integrity, and the collaborative supplement mechanism. The present application realizes flexible exploration of multi-perspective kinship, intelligently solves space conflicts, breaks through the interface space limit, supports self-improvement of the graph, and greatly improves the navigation efficiency and the coherence of perspective conversion.

Description

Technical Field

[0001] This invention relates to the field of data visualization and knowledge graph display technology, and more specifically, to a dynamic node mapping method and system based on a nine-square grid kinship graph. Background Technology

[0002] Existing kinship graph display technologies suffer from numerous limitations in practical applications, severely impacting users' comprehensive understanding and effective exploration of family relationships. Traditional kinship graphs employ a static, fixed layout, lacking necessary flexibility and adaptability. They cannot dynamically adjust to changing user focus, especially noticeable when viewing large family genealogies. Users are forced to view the family network from a single perspective, unable to easily switch to different family members' viewpoints to examine the overall relationships. In real-world scenarios, when users attempt to view family relationships from a grandfather's perspective, they often need to completely exit the current view and re-search, disrupting the seamless exploration experience. Limited interface space leads to the simple truncation or complete hiding of many key kinship nodes, particularly in large families where important collateral relatives are often sacrificed due to insufficient display space, resulting in incomplete information acquisition. Family knowledge graphs commonly suffer from data gaps and missing relationships, with unclear boundaries. Existing technologies lack intelligent automatic completion mechanisms, forcing users to spend significant time manually entering and maintaining relationship data. This data maintenance burden is particularly heavy for family history researchers who spend long periods tracing ancestral relationships. In terms of navigation experience, users can only access adjacent nodes through cumbersome step-by-step clicks, making it impossible to quickly locate and access distant relatives. When trying to find great-grandparents or distant cousins, multiple repetitive operations are often required, greatly reducing search efficiency. The perspective transition process is abrupt and lacks continuity; the node layout lacks correlation and spatial consistency before and after the switch, causing users to frequently lose their sense of spatial orientation and need to rebuild their mental model after each perspective change. Data quality differences lack intuitive visual distinction; complete and detailed kinship information and placeholder nodes with only basic data are presented in the same way, making it impossible for users to quickly identify information areas that require focus. Furthermore, information on the same person scattered throughout the map cannot be effectively integrated and utilized. The spatial conflict handling mechanism is lacking; when multiple important relatives are mapped to the same location, intelligent sorting based on relationship importance and closeness is not possible, often resulting in secondary relationships occupying core display positions while important immediate relatives are marginalized. Furthermore, as a carrier of family collective memory, kinship maps should support collaborative construction. However, existing technologies lack effective multi-user co-construction mechanisms, making it difficult to seamlessly integrate external data and failing to reflect the value of information contributed by all parties, thus limiting the accumulation and inheritance of family knowledge.

[0003] In view of this, the present invention proposes a dynamic node mapping method and system based on a nine-square grid kinship graph to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a dynamic node mapping method based on a nine-square grid kinship map, comprising: Receive a trigger command for a target relative entity, extract the entity identifier of the target relative entity, and retrieve the initial adjacency subgraph associated with the entity identifier in the pre-constructed family knowledge graph. The family knowledge graph is composed of relative nodes and relationship edges, and the relationship edges carry relationship type weights and bloodline confidence scores. The initial adjacency subgraph is input into the nine-grid spatial mapping engine. Based on the topological path between each relative node and the target relative entity in the initial adjacency subgraph, spatial conflict resolution is performed to allocate each relative node to the available slots in the nine-grid coordinate matrix and generate the initial mapping layout. Listen for node switching events initiated on the center slot of the 3x3 grid coordinate matrix, and extract the sliding direction vector and sliding speed parameters carried in the node switching event; Based on the sliding direction vector and sliding speed parameters, the candidate center node to be switched is determined, and the nine-square grid space mapping engine is triggered to perform local topological rotation reconstruction of the family knowledge graph with the candidate center node as the new anchor point, and calculate the topological deflection angle of the candidate center node relative to the original center node. Based on the topological deflection angle, the coordinate projection transformation is performed on the relatives nodes in the initial adjacency subgraph. The dangling or isolated nodes generated in the topological rotation reconstruction are mapped to the hidden layer of the nine-grid coordinate matrix, and virtual connections are established between the dangling or isolated nodes and the visible slot nodes in the nine-grid coordinate matrix in the hidden layer. The updated nine-grid interface is rendered based on the results of coordinate projection transformation, and the virtual connection state of the hidden layer is synchronized to the asynchronous topology correction module, so that the asynchronous topology correction module performs a completion operation on the broken connection boundary of the family knowledge graph in the background.

[0005] A dynamic node mapping system based on a nine-square grid kinship graph, the system being used to execute the aforementioned dynamic node mapping method based on a nine-square grid kinship graph, the system comprising: The graph retrieval module is used to receive trigger instructions for target kinship entities, extract the entity identifier of the target kinship entity, and retrieve the initial adjacency subgraph associated with the entity identifier in the pre-constructed family knowledge graph. The family knowledge graph is composed of kinship nodes and relationship edges, and the relationship edges carry relationship type weights and bloodline confidence scores. The spatial mapping module is used to input the initial adjacency subgraph into the nine-grid spatial mapping engine. Based on the topological path between each relative node and the target relative entity in the initial adjacency subgraph, it performs spatial conflict resolution processing to allocate each relative node to the available slots in the nine-grid coordinate matrix and generate the initial mapping layout. The event listening module is used to listen for node switching events initiated on the center slot of the 3x3 grid coordinate matrix and extract the sliding direction vector and sliding speed parameters carried in the node switching event. The topology reconstruction module is used to determine the candidate center node to be switched based on the sliding direction vector and sliding speed parameters, and trigger the nine-square grid space mapping engine to perform local topology rotation reconstruction of the family knowledge graph with the candidate center node as the new anchor point, and calculate the topology deflection angle of the candidate center node relative to the original center node. The coordinate projection module is used to perform coordinate projection transformation on the relatives nodes in the initial adjacency subgraph based on the topology deflection angle. It maps the dangling or isolated nodes generated in the topology rotation reconstruction to the hidden layer of the nine-grid coordinate matrix, and establishes virtual connections between the dangling or isolated nodes and the visible slot nodes in the nine-grid coordinate matrix in the hidden layer. The rendering and correction synchronization module is used to render the updated nine-grid interface based on the results of coordinate projection transformation, and to synchronize the virtual connection state of the hidden layer to the asynchronous topology correction module, so that the asynchronous topology correction module can perform a completion operation on the broken boundary of the family knowledge graph in the background.

[0006] The technical effects and advantages of the dynamic node mapping method and system based on a nine-square grid kinship map of the present invention are as follows: This invention achieves comprehensive optimization of kinship graph display, enabling dynamic adaptability of the interface layout and breaking free from the constraints of traditional fixed layouts. Topology rotation and reconstruction overcome the limitations of relationship cognition from a single perspective, enhancing the comprehensiveness of family network understanding. The design of hidden layers and virtual connections overcomes the physical limitations of the display space, achieving a logically complete presentation of the extended network. Asynchronous topology correction provides the graph's self-improvement capability, reducing data maintenance costs. The sliding navigation mechanism shortens the access path between nodes, simplifying the operation process of exploring complex structures. Topology deflection calculation ensures the coherence of perspective transitions, maintaining spatial cognition consistency. Multi-level rendering states provide intuitive data quality perception, enhancing information recognition efficiency. Weighted scoring conflict resolution ensures the priority presentation of core relationships. The structured filtering algorithm guarantees the ethical rationality of relationship display. The collaborative supplementation mechanism promotes distributed knowledge integration, forming a virtuous cycle of continuous optimization. The synergistic effect of these effects fundamentally enhances the interactive experience and information value of kinship graphs. Attached Figure Description

[0007] Figure 1This is a schematic diagram of a dynamic node mapping method based on a nine-square grid kinship map according to the present invention; Figure 2 This is an example diagram of the nine-grid coordinate matrix of the present invention; Figure 3 This is a schematic diagram of the structure of a dynamic node mapping system based on a nine-square grid kinship map according to the present invention. Detailed Implementation

[0008] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] like Figure 1 As shown, an embodiment of the present invention proposes a dynamic node mapping method based on a nine-square grid kinship graph, the method comprising the following steps: Step 1: Receive the trigger command for the target relative entity, extract the entity identifier of the target relative entity, and retrieve the initial adjacency subgraph associated with the entity identifier in the pre-constructed family knowledge graph. The family knowledge graph is composed of kinship nodes and relationship edges, and the relationship edges carry relationship type weights and bloodline confidence scores. Step 2: Input the initial adjacency subgraph into the nine-grid spatial mapping engine. Based on the topological path between each relative node and the target relative entity in the initial adjacency subgraph, perform spatial conflict resolution processing to assign each relative node to the available slots in the nine-grid coordinate matrix and generate the initial mapping layout. Step 3: Listen for node switching events initiated on the center slot of the 3x3 grid coordinate matrix, and extract the sliding direction vector and sliding speed parameters carried in the node switching event. Step 4: Based on the sliding direction vector and sliding speed parameters, determine the candidate center node to be switched, and trigger the nine-square grid space mapping engine to perform local topological rotation reconstruction of the family knowledge graph with the candidate center node as the new anchor point, and calculate the topological deflection angle of the candidate center node relative to the original center node. Step 5: Perform coordinate projection transformation on the relatives nodes in the initial adjacency subgraph based on the topological deflection angle. The dangling or isolated nodes generated in the topological rotation reconstruction will be mapped to the hidden layer of the nine-grid coordinate matrix. In the hidden layer, virtual connections will be established between the dangling or isolated nodes and the visible slot nodes in the nine-grid coordinate matrix. Step 6: Render the updated nine-grid interface based on the result of coordinate projection transformation, and synchronize the virtual connection state of the hidden layer to the asynchronous topology correction module, so that the asynchronous topology correction module can perform a completion operation on the broken connection boundary of the family knowledge graph in the background.

[0010] In this embodiment of the invention, because the invention employs the following technical means: receiving the trigger command of the target kinship entity and retrieving the initial adjacent subgraph; inputting the subgraph into the nine-square grid spatial mapping engine for spatial conflict resolution; listening to the switching event of the central slot node and extracting the sliding parameters; determining the candidate central node based on the sliding parameters and calculating the topological deflection angle; performing coordinate projection transformation and mapping the dangling node to the hidden layer to establish a virtual connection; rendering and updating the interface and performing asynchronous topological correction, the invention effectively overcomes the technical problems in existing kinship graph display methods, such as fixed interface layout, inability to dynamically view different kinship perspectives, incomplete display of kinship networks, limited space causing some nodes to be undisplayed, and incomplete kinship graphs requiring manual completion. This achieves the technical effects of flexible layout and efficient switching of kinship nodes based on the nine-square grid, supporting multi-perspective kinship exploration through topological rotation reconstruction, solving the limited space problem by combining hidden layer design, and improving the completeness of the family knowledge graph through asynchronous topological correction.

[0011] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1: Starting with the entity identifier as the initial retrieval point, the system performs a one-hop expansion along the relationship edges in the family knowledge graph to extract the set of kinship nodes directly connected to the target kinship entity. Specifically, this involves: First, identifying the target kinship entity, i.e., the kinship individual currently selected by the user as the central focus. For example, if the user selects "Zhang San" as the target kinship entity, the system identifies the entity's unique identifier, such as "ID_ZhangSan_001". Next, starting from this identifier, the system calls the family knowledge graph's query interface to send a one-hop query request to the graph database. This request requests all kinship nodes directly connected to "Zhang San," typically represented in the graph as adjacent nodes with only one relationship edge to the central node. The system traverses all relationship edges directly connected to "Zhang San" in the graph, extracting the corresponding target kinship nodes along these edges. These nodes may include "Zhang San's" parents, spouse, children, siblings, and other direct or collateral relatives, forming a set of kinship nodes directly connected to the target kinship entity. It ensures that each node in this set has a clear kinship type label for subsequent reading of relationship edge attributes and node sorting.

[0012] Step 1.2: For each relative node in the relative node set, read its corresponding relationship edge attributes. These attributes include generational difference, peer seniority number, and relationship edge timeliness marker. Specifically, for the relative node set obtained in the previous step, the system processes each node one by one. For the relationship edge between the target relative entity "Zhang San" and that relative node, read three types of key attribute information: First, the generational difference, representing the generational distance between the relative and "Zhang San." For example, the generational difference for parents is -1 (previous generation), and for children it is +1 (the previous generation). The system employs a hierarchical structure, with each generation having several key relationships. First, it assigns a seniority number to each sibling, distinguishing their order within the same generation. For example, for Zhang San's siblings, different numbers are assigned based on age; the elder brother might be -2 or -1, and the younger brother might be +1 or +2. Finally, it reads the validity period flag of each relationship edge to indicate whether the relationship is still valid. Invalid relationships are marked as "invalid" or "cancelled," while existing relationships are marked as "valid." The system ensures accurate reading of all attribute information for each relationship edge, laying the foundation for subsequent node filtering and sorting.

[0013] Step 1.3 filters out kinship nodes whose relationship edge expiration markers indicate they are invalid or cancelled, and initially sorts the remaining kinship nodes based on generational difference and peer seniority number to generate an initial adjacency subgraph. Specifically, the system analyzes the read relationship edge attributes. First, it performs a filtering operation based on the expiration marker, removing kinship nodes corresponding to relationship edges marked "invalid" or "cancelled" from the set. This ensures that only kinship relationships still valid in the current context are displayed in the 3x3 grid. Next, the remaining valid kinship nodes are sorted based on two main sorting keys. The primary key is the generational difference; the system arranges them in ascending order of generational difference, placing elders (negative values) first, peers (0 values) in the middle, and juniors (positive values) last. The secondary key is the peer seniority number; when generational differences are the same, they are sorted by peer seniority number to ensure that kinship nodes of the same generation are arranged in seniority order. After sorting, these nodes and their relationships with the target relative entity "Zhang San" are organized into a structured initial adjacency subgraph. This subgraph clearly shows the first-level relationship network between "Zhang San" and his directly related relatives, and has been initially sorted based on generational and seniority information, preparing for subsequent spatial mapping in the nine-square grid.

[0014] In this embodiment of the invention, the technical means of extracting directly connected kinship nodes in the family knowledge graph by using the identifier of the target kinship entity as the starting retrieval point, reading the relationship edge attributes corresponding to each kinship node including generational difference, peer seniority number and relationship edge timeliness mark, filtering out kinship nodes whose timeliness mark indicates failure or cancellation, and initially sorting the remaining nodes according to generational difference and peer seniority number to generate an initial adjacency subgraph, overcomes the technical problems in the prior art of lacking effective data filtering in the display of kinship relationships, resulting in invalid relationships mixed in, disordered and irregular sorting of relationship nodes, inability to distinguish generational and seniority attributes of relationships, and inability to adaptively filter the most relevant kinship nodes. Thus, it achieves the technical effect of accurately extracting effective kinship nodes directly related to the target kinship entity, forming a structured initial adjacency subgraph based on generational and seniority features, providing high-quality data support for the nine-square grid spatial mapping, and ensuring that the initial display of kinship relationships in the nine-square grid conforms to the family ethical sequence and has intuitive and understandable technical effects.

[0015] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1: For each kinship node in the initial adjacency subgraph, calculate the expected directional coordinates of the kinship node in the nine-square grid coordinate matrix based on its corresponding generational difference and peer seniority number. Specifically, the system defines the nine-square grid coordinate matrix as a 3×3 two-dimensional plane, with the central position (1,1) reserved for the target kinship entity, and the other eight positions corresponding to kinships of different generations and seniority relationships. During the calculation, the system first establishes a mapping rule between generational difference and vertical coordinates: elders (such as parents) with a generational difference of -1 are mapped to the upper row (0,y) position; peers (such as siblings, spouses) with a generational difference of 0 are mapped to the middle row (1,y) position; and younger generations (such as children) with a generational difference of +1 are mapped to the lower row (2,y) position. For kinships with an absolute generational difference greater than 1 (such as grandparents or grandchildren), their priority needs to be additionally determined by the algorithm based on their importance in the family tree. Next, the system establishes a mapping rule between peer seniority numbers and horizontal coordinates: relatives with negative peer seniority numbers (such as older brothers) are mapped to the left (x,0) position; relatives with positive peer seniority numbers (such as younger brothers) are mapped to the right (x,2) position; special relationships such as spouses are usually assigned fixed positions such as (1,2). Based on the above rules, the expected orientation coordinates of each relative node in the 3x3 grid are calculated. For example, the father may be mapped to the (0,0) position, the older brother to the (1,0) position, the spouse to the (1,2) position, and the children to the (2,1) position, etc., ensuring that the initial positions of relative nodes in the 3x3 grid conform to traditional family ethics and customary understanding.

[0016] like Figure 2The image shown is an example of a nine-square grid coordinate matrix. The target relative entity is the central slot, with the older brother above, the younger brother below, the father to the left, the second son to the right, the uncle to the upper left, the uncle to the lower left, the eldest son to the upper right, and the third son to the lower right.

[0017] Step 2.2 involves detecting whether the expected location coordinates of at least two relative nodes overlap. If overlap occurs, the relationship type weight and bloodline confidence score of each overlapping relative node are extracted. Specifically, after calculating the expected location coordinates of all relative nodes, a coordinate overlap detection algorithm is executed to determine whether multiple relative nodes are assigned to the same slot in the 3x3 grid. The detection algorithm iterates through the expected coordinates of all relative nodes and identifies groups of nodes with the same coordinate values ​​using a hash table or coordinate comparison technology. For example, when the target relative entity "Zhang San" has two older sisters, based on the calculation rules of generational difference and peer seniority, both older sister nodes may be assigned to the (1,0) position, resulting in coordinate overlap. If the system detects coordinate overlap—that is, at least two different kinship nodes mapped to the same slot in the 3x3 grid—it immediately extracts additional attribute information for these overlapping nodes from the family knowledge graph. This includes: relationship type weights, representing the importance of different kinship relationships within a cultural context (e.g., in traditional Chinese family concepts, paternal relatives typically have higher weights than maternal relatives); and bloodline confidence scores, quantifying the reliability and closeness of kinship relationships (direct blood relatives usually have higher confidence scores, while in-law relationships have relatively lower scores). The system ensures accurate extraction of these two key scores for each overlapping node, providing a basis for subsequent spatial conflict resolution decisions.

[0018] Step 2.3: Sort the overlapping kinship nodes in descending order according to the weighted sum of the relationship type weight and the bloodline confidence score, and map the kinship node with the highest weighted sum to the available slot corresponding to the desired orientation coordinate. Specifically, this includes: performing conflict resolution processing on the kinship node group with coordinate overlap detected in the previous step.

[0019] First, the system defines a comprehensive score calculation formula: The overall score is calculated as α × relationship type weight + β × bloodline confidence score, where α and β are preset weight coefficients that can be adjusted according to the application scenario. For example, in a family history research scenario, the weight β of the bloodline confidence score may be higher; while in everyday family relationship displays, the coefficient α of the relationship type weight may be larger. The overall score of each overlapping node is calculated, and the nodes are sorted in descending order of score value to form a priority queue. Then, the relative node with the highest overall score is retained in the nine-square grid slot corresponding to the original expected orientation coordinates. For example, when "Zhang San's" biological mother and adoptive mother are both mapped to position (0,2), if the biological mother's overall score is higher, the biological mother node will be retained in slot (0,2), ensuring that the most important kinship relationship occupies the limited nine-square grid space first.

[0020] Step 2.4: For the remaining overlapping kinship nodes not mapped to available slots, calculate the coordinates of overflow slots adjacent to available slots based on their peer seniority number, and map the remaining overlapping kinship nodes to the overflow slot coordinates. Simultaneously, add a collapse / expand marker to the nodes corresponding to the overflow slot coordinates. Specifically, for kinship nodes that failed to obtain their originally desired location slots during conflict resolution, the system does not simply discard them but implements an "overflow slot" strategy for placement. First, analyze the peer seniority number of these nodes to determine their relative positional relationship with already occupied slot nodes. For example, if two older sisters are vying for slot (1,0), after the older sister obtains the slot, calculate the adjacent overflow coordinates for the younger sister, which may be a "virtual slot" formed by logically extending the nine-square grid, such as (1,-1) or (0.5,0). The calculation process considers the size and direction of the peer seniority number to ensure that the allocation of overflow slots still follows the family ethical order and maintains intuitive understanding. These remaining overlapping nodes are mapped to the calculated overflow slot coordinates, and special "fold-and-expand indicators" are added to these nodes. These indicators will appear as visual cues during interface rendering, such as small arrows or fold icons, suggesting to the user that multiple relative nodes exist at that location and can be expanded for viewing. This fold indicator design allows the 3x3 grid to maintain a clean layout while elegantly handling situations where the number of relative nodes exceeds the grid limit, providing users with a more comprehensive experience in exploring kinship relationships.

[0021] In this embodiment of the invention, the technical means employed—calculating the expected directional coordinates of each kinship node in the nine-square grid based on generational differences and peer seniority in the initial adjacent subgraph, detecting and identifying overlapping node groups and extracting their relationship type weights and bloodline confidence scores, arranging overlapping nodes in descending order according to the weighted sum of the two scores and retaining the highest-scoring node in its original slot, calculating adjacent overflow slots for the remaining overlapping nodes and adding fold / expand markers—overcomes the technical problems in existing kinship graph displays caused by fixed space, such as node crowding and overlap, inability to distinguish between important and secondary relationships, inability to elegantly resolve spatial conflicts, and chaotic interface layout affecting user understanding. This achieves the technical effect of maximizing the display of kinship information within a limited space while maintaining an intuitive and understandable layout, based on reasonable allocation of nine-square grid coordinates according to family ethics, ensuring priority display of important relatives through a comprehensive scoring mechanism, and elegantly handling spatial conflicts using overflow slots and fold markers.

[0022] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1 maps the sliding direction vector to the standard azimuth axis of the 3x3 grid coordinate matrix to determine the candidate azimuth interval. Specifically, this involves: first, receiving the user's sliding operation performed in the center slot of the 3x3 grid. This operation generates a sliding direction vector with direction and magnitude; for example, a user sliding from the center to the upper right might generate a vector of (0.7, 0.7). To accurately interpret the user's intent, this original vector is mapped to the standard azimuth axis system of the 3x3 grid. The standard azimuth axis system divides the area around the 3x3 grid into 8 azimuth sectors: top (0, -1), upper right (1, -1), right (1, 0), lower right (1, 1), lower right (0, 1), lower left (-1, 1), left (-1, 0), and upper left (-1, -1). The system calculates the angle between the original sliding vector and these 8 standard azimuth vectors and selects the standard azimuth with the smallest angle as the primary mapping direction. For example, if the angle between the original vector (0.7, 0.7) and the upper right vector (1, -1) is the smallest, then the swipe operation is mapped to the upper right direction. The system will also determine a candidate orientation interval, which includes the main mapping direction and its adjacent sectors. For example, the candidate orientation interval for the upper right direction may include three sectors: directly up, upper right, and directly right. This sector division and expansion design enhances the accuracy of understanding the user's swipe intention, and has better fault tolerance, especially for imprecise touch operations.

[0023] Step 4.2: When the swipe speed parameter is less than or equal to a preset speed threshold, the relative node directly adjacent to the center slot within the candidate orientation interval is determined as the candidate center node. This specifically includes detecting the speed parameter of the user's swipe operation. This parameter reflects the speed at which the user swipes their finger or mouse, typically measured in pixels per second. A preset speed threshold, such as 200 pixels per second, is set as a benchmark for judging the user's intent. When the detected swipe speed is less than or equal to this threshold, it is determined that this is a fine swipe, and the user's intent may be to view the nearest relative directly connected to the current center relative. In this case, within the candidate orientation interval determined in the previous step, a relative node directly adjacent to the center slot is searched. For example, if the user slowly swipes from the center "Zhang San" to the upper right with a speed parameter of 150 pixels per second (less than the preset threshold of 200 pixels per second), the system will search for a relative node in the nine-grid slot in the upper right direction. If a mother node of "Zhang San" exists at that position, it is determined as a candidate center node. This low-speed sliding, near-distance node switching mechanism allows users to navigate precisely between immediate family members, making it suitable for exploring closely connected core family relationships.

[0024] Step 4.3: When the sliding speed parameter exceeds a preset speed threshold, calculate the speed gradient level to which the sliding speed parameter belongs, and perform a jump-style search along the generational path indicated by the candidate location interval in the family knowledge graph. The Nth generation relative node that matches the speed gradient level is determined as the candidate center node. Here, N is positively correlated with the speed gradient level. Specifically, when the user's sliding speed parameter exceeds a preset threshold (e.g., 200 pixels / second), it is determined that this is a fast slide, indicating that the user may want to view more distant generational relationships. First, the sliding speed parameter is mapped to a predefined speed gradient level. For example: 200-400 pixels / second is level one, corresponding to N=2; 400-600 pixels / second is level two, corresponding to N=3; above 600 pixels / second is level three, corresponding to N=4 or higher. Here, the N value represents the desired generational distance to jump. Next, based on the directional characteristics indicated by the candidate location intervals, the retrieval direction of the intergenerational path is determined: for example, the upward direction indicates retrieval of the elder generation's path, the downward direction indicates retrieval of the younger generation's path, and the horizontal direction indicates retrieval of the peer generation's extended path. A jump-style search is performed in the family knowledge graph. For example, if a user quickly swipes upwards from "Zhang San" at a speed of 500 pixels / second (level 2), it will jump three generations along the elder generation's path, directly locating "Zhang San's" great-grandfather / great-grandmother node; if the user quickly swipes downwards to the right, the system may locate "Zhang San's" grandnephew / grandniece. Simultaneously, the actual number of nodes existing in the graph is considered; if there is no node for the target generation, the closest valid node is selected. This speed-driven intergenerational jump mechanism allows users to quickly navigate long distances within the vast family network and efficiently explore family member relationships across different generations.

[0025] In this embodiment of the invention, by employing technical means such as mapping the sliding direction vector to the standard orientation axis of a nine-square grid to determine the candidate orientation interval, selecting directly adjacent nodes within the candidate interval when the sliding speed parameter is less than or equal to a preset threshold, and calculating the speed gradient level and performing a jump-type intergenerational retrieval matching the level when the speed parameter is greater than the threshold, the technical problems of existing kinship map displays, such as only being able to click to view adjacent nodes one by one, being unable to quickly reach distant relatives, having low navigation efficiency, and having a poor user interaction experience, are overcome. Thus, the technical effect of achieving kinship node switching through intuitive sliding operations, intelligently judging the user's navigation intention based on the sliding speed, supporting precise viewing of adjacent nodes and long-distance intergenerational jumps, and greatly improving the browsing efficiency of family relationship networks is achieved.

[0026] In a preferred embodiment of the present invention, step 4 above may include: Step 5.1: In the family knowledge graph, extract the shortest relationship path between the original central node and the candidate central node. Specifically, the system first determines the entity identifiers of the original central node (e.g., "Zhang San") and the new candidate central node (e.g., "Zhang San's grandfather") in the family knowledge graph. Then, it calls the graph's path retrieval algorithm to search for all possible paths connecting these two nodes. The retrieval process uses a breadth-first search strategy, starting from the original central node and expanding outwards along various relationship edges (e.g., father-son, brother, marriage, etc.) until a path to the candidate central node is found. For multiple paths found, the system calculates the "relationship distance" for each path, which is a comprehensive score of the number of relationship edges and their weights. For example, the edge weight of direct blood relatives is lower, indicating a closer relationship distance; while the edge weight of relatives by marriage or distant relatives is higher, indicating a farther relationship distance. The system selects the path with the lowest comprehensive score as the shortest relationship path, which is usually the most direct and core kinship connection between the two relatives. For example, the shortest path between "Zhang San" and "Zhang San's grandfather" might be "Zhang San → Zhang San's father → Zhang San's grandfather," rather than a roundabout path through collateral relatives. This shortest relationship path will serve as the basis for subsequent computational topological rotations, ensuring that perspective shifts in the family knowledge graph follow the most natural kinship chain.

[0027] Step 5.2 determines the target generational vector and target peer vector corresponding to the shortest relationship path. This specifically includes: based on the shortest relationship path extracted in the previous step, performing path decomposition and vector calculation. First, analyze each relationship edge on the path and extract its generational and peer attributes. The generational attribute represents the generational difference between nodes; for example, the generational difference for a parent-child relationship is 1, and the generational difference for a grandparent-grandchild relationship is 2. The peer attribute represents the sequential relationship between nodes of the same generation; for example, the peer difference between an elder brother and younger brother in a sibling relationship may be 1 or more. Along the shortest path, accumulate the generational differences of all relationship edges to obtain the total generational change from the original center node to the candidate center node, forming the target generational vector. For example, on the path from "Zhang San" to "Zhang San's grandfather," the generational difference of each parent-child relationship edge is -1 (upward one generation), and the total generational change of the two edges is -2, representing a movement two generations upward. Similarly, accumulate the peer differences of all relationship edges on the path to form the target peer vector. For example, on the path from "Zhang San" to "Zhang San's father's elder brother (Zhang San's uncle)," in addition to the generational change -1, there is also a peer change -1 (towards the elder generation). The final calculated target generational vector and target peer vector accurately describe the relative positional movement from the original central node to the candidate central node in the family structure, providing directional guidance for subsequent coordinate transformations.

[0028] Step 5.3 calculates the first angle between the target generation vector and the horizontal axis of the nine-square grid coordinate matrix, and the second angle between the target peer vector and the vertical axis of the nine-square grid coordinate matrix. Specifically, this involves mapping the target generation vector and the target peer vector determined in the previous step into the two-dimensional space of the nine-square grid coordinate matrix. Under standard settings, the horizontal axis of the nine-square grid typically represents the extension of peer relationships, and the vertical axis represents the extension of generational relationships. First, the target generation vector is oriented and projected onto the nine-square grid space. Then, the angle between this vector and the horizontal axis of the nine-square grid is calculated to obtain the first angle θ1. This angle represents the degree of influence of generational change on the horizontal direction in the family topology. For example, if the target generation vector is parallel to the horizontal axis, θ1 is 0°; if it is perpendicular, θ1 is 90°; if there is a certain tilt, θ1 is an intermediate value. Similarly, the angle between the target peer vector and the vertical axis of the nine-square grid is calculated to obtain the second angle θ2. This angle represents the degree of influence of peer relationship change on the vertical direction. The system ensures that angle calculations take into account the directionality of vectors; for example, the angles between the upward direction (for elders) and the downward direction (for younger generations) have opposite signs. These two angles together describe the direction and extent of spatial rotation required when the family relationship network changes perspective, laying the foundation for subsequent calculations of topological deflection angles.

[0029] Step 5.4: Determine the vector sum of the first included angle and the second included angle as the topological deflection angle. The topological deflection angle is used to indicate the angle parameter of the initial adjacent subgraph rotating around the candidate center node. Specifically, it includes: performing vector synthesis calculation based on the first included angle θ1 and the second included angle θ2 calculated in the previous step to obtain the final topological deflection angle α.

[0030] The calculation formula can be expressed as: α = ω1 × θ1 + ω2 × θ2, where ω1 and ω2 are weighting coefficients preset according to the characteristics of the family structure, used to balance the relative importance of intergenerational changes and peer changes in the perspective transformation. For example, in a family tree that emphasizes clan inheritance, intergenerational relationships may be more important, in which case ω1 > ω2; while in a family with close sibling relationships, peer relationships may be more important, in which case ω1 < ω2. The calculated topological deflection angle α is an angle parameter that integrates intergenerational changes and peer changes. It accurately describes the rotation transformation that the entire family structure needs to undergo when changing from the perspective of the original central node to the perspective of the candidate central node. For example, in the perspective transformation from "Zhang San" to "Zhang San's father", the topological deflection angle may be -45°, indicating that the entire family network needs to be rotated 45 degrees counterclockwise to correctly show the kinship structure centered on "Zhang San's father". This topological deflection angle will be used for subsequent coordinate projection transformation to ensure that the layout of kinship nodes in the nine-square grid can smoothly and naturally transition to the new central perspective.

[0031] In this embodiment of the invention, by employing the technical means of extracting the shortest relationship path between the original central node and the candidate central node, determining the target generation vector and target peer vector corresponding to the path, calculating the angle between these vectors and the nine-square grid coordinate axis, and determining the final topological deflection angle through vector summation, the technical problems of abrupt perspective switching, discontinuous node layout adjustment, inability to reflect the topological continuity of kinship relationships, and weak interface correlation before and after switching, which lead to heavy cognitive burden on users, are overcome in the existing kinship map display. Thus, the technical effect of calculating accurate topological deflection angle based on actual kinship paths, ensuring the continuity and smoothness of perspective switching, maintaining the logical consistency of family structure, and ultimately improving the user's spatial cognitive efficiency and experience comfort during multi-perspective switching is achieved.

[0032] In a preferred embodiment of the present invention, step 5 above may include: Step 6.1: For each relative node in the initial adjacency subgraph, transform its original coordinates using a rotation matrix based on the topological deflection angle to obtain the transformed coordinates. Specifically, this includes: first, constructing a standard two-dimensional rotation matrix. This matrix is ​​based on the topological deflection angle calculated in the previous step. , represented as: For each relative node in the initial adjacency subgraph, obtain its 3x3 grid coordinates from the perspective of the original center node. Subtract the center point coordinates (1,1) from the coordinates to convert them to relative coordinates, and then apply the rotation matrix R for transformation: ; Adding the transformed relative coordinates back to the center point coordinates yields a 3x3 grid of coordinates with the candidate center node as the new anchor point: For example, if a relative node's coordinates in the original 3x3 grid are (0,0) (top left corner) and the topological rotation angle is 45°, then after the rotation transformation, the node's new coordinates might become (-0.41, 0.41), indicating that the node's position has changed according to the new perspective. This rotation transformation process is performed on all nodes in the subgraph one by one, ensuring that the entire kinship network can undergo consistent topological rotations as the central perspective changes, maintaining the logical integrity of the family structure.

[0033] Step 6.2, determine whether the transformed coordinates fall within the visible slots of the 3x3 grid coordinate matrix, specifically including: for each node coordinate after the previous rotation transformation. Perform a boundary check to determine if it falls within the visible area of ​​the 3x3 grid. The visible slot range of the 3x3 grid is typically defined as a 3x3 matrix with the following coordinate range: and Perform precise numerical comparisons to check the transformed coordinates. and Are all the values ​​within this range? If the coordinate values ​​meet the condition, then the relative node is determined to still be directly visible in the 3x3 grid main interface after the viewpoint is switched; if the coordinate values ​​are outside the range ( or or or If the node is outside the visible area of ​​the 3x3 grid from the new perspective, it is determined that the node has exceeded the visible area of ​​the 3x3 grid. The system will also perform special processing for boundary cases, such as coordinate values ​​that are close to the boundary but have slight deviations (e.g., The node may be mapped to the nearest valid boundary location through rounding or constraint operations to enhance interface stability. This judgment process provides a clear basis for subsequent processing of dangling or isolated nodes, ensuring that the system can adopt appropriate processing strategies for nodes in different positional states.

[0034] Step 6.3: If not found, determine that the relative node is a dangling node or an isolated node generated during topology rotation reconstruction, and assign hidden coordinates in the hidden layer to the dangling or isolated node. Specifically, when the transformed coordinates of a relative node exceed the visible area of ​​the 3x3 grid, it will be classified as a "dangling node" or an "isolated node." A dangling node refers to a node that, although exceeding the visible area, still has a direct connection with nodes within the visible area; an isolated node refers to a node that is both beyond the visible area and has no direct connection with visible nodes. A virtual "hidden layer" space is constructed for these nodes. This space is located outside the main interface of the 3x3 grid but logically connected to the main interface. Based on the transformed coordinates of the node... The system calculates the node's position within the hidden layer. The calculation logic maintains the relative positions between nodes; for example, if a node was originally located outside the right side of the visible area, its hidden coordinates will remain in the right-side area, with its specific position determined based on the degree of deviation. A unique hidden layer coordinate is assigned to each dangling or isolated node, and its hidden state is marked in the data structure. This ensures that although these nodes are not directly visible on the main interface, they remain within the logical structure of the overall family relationship network and can be accessed through specific interactive methods.

[0035] Step 6.4 establishes a virtual connection edge between the hidden coordinates of the dangling or isolated node and the nearest visible slot node. This virtual connection edge carries a virtual connection pointer identifier, used to render hidden entry controls on the visible slot node. Specifically, this involves establishing a connection mechanism between each dangling or isolated node in the hidden layer and the visible area. First, the distance between the hidden node and all nodes in the 3x3 visible area is calculated, and the nearest visible node is selected as the connection point. The distance calculation is based on the transformed coordinates, considering Euclidean or Manhattan distances to ensure the closest spatially located node is found. A virtual connection edge is created connecting the hidden node and the selected visible node. This edge records the identifiers of the nodes at both ends, the connection type (dangling or isolated), and the pointing direction in the data structure. The virtual connection edge carries a "virtual connection pointer identifier," which contains metadata such as the location information, relationship type, and quantity of the hidden node. During interface rendering, based on this identifier information, hidden entry controls, such as directional arrows, expand icons, or number badges, are generated on the connected visible nodes to visually indicate to the user that there are more undisplayed related nodes in that direction. For example, if three dangling nodes connect to a visible node on the right edge, that node might display a right arrow icon marked "3+", indicating that there are more than three related relatives to explore on the right. This virtual connection design allows the grid interface to suggest a complete family network structure even with limited space, enhancing the user's exploration experience.

[0036] In this embodiment of the invention, by employing technical means such as performing a rotation matrix transformation on each relative node in the initial adjacency subgraph based on the topological deflection angle, determining whether the transformed coordinates fall within the visible area of ​​the nine-square grid, assigning hidden layer coordinates to hanging or isolated nodes that exceed the area, and establishing virtual connection edges with the nearest visible node, the technical problems in existing kinship graph displays caused by space limitations, such as the simple truncation of important nodes, the loss of associated nodes after perspective switching, the inability of the interface to suggest the complete family network structure, and the difficulty for users to perceive the explorable direction, the invention achieves the technical effect of realizing a smooth transition of relative nodes from the visible area to the hidden layer, maintaining the logical integrity of the family network through virtual connections, guiding users to explore a wider range of kinship relationships with intuitive visual cues, and ultimately providing infinite possibilities for family network exploration within a limited interface space.

[0037] In a preferred embodiment of the present invention, step 6 above may include: Step 7.1: The asynchronous topology correction module parses the status of virtual connections and identifies target boundary nodes in the family knowledge graph that lack missing relationships. Specifically, the asynchronous topology correction module receives and analyzes all virtual connection information established in the previous step, focusing on connections with poor quality or potential problems. Analysis metrics include the relationship certainty score, data integrity ratio, and topology consistency evaluation. For example, if a virtual connection edge is marked as "relationship uncertain" or "data incomplete," it may indicate a potential kinship relationship between the connected nodes that has not yet been included in the knowledge graph. Based on these analyses, the target boundary nodes most likely to have missing relationships in the family knowledge graph are identified. These nodes are typically located at the topological boundaries of the knowledge graph, such as the oldest ancestor, the youngest descendant, or distant relatives of a collateral branch. The entity identifiers, relationship states, and possible association directions of these target boundary nodes are recorded in the correction queue to provide target directions for subsequent relationship completion.

[0038] Step 7.2: Based on the entity identifier of the target boundary node, initiate a breadth-first search in the global data of the family knowledge graph to extract a set of candidate completion nodes that are potentially related to the target boundary node. Specifically, starting from the identified target boundary node, initiate a breadth-first search algorithm in the global database of the entire family knowledge graph. The search process is not limited to the local graph currently viewed by the user, but extends to the entire knowledge base, including nodes that may come from other family branches. Set reasonable search depth and expansion rules to avoid excessive consumption of computing resources. The search strategy considers multiple potential association paths: direct bloodline paths, collateral kinship paths, affinal relationships, etc., and applies heuristic rules during the search process to prioritize exploring directions that are more likely to have associations. For example, for the older generation target node at the top of the family tree, the system will prioritize searching other elderly people of the same generation to find possible sibling relationships; for younger members at the end of the family branch, it may prioritize searching the peer group. From the search results, select a set of candidate completion nodes that are most likely to be related to the target boundary node. These candidate nodes either have similar surname characteristics, regional backgrounds, or show connection possibilities through indirect relationships.

[0039] Step 7.3 calculates the kinship matching score between the target boundary node and each candidate completion node in the candidate completion node set. This specifically includes: conducting in-depth analysis of the candidate completion node set selected in the previous step, and calculating a matching score for the potential relationship between each candidate node and the target boundary node. The score calculation is based on multi-dimensional feature comparison: first, basic demographic feature matching, such as whether the age difference conforms to the typical generational interval of a specific kinship relationship, and whether the surname pattern conforms to the family inheritance pattern; second, social relationship overlap, detecting whether the two nodes have a common kinship network, such as mutually known third-party relatives; third, geographic and spatiotemporal consistency, judging whether the life trajectories of the two people overlap in time and space, supporting the formation of a kinship relationship; and fourth, transitive reasoning of known relationships, using confirmed kinship links for logical deduction to assess the rationality of the potential relationship. Combining the evaluation results of these dimensions, a kinship matching score between 0 and 1 is generated for each pair of "target boundary node - candidate completion node" combinations, with a higher score indicating a greater likelihood of a kinship relationship between the two nodes. At the same time, the system will also deduce the most likely specific kinship type, such as "may be father and son (0.87 points)" or "may be cousins ​​(0.65 points)".

[0040] Step 7.4: When a candidate completion node has a kinship matching score greater than a preset matching threshold, a candidate relationship edge is established between the target boundary node and the candidate completion node, and pushed to a verification queue for manual confirmation or automatic fusion. Specifically, this involves comparing the kinship matching score of each pair of nodes with a preset matching threshold (e.g., 0.75). When the matching score between a candidate completion node and the target boundary node exceeds this threshold, it is determined that there is a high probability of an actual kinship relationship between them, warranting further verification. The system creates a candidate relationship edge connecting these two nodes in the family knowledge graph data structure. The edge attributes include: the inferred relationship type (e.g., "father-son", "siblings"), relationship confidence (i.e., matching score), a summary of the reasoning basis, and a creation timestamp. These newly created candidate relationship edges are pushed to a dedicated verification queue for further processing. The verification queue has two subsequent processing paths: for relationships with exceptionally high confidence (e.g., scores exceeding 0.9), automatic fusion logic may be triggered, directly adding the relationship to the knowledge graph but marking it as "pending confirmation"; for other candidate relationships, a verification request will be displayed to the user at an appropriate time, such as "We have discovered that Zhang San may be Li Si's cousin. Do you confirm this relationship?", awaiting manual confirmation before being formally included in the knowledge graph. This dual-path verification mechanism ensures a balance between efficiency and accuracy in knowledge graph expansion.

[0041] Step 7.5: After the candidate relationship edge is confirmed to be effective, the nine-square grid spatial mapping engine is triggered to update the virtual connection state of the hidden layer in the next node switching event. Specifically, when a candidate relationship edge is officially accepted as a valid relationship edge of the family knowledge graph through manual confirmation or automatic fusion process, the system will record this state change and set an update flag. This flag indicates that the nine-square grid spatial mapping engine needs to refresh the topology of the relevant area when the next node switching event occurs. When the user performs the next node switching operation (such as swiping to select a new central kin), the update flag is detected, and the nine-square grid spatial mapping engine will recalculate and update the virtual connection state of the affected area. For newly confirmed relationship edges, the related virtual connections may undergo the following changes: the originally vague "possible association" virtual connection is upgraded to a clear entity relationship connection; the originally completely isolated nodes are connected to the main graph through the added relationship and become accessible normal nodes; some complex virtual paths may be replaced by new direct relationship edges, simplifying the navigation structure. While updating these connection states, the interface visual elements will also be adjusted accordingly, such as updating the dashed edges representing uncertain relationships to solid edges, or adding clear relationship type labels to newly confirmed relationships. This dynamic update mechanism ensures that users always see the latest and most complete kinship information when exploring family networks, enhancing the continuity of the user experience and the sense of surprise in knowledge discovery.

[0042] In this embodiment of the invention, the technical means of using an asynchronous topology correction module to parse the virtual connection state to identify target boundary nodes with missing relationships, initiate a breadth-first search in the global data based on the boundary nodes to extract candidate completion nodes with potential relationships, calculate the kinship matching score between the two types of nodes, establish candidate relationship edges for node pairs with scores exceeding a threshold and push verification, and update the virtual connection state after the relationship is confirmed, thus overcoming the technical problems of incomplete family network data, unclear boundary relationships, lack of automatic completion mechanism, and the need for users to manually enter a large amount of relationship data in the existing kinship graph display. This achieves the technical effect of intelligently detecting and completing missing kinship relationships during normal user use, reducing the data maintenance burden, gradually improving the completeness and accuracy of the family knowledge graph, and ensuring data quality through manual confirmation, ultimately realizing the continuous optimization and self-improvement of the family relationship network.

[0043] In a preferred embodiment of the present invention, after rendering the updated nine-grid interface based on the result of coordinate projection transformation, the method further includes: Step 8.1 involves detecting the data completeness label of kinship nodes in each visible slot of the nine-square grid coordinate matrix. The data completeness label is generated based on the missing attribute field rate of the kinship node in the family knowledge graph. Specifically, after completing the basic rendering of the nine-square grid interface, a data quality assessment process is initiated. For each visible slot of the kinship node in the nine-square grid, the system retrieves its complete data records stored in the family knowledge graph and extracts all attribute field information. These attribute fields typically include: basic population information (name, gender, date of birth, marital status, etc.), social characteristics (educational background, occupation, place of residence, etc.), family-specific information (generational ranking, family role, etc.), and system records (last update time, data source, etc.). The missing attribute field rate for each node is calculated, which is the ratio of the number of missing attribute fields to the total number of fields that the node type should have. For example, if a kinship node type defines 20 standard attribute fields, but currently only 12 fields have valid values, the missing rate is 40%. Based on the calculation results, a data completeness label is generated for each relative node, typically divided into several levels: such as "complete" (missing rate <10%), "partially missing" (10% ≤ missing rate <50%), "severely missing" (50% ≤ missing rate <90%), and "placeholder" (missing rate ≥90%, only the most basic identification information). These completeness labels are stored along with the node to guide subsequent differentiated rendering processing.

[0044] Step 8.2: Based on the data integrity label, perform dynamic downgrade or upgrade processing of the visual rendering level for the visible slots. The visual rendering level includes a first rendering state representing completeness, a second rendering state representing partial missingness, and a third rendering state representing severe missingness or only placeholder nodes. Specifically, based on the data integrity label evaluated in the previous step, assign a corresponding visual rendering level to each relative node in the 3x3 grid. Three main rendering states are defined, each with its own distinct visual presentation: The first rendering state applies to nodes labeled "Complete," providing the richest visual presentation, including a clear personal photo or high-resolution headshot, complete name and dates of birth and death, key identity information, and supporting rich interactive effects and animations. The second rendering state applies to nodes labeled "Partially Missing," providing a moderate level of visual presentation, possibly using simplified icons instead of photos, displaying basic names but omitting detailed biographical information, using neutral tones instead of vibrant colors, and correspondingly simplifying interactive responses. The third rendering state applies to nodes labeled "Severely Missing" or "Placeholder," employing the most minimalist visual presentation, displaying only silhouettes or generic gender icons, possibly only showing a surname or appellation (such as "great-grandfather") without a specific name, using semi-transparent or dashed borders to indicate incomplete data, and limiting interactive options to basic functions. Differentiated rendering of the interface based on these three states intuitively reflects differences in data quality, helping users identify nodes that require supplementary information. As node data is updated and improved, the rendering level is dynamically adjusted to upgrade or downgrade the node's visual presentation, promptly reflecting changes in data status.

[0045] Step 8.3: For placeholder nodes in the third rendering state, search the family knowledge graph for confirmed heterogeneous nodes with the same name. Specifically, pay special attention to nodes in the third rendering state (severely missing or only placeholders). These nodes often only have basic identification information, such as a vague name or role description (e.g., "Li, grandfather's sister"). For each such placeholder node, initiate a special heterogeneous node search operation across the entire family knowledge graph. Heterogeneous nodes with the same name refer to nodes in different positions within the graph, potentially belonging to different subgraphs, but sharing a potential identity. The search strategy comprehensively considers multiple matching factors: basic identifier matching, such as name similarity calculation, nickname or alias comparison; relational environment matching, checking for overlap or compatibility in the node's relational network; spatiotemporal consistency verification, assessing whether spatiotemporal information such as birth and death dates and activity areas allow two nodes to point to the same person; and data source relevance, examining whether the information of different nodes comes from related data sources. Through comprehensive comparison across these dimensions, calculate the identity probability score for each pair of "placeholder node - candidate homogeneous node". When the similarity score between a candidate node and a placeholder node exceeds a preset threshold (e.g., 0.85 points), it is determined that a confirmed heterogeneous node with the same name has been discovered, which means that there may be a more detailed record of the placeholder person somewhere in the graph.

[0046] Step 8.4: If a placeholder node exists, asynchronously inject some attribute fields from the heterogeneous node with the same name into the placeholder node, and upgrade the rendering state of the placeholder node from the third rendering state to the second rendering state. Specifically, this includes: once a heterogeneous node with high similarity to the placeholder node is found, the data fusion process is initiated. First, the target heterogeneous node with the same name is identified. This node usually has more complete data and contains more valid attribute field information. Controlled asynchronous injection of attribute fields is performed: select key attribute fields with high reliability and missing information from the heterogeneous node with the same name, such as basic population information and relationship identifiers, and copy these field values ​​to the corresponding attributes of the placeholder node; however, the unique information of the placeholder node itself is retained, such as relationship records under specific circumstances, to avoid overwriting valuable differential data; add special markers to the injected attribute fields to indicate that the data source is cross-node fusion, which facilitates future verification and management. After the data injection is completed, the system recalculates the attribute field missing rate of the node. Due to the addition of a large amount of information, the missing rate is usually significantly reduced, for example, from the original 95% (placeholder only) to 35% (partially missing). Based on the updated missing rate, the data completeness label for this node was upgraded from "placeholder" or "severely missing" to "partially missing." Correspondingly, the node's visual rendering state was also upgraded from the third rendering state (most minimalist) to the second rendering state (medium richness). This improvement was immediately reflected in the interface, making the previously vague placeholder node more specific and information-rich, such as displaying more detailed names and basic biographical information, significantly improving the user's information retrieval efficiency and experience.

[0047] In this embodiment of the invention, by employing technical means such as detecting the data integrity labels of kinship nodes in each visible slot of the nine-square grid, performing dynamic downgrading or upgrading of visual rendering levels based on the labels, and retrieving heterogeneous nodes with the same name for placeholder nodes in the third rendering state and injecting some of their attributes into the placeholder nodes to improve the rendering state, the invention overcomes the technical problems in existing kinship graph displays, such as the lack of intuitive expression of data integrity, the consistent visual representation of different quality data leading to user misjudgment, the inability to effectively utilize the scattered information of the same person in the graph, and the lack of information in placeholder nodes affecting user understanding. This achieves the technical effect of intuitively reflecting data integrity through differentiated visual rendering, guiding users to identify nodes requiring supplementary information through multi-level rendering states, and improving the richness of placeholder node information through the retrieval of heterogeneous nodes with the same name and attribute injection. Ultimately, while maintaining the aesthetic appeal of the interface, it provides data quality transparency, enhancing users' understanding and trust in the content of the family knowledge graph.

[0048] In a preferred embodiment of the present invention, the method further includes: Step 9.1: Receive the collaborative completion data packet returned from the external sharing interface, and extract the target slot identifier and completion field information contained in the collaborative completion data packet. Specifically, this includes: designing an open data collaboration mechanism that allows receiving supplementary family information from other users or external data sources through the external sharing interface. When other users supplement information about a relative through the sharing function, or when external systems (such as genealogy archives or social networking platforms) provide relevant data, this information is packaged into a standard format collaborative completion data packet and transmitted to the system. Upon receiving the data packet, first verify its digital signature and security tag to ensure that the data source is trustworthy and the content has not been tampered with. After successful verification, parse the data packet structure and extract two types of core information: first, the target slot identifier, which indicates the specific relative node location for which supplementary information is needed, including the node's unique identifier, path marker in the family knowledge graph, and current slot number in the nine-square grid (if any); second, completion field information, which includes new or updated data provided for the target node, such as more accurate birth and death dates, missing educational background, newly discovered relationship connections, etc. Each field is accompanied by a data source description and confidence score. The system temporarily stores this extracted information in a verification buffer, in preparation for subsequent consistency verification.

[0049] Step 9.2: Locate the node to be completed in the family knowledge graph corresponding to the target slot identifier, and verify the logical consistency between the completed field information and the existing relational edge constraints of the node to be completed. Specifically, this includes: accurately locating the relative node to be completed in the family knowledge graph based on the extracted target slot identifier. The location process considers the node's unique identifier, relational path, and current view state to ensure that the correct target node is found. After finding the target node, the system initiates a logical consistency verification process to comprehensively check whether the completed field information matches the node's existing relational edge constraints. The verification process includes multiple checks: time consistency check, ensuring that the supplemented birth and death dates do not lead to absurd age relationships, such as children being born before their parents; relation compatibility check, verifying whether the new relationship conflicts with the existing family structure, such as not being both someone's father and brother at the same time; cultural norm check, assessing whether the supplemented information conforms to naming habits, marriage rules, etc., under a specific family cultural background; and data source reliability assessment, judging the credibility of the information based on the provider's historical accuracy and supporting materials. An evaluation result of pass / warning / fail is generated for each check, and the overall logical consistency score is calculated. If a serious logical conflict is found (such as an age relationship that clearly violates the laws of nature), the system will directly reject the relevant field; for minor conflicts, a warning will be issued but processing will continue.

[0050] Step 9.3: If the verification passes, the completed field information is written into the attributes of the node to be completed, and the asynchronous topology correction module is triggered to recalculate the kinship confidence score between the node to be completed and its neighboring nodes. Specifically, this includes: after the logical consistency verification of the completed field information passes, a data integration operation is performed. First, the verified completed field information is written into the corresponding attributes of the node to be completed one by one. For attributes with the same name, if the confidence of the new data is higher than that of the existing data, it is overwritten and updated; if the confidence is similar, both records are retained and marked as "pending confirmation". While the data is being written, the system records the update operation log, including metadata such as modification time, data source, and operation type, to support possible future data backtracking and auditing needs. After the data update is completed, the system triggers the recalculation process of the asynchronous topology correction module. This process focuses on re-evaluating the strength of the relationship between the completed node and its surrounding neighboring nodes. A kinship assessment algorithm is applied, and based on the newly supplemented information (such as confirmed parent-child relationships, more accurate birth information, etc.), the kinship confidence score between the node and all directly connected nodes is recalculated. For example, if newly added information confirms "Zhang San's" identity as his biological father, the system will increase the bloodline confidence score between "Zhang San" and his father node, potentially raising it from 0.7 (based on indirect evidence) to 0.95 (based on direct document proof). This dynamic update mechanism ensures that the relationship strength assessment in the family knowledge graph always reflects the latest and most complete data state.

[0051] Step 9.4: Based on the updated bloodline confidence score, determine whether the node to be completed meets the condition for recovery from the overflow slot to the desired orientation coordinates. If it does, the node to be completed will be remapped to the available slot corresponding to the desired orientation coordinates when the next node switching event is triggered. This includes: analyzing the recalculated bloodline confidence score, paying particular attention to relatives who were originally assigned to overflow slots due to low scores. Define the slot priority recovery condition: if the bloodline confidence score of a node increases by more than a preset threshold (e.g., by more than 0.3 points), and the new score is higher than the score of the node currently occupying the desired orientation coordinates, then the node is eligible to return to its desired slot. For example, if "Zhang San's" adoptive father originally occupied the father slot (0,0), while the biological father was placed in an overflow slot due to insufficient information, and now the supplemented information confirms the biological father's identity and significantly increases the bloodline confidence score, then the biological father node may meet the recovery condition. Once a node to be completed is determined to meet the restoration conditions, its position is not immediately adjusted. Instead, a "pending remapping" flag is set, awaiting the next node switching event triggered by the user. When the user performs the next node switching operation (such as swiping to select a new central relative), the system detects this flag and, during the reconstruction of the 3x3 grid layout, moves the node to be completed that meets the conditions from its original overflow slot back to the available slot corresponding to the desired orientation coordinates. Simultaneously, the node that originally occupied that slot may be moved to another suitable location or an overflow slot. This layout adjustment method, triggered by user actions, avoids sudden changes while the user is viewing the content, providing a smoother user experience, while ensuring that the display of family relationships always reflects the most accurate and important kinship connections.

[0052] In this embodiment of the invention, by employing technical means such as receiving collaborative completion data packets returned from an external sharing interface and extracting target slot identifiers and completion field information, locating nodes to be completed in the graph and verifying the logical consistency of the supplementary information, updating node attributes and recalculating kinship confidence scores after verification, determining whether a node meets the conditions for recovering from an overflow slot to the desired position based on the new score, and performing remapping during the next switch, the technical problems of existing kinship graph displays, such as lack of multi-user collaborative completion mechanisms, difficulty in integrating external data, information updates not affecting node display priority, and inability to intuitively reflect user contributions, are overcome. This achieves the technical effects of realizing multi-channel information collaborative completion, rigorous verification to ensure data quality and consistency, dynamic adjustment of node priorities to reflect the latest understanding, and encouraging user participation in the co-construction of the family knowledge graph, ultimately building a more complete, accurate, and continuously evolving family relationship network.

[0053] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0054] Figure 3 This is a schematic diagram of the structure of a dynamic node mapping system based on a nine-square grid kinship map provided in an embodiment of this application. Figure 3 As shown, the dynamic node mapping system based on the nine-square grid kinship graph includes: The graph retrieval module is used to receive trigger instructions for target kinship entities, extract the entity identifier of the target kinship entity, and retrieve the initial adjacency subgraph associated with the entity identifier in the pre-constructed family knowledge graph. The family knowledge graph is composed of kinship nodes and relationship edges, and the relationship edges carry relationship type weights and bloodline confidence scores. The spatial mapping module is used to input the initial adjacency subgraph into the nine-grid spatial mapping engine. Based on the topological path between each relative node and the target relative entity in the initial adjacency subgraph, it performs spatial conflict resolution processing to allocate each relative node to the available slots in the nine-grid coordinate matrix and generate the initial mapping layout. The event listening module is used to listen for node switching events initiated on the center slot of the 3x3 grid coordinate matrix and extract the sliding direction vector and sliding speed parameters carried in the node switching event. The topology reconstruction module is used to determine the candidate center node to be switched based on the sliding direction vector and sliding speed parameters, and trigger the nine-square grid space mapping engine to perform local topology rotation reconstruction of the family knowledge graph with the candidate center node as the new anchor point, and calculate the topology deflection angle of the candidate center node relative to the original center node. The coordinate projection module is used to perform coordinate projection transformation on the relatives nodes in the initial adjacency subgraph based on the topology deflection angle. It maps the dangling or isolated nodes generated in the topology rotation reconstruction to the hidden layer of the nine-grid coordinate matrix, and establishes virtual connections between the dangling or isolated nodes and the visible slot nodes in the nine-grid coordinate matrix in the hidden layer. The rendering and correction synchronization module is used to render the updated nine-grid interface based on the results of coordinate projection transformation, and to synchronize the virtual connection state of the hidden layer to the asynchronous topology correction module, so that the asynchronous topology correction module can perform a completion operation on the broken boundary of the family knowledge graph in the background.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0056] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A dynamic node mapping method based on a nine-square grid kinship graph, characterized in that, include: Receive a trigger command for a target relative entity, extract the entity identifier of the target relative entity, and retrieve the initial adjacency subgraph associated with the entity identifier in a pre-constructed family knowledge graph, wherein the family knowledge graph is composed of relative nodes and relationship edges, and the relationship edges carry relationship type weights and bloodline confidence scores; The initial adjacency subgraph is input into the nine-grid spatial mapping engine. Based on the topological path between each relative node in the initial adjacency subgraph and the target relative entity, spatial conflict resolution processing is performed to allocate each relative node to the available slots in the nine-grid coordinate matrix and generate the initial mapping layout. Listen for node switching events initiated on the center slot of the nine-grid coordinate matrix, and extract the sliding direction vector and sliding speed parameters carried in the node switching event; Based on the sliding direction vector and the sliding speed parameter, the candidate center node to be switched is determined, and the nine-square grid space mapping engine is triggered to perform local topological rotation reconstruction of the family knowledge graph with the candidate center node as the new anchor point, and the topological deflection angle of the candidate center node relative to the original center node is calculated. Based on the topology deflection angle, coordinate projection transformation is performed on the kinship nodes in the initial adjacency subgraph, and the dangling or isolated nodes generated in the topology rotation reconstruction are mapped to the hidden layer of the nine-grid coordinate matrix. In the hidden layer, virtual connections are established between the dangling or isolated nodes and the visible slot nodes in the nine-grid coordinate matrix. The updated nine-grid interface is rendered based on the result of the coordinate projection transformation, and the virtual connection state of the hidden layer is synchronized to the asynchronous topology correction module, so that the asynchronous topology correction module performs a completion operation on the broken boundary of the family knowledge graph in the background.

2. The method according to claim 1, characterized in that, The step of retrieving the initial adjacency subgraph associated with the entity identifier in the pre-constructed family knowledge graph includes: Starting from the entity identifier, a one-hop expansion is performed along the relationship edge in the family knowledge graph to extract the set of kinship nodes directly connected to the target kinship entity; For each relative node in the set of relative nodes, read its corresponding relationship edge attributes, which include generational difference, peer seniority number and relationship edge timeliness marker. Filter out the kinship nodes whose relationship edge timeliness marker indicates that they are invalid or cancelled, and perform initial sorting on the remaining kinship nodes according to the generational difference and the peer seniority number to generate the initial adjacency subgraph.

3. The method according to claim 1, characterized in that, The step of performing spatial conflict resolution based on the topological paths between each relative node in the initial adjacency subgraph and the target relative entity, to assign each relative node to an available slot in the nine-grid coordinate matrix, includes: For each kin node in the initial adjacency subgraph, the expected directional coordinates of the kin node in the nine-square coordinate matrix are calculated based on its corresponding generational difference and peer seniority number. Detect whether there is an overlap between the expected directional coordinates of at least two kinship nodes. If an overlap occurs, extract the relationship type weight and bloodline confidence score of each overlapping kinship node. Based on the weighted sum of the relationship type weight and the bloodline confidence score, the overlapping kinship nodes are sorted in descending order, and the kinship node with the highest weighted sum is mapped to the available slot corresponding to the expected directional coordinates. For the remaining overlapping relative nodes that are not mapped to available slots, calculate the coordinates of the overflow slot adjacent to the available slot based on their peer seniority number, map the remaining overlapping relative nodes to the overflow slot coordinates, and add a fold / expand identifier to the node corresponding to the overflow slot coordinates.

4. The method according to claim 1, characterized in that, The step of determining the candidate center node to be switched based on the sliding direction vector and the sliding speed parameter includes: The sliding direction vector is mapped to the standard azimuth axis of the nine-grid coordinate matrix to determine the candidate azimuth interval; When the sliding speed parameter is less than or equal to a preset speed threshold, the relative node that is directly adjacent to the center slot within the candidate orientation interval is determined as the candidate center node. When the sliding speed parameter is greater than the preset speed threshold, the speed gradient level to which the sliding speed parameter belongs is calculated, and a jump search is performed in the family knowledge graph along the generational path indicated by the candidate orientation interval. The Nth generation relative node that is found and matches the speed gradient level is determined as the candidate center node.

5. The method according to claim 1, characterized in that, The step of using the candidate center node as the new anchor point to perform local topological rotation reconstruction of the family knowledge graph, and calculating the topological deflection angle of the candidate center node relative to the original center node, includes: In the family knowledge graph, extract the shortest relationship path between the original center node and the candidate center node; Determine the target generation vector and target peer vector corresponding to the shortest relationship path; Calculate the first angle between the target generation vector and the horizontal axis of the nine-square grid coordinate matrix, and the second angle between the target peer vector and the vertical axis of the nine-square grid coordinate matrix; The vector sum of the first included angle and the second included angle is determined as the topological deflection angle.

6. The method according to claim 1, characterized in that, The step of performing coordinate projection transformation on the kinship nodes in the initial adjacency subgraph based on the topological deflection angle, mapping the dangling or isolated nodes generated in the topological rotation reconstruction to the hidden layer of the nine-grid coordinate matrix, includes: For each relative node in the initial adjacency subgraph, its original coordinates are transformed by a rotation matrix based on the topological deflection angle to obtain the transformed coordinates; Determine whether the transformed coordinates fall within the visible slot range of the nine-grid coordinate matrix; If it does not fall into the category, the relative node is determined to be a dangling node or an isolated node generated in the topology rotation reconstruction, and the dangling node or isolated node is assigned hidden coordinates in the hidden layer; Establish a virtual connection edge between the hidden coordinates of the suspended or isolated node and the nearest visible slot node.

7. The method according to claim 1, characterized in that, The step of having the asynchronous topology correction module perform a completion operation on the broken boundaries of the family knowledge graph in the background includes: The asynchronous topology correction module parses the state of the virtual connection and identifies the target boundary node with missing associations in the family knowledge graph; Based on the entity identifier of the target boundary node, a breadth-first search is initiated in the global data of the family knowledge graph to extract a set of candidate completion nodes that have potential association with the target boundary node. Calculate the kinship matching score between the target boundary node and each candidate completion node in the candidate completion node set; When there is a candidate completion node whose kinship matching score is greater than the preset matching threshold, a candidate relationship edge is established between the target boundary node and the candidate completion node, and pushed to the verification queue to wait for manual confirmation or automatic fusion. After the candidate relationship edge is confirmed to be effective, the nine-square grid space mapping engine is triggered to update the virtual connection state of the hidden layer in the next node switching event.

8. The method according to any one of claims 1 to 7, characterized in that, After rendering the updated 3x3 grid interface based on the result of the coordinate projection transformation, the method further includes: The data integrity label of the relative node in each visible slot of the nine-square coordinate matrix is ​​detected. The data integrity label is generated based on the missing attribute field rate of the relative node in the family knowledge graph. Based on the data integrity label, the visible slots are subjected to dynamic downgrade or upgrade processing of the visual rendering level, wherein the visual rendering level includes a first rendering state representing completeness, a second rendering state representing partial missingness, and a third rendering state representing severe missingness or only placeholder nodes. For the placeholder node in the third rendering state, search the family knowledge graph to see if there is a confirmed heterogeneous node with the same name for the placeholder node. If it exists, some attribute fields of the heterogeneous node with the same name are asynchronously injected into the placeholder node, and the rendering state of the placeholder node is promoted from the third rendering state to the second rendering state.

9. The method according to claim 8, characterized in that, The method further includes: Receive the collaborative completion data packet returned by the external sharing interface, and extract the target slot identifier and completion field information contained in the collaborative completion data packet; Locate the node to be completed in the family knowledge graph corresponding to the target slot identifier, and verify the logical consistency between the completion field information and the existing relation edge constraints of the node to be completed; If the verification is successful, the completed field information is written into the attribute of the node to be completed, and the asynchronous topology correction module is triggered to recalculate the lineage confidence score of the node to be completed and its neighboring nodes. Based on the updated lineage confidence score, it is determined whether the node to be completed meets the condition of restoring from the overflow slot to the desired orientation coordinates. If it does, the node to be completed will be remapped to the available slot corresponding to the desired orientation coordinates when the next node switching event is triggered.

10. A dynamic node mapping system based on a nine-square grid kinship map, characterized in that, The system is used to perform the method as described in any one of claims 1 to 9, the system comprising: The graph retrieval module is used to receive a trigger command for a target kinship entity, extract the entity identifier of the target kinship entity, and retrieve the initial adjacency subgraph associated with the entity identifier in a pre-constructed family knowledge graph. The family knowledge graph is composed of kinship nodes and relationship edges, and the relationship edges carry relationship type weights and bloodline confidence scores. The spatial mapping module is used to input the initial adjacency subgraph into the nine-grid spatial mapping engine, and perform spatial conflict resolution processing based on the topological path between each relative node in the initial adjacency subgraph and the target relative entity, so as to allocate each relative node to the available slots of the nine-grid coordinate matrix and generate the initial mapping layout. The event listening module is used to listen for node switching events initiated on the center slot of the nine-grid coordinate matrix and extract the sliding direction vector and sliding speed parameters carried in the node switching event. The topology reconstruction module is used to determine the candidate center node to be switched based on the sliding direction vector and the sliding speed parameter, and to trigger the nine-square grid space mapping engine to perform local topology rotation reconstruction of the family knowledge graph with the candidate center node as the new anchor point, and calculate the topology deflection angle of the candidate center node relative to the original center node. The coordinate projection module is used to perform coordinate projection transformation on the relatives nodes in the initial adjacency subgraph based on the topology deflection angle, map the dangling or isolated nodes generated in the topology rotation reconstruction to the hidden layer of the nine-grid coordinate matrix, and establish virtual connections between the dangling or isolated nodes and the visible slot nodes in the nine-grid coordinate matrix in the hidden layer. The rendering and correction synchronization module is used to render the updated nine-grid interface based on the result of the coordinate projection transformation, and to synchronize the virtual connection state of the hidden layer to the asynchronous topology correction module, so that the asynchronous topology correction module performs a completion operation on the broken boundary of the family knowledge graph in the background.

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