A root and affix anchoring graph learning method, system and device for professional English terminology learning and a storage medium

Abstract

The application discloses a root and affix anchoring graph learning method and system for professional English terminology learning, equipment and a storage medium. The method constructs a root and affix anchoring relationship graph through standardized knowledge unit data, and generates one or more display views such as a network graph, a mind map, a tree graph, a list, a card, a folding outline, a spherical projection graph or a three-dimensional relationship graph of the earth type based on the same anchoring relationship graph. In the display view, the node relationship is retained in at least one of the following ways: explicit connection, hierarchical inclusion, group ownership, spatial proximity, sequential arrangement, label mapping, index jumping, spherical coordinate position or three-dimensional space position. User interaction events are collected and mapped to a state machine, the learning state is updated according to the event sequence, and deduplication and local storage are performed. The scheme reduces the possibility of evading by replacing the display form, hiding the connection, switching the two-dimensional and three-dimensional structure or changing the interface organization method.

Description

I. Technical Field

[0002] This invention relates to the fields of computer software and intelligent education technology, specifically to a method, system, device, and storage medium for constructing root and affix anchoring maps, multi-form display projection, relation preservation and expression, interactive event parsing, and learning status updates for professional English terminology learning scenarios. II. Background Technology

[0004] Existing professional English learning applications mostly organize content using vocabulary lists, question banks, or fixed chapters. Common technical issues include:

[0005] 1) The word-formation path between roots, affixes, and derived words is not well expressed, making it difficult to form an interpretable "root-affix-word-term" learning link;

[0006] 2) The diagram display is coupled with the underlying learning logic. Once it is replaced with mind maps, tree diagrams, lists, cards, folded outlines, spherical projection diagrams, earth-shaped 3D relationship diagrams or other interface forms, the core logic needs to be modified.

[0007] 3) Some products achieve the same learning logic by hiding the connections, only retaining the grouping, indentation, labels, sorting, jump relationships, or three-dimensional spatial relative positions, which can easily circumvent the protection that is only for the "graphical connection interface" on the surface.

[0008] 4) Learning status recording relies on a single trigger condition, which leads to issues such as misrecording, omissions, duplicate writing, and inconsistencies across platforms;

[0009] 5) The learning of professional terms such as medical English, legal English, financial English, accounting and auditing English, business and trade English, computer and information technology English, engineering and technology English, and intellectual property English requires higher requirements for root and affix anchoring and term derivation tracking, and general vocabulary lists are difficult to reuse stably.

[0010] Therefore, a learning solution is needed that takes root and affix maps as its core, while also being compatible with multiple display formats, implicit relationship expressions, and multiple professional English terminology databases. III. Summary of the Invention

[0012] (a) Purpose of the invention

[0013] This invention aims to provide a word root and affix anchor graph-driven learning scheme. Through the mechanism of "anchor graph + equivalent view projection + relation preservation expression + event state machine + persistence and synchronization", it avoids being circumvented by a single display form, hidden connections or replacement interface structure, and improves the accuracy of learning state recording and system scalability.

[0014] (II) Technical Solution

[0015] To achieve the above objectives, the present invention provides the following method:

[0016] S1, Knowledge Unit Standardization: Acquire learning data and standardize it to form a data structure that includes unit identifier, unit type, word form, definition, tag, difficulty, and prerequisite relationship; the unit type includes at least one of the following: root word, affix, word-forming morpheme, derived word, term, and example sentence.

[0017] S2, Root and Affix Anchoring Graph Construction: An anchoring relationship graph is generated based on the data structure, where nodes are knowledge units and edges are relationship connections; it includes at least the word formation relationship of "root / affix / morpheme-derived word", and can be expanded to include one or more of the following: semantic relationship, topic relationship, collocation relationship, and industry tag relationship;

[0018] S3, Multi-form display projection: Generate at least one target display view based on the same anchoring relationship graph. The target display view includes two-dimensional or three-dimensional views. The view includes one or more of the following: graph network diagram, mind map, tree diagram, list view, card view, collapsible outline view, spherical projection diagram, earth-shaped three-dimensional relationship diagram, and orbit-shaped three-dimensional relationship diagram.

[0019] S4, Relationship Preservation Expression: In the target display view, the node associations in the anchored relationship graph are preserved through at least one relationship expression method, wherein the relationship expression method includes one or more of the following: explicit connection, hierarchical inclusion, grouping, spatial proximity, sequential arrangement, label mapping, index jump, and two-dimensional or three-dimensional spatial location mapping.

[0020] S5, Adaptive rendering: Selects and renders the target layout strategy based on graph size parameters and terminal resource parameters;

[0021] S6, Interaction Event Parsing: Collect user interaction events and map them to state machine inputs. The interaction events include at least one of the following: click, expand, collapse, search, rotate, zoom, drag, detail dwell, practice submission, and voice repetition.

[0022] S7, Learning status update: Update the learning status according to the event sequence and threshold rules. The learning status includes at least not learned, learning in progress, and mastered.

[0023] S8, Storage and Synchronization: Writes update results to local storage and performs cross-device synchronization when the network is available. In case of conflicts, merges according to version number and timestamp rules.

[0024] Optionally, in S3, the same anchoring relationship graph can be output as both a graphical view and a non-graphical view. The graphical view includes a planar view and a three-dimensional view, while the non-graphical view includes a grouped list, a card stream, a collapsible outline, or a search result stream, to ensure equivalent learning logic under different terminal capabilities.

[0025] Optionally, even if the target display view does not show visible connections in S4, as long as at least some node relationships are preserved through indentation levels, group boundaries, label mapping, sorting rules, jump references, proximity, or three-dimensional spherical coordinate positions, it is still considered a display projection of the anchoring relationship map.

[0026] Optionally, the target display views can be transformed through dynamic switching or animated transitions from planar to 3D, 2D to list, spherical to track layout, while maintaining consistency in node identification and relational semantics before and after the transformation.

[0027] Optionally, S2 employs a central root / affix anchoring mechanism, using roots and affixes as anchoring units and establishing derived word path relationships; the same derived word can be associated with multiple root or affix anchor points; the anchoring unit can be a single center, multiple centers, or grouped centers.

[0028] Optionally, the layout strategy in S5 includes at least two of the following: radial layout, force-oriented layout, and hierarchical layout; the graph scale parameters include at least one of the following: number of nodes, edge density, and clustering coefficient; and the terminal resource parameters include at least one of the following: frame rate, memory usage, and rendering latency.

[0029] Optionally, the learning state scoring function in S7 is:

[0030] Score = alpha*C + beta*R + gamma*T + delta*Q,

[0031] Wherein, C is the accuracy rate indicator, R is the review interval compliance indicator, T is the effective dwell time indicator, and Q is the application task completion indicator.

[0032] Optionally, before S7 executes, event deduplication is performed, based on the event fingerprint consisting of "user identifier + unit identifier + event type + time window".

[0033] Optionally, the learning data is professional English terminology data, at least in part from one or more of the following: medical English, legal English, financial English, accounting and auditing English, business and trade English, computer and information technology English, engineering and technical English, and intellectual property English.

[0034] (III) Beneficial Effects

[0035] Compared with the prior art, the present invention has at least the following effects:

[0036] 1) The underlying root and affix anchoring map is decoupled from the interface representation, so that even if it is changed to a mind map, tree diagram, list, card, folded outline, spherical projection map or earth-shaped 3D relationship diagram, the same learning engine is maintained;

[0037] 2) By using the "relationship preservation and expression" mechanism, both explicit and implicit connection methods are covered, reducing the possibility of circumventing the restrictions by hiding relationship lines or using grouping and sorting.

[0038] 3) Improve the accuracy of learning state recording through event state machines and deduplication mechanisms;

[0039] 4) Improve display stability in high-load scenarios through adaptive rendering strategies;

[0040] 5) Enhance the interpretability of learning paths for professional terms such as medical English, legal English, financial English, accounting and auditing English, business and trade English, computer and information technology English, engineering and technology English, and intellectual property English through the root and affix anchoring mechanism;

[0041] 6) Improve the consistency of state across multiple devices through cross-device merging mechanism. IV. Description of the attached drawings

[0043] Figure 1 A flowchart for constructing and displaying a root and affix graph;

[0044] Figure 2 Example of a graph type: network graph;

[0045] Figure 3 Flowchart for event state machine and deduplication writing;

[0046] Figure 4 A diagram illustrating the anchoring and derivation paths of word roots and affixes;

[0047] Figure 5 A schematic diagram of the equivalent projection of multiple forms of the same underlying anchoring pattern;

[0048] Figure 6 Example of a graph type: tree graph;

[0049] Figure 7 Example of a graph type: list projection;

[0050] Figure 8 Example of a map type: card projection;

[0051] Figure 9 This is a schematic diagram of a three-dimensional relationship diagram projected onto a globe.

[0052] Figure 10 This is a schematic diagram of the orbital three-dimensional relationship diagram projection. V. Detailed Implementation Methods

[0054] Example 1 (Medical English Terminology Scenario)

[0055] The system accesses a medical English terminology database and establishes root and affix anchoring maps by departmental theme and terminology tags; it uses list projection on low-performance terminals and graphical projection on high-performance terminals, while maintaining consistent learning state rules.

[0056] Example 2 (Legal English, Financial English, and Accounting / Auditing English Scenarios)

[0057] The system integrates legal English terminology databases, financial English terminology databases, and accounting and auditing English terminology databases, incorporating contract terms, litigation terms, securities terms, accounting and auditing terms into the same anchoring graph, and organizing the display and review paths based on industry tags.

[0058] Example 3 (Cross-industry professional English scenario)

[0059] The system integrates terminology databases for computer and information technology English, engineering and technology English, intellectual property English, and business and trade English, allowing for the switching of industry-specific corpora without altering the underlying schema and state machine. The business and trade English scenario can include terminology sets such as customs declaration, logistics, procurement, international settlement, letters of credit, and foreign trade contracts, enabling cross-industry reuse.

[0060] Example 4 (Multi-form projection scene)

[0061] The system generates network diagrams, mind maps, tree diagrams, lists, cards, globe-shaped 3D relationship diagrams, and orbit-shaped 3D relationship diagrams for the same anchored graph. Some views do not display explicit connections but express node relationships through hierarchical indentation, group boundaries, label mapping, sorting position, or 3D spatial coordinates, but the learning path and state update rules remain consistent.

[0062] Example 5 (Root and Affix Anchoring Scenario)

[0063] The system uses word roots and affixes as anchor units to establish a path of "word roots and affixes - derived words - terminology application", supporting the tracking of mastery progress and review priorities by word roots.

[0064] Example 6 (Avoiding morphological verification)

[0065] Without changing the underlying anchoring graph and state machine, the learning results are consistent when the display format is switched from a network diagram to a mind map, tree diagram, card flow, folded outline, earth-shaped 3D relationship diagram, orbit-shaped 3D relationship diagram, or a list view that only retains group labels. This shows that the present invention does not rely on a single UI form, a single dimension, or a single connection expression.

[0066] Terminology and Parameter Range

[0067] The “root and affix anchoring graph” in this specification includes knowledge graphs, relationship networks, knowledge trees, mind maps, and their equivalent representations.

[0068] The term "roots and affixes" in this specification includes roots, prefixes, suffixes, word-forming morphemes, morphological markers, and their equivalent terms.

[0069] The "relationship-preserving expressions" in this specification include explicit connections, hierarchical inclusion, grouping, spatial proximity, sequential arrangement, label mapping, index jump, spherical coordinate mapping, three-dimensional spatial coordinate mapping, and their equivalent relation expressions;

[0070] N1, N2, D1, P1, S_learn, and S_master are configurable parameters that are dynamically adjusted based on terminal performance and corpus size.

[0071] Alternative implementation methods

[0072] Without departing from the essence of this invention, the anchoring relationship connection can be represented by hyperedge, matrix mapping, or temporal relationship; the anchoring unit can be organized as a single center, multiple centers, or hidden center; the display projection can be expressed by a two-dimensional plane, a sphere, a three-dimensional space, or a combination thereof; the state model can be a rule model or a machine learning model; the deployment method can be terminal-side, server-side, or hybrid deployment, all of which should fall within the protection scope of this invention.

Claims

1. A root and affix anchoring map learning method for learning professional English terminology, characterized in that, include: Acquire and standardize learning data to form a data structure that includes knowledge unit identifiers, unit types, word forms, definitions, tags, and prerequisite relationships; Based on the data structure, a root and affix anchoring relationship graph is constructed. The anchoring relationship graph consists of knowledge unit nodes and relationship connections. The relationship connections include at least the word formation relationship between roots, affixes or word-forming morphemes and derived words. Generate at least one target display view based on the same anchoring relationship map; In the target display view, the node relationships of the anchoring relationship graph are preserved through at least one of the following methods: explicit connection, hierarchical inclusion, grouping, spatial proximity, sequential arrangement, label mapping, or index jump. Collect user interaction events and map them as state machine inputs; Update the learning status of knowledge units based on event sequences and threshold rules; Write the learning status to local storage.

2. The method according to claim 1, characterized in that, The knowledge unit types include at least one or more of the following: roots, affixes, word-forming morphemes, derived words, terms, and example sentences.

3. The method according to claim 1, characterized in that, The relationship connections also include one or more of the following: semantic relationships, topic relationships, collocation relationships, and industry tag relationships.

4. The method according to claim 1, characterized in that, The target display view includes two-dimensional or three-dimensional views, and includes one or more of the following: network diagram, mind map, tree diagram, list view, card view, collapsible outline view, spherical projection diagram, earth-shaped three-dimensional relationship diagram, and orbit-shaped three-dimensional relationship diagram.

5. The method according to claim 1, characterized in that, The generation of at least one target display view based on the same anchoring relationship map includes: simultaneously generating a graphical view and a non-graphical view based on the same anchoring relationship map.

6. The method according to claim 1, characterized in that, When the target display view does not show visible lines, the node relationship is expressed by at least one of the following methods: indentation level, group boundary, label mapping, sorting rules, jump reference, location proximity, spherical coordinate position, or three-dimensional spatial position.

7. The method according to claim 1, characterized in that, Also includes: Select a layout strategy based on graph size parameters and terminal resource parameters, and perform adaptive rendering.

8. The method according to claim 1, characterized in that, The user interaction events include at least one or more of the following: click, expand, collapse, search, rotate, zoom, drag, detail dwell, exercise submission, and voice repetition.

9. The method according to claim 1, characterized in that, The learning status includes at least three levels: not learning, learning, and mastering.

10. The method according to claim 1, characterized in that, Before updating the learning state, event deduplication is performed, based on an event fingerprint consisting of user identifier, knowledge unit identifier, event type, and time window.

11. The method according to claim 1, characterized in that, Also includes: Perform cross-platform synchronization when the network is available, and merge conflicts based on version number and timestamp.

12. The method according to claim 1, characterized in that, The learning data is derived, in part, from one or more specialized terminology databases, including but not limited to medical English, legal English, financial English, accounting and auditing English, business and trade English, computer and information technology English, engineering and technical English, and intellectual property English.

13. The method according to claim 1, characterized in that, The anchoring relationship map sets up a central root and affix anchoring unit and establishes the word formation path relationship between the root and affix and the derived words.

14. The method according to claim 13, characterized in that, The anchoring unit can be one or more of a single center, multiple centers, or grouped centers, and the same derived word can be associated with one or more anchoring units.

15. The method according to claim 13, characterized in that, Generate a priority list for derivation word review based on the learning status of the anchor unit for word roots and affixes.

16. A root and affix anchoring graph learning system for learning professional English terminology, characterized in that, include: The data standardization module is used to standardize learning data and generate knowledge unit structures. Anchoring graph construction module is used to generate a root and affix anchoring relationship graph consisting of knowledge unit nodes and relational connections; The multi-view projection module is used to generate at least one target display view based on the same anchoring relationship map; The relationship preservation module is used to preserve node relationships through at least one of the following methods: explicit connection, hierarchical inclusion, group affiliation, spatial proximity, sequential arrangement, label mapping, or index jump. The event parsing module is used to collect and parse user interaction events; The state update module is used to update the learning state based on the event sequence and threshold rules; The storage module is used to write learning status data.

17. The system according to claim 16, characterized in that, It also includes an adaptive rendering module, which selects a layout strategy based on graph size parameters and terminal resource parameters.

18. The system according to claim 16, characterized in that, It also includes a synchronization module for performing cross-platform synchronization and conflict merging.

19. The system according to claim 16, characterized in that, The system is deployed on one or more of mobile terminals, tablet terminals, web terminals, or servers.

20. An electronic device comprising a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method of any one of claims 1 to 15.

21. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1 to 15.

22. A computer program product comprising program instructions that, when executed on a processor, implement the method of any one of claims 1 to 15.

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