Group cognition three-stage screening input method and system based on micro-layout cursor

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a group cognition three-level screening input method and system based on a micro-layout cursor, and the method comprises the following steps: constructing a regional closed ecological medium code; constructing a micro-layout DMT static and dynamic data structure; performing primary screening to obtain a primary screening result; performing secondary screening to obtain a micro-layout with collaborative, emerging and aligned screening type characteristics; performing three-stage screening to obtain a page group with group cognitive features; performing quantitative evaluation on the group cognition effect to obtain a micro-layout group cognition evaluation result; and structured storage is carried out, and the whole process of group cognition based on the micro layout is completed. According to the method and the system provided by the invention, closed-loop design of cognition improvement is realized, the technical limitation of the existing input method is broken through from multiple aspects of group cooperation, cognition optimization, static and dynamic data integration, closed domain adaptation and cognition productivity improvement, and the dimension of group cognition of education and commercial organizations is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a group-recognizable three-level screening input method and system based on a micro-page cursor. Background Art

[0002] Traditional input method technologies primarily focus on the convenience and efficiency of user text input, but their expressiveness is significantly insufficient in complex scenarios such as group collaboration, cognitive optimization, and multi-dimensional dynamic data processing. Most existing input methods are centered around single-user input and offer limited functionality, making them unable to support the multi-role collaboration and cognitive resource optimization requirements of closed-domain scenarios. This is particularly true in scenarios requiring complex cognitive collaboration, such as education and business, where the technical bottlenecks of existing input methods are particularly pronounced, making them difficult to adapt to the advanced demands of these scenarios.

[0003] Furthermore, existing input methods lack the ability to integrate static and dynamic data, or quantify cognitive behavior, for group cognitive collaboration. Traditional input method systems typically only process single-dimensional static text input, failing to capture the multidimensional dynamic nature of the cognitive process and making it difficult to support collaborative input and cognitive optimization among group members. Existing technologies often view the input process as a one-way text generation process, ignoring the implicit cognitive value and collaborative potential of the input process. This simplified input model restricts users' cognitive efficiency and collaborative effectiveness in complex task scenarios.

[0004] In view of the shortcomings of traditional input method technology in digital cognitive scenarios, the problems can be summarized into the following four main aspects: non-collaborative problems, non-static and dynamic integration problems, non-cognitive measurement problems, and non-closed domain adaptation problems.

[0005] Existing input methods are insufficient in group collaboration scenarios, making it difficult to achieve collaborative input based on multi-role division of labor. Traditional systems typically only support simple vocabulary sharing or synchronization of input records, failing to capture the multi-dimensional dynamic interactions between group members and lacking support for cognitive optimization of the collaborative process. This limitation restricts the application of input methods in group tasks and makes them unable to meet the needs of cognitive collaboration in specific scenarios such as education and business.

[0006] Existing technologies struggle to achieve deep integration and processing of both static and dynamic data. They typically support only static text input or real-time voice input, lacking comprehensive analysis and filtering of dynamically generated data and static pre-set content. This lack of static and dynamic integration limits input methods' ability to fully explore and utilize valuable information from various data resources in complex task scenarios, hindering their ability to provide comprehensive cognitive support for users.

[0007] Existing input method technologies lack methods for measuring user cognition during the input process. Input methods typically focus solely on the efficiency and accuracy of text input, while ignoring the user's cognitive state and behavioral characteristics during the input process. Furthermore, existing technologies lack a mechanism for measuring user cognitive load, making it impossible to quantify users' cognitive stress and collaborative efficiency in complex tasks, and thus unable to effectively evaluate users' static and dynamic cognitive performance during the input process. This deficiency makes it difficult for input methods to optimize the user input experience and provide scientific support for cognitive resource allocation and task management in group collaboration, limiting their potential for application in cognitive optimization and complex task scenarios.

[0008] Regarding the issue of non-closed domain adaptation, existing input methods are mostly designed for universality and lack the adaptability to closed-domain scenarios. Traditional input methods cannot import closed-domain information such as class schedules and activity lists, making it difficult to support multi-role collaborative input and task decomposition. This non-closed domain adaptation issue seriously affects the practicality and effectiveness of input methods in specific scenarios. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a three-level screening input method and system based on micro-page cursor that can be used for group cognition. By expanding the three-level screening mechanism, cognitive behavior is further deeply analyzed and optimized, and quantifiable cognitive granular input is generated, thereby realizing a closed-loop design for cognitive improvement. It breaks through the technical limitations of existing input methods in many aspects, including group collaboration, cognitive optimization, static and dynamic data integration, closed domain adaptation, and cognitive productivity improvement, and can upgrade group cognition for educational and commercial organizations.

[0010] The present invention is achieved through the following technical solutions: A three-level screening input method based on a micro-page cursor capable of group recognition comprises the following steps: S1: Construct a regional closed ecological medium code that includes a closed domain deep strong screening code and a natural weak screening code; S2: Extract the preset static content and generated dynamic content in the closed domain deep strong screening code based on the regional closed ecological medium code, and construct the micro-page DMT static and dynamic data structure; S3: Perform a first-level screening based on the constructed micro-layout DMT static and dynamic data structure to obtain the first-level screening results including existence judgment, structural judgment, and OT quantification indicator data; S4: Calculate the five rhythm indicators based on the first-level screening results, calculate the collaborative screening indicator, emergence screening indicator and alignment screening indicator through the five rhythm indicators, and perform second-level screening to obtain micro-layouts with collaborative, emergence and alignment screening type characteristics; S5: Determine the classification type based on the collaborative screening index, the emergence screening index, and the alignment screening index, and perform three-level screening on the micro-pages with collaborative, emergence, and alignment screening type characteristics according to the determined classification type to obtain page groups with group cognitive characteristics; S6: Conduct a quantitative evaluation of the group cognitive effect based on the final screening results to obtain the micro-page group cognitive evaluation results; S7: The micro-layout group cognitive evaluation results are stored in a structured manner in XML format, and are finally output by the micro-layout cognitive input method to complete the entire micro-layout group cognitive process.

[0011] Optimized, the closed domain deep strong screening coding in step S1 includes pre-coding and post-coding, the pre-coding fields include medium and format, preset static content, generated dynamic content, group and generation path, and the post-coding fields include screening identification and verification.

[0012] Optimized, the fields of the natural weak screening code in step S1 include data source, spatiotemporal information and natural generation.

[0013] The optimized micro-layout DMT static and dynamic data structure constructed in step S2 includes static elements, dynamic elements, and organizational presentation. The static elements include spatial geometric relationships, structural mechanisms, content templates, and preset content. The spatial geometric relationships include up-down relationships, left-right relationships, and inside-out relationships. The structural mechanisms include a micro-layout unique identifier, a micro-layout O-zone structure, a micro-layout T-zone structure, and a micro-layout theme. The dynamic elements include dot-stroke symbol order summaries and DMT logical relationships. The dot-stroke symbol order summaries include dot-stroke symbol orders for writing traces, sound traces, body posture traces, and photographing traces. The DMT logical relationships include micro-layout screening relationships and micro-layout aggregation relationships. The organizational presentation includes organizational roles.

[0014] Furthermore, the process of the first-level screening in step S3 is as follows: S31: Based on the static elements and dynamic elements in the micro-layout DMT static and dynamic data structure and the micro-layout cursor API parameters, establish cross-media and cross-sensor data based on the micro-layout, perform DMT structure screening, OT flow existence screening, and synchronize micro-layout related information; S32: Integrate the scheduled learning activities into the micro-layout to provide the context for each operation. The system records the time when each handwriting occurs on the micro-layout. Combined with the activity schedule, it automatically imports the metadata preset at the corresponding time to complete the first-level screening and obtain the first-level screening results including existence judgment, structural judgment, and OT quantification indicator data.

[0015] The optimized five rhythm indicators include trace rhythm, relationship rhythm, degree rhythm, attenuation rhythm and de-invasive rhythm.

[0016] Further, the method of secondary screening is as follows: S41: Calculate five rhythm indicators: Calculate the trace rhythm index according to formula (1): (1); in: represents the trace rhythm index, The total number of dot-dash symbols in the area. Indicates the user's interaction time in the area, Represents the teaching cycle, Indicates the current time, represents the initial state of the trace rhythm; Calculate the relationship rhythm index according to formula (2): (2); in: Represents the relationship rhythm index, Indicates the number of recorded collaboration behaviors, Represents the number of all interactive behaviors, It represents the initial state of the relationship rhythm; Calculate the degree rhythm index according to formula (3): (3); in: Indicates the degree of rhythm index, The dot-dash symbol indicates the target task area. It represents the total number of dot and stroke symbols that occur on the micro-layout. Indicates the initial state of degree rhythm; Calculate the decay rhythm index according to formula (4): (4); in: represents the decay rhythm index, represents a natural constant, represents the initial state of the decay rhythm, represents the decay rate, Indicates the last interaction time; The elimination rhythm index is calculated according to formula (5): (5); in: Indicates the rhythm index of invasiveness. Indicates the number of newly generated contents. represents the quality score, represents the total number of questions, It represents the initial state of the elimination rhythm; S42: Calculate the collaborative screening index in the collaborative screening index according to formula (6) , according to formula (7) to calculate the collaborative screening preset in the collaborative screening index , and compare and The size of When retaining the micro layout, When abandoning micro layout: (6); (7); in: represents the average value of cooperative behavior, represents the standard deviation of the collaborative behavior, represents the sensitivity coefficient of collaborative screening; S43: Calculate the emergent screening index in the emergent screening index according to formula (8) , according to formula (9), the emerging screening preset in the emerging screening index is calculated , and compare and The size of When retaining the micro layout, When abandoning micro layout: (8); (9); in: represents the mean frequency of new content generation in historical data, represents the mean of the quality scores of new content in historical data, represents the emergent screening sensitivity coefficient, represents the standard deviation of emergent behavior; S44: Calculate the alignment screening index in the alignment screening index according to formula (10) , calculate the alignment screening preset in the alignment screening index according to formula (11) , and compare and The size of When retaining the micro layout, When abandoning micro layout: (10); (11); in: The weight coefficient representing the reference value of the degree rhythm, Indicates the baseline value of the degree rhythm, The weight coefficient representing the reference value of the relationship rhythm, Indicates the baseline value of the relationship rhythm; S45: Complete the secondary screening and obtain a micro-layout with collaborative, emergent and aligned screening type characteristics.

[0017] Furthermore, the process of performing the three-stage screening in step S5 is as follows: S51: According to the comparison result in the secondary screening process, a screening type having a screening value greater than a corresponding preset value in the screening index is selected as a determined screening type; If only one screening type has a screening value greater than the corresponding preset value, this screening type is selected as the determined screening type; If there is no screening type with a screening value greater than the corresponding preset value, it is considered invalid screening, and the process directly proceeds to step S6 by skipping the third-level screening; If there are multiple screening types whose screening values are greater than the corresponding preset values, the screening type with the highest priority is determined as the determined screening type according to the set priority; S52: Aggregating micro-pages according to the determined screening type to generate corresponding preliminary page groups; S53: Perform three-level screening based on the generated preliminary page group, implement behavioral resolution screening, and optimize the cognitive contribution of the micro-page group.

[0018] Furthermore, in step S6, the following method is used to quantitatively evaluate the group cognitive effect to obtain the micro-format group cognitive evaluation result: Quantification of internal cognitive load in group cognitive effects: Record the group's activity time on the micro-board, calculate the total duration, count the total number of operations on the micro-board, and quantify the internal cognitive load; Quantification of external cognitive load in group cognitive effects: Scoring the degree of synergy and alignment separately; Quantification of relevant cognitive load: The creative rhythm was scored based on the frequency and quality of creative activities on the micropages; Evaluate the screening results at each level: Assign values layer by layer based on the reflection of each level screening result in the five-layer concept; Directly extract the index data from each level of screening, and map the screening results of each level to the scoring criteria of the five-level concept to assign points layer by layer; Through the feedback of static rules and dynamic screening steps, the layer-by-layer scoring is coordinated and integrated with the cognitive level and the screening level to obtain a fusion score of the cognitive level and the screening level; Refine the integrated scores of cognitive level and screening level; Each level of screening results is evaluated based on the refined score. If the score is lower than the set value, the insufficient level is improved and steps S3 to S6 are repeated to re-evaluate until the score is greater than or equal to the set value.

[0019] A micro-layout cursor-based group-recognizable three-level screening system, for executing any of the above-described micro-layout cursor-based group-recognizable three-level screening input methods, comprising a regional closed ecological medium encoding module, a micro-layout DMT static and dynamic data structure construction module, a primary screening module, a secondary screening module, a tertiary screening module, a quantitative evaluation module, and a structured storage module; The regional closed ecological medium coding module is connected to the micro-format DMT static and dynamic data structure construction module for regional closed ecological medium coding; The micro-format DMT static and dynamic data structure construction module is respectively connected to the primary screening module, the secondary screening module, the tertiary screening module, and the quantitative evaluation module for constructing the micro-format DMT static and dynamic data structure; The primary screening module is connected to the secondary screening module and the quantitative evaluation module, and is used to perform primary screening on the constructed micro-format DMT static and dynamic data structure; The secondary screening module is connected to the tertiary screening module and the quantitative evaluation module, and is used to perform secondary screening on the constructed micro-format DMT static and dynamic data structure; The three-level screening module is connected to the quantitative evaluation module and is used to perform three-level screening on the constructed micro-format DMT static and dynamic data structure; The quantitative evaluation module is connected to the structured storage module and is used to perform quantitative evaluation on the group cognitive effect; The structured storage module is used to store the micro-page group cognitive evaluation results.

[0020] Beneficial effects of the invention: The micro-page cursor-based three-level screening input method and system provided by the present invention has the following advantages: 1. Improve group collaboration efficiency: Support for multi-role division of labor and collaboration: The system can achieve dynamic collaboration among multiple roles by building a granular chain input mechanism for group cognitive blocks. It supports task decomposition and real-time data synchronization among members, significantly enhancing the efficiency of collaboration among group members. Optimizing collaborative cognitive behavior: The system can capture the multi-dimensional input behavior of group members, and optimize collaborative cognitive behavior through organized expression and data chain integration, providing efficient collaborative input tools for scenarios such as education and business.

[0021] 2. Improve input accuracy and quality: Precise input based on lossless closed-loop screening: This algorithm uses a lossless closed-loop screening algorithm based on the coordination, emergence, and alignment of the five rhythms of the micro-layout to perform multi-dimensional screening and aggregation of static and dynamic input data, screening out high-quality cognitive particles. This can avoid redundant information interference and effectively improve the accuracy of input content and task completion. Deep integration of static and dynamic data: Through the construction of regional closed ecological media coding and micro-format DMT static and dynamic data structure, the present invention realizes the deep integration of static data and dynamic data, ensuring the comprehensiveness and accuracy of input data in complex task scenarios.

[0022] 3. Improve cognitive load management and optimization capabilities: Through the design of a quantitative evaluation system, we can comprehensively quantify the user's cognitive state and behavioral characteristics during the input process, providing scientific support for the optimal allocation of cognitive resources and the improvement of task completion. Dynamically optimize cognitive resource allocation: Through a comprehensive assessment of the user's internal cognitive load, collaboration efficiency, and the related load of innovative tasks, the input method can dynamically optimize cognitive resource allocation, significantly reduce the user's cognitive stress in complex tasks, and improve input efficiency and cognitive performance.

[0023] 4. Accuracy and flexibility in adapting to closed domain scenarios: Closed-domain information import and collaborative adaptation: The input method is designed specifically for closed-domain scenarios such as education and commerce. It supports the import of metadata such as course schedules and activity lists, accurately adapting to collaborative tasks and dynamic management needs in closed domains, solving the problem of insufficient adaptability of existing input methods in specific scenarios. Support for multi-dimensional collaborative input: The input method can dynamically adjust data input and collaboration modes based on the needs of multiple roles in a closed domain, flexibly adapt to complex tasks in different scenarios, and improve users' work and learning efficiency in a closed domain environment.

[0024] 5. Significantly improve cognitive productivity: Transformation from single input to cognitive optimization tool: The method provided by this invention breaks through the limitation of traditional input methods that only process single text input. Through chained input micro-format cognitive particles and automated measurement functions, it achieves an efficient transformation from input to cognitive behavior optimization, promoting the development of input methods in the direction of cognitive productivity tools. Supporting knowledge generation and collaborative innovation: Through lossless screening and cognitive load optimization mechanisms, the present invention can help users generate high-value cognitive particles during the input process, promote knowledge generation and collaborative innovation, and significantly improve cognitive productivity in education and business scenarios.

[0025] 6. Resource saving and efficiency optimization friendliness: Reduce repetitive operations and resource waste: Through precise cognitive optimization and data screening mechanisms, this invention can reduce repetitive input and invalid operations in group tasks, reduce waste of time and resources, and improve task completion efficiency; Efficient use of cognitive resources: The system improves the efficient use of cognitive resources through quantitative assessment and optimized configuration of cognitive load, providing more environmentally friendly technical support for group collaboration and knowledge management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flow chart of the present invention.

[0027] Figure 2 This is a schematic diagram of the coding of the regional closed ecological medium of the present invention.

[0028] Figure 3 It is a schematic diagram of the static and dynamic data structure of the micro-format DMT of the present invention. DETAILED DESCRIPTION

[0029] A three-level screening input method based on micro-page cursor with group recognition includes the following steps, and its flow chart is as follows: Figure 1 As shown: S1: Construct a regional closed ecological medium code that includes a closed domain deep strong screening code and a natural weak screening code; The coding basis of the three-level screening input method for micro-page group cognition is the regional closed ecological medium coding. Its core goal is to provide a unique identifier for the static and dynamic data of the micro-page, supporting the entire process from collection to screening to aggregation.

[0030] The schematic diagram of regional closed ecological medium coding is as follows Figure 2 As shown, the regional closed ecological medium coding includes the closed domain deep strong screening coding and the natural weak screening coding; Closed domain deep strong screening coding is a high-precision coding system for school domains or institutional domains, which is used to identify the static definition and dynamic generation data structure of micro-layouts to ensure the integrity, consistency and traceability of micro-layouts within the closed domain.

[0031] Closed domain deep strong screening coding includes pre-coding and post-coding. The prefix code is used to preset the basic physical properties and scope information of the microlayout. It stores static metadata at the time of microlayout creation in the form of fixed fields and is associated with the system's closed ecosystem, such as schools and institutions. The prefix code fields include media and format, preset static content, generated dynamic content, group, and generation path.

[0032] Medium and format are used to define the physical medium information and layout structure of the micro-page; Pre-set static content refers to pre-defined static content in the micro layout, such as themes, large units, stages, etc. Generated dynamic content refers to the dynamic content generated by the micro-layout during use, such as cards, grade quality, etc. Groups are used to determine the school or institution domain to which a micropage belongs, bind closed domain metadata, mark specific groups or organizations, and store user roles, group hierarchies, and relationships; The generated path is used to record the sensing method and path information of the micro-page from creation to sharing, submission and other operations.

[0033] Post-coding is used to dynamically record the data generated during the use of micro-layouts, and can support the screening and aggregation of static and dynamic content of micro-layouts.

[0034] The post-coded fields include screening identification and verification.

[0035] The screening mark is used to mark the status and key data structure of the micro-panel at different screening stages.

[0036] Checksum refers to the use of hash checksum for data integrity verification.

[0037] Natural weak sieving codes are a weakly bound encoding system automatically generated during natural interactions. They primarily record the environmental data naturally introduced by micro-layouts during the sieving and aggregation process. Compared to closed-domain deep strong sieving codes, weak codes focus more on the external attributes of micro-layout data and naturally generated contextual information.

[0038] The fields of natural weak screening coding include data source, spatiotemporal information and natural generation.

[0039] Among them: data sources include data update and iteration types; Data update mainly records the frequency and method of data update, such as real-time update, batch import, etc. Iteration type refers to the form of data evolution, such as incremental, full, or version iteration; Spatiotemporal information includes production time and location; Production time refers to the timestamp of data generation, which is used to mark the time attribute of data; Location refers to the geographical location information generated by the data, which is used to identify spatiotemporal characteristics; Natural generation includes device metadata, operation objects, and corresponding scenarios; Device metadata refers to the hardware environment that records the data, including information such as device type, operating system, and resolution; Operation objects refer to external resources associated with the micro-page, such as cited teaching materials, collaborative content, etc. The corresponding scenario describes the actual application scenario of data generation, highlighting its naturally generated context.

[0040] In order to support the three-level screening system of the micro-page input method, the regional closed ecological medium coding field is progressively designed in a layer-by-layer streamlined manner. At the same time, a finer-grained screening aggregation is achieved through the classification standards of field values, which can adapt to the step-by-step screening needs from static data to dynamic data, from basic structure to advanced cognition.

[0041] S2: Extract the preset static content and generated dynamic content in the closed domain deep strong screening code based on the regional closed ecological medium code, and construct the micro-page DMT static and dynamic data structure; The three types of tightly coupled micro-layouts, as important layout elements, effectively support DMT screening and aggregation operations. The core of micro-layout DMT relationships lies in describing the interrelationships between data, models, and tasks. By establishing these relationships, dynamic organization of micro-layout cognitive resources and data synchronization across linked micro-layouts are achieved.

[0042] Relationships between micro-layouts can be described in several ways. First, spatial relationships describe the relative spatial positions of micro-layouts. For example, on a two-dimensional layout, two adjacent micro-layouts may form a top-bottom or left-right relationship. Next, logical relationships encompass screening and aggregation relationships, which are relationships between micro-layouts generated based on the two primary operations of micro-layout screening and aggregation. These relationships are not only an important component of the micro-layout data structure but also provide support for DMT screening and aggregation operations. Through screening and aggregation operations, dynamic organizational communities of micro-layouts with different hierarchical structures, namely page groups, can be established.

[0043] Page clusters are dynamic structures generated based on the cognition of static and dynamic micro-layout data, designed to dynamically organize micro-layout cognition. Page clusters are dynamically generated by screening and aggregating related cognitive micro-layouts driven by target tasks. Once the target tasks are completed, page clusters naturally dissolve and disappear. Page clusters can be divided into three levels. The smallest, indivisible micro-layout cognitive units are called D (Down) micro-layouts, such as a micro-layout containing only one problem in a workbook. Medium-sized page clusters are called M (Middle) micro-layouts, such as a workbook containing multiple problems. The largest page clusters are called T (Top) micro-layouts, such as learning materials containing multiple workbooks.

[0044] A D-type micro-layout contains the data flows occurring within it: the target flow or task flow, the assignment flow or log flow, and related static and dynamic data structures. Two or more related D-type micro-layouts can be logically aggregated into an M-type micro-layout. An M-type micro-layout contains the layout elements of a D-type micro-layout and also accommodates its own data flows. An M-type micro-layout can be further divided into one or more D-type micro-layouts, a process known as a subdivision relationship. Similarly, two or more related M-type micro-layouts can be aggregated into a T-type micro-layout. DMT layout elements together constitute three types of tightly coupled micro-layouts.

[0045] The three-level tightly coupled organization, serving as the organizing element between micro-layouts, describes the organizational hierarchy and relationships of group roles within the micro-layout input method. Organizational relationships are based on the micro-layout cognitive associations of group roles. Micro-layouts within the same group or group of roles can synchronize group cognitive data through organizational relationships. This organizational structure clearly expresses the division of labor and collaboration from the teacher level to the teaching assistant level and then to the group level, providing fundamental support for micro-layout collaboration and screening.

[0046] The teacher layer, as the first-level organizational element, is usually a teacher or teaching manager, responsible for formulating overall learning goals, task frameworks, and resource allocation. The teaching assistant layer is the second-level task executor, which can be further divided into two or more teaching assistant roles, corresponding to different task sets and the underlying groups they manage: group members and partner groups. The group layer is the third level of the organization, consisting of specific learning groups and point-to-point partner groups, which directly participate in the creation, operation, and synchronization of micro-pages. Each group contains member roles, which perform their assigned tasks and submit micro-page data; the third-level partner group uses groups as units for cross-group communication and collaboration, and synchronizes group cognitive data through synchronized micro-pages.

[0047] The constructed micro-layout DMT static and dynamic data structure is shown in Figure 3, which includes static elements, dynamic elements, and organizational presentation. The static elements include spatial geometric relationships, structural mechanisms, content templates, and preset content. The spatial geometric relationships include up-down relationships, left-right relationships, and inside-out relationships. The structural mechanisms include the micro-layout unique identifier, micro-layout O-zone structure, micro-layout T-zone structure, and micro-layout theme. The dynamic elements include dot-stroke order summaries and DMT logical relationships. The dot-stroke order summaries include dot-stroke order summaries of writing traces, sound traces, body posture traces, and photographic traces. The DMT logical relationships include micro-layout screening relationships and micro-layout aggregation relationships. The organizational presentation includes organizational roles.

[0048] Micro-page DMT basic data metrics: An effective micro-layout measurement model is the basis for resource data valuation and DMT screening and aggregation. The basic measurements within the micro-layout can be divided into static measurements and dynamic measurements according to the static and dynamic properties of the measured resource objects.

[0049] For static measurement, according to the preset static content, specify the micro-page ID, micro-page theme S and calculate the preset duty cycle and assign a value; The micro-layout ID is a unique identifier of the digital cognitive space of the micro-layout input method, used for lossless association of cross-micro-layout data, and different micro-layouts are distinguished by the ID; The micro-page theme S is a summary of the content, divided into main numbers and sub-numbers to ensure its traceability. Different themes are expanded in a non-linear way and indexed by internal references and keywords. The preset duty cycle is the ratio of the area occupied by the preset content of the micro layout to the total area of the micro layout, which effectively describes the magnitude of the interactive resources in the micro layout in the initial state.

[0050] Micro-layout dynamic measurement calculates the dot-stroke order summary and dynamic duty cycle during the dynamic content generation process based on the encoding of the dynamic content.

[0051] Micro-layout dynamic measurement focuses on depicting the temporal variation characteristics and cumulative effects of dynamic resources, including dot-stroke order summaries and dynamic duty cycle; Abstract: Standardized description of dynamic handwriting, sound traces, and beat traces data on micro-layouts. Dynamic statistical measurements of dot-stroke order within a micro-layout can be defined by a five-tuple: Dot: the smallest writing unit, recording the position of the pen; Stroke: the trajectory from the time the pen is put down to the time it is lifted; Symbol: A written symbol composed of multiple strokes, which may be an instruction or ordinary handwriting; Command: A collection of writing symbols constitutes a micro-layout operation; times: the complete set of operations divided by time; The dynamic duty cycle is the ratio of the space occupied by all existing content in the micro-page to the total area of the micro-page, reflecting the efficiency of dynamic information carrying.

[0052] S3: Perform a first-level screening based on the constructed micro-layout DMT static and dynamic data structure to obtain the first-level screening results including existence judgment, structural judgment, and OT quantification indicator data; Specifically, the process of primary screening is as follows: S31: Micro-layout cursor basic cognitive data integration: Based on the static and dynamic elements in the micro-layout DMT static and dynamic data structure and the micro-layout cursor API parameters, establish cross-media and cross-sensor data based on the micro-layout, perform DMT structure screening, OT flow existence screening, and synchronize micro-layout related information; Through the dynamic summary calculation and data screening capabilities of high-dimensional cursors, a series of micro-page cursor API parameters can be provided. This part is the existing technology. The micro-page cursor API parameters include: Role existence determination results: Determine the current user's group role, group to which they belong, and associated companion groups, providing basic support for group collaborative screening in the input method; DMT structure matching results: including the matching results of static and dynamic elements of the micro-layout, as well as the presentation of the organizational structure, are used to build the hierarchical screening infrastructure of the input method; OT Existence Results: This includes the existence determination and corresponding coordinate positioning of the target completion area (O area) and the task completion area (T area), providing support for the emergence screening and alignment screening of the input method's secondary screening. Summary of dot-stroke symbol frequency: including the quantitative results of dot-stroke symbol frequency in the micro-layout area and dynamic duty cycle statistics, providing a basis for the five-rhythm calculation and collaborative screening of the input method.

[0053] Collaboration base, emergence base and alignment base classification: Use NK-Cola instructions to mine and screen collaboration, emergence and alignment base data, including characteristic data of static classes, dynamic classes and synchronization classes, for secondary screening of input methods.

[0054] S32: Closed domain metadata import: Integrate the scheduled learning activities into the micro-layout to provide the context of each operation. The system records the time when each handwriting occurs on the micro-layout. Combined with the activity schedule, it automatically imports the metadata preset at the corresponding time to complete the first-level screening and obtain the first-level screening results including existence judgment, structural judgment, and OT quantification indicator data.

[0055] In the first-level screening, the coding fields include: medium and format, preset static content, generated dynamic content, group, generated path, screening identification, and verification. Static structure verification and dynamic data quantification are performed through the above fields to screen out micro-layouts that meet the basic requirements.

[0056] S4: Calculate the five rhythm indicators based on the first-level screening results, calculate the collaborative screening indicator, emergence screening indicator and alignment screening indicator through the five rhythm indicators, and perform second-level screening to obtain micro-layouts with collaborative, emergence and alignment screening type characteristics; The five rhythm indicators include trace rhythm, relationship rhythm, degree rhythm, attenuation rhythm and de-invasive rhythm.

[0057] Trace rhythm is a precise quantification of the density and time distribution characteristics of users' interactive behaviors on micro-pages. It focuses on describing the cumulative frequency of users' operation behaviors such as points, strokes, symbols, commands, and sub-operations within a specific time period and their changes over time, thereby revealing the rhythm of users' input behaviors and the dynamic interaction efficiency, and providing a basis for the rhythmic analysis of cognitive behaviors.

[0058] Relationship rhythm systematically depicts the dynamic relationship changes in the collaborative input process by quantifying the interaction frequency, collaborative behavior and communication quality among group members, revealing the collaborative efficiency of the group in task execution, the role division among members and the intensity of interaction, thereby providing data support for collaborative screening and optimization of group behavior.

[0059] By analyzing the user's operational focus, the proportion of dot-dash commands, and task completion in the target area (O area) and task area (T area), the degree rhythm accurately describes the dynamic changes in the user's alignment ability and focus behavior with the task goal, revealing the accuracy, efficiency, and goal consistency of task completion in the input behavior, and providing a scientific basis for task alignment optimization.

[0060] The decay rhythm is based on the time decay model, which dynamically quantifies the memory forgetting patterns and review needs of users in specific micro-page interaction behaviors, revealing the decay characteristics of cognitive behavior over time and the interval distribution characteristics of input behavior, thereby providing theoretical support for identifying users' repeated operation patterns and content optimization.

[0061] The consumption-creation rhythm comprehensively reflects the user's creative knowledge construction ability and innovative input characteristics in the input environment by quantifying the amount of new content generated by users in micro-pages, the frequency of innovative behaviors, and the quality of output content. It provides a quantitative basis for emergence screening and knowledge generation optimization, and helps to deeply explore personalized cognitive behaviors.

[0062] The method of secondary screening is as follows: S41: Calculate five rhythm indicators: Calculate the trace rhythm index according to formula (1): (1); in: represents the trace rhythm index, The total number of dot-dash symbols in the area. Indicates the user's interaction time in the area, Represents the teaching cycle, Indicates the current time, represents the initial state of the trace rhythm; Calculate the relationship rhythm index according to formula (2): (2); in: Represents the relationship rhythm index, Indicates the number of recorded collaboration behaviors, Represents the number of all interactive behaviors, It represents the initial state of the relationship rhythm; Calculate the degree rhythm index according to formula (3): (3); in: Indicates the degree of rhythm index, The dot-dash symbol indicates the target task area. It represents the total number of dot and stroke symbols that occur on the micro-layout. Indicates the initial state of degree rhythm; Calculate the decay rhythm index according to formula (4): (4); in: represents the decay rhythm index, represents a natural constant, represents the initial state of the decay rhythm, represents the decay rate, Indicates the last interaction time; The elimination rhythm index is calculated according to formula (5): (5); in: Indicates the rhythm index of invasiveness. Indicates the number of newly generated contents. represents the quality score, represents the total number of questions, It represents the initial state of the elimination rhythm; S42: Calculate the collaborative screening index in the collaborative screening index according to formula (6) , according to formula (7) to calculate the collaborative screening preset in the collaborative screening index , and compare and The size of When retaining the micro layout, When abandoning micro layout: (6); (7); in: represents the average value of cooperative behavior, represents the standard deviation of the collaborative behavior, represents the sensitivity coefficient of collaborative screening; S43: Calculate the emergent screening index in the emergent screening index according to formula (8) , according to formula (9), the emerging screening preset in the emerging screening index is calculated , and compare and The size of When retaining the micro layout, When abandoning micro layout: (8); (9); in: represents the mean frequency of new content generation in historical data, represents the mean of the quality scores of new content in historical data, represents the emergent screening sensitivity coefficient, represents the standard deviation of emergent behavior; S44: Calculate the alignment screening index in the alignment screening index according to formula (10) , calculate the alignment screening preset in the alignment screening index according to formula (11) , and compare and The size of When retaining the micro layout, When abandoning micro layout: (10); (11); in: The weight coefficient representing the reference value of the degree rhythm, Indicates the baseline value of the degree rhythm, The weight coefficient representing the reference value of the relationship rhythm, Indicates the baseline value of the relationship rhythm; S45: Complete the secondary screening and obtain a micro-layout with collaborative, emergent and aligned screening type characteristics.

[0063] The coding fields of the secondary screening include: format, preset static content, generated dynamic content, group, screening identification, and verification. The secondary screening further refines the fields and combines the five rhythm-derived indicators to screen out micro-layouts with synergy, emergence, and alignment value.

[0064] S5: Determine the classification type based on the collaborative screening index, the emergence screening index, and the alignment screening index, and perform three-level screening on the micro-pages with collaborative, emergence, and alignment screening type characteristics according to the determined classification type to obtain page groups with group cognitive characteristics; The third-level screening analyzes group collaborative emergence and alignment behaviors, aggregates high-quality micro-layouts to generate cognitive particles, and provides a basis for the structured expression of group cognition. The third-level screening cognitive indicators are micro-layout collaboration, emergence, and alignment page groups; Collaborative Page Group: This is formed by the aggregation of multiple highly collaborative micro-pages, reflecting the collaborative results among team members. Its data sources include micro-page collaboration data and relationship rhythms. Micro-page collaboration data: such as annotations, synchronized interaction information, discussion records, etc. Relationship rhythm: the frequency and quality of interactions between group members.

[0065] Emergent page group: Aggregate the emerging innovative micro-pages, which are manifested as new knowledge perspectives or solutions generated by group members when solving problems.

[0066] Its data sources include: micro-page dynamic data, such as new task generation and problem-solving records; creative rhythm: the frequency and quality scores of students raising new questions.

[0067] Aligned page groups: Aligned page groups contain micro-pages with consistent goals and high degree rhythm, reflecting the consistency of team members on task goals and their mastery of knowledge points.

[0068] Its data sources are: micro-page target task (O or T) records, dynamic interaction data; Level rhythm: the ratio of the number of strokes in the task area to the total number of strokes.

[0069] Specifically, the process of three-stage screening is as follows: S51: According to the comparison result in the secondary screening process, a screening type having a screening value greater than a corresponding preset value in the screening index is selected as a determined screening type; If only one screening type has a screening value greater than the corresponding preset value, this screening type is selected as the determined screening type; If there is no screening type with a screening value greater than the corresponding preset value, it is considered invalid screening, and the process directly proceeds to step S6 by skipping the third-level screening; If there are multiple screening types whose screening values are greater than the corresponding preset values, the screening type with the highest priority is determined as the determined screening type according to the set priority; S52: Aggregating micro-pages according to the determined screening type to generate corresponding preliminary page groups; S53: Perform three-level screening based on the generated preliminary page group, implement behavioral resolution screening, and optimize the cognitive contribution of the micro-page group.

[0070] The main coding fields of the three-level screening are: pre-setting static content, generating dynamic content, group, screening identification, and verification. The three-level screening focuses on the generation of cognitive particles, combined with the characteristics of the three-trend page groups, to screen out high-quality micro-layouts that can support the resolution of group behavior.

[0071] S6: Conduct a quantitative evaluation of the group cognitive effect based on the final screening results to obtain the micro-page group cognitive evaluation results; Specifically, the following method is used to quantitatively evaluate the group cognitive effect and obtain the micro-page group cognitive evaluation results: Quantification of internal cognitive load in group cognitive effects: Record the group's activity time on the micro-board, calculate the total duration, count the total number of operations on the micro-board, and quantify the internal cognitive load; Quantification of external cognitive load in group cognitive effects: Scoring the degree of synergy and alignment separately; Specifically, the scores for the degree of synergy are shown in Table 1, and the scores for the degree of alignment are shown in Table 2: Table 1

[0072] Table 2

[0073] Quantification of Relevant Cognitive Load: Creative Rhythm is scored based on the frequency and quality of creative activities on the micro-board. Quantitative indicators include: Creative Rhythm: This reflects students' ability to solve practical problems, create, and apply knowledge. Quantification Method: Creative Rhythm Scoring: This scoring is based on the frequency and quality of students' creative activities on the micro-board. The scoring range is 1-5, with higher scores indicating higher relevant cognitive load.

[0074] The scoring criteria for the invasive rhythm are shown in Table 3: Table 3

[0075] Evaluate the screening results at each level: Assign values layer by layer based on the reflection of each level screening result in the five-layer concept; Directly extract the index data from each level of screening, and map the screening results of each level to the scoring criteria of the five-level concept to assign points layer by layer; Specifically, the five-layer concept is as follows: The first layer (humanities + technology): reflects the combination of structuring of static cognitive data (such as target task area, group role) and dynamic screening algorithms (such as five rhythm indicators).

[0076] The second level (cognition + action): focuses on the closed-loop relationship between cognition and action, and evaluates whether the screening results provide theoretical support for cognitive behavior, and whether the actions reversely verify the cognitive results.

[0077] The third layer (top-down and bottom-up): Evaluate the role of screening results in group collaboration, whether it supports distributed collaboration from the teacher level to the group member level, while stimulating group members' innovative feedback and emergent behavior; whether the DMT logical relationship supports micro-page screening and micro-page aggregation operations.

[0078] The fourth level (goals + tasks): evaluates whether the screening results clearly reflect the decomposition (static) and execution (dynamic) of the target tasks, as well as the performance of group members in task completion and goal consistency.

[0079] The fifth layer (dam + torrent): Through static rules (dam) and dynamic screening mechanisms (torrent), evaluate whether the screening can screen out high-quality cognitive particles while supporting the entropy reduction ecology of group symbiosis.

[0080] Through the feedback of static rules and dynamic screening steps, the layer-by-layer scoring is coordinated and integrated with the cognitive level and the screening level to obtain a fusion score of the cognitive level and the screening level; The integrated scoring criteria for the cognitive level and the screening level are shown in Table 4: Table 4

[0081] Refine the integrated scores of cognitive level and screening level; Specifically, a 0.5-point step method can be used to refine the fusion scores of the cognitive level and the screening level.

[0082] Each level of screening results is evaluated based on the refined score. If the score is lower than the set value, the insufficient level is improved and steps S3 to S6 are repeated to re-evaluate until the score is greater than or equal to the set value.

[0083] Based on the static and dynamic data, five rhythms and screening results generated during the micro-layout screening process, a quantitative assessment of the three types of cognitive load involved in the micro-layout during group task execution is conducted. By scoring through quantitative means, the overall cognitive load level can be clarified, and targeted reduction strategies can be proposed based on the evaluation results to optimize cognitive behavior and task efficiency.

[0084] By scoring the micro-layout screening process based on the five-layer concept, quantifying the value of the screening results in cognitive behavior optimization, and recording the scores and suggestions, the degree of optimization of cognitive behavior and the effect of technical support can be reflected.

[0085] S7: The micro-layout group cognitive evaluation results are stored in a structured manner in XML format, and are finally output by the micro-layout cognitive input method to complete the entire micro-layout group cognitive process.

[0086] The connotation of output granules: The final output of the micro-board cognitive input method is group or individual cognitive granules. These granules record and express the micro-board screening and aggregation through structured data in XML format. These output granules are quantitative storage units of cognitive behavior, reflecting the cognitive improvement process of groups or individuals and serving as the basic data modules in the group cognitive closed loop.

[0087] Its core connotations include: structured expression, basic units of cognitive particles and block design; Structured Expression: Clearly describe micro-panel screening results in XML format, supporting organized storage of static and dynamic data. Each particle corresponds to a cognitive unit, containing data from the entire process from screening to cognitive optimization.

[0088] The basic unit of cognitive granules: Output granules serve as the basic unit of the micro-layout group cognitive input method. They are used to record micro-layout screening results, cognitive load assessment indicators, and the collaborative relationship of group behavior. They support the natural and autonomous generation of cognitive data and the quantitative storage with clear boundaries, providing support for subsequent analysis, optimization, and expansion.

[0089] Blockchain design: Each XML output particle is both an independent cognitive unit and can be aggregated at the group level to form a larger cognitive blockchain, achieving data traceability (metadata) and decentralization (blockchain storage), facilitating collaboration and reuse.

[0090] The core components of XML output particles include entity data, metadata, and relationships; Entity data: It is the core content data in the micro-page, directly reflecting the specific content of cognitive activities in the screening process, covering static and dynamic data; Static data: This includes the microlayout ID (for unique identification), the definition of the target task (O zone) and task area (T zone), and the layout type (T, M, D). Static data describes the basic structure of the microlayout in the cognitive system.

[0091] Dynamic data: This includes the dot-dash summary (reflecting the frequency and density of user interaction), dynamic duty cycle (the ratio of dynamically generated content to the total area of the micro-layout), and interaction time (the length of time users spend interacting within the micro-layout). Dynamic data captures the dynamic nature of cognitive behavior.

[0092] Metadata: It is the background information during the generation and use of micro-layouts. It describes the context of screening and cognitive activities, ensures the traceability and relevance of data, and includes generation time, user role, and device information. Generation time: The timestamp of micro-page creation and data update, used to record the time node of cognitive activity.

[0093] User Role: This identifies the group level of participating users, including teacher (T), teaching assistants (TA1, TA2), and team members (team members, partner groups). User roles help analyze the division of labor and collaboration among different levels in the cognitive process.

[0094] Device information: records the hardware environment when the micro-layout is generated, including device type (such as tablet, computer), operating system version, screen resolution, etc., which is used to reflect the technical support environment of cognitive behavior.

[0095] Relationship R: describes the relationship between a micro-page and other micro-pages, including the logical relationship formed during the screening and aggregation process, and the position of the micro-page in the cognitive closed loop; including DMT screening and aggregation relationships, blockchain clusters, and group association relationships; DMT screening aggregation relationship: describes the hierarchical position of micro-pages in Down (minimum cognitive unit), Middle (medium-sized unit) and Top (overall cognitive structure), and supports the dynamic aggregation and decomposition of micro-page organizations.

[0096] Blockchain cluster: records the comings and goings between micro-pages (data flow), indicates the logical links between micro-pages and other micro-pages, and facilitates the tracking of data sources and flow paths.

[0097] Group association: reflects the position of the micro-page in the group task, marks the group or partner group to which it belongs, and supports the simultaneous analysis of group collaboration and cognitive data.

[0098] The present invention provides a micro-layout cursor-based, group-recognizable, three-level screening input method. By constructing a group cognitive block granular chain input mechanism, it achieves dynamic collaboration with multiple roles, supports task decomposition and real-time data synchronization between members, and significantly enhances the efficiency of collaboration between group members. The system can also capture the multi-dimensional input behavior of group members, optimize collaborative cognitive behavior through organized expression and data chain integration, and provide efficient collaborative input tools for scenarios such as education and business. At the same time, based on precise input through lossless closed-loop screening, the micro-layout five-rhythm collaborative, emergent, and aligned lossless closed-loop screening algorithm is used to perform multi-dimensional screening and aggregation of static and dynamic input data, screening out high-quality cognitive particles, avoiding redundant information interference, and effectively improving the accuracy of input content and task completion. Through the construction of regional closed ecological medium coding and micro-layout DMT static and dynamic data structure, the deep integration of static and dynamic data is achieved, ensuring the comprehensiveness and accuracy of input data in complex task scenarios. Through the design of a quantitative evaluation system, we can comprehensively quantify the user's cognitive state and behavioral characteristics during the input process, providing scientific support for the optimal allocation of cognitive resources and the improvement of task completion; through a comprehensive evaluation of the user's internal cognitive load, collaborative efficiency and the related load of innovative tasks, we can dynamically optimize the allocation of cognitive resources, significantly reduce the user's cognitive pressure in complex tasks, and improve input efficiency and cognitive performance. The input method is designed for closed-domain scenarios such as education and business. It supports the import of metadata such as course schedules and activity lists, and accurately adapts to collaborative tasks and dynamic management needs in closed domains, solving the problem of insufficient adaptability of existing input methods in specific scenarios. In addition, the input method can dynamically adjust data input and collaboration modes according to the multi-role needs of the closed domain, flexibly adapt to complex tasks in different scenarios, and improve users' work and learning efficiency in closed-domain environments. It breaks through the limitation of traditional input methods that only handle single text input. Through chained input of micro-layout cognitive particles and automated measurement functions, it realizes an efficient transformation from input to cognitive behavior optimization, and promotes the development of input methods towards cognitive productivity tools. Through lossless screening and cognitive load optimization mechanisms, it can help users generate high-value cognitive particles during the input process, promote knowledge generation and collaborative innovation, and significantly improve cognitive productivity in education and business scenarios. Reduce repetitive operations and resource waste: Through precise cognitive optimization and data screening mechanisms, the present invention can reduce repetitive input and invalid operations in group tasks, reduce the waste of time and resources, and improve task completion efficiency; through quantitative evaluation and optimized configuration of cognitive load, it improves the efficient utilization of cognitive resources and provides more environmentally friendly technical support for group collaboration and knowledge management.

[0099] Minimum scenario verification: Minimum scenario validation aims to verify the core functionality of the aforementioned technologies through specific scenarios within educational or commercial organizations, such as classes and meetings. This includes the visibility and invisibility of the micro-page cursor, data aggregation, the five-rhythm screening of the micro-page input method, and the penetration and emergence of the Red, Blue, and Double O goal system in group collaboration. The validation focuses on three phases: snapping simple cards, taking and stacking secondary notes, and sharing in groups. This will gradually establish a high-dimensional cognitive closed loop from individual record-keeping to cognitive iteration to group collaboration.

[0100] Scenario setting: The verification scenario focuses on group cognitive collaboration in teaching or conference scenarios. Based on the principle of minimizing verification, the following conditions are preset: Task objectives: Guided by the red and blue Os, preset course objectives (red) and organizational objectives (blue) point to individual learning goals and group collaboration goals respectively.

[0101] Verification environment: includes PC and Android devices, supports multiple input media such as electromagnetic pen, dot pen, camera, voice, etc., and imports course schedule metadata as closed domain information.

[0102] Participating groups: include multiple roles such as teachers (T), teaching assistants (TA), team members (groups), etc., and a collaborative relationship is formed between the roles.

[0103] Verification steps (1) General Shooting Card Objective: Through ordinary photography and simple merging operations, convert the key content of the class or meeting into digital card notes, and build the basic data container of micro-page.

[0104] Process: Team members use their mobile phone cameras to capture key and difficult content (such as blackboard notes and lab equipment) during class, creating note cards. These cards are automatically populated with red and blue double-O targets and undergo multi-dimensional data structured processing. A micro-layout cursor, based on broadly defined handwriting, sound, and photogrammetry, generates cognitively explicit data carriers, which can be presented in both explicit and implicit forms.

[0105] Explicit cursor: On visual platforms such as PCs, it dynamically captures user operations in real time, and filters and structures static and dynamic data within the micro-page, similar to a mouse on a PC.

[0106] Hidden cursor: On multiple platforms (such as Android), it implicitly captures the trajectory of user operation behavior through abstract coding and multi-dimensional space mapping, assists in data connection and screening in high-dimensional cognitive space, and is only explicitly called when sensor traces are triggered. It is usually hidden in the platform backend.

[0107] The uniqueness of the micro-layout cursor lies in its ability to filter and coordinate cognitive data. Within a multi-layout, multi-group data environment, it can create a high-dimensional space of cognitive data for micro-layout groups by filtering and connecting static and dynamic data particles. This high-dimensional concurrent space of micro-layout cursors aggregates static and dynamic data from different visible and invisible cursors and supports synchronized display between them.

[0108] (2) Take secondary notes Objective: Based on the generated cards, team members will iteratively complete secondary revisions through handwriting or audio recordings to refine cognitive records and stimulate cognitive enlightenment.

[0109] Process: Team members use electromagnetic pens or dot pens to make handwritten changes on the cards or supplement content via voice commands. The micro-board cursor records the operation trajectory and triggers data connection through relative coordinates. Data synchronization between multiple roles is achieved through high-dimensional cursors, allowing team members to access other roles' cognitive data and collaboratively optimize. Team members use electromagnetic pens and dot pens to write on the electromagnetic board and dot paper, respectively. PC and Android cursors track the user's operation trajectory on the card in real time, generating a highly accurate record of cognitive behavior. At the same time, the micro-board cursor dynamically captures subtle changes in the user's operation through a relative coordinate system, enabling connection and triggering between different data tracks. A character's micro-board high-dimensional cursor obtains cognitive data from other characters in the same or companion group on the current micro-board through data synchronization, enabling real-time calculation, feedback, and collaboration of high-dimensional dynamic summary information across roles. The PC cursor and Android cursor upload data sources, group role information, sensing methods, media, micro-page related metadata, dot-stroke symbol order summary and other information to the multi-dimensional table digital space respectively. Through the high-dimensional cursor real-time synchronization feedback mechanism, the data of each role in the group or the associated companion group is synchronized in real time on the micro-page, obtained and presented on the corresponding cursor UI. Team members can write symbolic instructions based on the cards to leave traces of cognitive behavior.

[0110] (3) Group symbiotic sharing Objective: By submitting and sharing card notes, group members can form cognitive emergence and promote group collaboration, thereby improving the overall cognitive level of the group.

[0111] Process: Team members upload the iterated card notes to the group's shared space. The micro-layout input method uses a five-rhythm model to quantitatively analyze the cognitive value of cards, screen out high-quality cards, and share them with the group through AR data registration, achieving cognitive collaboration and goal alignment. Specifically, the micro-layout three-level screening input method is first and foremost an input method application that meets basic text input needs in various scenarios. The micro-layout input method supports Chinese Pinyin, Chinese Double Pinyin, and English input, and can normally output text and characters in text editors, dialog boxes, and code editors. In addition, the micro-layout input method also supports cognitive input, primarily including electromagnetic input using a pen and camera-based camera input, namely micro-layout fast and slow snapshot notes. Fast snapshots refer to direct capture similar to mobile phone photography, while slow snapshots incorporate interactive information such as QR code recognition, layout correction, and cutouts. Both fast and slow snapshots are lossless layout descriptions, and layout-related information is primarily stored in the QR code.

[0112] The micro-page input method screening first reconstructs the closed domain of cursor-aggregated data, and then screens out cognitive data with the basis of collaboration, emergence, and alignment through the first level of screening; then, the group cognitive data is quantitatively analyzed through the five-rhythm model to capture the group's collaborative emergence and alignment behavior, forming support for the improvement of group cognition; finally, a general description of the group's cognitive process is given, including the access of cognitive load cards and the manifestation of output granularity.

[0113] The present invention demonstrates significant beneficial effects in many aspects, including group collaboration, cognitive optimization, integration of static and dynamic data, closed domain adaptation, and improvement of cognitive productivity. It breaks through the technical limitations of existing input methods, and can upgrade group cognition in education and commercial organizations, providing basic digital AI symbiotic support including digital stationery.

[0114] A micro-layout cursor-based group-recognizable three-level screening system, for executing any of the above-described micro-layout cursor-based group-recognizable three-level screening input methods, comprising a regional closed ecological medium encoding module, a micro-layout DMT static and dynamic data structure construction module, a primary screening module, a secondary screening module, a tertiary screening module, a quantitative evaluation module, and a structured storage module; The regional closed ecological medium coding module is connected to the micro-format DMT static and dynamic data structure construction module for regional closed ecological medium coding; The micro-format DMT static and dynamic data structure construction module is respectively connected to the primary screening module, the secondary screening module, the tertiary screening module, and the quantitative evaluation module for constructing the micro-format DMT static and dynamic data structure; The primary screening module is connected to the secondary screening module and the quantitative evaluation module, and is used to perform primary screening on the constructed micro-format DMT static and dynamic data structure; The secondary screening module is connected to the tertiary screening module and the quantitative evaluation module, and is used to perform secondary screening on the constructed micro-format DMT static and dynamic data structure; The three-level screening module is connected to the quantitative evaluation module and is used to perform three-level screening on the constructed micro-format DMT static and dynamic data structure; The quantitative evaluation module is connected to the structured storage module and is used to perform quantitative evaluation on the group cognitive effect; The structured storage module is used to store the micro-page group cognitive evaluation results.

[0115] In summary, the three-level screening input method and system based on micro-page cursor and group cognition provided by the present invention realizes a closed-loop design for cognitive improvement, breaking through the technical limitations of existing input methods in many aspects such as group collaboration, cognitive optimization, static and dynamic data integration, closed domain adaptation and cognitive productivity improvement, and upgrading group cognition for education and commercial organizations.

[0116] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A three-level screening input method based on a micro-page cursor and capable of group recognition, characterized by: The steps include: S1: Construct a regional closed ecological medium code that includes a closed domain deep strong screening code and a natural weak screening code; S2: Extract the preset static content and generated dynamic content in the closed domain deep strong screening code based on the regional closed ecological medium code, and construct the micro-page DMT static and dynamic data structure; S3: Perform a first-level screening based on the constructed micro-layout DMT static and dynamic data structure to obtain the first-level screening results including existence judgment, structural judgment, and OT quantification indicator data; S4: Calculate the five rhythm indicators based on the first-level screening results, calculate the collaborative screening indicator, emergence screening indicator and alignment screening indicator through the five rhythm indicators, and perform second-level screening to obtain micro-layouts with collaborative, emergence and alignment screening type characteristics; S5: Determine the classification type based on the collaborative screening index, the emergence screening index, and the alignment screening index, and perform three-level screening on the micro-pages with collaborative, emergence, and alignment screening type characteristics according to the determined classification type to obtain page groups with group cognitive characteristics; S6: Conduct a quantitative evaluation of the group cognitive effect based on the final screening results to obtain the micro-page group cognitive evaluation results; S7: The micro-layout group cognitive evaluation results are stored in a structured manner and finally output by the micro-layout cognitive input method to complete the entire micro-layout group cognitive process.

2. The micro-page cursor-based group-recognizing three-level screening input method according to claim 1, characterized in that: The closed domain deep strong screening coding in step S1 includes pre-coding and post-coding. The fields of the pre-coding include medium and format, preset static content, generated dynamic content, group and generation path, and the fields of the post-coding include screening identification and verification.

3. The micro-page cursor-based group-recognizable three-level screening input method according to claim 1, characterized in that: The fields of the natural weak screening code in step S1 include data source, spatiotemporal information and natural generation.

4. The micro-page cursor-based group-recognizable three-level screening input method according to claim 1, characterized in that: The micro-layout DMT static and dynamic data structure constructed in step S2 includes static elements, dynamic elements, and organizational presentation. The static elements include spatial geometric relationships, structural mechanisms, content templates, and preset content. The spatial geometric relationships include up-down relationships, left-right relationships, and inside-out relationships. The structural mechanisms include the micro-layout unique identifier, the micro-layout O-zone structure, the micro-layout T-zone structure, and the micro-layout theme. The dynamic elements include dot-stroke symbol order summaries and DMT logical relationships. The dot-stroke symbol order summaries include dot-stroke symbol orders for writing traces, sound traces, body posture traces, and photographic traces. The DMT logical relationships include micro-layout screening relationships and micro-layout aggregation relationships. The organizational presentation includes organizational roles.

5. The micro-page cursor-based group-recognizable three-level screening input method according to claim 1, characterized in that: The process of primary screening in step S3 is as follows: S31: Based on the static elements and dynamic elements in the micro-layout DMT static and dynamic data structure and the micro-layout cursor API parameters, establish cross-media and cross-sensor data based on the micro-layout, perform DMT structure screening, OT flow existence screening, and synchronize micro-layout related information; S32: Integrate the scheduled learning activities into the micro-layout to provide the context for each operation. The system records the time when each handwriting occurs on the micro-layout. Combined with the activity schedule, it automatically imports the metadata preset at the corresponding time to complete the first-level screening and obtain the first-level screening results including existence judgment, structural judgment, and OT quantification indicator data.

6. The micro-page cursor-based group-recognizable three-level screening input method according to claim 1, characterized in that: The five rhythm indicators include trace rhythm, relationship rhythm, degree rhythm, attenuation rhythm and de-invasive rhythm.

7. The micro-page cursor-based group-recognizable three-level screening input method according to claim 1, characterized in that: The method of the secondary screening is as follows: S41: Calculate five rhythm indicators: Calculate the trace rhythm index according to formula (1): （1）； in: represents the trace rhythm index, The total number of dot-dash symbols in the area. Indicates the user's interaction time in the area, Represents the teaching cycle, Indicates the current time, represents the initial state of the trace rhythm; Calculate the relationship rhythm index according to formula (2): （2）； in: Represents the relationship rhythm index, Indicates the number of recorded collaboration behaviors, Represents the number of all interactive behaviors, It represents the initial state of the relationship rhythm; Calculate the degree rhythm index according to formula (3): （3）； in: Indicates the degree of rhythm index, The dot-dash symbol indicates the target task area. It represents the total number of dot and stroke symbols that occur on the micro-layout. Indicates the initial state of degree rhythm; Calculate the decay rhythm index according to formula (4): （4）； in: represents the decay rhythm index, represents a natural constant, represents the initial state of the decay rhythm, represents the decay rate, Indicates the last interaction time; The elimination rhythm index is calculated according to formula (5): （5）； in: Indicates the rhythm index of elimination. Indicates the number of newly generated content. represents the quality score, represents the total number of questions, It represents the initial state of the elimination rhythm; S42: Calculate the collaborative screening index in the collaborative screening index according to formula (6) , according to formula (7) to calculate the collaborative screening preset in the collaborative screening index , and compare and The size of When retaining the micro layout, When abandoning micro layout: （6）； （7）； in: represents the average value of cooperative behavior, represents the standard deviation of the collaborative behavior, represents the sensitivity coefficient of collaborative screening; S43: Calculate the emergent screening index in the emergent screening index according to formula (8) , according to formula (9), the emerging screening preset in the emerging screening index is calculated , and compare and The size of When retaining the micro layout, When abandoning micro layout: （8）； （9）； in: represents the mean frequency of new content generation in historical data, represents the mean of the quality scores of new content in historical data, represents the emergent screening sensitivity coefficient, represents the standard deviation of emergent behavior; S44: Calculate the alignment screening index in the alignment screening index according to formula (10) , calculate the alignment screening preset in the alignment screening index according to formula (11) , and compare and The size of When retaining the micro layout, When abandoning micro layout: （10）； （11）； in: The weight coefficient representing the reference value of the degree rhythm, Indicates the baseline value of the degree rhythm, The weight coefficient representing the reference value of the relationship rhythm, Indicates the baseline value of the relationship rhythm; S45: Complete the secondary screening and obtain a micro-layout with collaborative, emergent and aligned screening type characteristics.

8. The micro-page cursor-based group-recognizable three-level screening input method according to claim 7, characterized in that: The process of performing three-stage screening in step S5 is as follows: S51: According to the comparison result in the secondary screening process, a screening type having a screening value greater than a corresponding preset value in the screening index is selected as a determined screening type; If only one screening type has a screening value greater than the corresponding preset value, this screening type is selected as the determined screening type; If there is no screening type with a screening value greater than the corresponding preset value, it is considered invalid screening, and the process directly proceeds to step S6 by skipping the third-level screening; If there are multiple screening types whose screening values are greater than the corresponding preset values, the screening type with the highest priority is determined as the determined screening type according to the set priority; S52: Aggregating micro-pages according to the determined screening type to generate corresponding preliminary page groups; S53: Perform three-level screening based on the generated preliminary page group, implement behavioral resolution screening, and optimize the cognitive contribution of the micro-page group.

9. The micro-page cursor-based group-recognizable three-level screening input method according to claim 1, characterized in that: In step S6, the following method is used to quantitatively evaluate the group cognitive effect and obtain the micro-page group cognitive evaluation result: Quantification of internal cognitive load in group cognitive effects: Record the group's activity time on the micro-board, calculate the total duration, and count the total number of operations on the micro-board to quantify the internal cognitive load; Quantification of external cognitive load in group cognitive effects: scoring the degree of synergy and alignment separately; Quantification of relevant cognitive load: The creative rhythm was scored based on the frequency and quality of creative activities on the micropages; Evaluate the screening results at each level: Assign values layer by layer based on the reflection of each level screening result in the five-layer concept; Directly extract the index data from each level of screening, and map the screening results of each level to the scoring criteria of the five-level concept to assign points layer by layer; Through the feedback of static rules and dynamic screening steps, the layer-by-layer scoring is coordinated and integrated with the cognitive level and the screening level to obtain a fusion score of the cognitive level and the screening level; Refine the integrated scores of cognitive level and screening level; Each level of screening results is evaluated based on the refined score. If the score is lower than the set value, the insufficient level is improved and steps S3 to S6 are repeated to re-evaluate until the score is greater than or equal to the set value.

10. A micro-page cursor-based group-recognizable three-level screening system, for executing the micro-page cursor-based group-recognizable three-level screening input method according to any one of claims 1 to 9, characterized in that: It includes regional closed ecological medium coding module, micro-format DMT static and dynamic data structure construction module, first-level screening module, second-level screening module, third-level screening module, quantitative evaluation module and structured storage module; The regional closed ecological medium coding module is connected to the micro-format DMT static and dynamic data structure construction module for regional closed ecological medium coding; The micro-format DMT static and dynamic data structure construction module is respectively connected to the primary screening module, the secondary screening module, the tertiary screening module, and the quantitative evaluation module for constructing the micro-format DMT static and dynamic data structure; The primary screening module is connected to the secondary screening module and the quantitative evaluation module, and is used to perform primary screening on the constructed micro-format DMT static and dynamic data structure; The secondary screening module is connected to the tertiary screening module and the quantitative evaluation module, and is used to perform secondary screening on the constructed micro-format DMT static and dynamic data structure; The three-level screening module is connected to the quantitative evaluation module and is used to perform three-level screening on the constructed micro-format DMT static and dynamic data structure; The quantitative evaluation module is connected to the structured storage module and is used to perform quantitative evaluation on the group cognitive effect; The structured storage module is used to store the micro-page group cognitive evaluation results.

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