A method for generating multidimensional information cursors based on micro-layout data screening

Abstract

This invention relates to the field of mobile internet technology and provides a method for generating multi-dimensional information cursors based on micro-layout data screening. The method includes: generating an initial media connectivity code based on static and dynamic encoding information; constructing a computational space and a data space; scanning the initial media connectivity code to obtain initial static and dynamic cognitive data including interactive data; updating the initial media connectivity code through interactive data to obtain a target media connectivity code; obtaining target static and dynamic cognitive data through primary and secondary screening; obtaining a target cognitive data chain and a target cognitive data cluster through the target static and dynamic cognitive data; obtaining a reference task target through the target cognitive data chain and target cognitive data cluster; selecting a media device, reading the target media connectivity code through the media device, and generating a multi-dimensional information cursor. This invention effectively improves the cursor's adaptability to multi-dimensional data environments and its multi-device collaborative capabilities.

Description

Technical Field

[0001] This invention relates to the field of mobile Internet technology, and in particular to a method for generating multi-dimensional information cursors based on micro-layout data screening. Background Technology

[0002] Traditional graphical user interface (GUI) cursors are primarily used for positional indication and simple operations on a two-dimensional plane. Their functionality and expressiveness are insufficient in complex, multi-dimensional data environments. They struggle to effectively represent and manipulate multi-dimensional data structures and cannot effectively filter data, making them unsuitable for the demands of modern educational and office environments that require processing multi-level, multi-dimensional data. This limitation is particularly pronounced in large-scale group collaborations and cross-platform data interaction, rendering traditional GUI cursors unsuitable for the complex needs of these scenarios.

[0003] Traditional graphical user interface (GUI) cursors lack cross-platform and cross-device data connectivity capabilities. This makes it difficult to achieve seamless data integration and unified labeling across different systems, devices, and media, resulting in a large amount of valuable information being difficult to effectively integrate and utilize. Furthermore, traditional GUI cursors do not have cross-device group collaboration capabilities, and they do not support the display of interactive information, including handwriting and sound. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a method for generating multi-dimensional information cursors based on micro-layout data screening, which realizes the generation of multi-dimensional information cursors including multi-dimensional data and interactive data, and the multi-dimensional information cursors can be used to achieve cross-platform and cross-device data connectivity.

[0005] This invention provides a method for generating multidimensional information cursors based on micro-layout data screening, comprising:

[0006] S1: Obtain static and dynamic coding information, and generate initial medium connectivity coding based on the static and dynamic coding information;

[0007] S2: Construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster;

[0008] S3: Select an interactive medium, scan the initial medium connectivity code through the interactive medium to obtain the scanning result, interact with the scanning result through the interactive medium to obtain interactive data and initial static and dynamic cognitive data including the interactive data; update the initial medium connectivity code through the interactive data to obtain the target medium connectivity code;

[0009] S4: Obtain the task objective; in the computing space, perform a first-level screening of the initial static and dynamic cognitive data using the task objective and resource static and dynamic cognitive data to obtain the first-level screening data; perform a second-level screening of the first-level screening data using interaction data to obtain the target static and dynamic cognitive data including the interaction data; in the data space, update the initial cognitive data chain and the initial cognitive data cluster using the target static and dynamic cognitive data to obtain the target cognitive data chain and the target cognitive data cluster respectively.

[0010] S5: Import the task objective into the data space, and modify the task objective through the target cognition data chain and target cognition data cluster to obtain the reference task objective;

[0011] S6: Select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multi-dimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

[0012] According to the present invention, a method for generating multidimensional information cursors based on micro-layout data screening is provided, wherein step S4 includes:

[0013] S41: Obtain the task target and store it in the computing space, allocate a data container environment in the computing space, and import the resource static and dynamic cognition data into the data container environment;

[0014] S42: Analyze the task objective to obtain the task objective analysis result; index the resource static and dynamic cognitive data using the task objective analysis result to obtain associated static and dynamic cognitive data; filter the initial static and dynamic cognitive data using the task objective analysis result to obtain the initial first-stage filtered data.

[0015] S43: The associated static and dynamic cognitive data and the initial first screening data are fused to obtain the first screening data;

[0016] S44: Obtain instruction interaction data through the interaction data, and perform secondary screening on the first screening data through the instruction interaction data to obtain the target static and dynamic cognition data including the interaction data;

[0017] S45: Import the target static and dynamic cognitive data into the data space, and incorporate the target static and dynamic cognitive data into the initial cognitive data chain to obtain the target cognitive data chain; incorporate the target static and dynamic cognitive data into the initial cognitive data cluster to obtain the target cognitive data cluster.

[0018] According to the multidimensional information cursor generation method based on micro-layout data screening provided by the present invention, step S44 further includes:

[0019] S441: The interactive data is split to obtain discrete interactive data;

[0020] S442: Obtain the instruction library, compare the discrete interactive data with the instruction library, and obtain the instruction interactive data;

[0021] S443: The first screening data is labeled by the instruction interaction data to obtain the label. The first screening data is then screened by the label to obtain the target static and dynamic cognition data.

[0022] According to the present invention, a method for generating multidimensional information cursors based on micro-layout data screening is provided, wherein step S5 includes:

[0023] S51: Import the task objective into the data space, and evaluate the target cognitive data chain or the target cognitive data cluster to obtain the task completion rate;

[0024] S52: Calculate the gap between the task objective and the task completion rate to obtain the task difference, evaluate the expected task progress through the target cognitive data chain and the target cognitive data cluster, and generate the reference task objective through the task difference and the expected task progress.

[0025] According to the present invention, a method for generating multidimensional information cursors based on micro-layout data screening is provided, wherein step S6 includes:

[0026] S61: Select the medium device according to the form of the target medium connection code, and read the target medium connection code through the medium device to obtain the reading result;

[0027] S62: Based on the reading results, search the data space to find the reference task target and the target static and dynamic cognitive data, extract the target static and dynamic cognitive data to obtain interactive data, and read the reference task target and interactive data into the computing space;

[0028] S63: Extract and summarize interactive data in the computing space to generate brief interactive information, and generate a multi-dimensional information cursor that matches the media device based on the brief interactive information, reference task objectives, and interactive data.

[0029] According to the present invention, a multidimensional information cursor generation method based on micro-layout data screening is provided, wherein both the initial medium connectivity encoding and the target medium connectivity encoding include encoded fields and non-encoded fields.

[0030] This invention also provides a multi-dimensional information cursor generation system based on micro-layout data screening, used to execute a multi-dimensional information cursor generation method based on micro-layout data screening as described in any of the above claims, including:

[0031] Encoding module: Used to acquire static and dynamic encoding information, and generate initial medium connectivity code based on the static and dynamic encoding information;

[0032] Space construction module: used to construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster;

[0033] Interaction module: Used to select an interaction medium, scan the initial medium connectivity code through the interaction medium to obtain the scanning result, interact with the scanning result through the interaction medium to obtain interaction data and initial static and dynamic cognitive data including the interaction data; update the initial medium connectivity code through the interaction data to obtain the target medium connectivity code;

[0034] Data screening module: Used to acquire task objectives, perform primary screening of initial static and dynamic cognitive data in the computing space using task objectives and resource static and dynamic cognitive data to obtain first-screened data; perform secondary screening of the first-screened data using interactive data to obtain target static and dynamic cognitive data including interactive data; update the initial cognitive data chain and initial cognitive data cluster using the target static and dynamic cognitive data in the data space to obtain the target cognitive data chain and target cognitive data cluster respectively.

[0035] Reference task target generation module: It is used to import the task target into the data space, and correct the task target through the target cognition data chain and target cognition data cluster to obtain the reference task target;

[0036] Multidimensional information cursor generation module: Used to select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multidimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a multi-dimensional information cursor generation method based on micro-layout data screening as described in any of the above claims.

[0038] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements a multi-dimensional information cursor generation method based on micro-layout data screening as described in any of the preceding claims.

[0039] The present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer is able to execute a multi-dimensional information cursor generation method based on micro-layout data screening as described in any of the above claims.

[0040] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0041] This invention provides a method for generating multi-dimensional information cursors based on micro-layout data screening. By introducing multi-dimensional information cursors, it enables seamless communication and collaboration between multiple devices. Multiple devices can communicate and interact with the medium simultaneously or at different times, and synchronize their various data in real time or at different times, thereby ensuring information consistency and improving collaboration efficiency.

[0042] Multidimensional information cursors can also effectively filter data and manipulate and express multidimensional data structures. In particular, they can process and utilize interactive data, including handwriting and sound information, which effectively improves the functionality and expressiveness of cursors in complex multidimensional data environments. This allows multidimensional information cursors to meet the cursor requirements in large-scale group collaboration and cross-platform data interaction.

[0043] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a multi-dimensional information cursor generation method based on micro-layout data screening provided by the present invention.

[0046] Figure 2 This is a schematic diagram of the structure of a multidimensional information cursor provided by the present invention, which is a method for generating multidimensional information cursors based on micro-layout data screening.

[0047] Figure 3 This is a schematic diagram of the structure of the multidimensional information cursor call interface of a multidimensional information cursor generation method based on micro-layout data screening provided by the present invention.

[0048] Figure 4This is a schematic diagram of the structure of a multi-dimensional information cursor generation system based on micro-layout data screening provided by the present invention.

[0049] Figure 5 This is a schematic diagram of the structure of a multi-dimensional information cursor generation device based on micro-layout data screening provided by the present invention.

[0050] Figure label:

[0051] 1. Multidimensional information cursor; 11. First identity identification area; 12. Second identity identification area; 13. Team member status area; 14. Uplink data transmission indicator area; 15. Downlink data transmission indicator area; 16. Media device status area; 17. User interaction information area; 18. Reference task target area; 19. Team member interaction information area; 21. Interface; 22. Interface task target area; 23. Interface reference task target area; 24. Notes area; 25. Voice area; 251. Voice icon; 100. Encoding module; 200. Space construction module; 300. Interaction module; 400. Data screening module; 500. Reference task target generation module; 600. Multidimensional information cursor generation module; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0053] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0054] The following is combined Figures 1 to 5 Description of embodiments of the present invention:

[0055] Figure 1 This invention provides a flowchart illustrating a method for generating a multidimensional information cursor based on micro-layout data screening. The method comprises six steps: S1 generates an initial media connectivity code, which is generated using static and dynamic encoding information. S2 constructs a computational space and a data space; additionally, a cloud data space can be constructed to improve the reliability and stability of the data space. S3 generates a target media connectivity code, which involves first selecting an interactive medium for scanning to obtain initial static and dynamic cognitive data, then updating the initial media connectivity code, and finally obtaining the target media connectivity code. S4 performs primary and secondary screening to obtain a target cognitive data chain and a target cognitive data cluster, including primary screening based on the task objective and secondary screening based on interactive data, obtaining target static and dynamic cognitive data, a target cognitive data chain, and a target cognitive data cluster. Furthermore, the target static and dynamic cognitive data can be incorporated into resource static and dynamic cognitive data. S5 corrects the task objective using the target cognitive data chain and target cognitive data cluster to obtain a reference task objective. S6 uses a media device to read the target media connectivity code and generate a multidimensional information cursor.

[0056] This invention provides a method for generating multidimensional information cursors based on micro-layout data screening, comprising:

[0057] S1: Obtain static and dynamic coding information, and generate initial medium connectivity coding based on the static and dynamic coding information;

[0058] Furthermore, the objective of this stage is to generate initial media connectivity codes for subsequent use. Media connectivity codes can be implemented in various ways, such as digital serial numbers, one-dimensional barcodes, QR codes, and dot matrix codes for smart pens. Each encoding format is suitable for different application scenarios. For example, QR codes can store more complex information and are suitable for screen and camera scanning; digital serial numbers are typically used to provide a matching index for the resource filenames corresponding to the media connectivity codes; dot matrix codes are typically used to adapt to smart dot matrix pens and collect data through scanning and sensing. Media connectivity codes are used to establish the connection between the device and content data. Each media connectivity code corresponds to a set of content data, such as a research topic or an exercise. The micro-layout includes initial media connectivity codes and target media connectivity codes.

[0059] Generating the initial media connectivity code requires acquiring both static and dynamic encoding information. Static encoding information includes fixed, unchanging details such as the basic physical attributes and scope of the initial media connectivity code, including the type of the corresponding interactive medium, its size, location, and the content data it points to; this information remains unchanged after creation. Dynamic encoding information, on the other hand, includes dynamic data generated by the user during the generation of the initial media connectivity code, such as user identity and permissions, whether editing was performed, the device used, and the time the operation was completed; this information is variable. After acquiring both static and dynamic encoding information, the initial media connectivity code can be generated based on them.

[0060] S2: Construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster;

[0061] Furthermore, the purpose of this stage is to construct a computational space and a data space for use in subsequent steps. The computational space is the area where users operate and interact in real time, and it is also the area for identifying, filtering, comparing, splitting, and updating various data, information, and codes. In addition, this stage also acquires static and dynamic cognitive data of resources, initial cognitive data chains, and initial cognitive data clusters, and constructs the data space using these resources. The initial cognitive data chain includes resources necessary for forming cognition of content data and possessing a cognitive process, such as the relevant knowledge network needed to answer a question in the content data, the execution flow for performing a process in the content data, and mind maps of the content data. The initial cognitive data cluster includes relevant resources needed for forming cognition of content data, such as references related to the content data and other content data related to the content data. The static and dynamic cognitive data of resources combines the characteristics of the initial cognitive data chain and the initial cognitive data cluster; it not only possesses a cognitive process but also has more detailed and specific content. For example, when the content data is an exercise, the static and dynamic cognitive data of resources can be a detailed analysis and explanation of that exercise. The data space is used to store and update this data. In addition, the data space also undertakes some data operations and data processing tasks.

[0062] To ensure data security and accuracy, a separate cloud data space can be set up to provide mapping and backup for the data space, improving its reliability. The cloud data space and the data space can be synchronized non-real-time via specific operations. This non-real-time synchronization ensures consistency and coordination between the cloud data space and the data space, while avoiding the high network performance and system resource consumption of real-time synchronization.

[0063] S3: Select an interactive medium, scan the initial medium connectivity code through the interactive medium to obtain the scanning result, interact with the scanning result through the interactive medium to obtain interactive data and initial static and dynamic cognitive data including the interactive data; update the initial medium connectivity code through the interactive data to obtain the target medium connectivity code;

[0064] Furthermore, the purpose of this step is to select an interactive medium, scan the initial medium connectivity code through the interactive medium to obtain the scan result, then interact with the scan result to obtain initial static and dynamic cognitive data, and simultaneously generate interactive dynamic coding information based on the interactive data. Finally, the target medium connectivity code is obtained through the interactive dynamic coding information. Specifically, this step requires selecting an interactive medium that can interact with the initial medium connectivity code according to its specific implementation method, and scanning the initial medium connectivity code through the interactive medium to obtain the scan result, which is the content data corresponding to the initial medium connectivity code. Then, the interactive medium interacts with the scan result to obtain interactive data and initial static and dynamic cognitive data including the interactive data. In this embodiment, the interactive data includes handwriting data of notes made by the user during interaction and recorded audio data, wherein the handwriting data and audio data are collected by corresponding sensors on the interactive medium. After the interactive data is generated, interactive dynamic coding information needs to be generated based on information such as the user's identity, the device used, and the time when the operation is completed. Then, the interactive dynamic coding information is fused with the initial medium connectivity code to update the initial medium connectivity code, and finally, the target medium connectivity code is obtained. Both initial medium connectivity coding and target medium connectivity coding can include coded fields; they can also include non-coded fields, such as natural language, symbols, and graphics. Non-coded fields can improve users' ability to distinguish between different initial and target medium connectivity codings.

[0065] S4: Obtain the task objective; in the computing space, perform a first-level screening of the initial static and dynamic cognitive data using the task objective and resource static and dynamic cognitive data to obtain the first-level screening data; perform a second-level screening of the first-level screening data using interaction data to obtain the target static and dynamic cognitive data including the interaction data; in the data space, update the initial cognitive data chain and the initial cognitive data cluster using the target static and dynamic cognitive data to obtain the target cognitive data chain and the target cognitive data cluster respectively.

[0066] Furthermore, the purpose of this stage is to obtain the task objective, and to filter the initial static and dynamic cognitive data through the task objective, interaction data, and resource static and dynamic cognitive data to obtain the target static and dynamic cognitive data. The target static and dynamic cognitive data is then imported into the data space, and finally, the target cognitive data chain and target cognitive data cluster are updated within the data space using the target static and dynamic cognitive data.

[0067] Step S4 further includes:

[0068] S41: Obtain the task target and store it in the computing space, allocate a data container environment in the computing space, and import the resource static and dynamic cognition data into the data container environment;

[0069] S42: Analyze the task objective to obtain the task objective analysis result; index the resource static and dynamic cognitive data using the task objective analysis result to obtain associated static and dynamic cognitive data; filter the initial static and dynamic cognitive data using the task objective analysis result to obtain the initial first-stage filtered data.

[0070] S43: The associated static and dynamic cognitive data and the initial first screening data are fused to obtain the first screening data;

[0071] S44: Obtain instruction interaction data through the interaction data, and perform secondary screening on the first screening data through the instruction interaction data to obtain the target static and dynamic cognition data including the interaction data;

[0072] S45: Import the target static and dynamic cognitive data into the data space, and incorporate the target static and dynamic cognitive data into the initial cognitive data chain to obtain the target cognitive data chain; incorporate the target static and dynamic cognitive data into the initial cognitive data cluster to obtain the target cognitive data cluster.

[0073] Step S44 also includes:

[0074] S441: The interactive data is split to obtain discrete interactive data;

[0075] S442: Obtain the instruction library, compare the discrete interactive data with the instruction library, and obtain the instruction interactive data;

[0076] S443: The first screening data is labeled by the instruction interaction data to obtain the label. The first screening data is then screened by the label to obtain the target static and dynamic cognition data.

[0077] The specific implementation method for the above steps in this embodiment is as follows:

[0078] First, the task objective needs to be obtained from a user. Then, a storage space is allocated in the computing space as a data container environment, and the static and dynamic resource cognitive data is imported into this environment. During import, the static and dynamic resource cognitive data can be filtered based on the task objective, selecting data relevant to both the task objective and the content data. Next, the task objective is parsed to obtain the parsing results, which involve breaking down the task objective into multiple sub-task objectives. Then, the static and dynamic resource cognitive data is indexed based on these parsing results, and the indexed data related to the parsing results are used as associated static and dynamic cognitive data. The initial static and dynamic cognitive data is then filtered using the parsing results to obtain the initial first-stage filtered data. Finally, the associated static and dynamic cognitive data and the initial first-stage filtered data are merged to obtain the first-stage filtered data, thus completing the first-level filtering. Here, filtering means clustering data based on a certain condition; for example, filtering the initial static and dynamic cognitive data using the parsing results of the task objective is equivalent to clustering the initial static and dynamic cognitive data based on the sub-task objectives.

[0079] Next, the interactive data is split into discrete interactive data. In this embodiment, handwriting data is split into basic handwriting units, such as individual Chinese characters, words, and symbols; and audio data is split into basic language units, such as individual sounds, words, and symbols. Here, splitting the interactive data does not affect the integrity of the interactive data itself. Then, an instruction library is obtained, containing the meanings of common handwriting and language units, such as a checkmark representing correct, an cross representing completely wrong, and "agree" representing approval. The discrete interactive data is then compared with the instruction library to find handwriting and language units with instruction meanings, thereby assigning instruction meanings to the handwriting and language units in the discrete interactive data and forming instruction interactive data. Finally, the first screening data is labeled using the instruction interactive data to obtain label tags. These label tags can be used to represent the evaluation metrics of the first screening data and are determined based on the instruction meanings contained in the instruction interactive data. If a portion of the discrete interactive data in the first screening data corresponds to a checkmark and the instruction it contains means "correct," then the generated label is "completely correct." If the discrete interactive data is a half-checkmark and the instruction it contains means "partially correct," then the generated label is "partially correct." If the discrete interactive data is an X and the instruction it contains means "completely incorrect," then the generated label is "incorrect." Finally, the first screening data is further screened using these labels to obtain the target static and dynamic cognitive data, which completes the second-level screening. The target static and dynamic cognitive data includes interactive data.

[0080] After obtaining the target static and dynamic cognitive data, the target static and dynamic cognitive data is imported into the data space and incorporated into the initial cognitive data chain to obtain the target cognitive data chain; the target static and dynamic cognitive data is then incorporated into the initial cognitive data cluster to obtain the target cognitive data cluster. Here, the target static and dynamic cognitive data can also be incorporated into the resource static and dynamic cognitive data to achieve content iteration of the resource static and dynamic cognitive data, thereby effectively improving the adaptability and accuracy of the resource static and dynamic cognitive data.

[0081] In addition, users can export the target static and dynamic cognitive data according to their own needs, and use the associated static and dynamic cognitive data in the target static and dynamic cognitive data to provide inspiration and guidance for their own cognitive process.

[0082] S5: Import the task objective into the data space, and modify the task objective through the target cognition data chain and target cognition data cluster to obtain the reference task objective;

[0083] Furthermore, the objective of this stage is to evaluate and revise the task objectives through the target cognitive data chain and target cognitive data cluster, thereby obtaining a reference task objective.

[0084] Step S5 further includes:

[0085] S51: Import the task objective into the data space, and evaluate the target cognitive data chain or the target cognitive data cluster to obtain the task completion rate;

[0086] S52: Calculate the gap between the task objective and the task completion rate to obtain the task difference, evaluate the expected task progress through the target cognitive data chain and the target cognitive data cluster, and generate the reference task objective through the task difference and the expected task progress.

[0087] Specifically, the task objective is imported into the data space. Since both the target cognitive data chain and the target cognitive data cluster contain static and dynamic target cognitive data, the task completion rate can be assessed by evaluating the static and dynamic target cognitive data in the target cognitive data chain or cluster. Here, the completion rate of a sub-task objective can be evaluated based on a single label in the target static and dynamic cognitive data. For example, the completion rate of a sub-task objective with the label "completely correct" is set to 1, the completion rate of a sub-task objective with the label "partially correct" is set to 0.5, and the completion rate of a sub-task objective with the label "incorrect" is set to 0. If a sub-task objective has multiple labels, the completion rate of the sub-task objective is evaluated by calculating the average completion rate represented by the multiple labels. Calculating the average completion rate of all sub-task objectives yields the task completion rate. Then, the difference between the task objective and the task completion rate is calculated to obtain the task difference.

[0088] Subsequently, the expected task progress can be assessed through the target cognitive data chain and target cognitive data cluster. For example, when the task objective is to complete a certain study, the interaction data of the target static and dynamic cognitive data in the target cognitive data chain and target cognitive data cluster can be compared with the references, comparative documents, and related knowledge in the target cognitive data chain and target cognitive data cluster. Combined with the evaluation metrics contained in the annotation tags, the current completion level of the study and how much relevant knowledge the researcher has used can be assessed. It can also be assessed how the researcher's current progress compares with the comparative documents, thus obtaining a reasonable expected task progress. Then, by comprehensively considering the task difference and the expected task progress, a reference task objective can be generated. For example, when the task difference is large, the expected task progress can be increased to ensure that the task objective is achieved on time.

[0089] Here, the target static and dynamic cognitive data can also be combined with the expected task progress. When the user exports the target static and dynamic cognitive data and views the associated static and dynamic cognitive data, only the associated static and dynamic cognitive data that matches the expected task progress can be provided to the user to gradually guide the user to complete the cognitive process.

[0090] S6: Select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multi-dimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

[0091] Furthermore, the purpose of this step is to select a suitable media device, read the target media connectivity code through the media device, obtain the reading result, and finally generate a multi-dimensional information cursor that matches the media device.

[0092] Specifically, step S6 further includes:

[0093] S61: Select the medium device according to the form of the target medium connection code, and read the target medium connection code through the medium device to obtain the reading result;

[0094] S62: Based on the reading results, search the data space to find the reference task target and the target static and dynamic cognitive data, extract the target static and dynamic cognitive data to obtain interactive data, and read the reference task target and interactive data into the computing space;

[0095] S63: Extract and summarize interactive data in the computing space to generate brief interactive information, and generate a multi-dimensional information cursor that matches the media device based on the brief interactive information, reference task objectives, and interactive data.

[0096] First, a media device capable of interacting with the target medium's connectivity encoding is selected based on its specific implementation. Next, the target medium's connectivity encoding is read through the media device, yielding the reading result, i.e., the retrieval index corresponding to the target medium's connectivity encoding. Then, a search is performed in the data space based on the reading result to obtain the corresponding reference task target, target cognitive data chain, and target cognitive data cluster. The target cognitive data chain and target cognitive data cluster include target static and dynamic cognitive data, which are extracted to obtain interaction data. Subsequently, the reference task target and interaction data are read into the computing space. Then, the interaction data is extracted and summarized in the computing space to generate brief interaction information. A multi-dimensional information cursor matching the media device is generated using the brief interaction information, the reference task target, and the interaction data. Here, the extraction and summarization of interaction data is achieved by introducing an external extractive automatic text summarization algorithm. Furthermore, the data space can also monitor the number and identity of devices interacting with the target medium's connectivity encoding, thereby determining the identity information and interaction status of team members and presenting them in the multi-dimensional information cursor; it can also acquire the interaction data of team members and generate brief interaction information for each member.

[0097] Figure 2 This is a structural diagram of a multidimensional information cursor, such as... Figure 2 As shown, in this embodiment, the multi-dimensional information cursor includes a first identity identifier area 11 for representing the user's identity, a second identity identifier area 12 for representing the group member's identity, a group member status area 13 for displaying the current group member's participation status and interaction status, a reference task target area 18 for displaying the reference task target, a media device status area 16 for displaying the media device's working status, and an uplink data transmission indicator area 14 and a downlink data transmission indicator area 15 for indicating the data transmission status. The uplink data transmission indicator area 14 indicates whether data is being transmitted from the computing space to the data space by turning on and off, and the downlink data transmission indicator area 15 indicates whether data is being transmitted from the data space to the computing space by turning on and off. It also includes a user interaction information area 17 and a group member interaction information area 19 for displaying brief interaction information. The user interaction information area 17 displays the user's own brief interaction information, and the group member interaction information area 19 displays the brief interaction information of other group members. Here, the medium device corresponding to the multidimensional information cursor is a personal computer or a personal mobile terminal such as a mobile phone or tablet. When the medium device is another device, the presentation form and structure of the multidimensional information cursor can also be modified as needed to match other medium devices.

[0098] also, Figure 3 This is a structural diagram of the multi-dimensional information cursor call interface, such as... Figure 3As shown, in this embodiment, the media device can call up the multidimensional information cursor and display the task target in the interface task target area 22 on the interface 21, display the reference task target in the interface reference task target area 23, display the handwriting data in the interactive data in the note area 24, and display the audio data in the interactive data in the voice area 25. Here, when the voice icon 251 in the voice area 25 is clicked, the audio data can be played. In addition, the multidimensional information cursor 1 can also be displayed normally in the interface 21.

[0099] In summary, the multidimensional information cursor generation method based on micro-layout data screening provided by this invention can effectively enable seamless connection and collaboration between multiple devices by introducing multidimensional information cursors. It can also manipulate and express multidimensional data structures, especially interactive data including handwriting and sound information, effectively improving the functionality and expressiveness of the cursor in complex multidimensional data environments.

[0100] The following describes a multi-dimensional information cursor generation system based on micro-layout data screening provided by the present invention. The multi-dimensional information cursor generation system based on micro-layout data screening described below can be referred to in correspondence with the multi-dimensional information cursor generation method based on micro-layout data screening described above.

[0101] Figure 4 Example: A schematic diagram of a multi-dimensional information cursor generation system based on micro-layout data screening, such as... Figure 4 As shown, a multi-dimensional information cursor generation method based on micro-layout data screening, as described above, includes:

[0102] Encoding module 100: Used to acquire static encoding information and dynamic encoding information, and generate initial medium connectivity encoding based on the static encoding information and dynamic encoding information;

[0103] Space construction module 200: used to construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster;

[0104] Interaction module 300: Used to select an interaction medium, scan the initial medium connectivity code through the interaction medium to obtain the scanning result, interact with the scanning result through the interaction medium to obtain interaction data and initial static and dynamic cognitive data including the interaction data; update the initial medium connectivity code through the interaction data to obtain the target medium connectivity code;

[0105] Data screening module 400: Used to acquire task objectives, perform primary screening of initial static and dynamic cognitive data in the computing space using task objectives and resource static and dynamic cognitive data to obtain first-screened data; perform secondary screening of the first-screened data using interactive data to obtain target static and dynamic cognitive data including interactive data; update the initial cognitive data chain and initial cognitive data cluster using the target static and dynamic cognitive data in the data space to obtain the target cognitive data chain and target cognitive data cluster respectively.

[0106] Reference task target generation module 500: Used to import task targets into the data space, and correct the task targets through the target cognition data chain and target cognition data cluster to obtain reference task targets;

[0107] Multidimensional information cursor generation module 600: Used to select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multidimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

[0108] on the other hand, Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a multi-dimensional information cursor generation method based on micro-layout data screening, the method including:

[0109] S1: Obtain static and dynamic coding information, and generate initial medium connectivity coding based on the static and dynamic coding information;

[0110] S2: Construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster;

[0111] S3: Select an interactive medium, scan the initial medium connectivity code through the interactive medium to obtain the scanning result, interact with the scanning result through the interactive medium to obtain interactive data and initial static and dynamic cognitive data including the interactive data; update the initial medium connectivity code through the interactive data to obtain the target medium connectivity code;

[0112] S4: Obtain the task objective; in the computing space, perform a first-level screening of the initial static and dynamic cognitive data using the task objective and resource static and dynamic cognitive data to obtain the first-level screening data; perform a second-level screening of the first-level screening data using interaction data to obtain the target static and dynamic cognitive data including the interaction data; in the data space, update the initial cognitive data chain and the initial cognitive data cluster using the target static and dynamic cognitive data to obtain the target cognitive data chain and the target cognitive data cluster respectively.

[0113] S5: Import the task objective into the data space, and modify the task objective through the target cognition data chain and target cognition data cluster to obtain the reference task objective;

[0114] S6: Select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multi-dimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

[0115] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute a multi-dimensional information cursor generation method based on micro-layout data screening provided by the above methods, the method comprising:

[0117] S1: Obtain static and dynamic coding information, and generate initial medium connectivity coding based on the static and dynamic coding information;

[0118] S2: Construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster;

[0119] S3: Select an interactive medium, scan the initial medium connectivity code through the interactive medium to obtain the scanning result, interact with the scanning result through the interactive medium to obtain interactive data and initial static and dynamic cognitive data including the interactive data; update the initial medium connectivity code through the interactive data to obtain the target medium connectivity code;

[0120] S4: Obtain the task objective; in the computing space, perform a first-level screening of the initial static and dynamic cognitive data using the task objective and resource static and dynamic cognitive data to obtain the first-level screening data; perform a second-level screening of the first-level screening data using interaction data to obtain the target static and dynamic cognitive data including the interaction data; in the data space, update the initial cognitive data chain and the initial cognitive data cluster using the target static and dynamic cognitive data to obtain the target cognitive data chain and the target cognitive data cluster respectively.

[0121] S5: Import the task objective into the data space, and modify the task objective through the target cognition data chain and target cognition data cluster to obtain the reference task objective;

[0122] S6: Select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multi-dimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

[0123] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for generating a multi-dimensional information cursor based on micro-layout data screening provided by the methods described above, the method comprising:

[0124] S1: Obtain static and dynamic coding information, and generate initial medium connectivity coding based on the static and dynamic coding information;

[0125] S2: Construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster;

[0126] S3: Select an interactive medium, scan the initial medium connectivity code through the interactive medium to obtain the scanning result, interact with the scanning result through the interactive medium to obtain interactive data and initial static and dynamic cognitive data including the interactive data; update the initial medium connectivity code through the interactive data to obtain the target medium connectivity code;

[0127] S4: Obtain the task objective; in the computing space, perform a first-level screening of the initial static and dynamic cognitive data using the task objective and resource static and dynamic cognitive data to obtain the first-level screening data; perform a second-level screening of the first-level screening data using interaction data to obtain the target static and dynamic cognitive data including the interaction data; in the data space, update the initial cognitive data chain and the initial cognitive data cluster using the target static and dynamic cognitive data to obtain the target cognitive data chain and the target cognitive data cluster respectively.

[0128] S5: Import the task objective into the data space, and modify the task objective through the target cognition data chain and target cognition data cluster to obtain the reference task objective;

[0129] S6: Select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multi-dimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

[0130] The system, device, product, and medium embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0131] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating multi-dimensional information cursors based on micro-layout data screening, characterized in that, include: S1: Obtain static and dynamic coding information, and generate initial medium connectivity coding based on the static and dynamic coding information; S2: Construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster; S3: Select an interactive medium, scan the initial medium connectivity code through the interactive medium to obtain the scanning result, interact with the scanning result through the interactive medium to obtain interactive data and initial static and dynamic cognitive data including the interactive data; update the initial medium connectivity code through the interactive data to obtain the target medium connectivity code; S4: Obtain the task objective; in the computing space, perform a first-level screening of the initial static and dynamic cognitive data using the task objective and resource static and dynamic cognitive data to obtain the first-level screening data; perform a second-level screening of the first-level screening data using interaction data to obtain the target static and dynamic cognitive data including the interaction data; in the data space, update the initial cognitive data chain and the initial cognitive data cluster using the target static and dynamic cognitive data to obtain the target cognitive data chain and the target cognitive data cluster respectively. S5: Import the task objective into the data space, and modify the task objective through the target cognition data chain and target cognition data cluster to obtain the reference task objective; S6: Select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multi-dimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

2. The method for generating multi-dimensional information cursors based on micro-layout data screening according to claim 1, characterized in that, Step S4 includes: S41: Obtain the task target and store it in the computing space, allocate a data container environment in the computing space, and import the resource static and dynamic cognition data into the data container environment; S42: Analyze the task objective to obtain the task objective analysis result; index the resource static and dynamic cognitive data using the task objective analysis result to obtain associated static and dynamic cognitive data; filter the initial static and dynamic cognitive data using the task objective analysis result to obtain the initial first-stage filtered data. S43: The associated static and dynamic cognitive data and the initial first screening data are fused to obtain the first screening data; S44: Obtain instruction interaction data through the interaction data, and perform secondary screening on the first screening data through the instruction interaction data to obtain the target static and dynamic cognition data including the interaction data; S45: Import the target static and dynamic cognitive data into the data space, and incorporate the target static and dynamic cognitive data into the initial cognitive data chain to obtain the target cognitive data chain; incorporate the target static and dynamic cognitive data into the initial cognitive data cluster to obtain the target cognitive data cluster.

3. The method for generating multi-dimensional information cursors based on micro-layout data screening according to claim 2, characterized in that, Step S44 also includes: S441: The interactive data is split to obtain discrete interactive data; S442: Obtain the instruction library, compare the discrete interactive data with the instruction library, and obtain the instruction interactive data; S443: The first screening data is labeled by the instruction interaction data to obtain the label. The first screening data is then screened by the label to obtain the target static and dynamic cognition data.

4. The method for generating multi-dimensional information cursors based on micro-layout data screening according to claim 1, characterized in that, Step S5 includes: S51: Import the task objective into the data space, and evaluate the target cognitive data chain or the target cognitive data cluster to obtain the task completion rate; S52: Calculate the gap between the task objective and the task completion rate to obtain the task difference, evaluate the expected task progress through the target cognitive data chain and the target cognitive data cluster, and generate the reference task objective through the task difference and the expected task progress.

5. The method for generating multi-dimensional information cursors based on micro-layout data screening according to claim 1, characterized in that, Step S6 includes: S61: Select the medium device according to the form of the target medium connection code, and read the target medium connection code through the medium device to obtain the reading result; S62: Based on the reading results, search the data space to find the reference task target and the target static and dynamic cognitive data, extract the target static and dynamic cognitive data to obtain interactive data, and read the reference task target and interactive data into the computing space; S63: Extract and summarize interactive data in the computing space to generate brief interactive information, and generate a multi-dimensional information cursor that matches the media device based on the brief interactive information, reference task objectives, and interactive data.

6. The method for generating multi-dimensional information cursors based on micro-layout data screening according to claim 1, characterized in that, Both the initial medium connectivity code and the target medium connectivity code include coded fields and non-coded fields.

7. A multi-dimensional information cursor generation system based on micro-layout data screening, used to execute the multi-dimensional information cursor generation method based on micro-layout data screening as described in any one of claims 1 to 6, characterized in that, include Encoding module: Used to acquire static and dynamic encoding information, and generate initial medium connectivity code based on the static and dynamic encoding information; Space construction module: used to construct the computing space and acquire resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster, and construct the data space through resource static and dynamic cognitive data, initial cognitive data chain and initial cognitive data cluster; Interaction module: Used to select an interaction medium, scan the initial medium connectivity code through the interaction medium to obtain the scanning result, interact with the scanning result through the interaction medium to obtain interaction data and initial static and dynamic cognitive data including the interaction data; update the initial medium connectivity code through the interaction data to obtain the target medium connectivity code; Data screening module: Used to acquire task objectives, perform primary screening of initial static and dynamic cognitive data in the computing space using task objectives and resource static and dynamic cognitive data to obtain first-screened data; perform secondary screening of the first-screened data using interactive data to obtain target static and dynamic cognitive data including interactive data; update the initial cognitive data chain and initial cognitive data cluster using the target static and dynamic cognitive data in the data space to obtain the target cognitive data chain and target cognitive data cluster respectively. Reference task target generation module: It is used to import the task target into the data space, and correct the task target through the target cognition data chain and target cognition data cluster to obtain the reference task target; Multidimensional information cursor generation module: Used to select a medium device, read the target medium connectivity code through the medium device, obtain the reading result, search for the reference task target and target static and dynamic cognitive data in the data space based on the reading result, and generate a multidimensional information cursor that matches the medium device in the computing space based on the reference task target and target static and dynamic cognitive data.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-dimensional information cursor generation method based on micro-layout data screening as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-dimensional information cursor generation method based on micro-layout data screening as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that, When the program instructions are executed by the computer, the computer is able to perform the steps of a multi-dimensional information cursor generation method based on micro-layout data screening as described in any one of claims 1 to 6.

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