Multi-whiteboard data interaction method and system based on cloud platform

Abstract

The invention relates to a multi-whiteboard data interaction method and system based on a cloud platform. The method comprises the following steps: acquiring handwriting input data of a plurality of users through multi-node access and performing time sequence combination; performing sequential arrangement and conflict judgment on the merged data; when no conflict exists, extracting multi-user handwritten annotation track information and fusing the information to generate a unified handwritten data structure; the transmission priority is dynamically adjusted according to network delay in real-time distribution; updating the cooperation interface by adopting a synchronous display method and confirming a synchronous state through feedback; and finally analyzing the interaction consistency and updating the shared display platform. By adopting the method, the problem of content display disorder or coverage omission caused by insufficient data integration mechanism and difficulty in real-time synchronization under multi-node access can be effectively solved, real-time integration and synchronous smooth presentation of multi-user handwritten data are realized, and the interactive experience and working efficiency of remote multi-party cooperation are improved.

Description

Technical Field

[0001] This invention belongs to the field of information technology, and in particular relates to a multi-whiteboard data interaction method and system based on a cloud platform. Background Technology

[0002] In the field of modern information-based collaboration, remote interaction and data sharing technologies are particularly crucial, especially in multi-party meetings, teaching, or team collaboration scenarios. Whiteboards, as an important tool for information presentation and exchange, directly impact communication efficiency and the collaborative experience depending on their functionality. The development of whiteboard technology is not only about data display but also about achieving smooth and real-time multi-party interaction in complex scenarios. Research and innovation in this area have undeniable value in improving work efficiency and user experience.

[0003] However, current whiteboard applications have revealed some deep-seated shortcomings in practical use. Existing technical solutions are often limited to one-way data transmission and presentation, lacking support for dynamic multi-party participation. Especially in scenarios with multiple meeting rooms or multiple users participating simultaneously, the system cannot effectively handle the needs of multi-point input and real-time synchronization. This limitation often traps users in a passive acceptance or take-alternate operation dilemma during collaboration, making it difficult to achieve true interaction and collaboration.

[0004] A deeper technical challenge lies in the coordination of data interaction and the implementation of real-time linkage functions in a multi-node access environment. The primary issue is the lack of an effective integration mechanism for data input from different sources when multiple nodes are connected, resulting in handwritten content from different users not being displayed instantly on the same interface. As this problem becomes apparent, a further challenge emerges: insufficient real-time data transmission guarantees. When multiple users operate simultaneously, the system often experiences content display errors or loss due to delays or conflicts. For example, in a cross-regional remote conference, when multiple participants are writing or annotating on a whiteboard simultaneously, some content may not be updated in time due to network latency, or even be overwritten or omitted. This phenomenon severely impacts the smoothness of collaboration.

[0005] Therefore, ensuring the real-time integration and synchronized presentation of handwritten data from multiple users in complex scenarios with multiple meeting venues has become a key issue in improving whiteboard collaboration functionality. Solving this problem not only involves technological breakthroughs but also directly impacts the user's collaborative experience and efficiency in actual business operations. Summary of the Invention

[0006] Therefore, it is necessary to provide a cloud-based method and system for multi-whiteboard data interaction to address the aforementioned technical issues.

[0007] Firstly, this application provides a multi-whiteboard data interaction method based on a cloud platform, including:

[0008] S1. Collect handwriting input data from multiple users through multi-node access to obtain a handwriting input data set;

[0009] S2. Obtain the timestamp record information of each node based on the handwriting input data set. When the difference in the timestamp record information meets the preset time synchronization conditions, merge the handwriting input data set to obtain the preliminary merged handwriting input dataset.

[0010] S3. Arrange the data in the initially merged handwritten input dataset in order, determine whether there is a time conflict after the arrangement, and obtain the conflict judgment result.

[0011] S4. When the conflict judgment result is that there is no time conflict, an ordered handwriting input sequence is generated, and the handwriting annotation trajectory information of multiple users is extracted from the ordered handwriting input sequence. The handwriting annotation trajectory information is integrated through the data fusion method to determine the unified handwriting data structure after fusion.

[0012] S5. Obtain the unified handwritten data structure after fusion, distribute it to each collaborative device in real-time collaboration mode, and adjust the transmission priority strategy when the distribution delay exceeds the preset network threshold to obtain the optimized distribution data packet.

[0013] S6. Based on the optimized distribution data packet, the collaborative interface update is processed using a synchronous display method. The update status is confirmed through multi-node access feedback, and a confirmation signal for synchronous presentation is obtained.

[0014] S7. Analyze the consistency of multi-user interaction from the synchronously presented confirmation signals. When the interaction consistency meets the preset collaboration requirements, update the shared display platform to obtain the final real-time integrated handwriting input presentation result.

[0015] In one embodiment, the data in the initially merged handwritten input dataset is sequentially arranged, and it is determined whether there are any time conflicts after the arrangement, thus obtaining a conflict determination result, including:

[0016] S11. Obtain the timestamp records of each data item from the initially merged handwriting input dataset, and sort the data order by comparing the timestamps to obtain the sorted handwriting input dataset.

[0017] S12. For the sorted handwritten input dataset, the timestamp comparison method is used to detect whether the timestamps of adjacent data items overlap. If the timestamps overlap, there is a time conflict, and the time conflict detection information is obtained.

[0018] S13. Based on the time conflict detection information, perform handwriting trajectory adjustment processing. The adjusted handwriting coordinates are obtained by weighted fusion correction of multiple conflicting coordinate points using the following formula:

[0019]

[0020] in, This indicates the adjusted coordinates of the handwriting. Represents the original coordinate point. Indicates the fusion weight coefficient. Indicates the first The weights of conflicting data items, Indicates the first Conflicting coordinate points Indicates the total number of conflicting data items;

[0021] S14. Generate an adjusted handwriting input dataset based on the adjusted handwriting coordinate points.

[0022] S15. For the adjusted handwritten input dataset, obtain the trajectory continuity verification result, expand the dataset through multi-source data fusion, and obtain the conflict judgment result.

[0023] In one embodiment, when the conflict determination result indicates that there is no time conflict, an ordered handwritten input sequence is generated, and multi-user handwritten annotation trajectory information is extracted from the ordered handwritten input sequence. The handwritten annotation trajectory information is then integrated using a data fusion method to determine the unified handwritten data structure after fusion, including:

[0024] S21. By using an ordered sequence of handwritten inputs, extract handwritten annotation trajectory information involving multiple users to obtain the extracted trajectory information set.

[0025] S22. Use data fusion methods to integrate the extracted trajectory information set, incorporate user role weight adjustments, and determine the preliminary fused handwritten data structure.

[0026] S23. For the initially fused handwritten data structure, obtain the user weight records during the collaborative annotation process. When the user weight records show deviation, adjust the fusion weights to obtain the handwritten data structure after weight adjustment.

[0027] S24. Based on the handwritten data structure after weight adjustment, obtain the consistency index from the trajectory fusion distribution, and determine the unified handwritten data structure after fusion by comparing the consistency index with the preset threshold.

[0028] In one embodiment, a unified handwritten data structure after fusion is obtained and distributed to each collaborating device in real-time collaboration mode. When the distribution delay exceeds a preset network threshold, the transmission priority strategy is adjusted to obtain an optimized distribution data packet, including:

[0029] S31. Extract collaboration identification information from the fused unified handwritten data structure, distribute it to each collaborative device through a preset network channel, determine whether the distribution delay exceeds the preset network threshold, and obtain the delay judgment result.

[0030] S32. When the delay judgment result is that the threshold is exceeded, the transmission priority strategy is adjusted for the unified handwritten data structure, and the data packets are sorted by a weighted queue to determine the adjusted priority sequence.

[0031] S33. Construct a distribution confirmation mechanism based on the adjusted priority sequence, obtain the reception feedback information of each cooperating device, verify the completeness of the feedback by comparing the number of feedback data points, integrate the unified handwritten data structure, and obtain the confirmed distribution package.

[0032] S34. Perform packet integrity verification on the confirmed distribution packet, and use the cyclic redundancy check method to compare the checksum value to verify data consistency, and obtain the optimized distribution data packet.

[0033] In one embodiment, the collaborative interface update is processed using a synchronous display method based on the optimized distribution data packet. The update status is confirmed through multi-node access feedback, resulting in a confirmation signal for synchronous presentation, including:

[0034] S41. Obtain the updated content of the collaboration interface from the optimized distribution data packet, distribute the updated content to the target node through multi-node access, and obtain the distributed update queue.

[0035] S42. For the updated queue after distribution, the synchronous display method is used to process the interface of each node. The synchronous display method uses a unified clock mechanism to render the interface elements at the same time, obtain node feedback information, and determine the consistency of the update status.

[0036] S43. Based on the updated state consistency, collect response signals from multiple nodes accessing the system through a feedback confirmation mechanism to generate a preliminary confirmation identifier;

[0037] S44. Based on the preliminary confirmation identifier, execute the node feedback loop to retry the abnormal response. The node feedback loop repeatedly sends updated content to the abnormal node until the response is normal, obtains the set of responses after retry, and determines the intermediate signal for synchronous presentation.

[0038] S45. By combining the intermediate signals presented synchronously with the confirmation signal generation process, wherein the confirmation signal generation process adopts the aggregation of the response set after retry and verifies the overall consistency, and integrates the interface consistency guarantee, the final synchronously presented confirmation signal is obtained.

[0039] In one embodiment, the consistency of multi-user interaction is analyzed from the synchronously presented confirmation signals. When the interaction consistency meets preset collaboration requirements, the shared display platform is updated to obtain the final real-time integrated handwriting input presentation result, including:

[0040] S51. Collect synchronization confirmation signals through multi-user devices, obtain interactive consistency data in the synchronization confirmation signals, and determine the collaborative status of multi-user participation;

[0041] S52. For the collaboration status, analyze the interaction consistency. When the interaction consistency is lower than the preset collaboration requirements, use a conflict handwriting discrimination mechanism based on timestamp comparison to adjust the signal and obtain the adjusted confirmation signal.

[0042] S53. Extract handwritten input from the adjusted confirmation signal, determine the real-time integration possibility of handwritten input, and obtain the integrated input sequence;

[0043] S54. Based on the integrated input sequence, update the shared display platform, obtain the handwriting presentation version on the platform, and determine the resolution results of the differences between the versions;

[0044] S55. When the difference resolution result meets the preset collaboration requirements, the handwriting of multiple users is integrated to obtain the final real-time integrated handwriting input presentation result.

[0045] Secondly, this application also provides a cloud-based multi-whiteboard data interaction system, including:

[0046] The multi-node handwriting synchronous acquisition module is used to collect handwriting input data from multiple users through multi-node access, and obtain a set of handwriting input data.

[0047] The timing verification and data aggregation module is used to obtain the timestamp record information of each node based on the handwritten input data set. When the difference in the timestamp record information meets the preset time synchronization conditions, the handwritten input data set is merged to obtain a preliminary merged handwritten input dataset.

[0048] The asynchronous conflict pre-detection module is used to sort the data in the initially merged handwritten input dataset sequentially, determine whether there is a time conflict after sorting, and obtain the conflict judgment result.

[0049] The multi-trajectory semantic fusion module is used to generate an ordered handwritten input sequence when the conflict judgment result is that there is no time conflict, and extract handwritten annotation trajectory information involving multiple users from the ordered handwritten input sequence. The handwritten annotation trajectory information is integrated through data fusion method to determine the unified handwritten data structure after fusion.

[0050] The adaptive network distribution optimization module is used to obtain the unified handwritten data structure after fusion and distribute it to each collaborating device in real-time collaboration mode. When the distribution delay exceeds the preset network threshold, the transmission priority strategy is adjusted to obtain the optimized distribution data packet.

[0051] The interface consistency synchronization module is used to process collaborative interface updates using a synchronous display method based on the optimized distribution data packets. It confirms the update status through multi-node access feedback and obtains a confirmation signal for synchronous presentation.

[0052] The collaborative presentation and consistency verification module is used to analyze the consistency of multi-user interaction from the confirmation signals of synchronous presentation. When the interaction consistency meets the preset collaboration requirements, the shared display platform is updated to obtain the final real-time integrated handwriting input presentation result.

[0053] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0054] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0055] The aforementioned cloud-based multi-whiteboard data interaction method and system sequentially performs multi-node handwriting collection and initial merging based on timestamp difference verification. The merged data is then ordered and time conflict is checked. When there are no conflicts, an ordered sequence is generated, and multi-user handwriting trajectories are extracted and fused to form a unified data structure. The transmission priority of this data structure is dynamically adjusted and distribution optimized based on real-time network latency. Subsequently, a synchronous display method combined with multi-node feedback confirms the update status to ensure interface consistency. Finally, the shared platform is updated by analyzing interaction consistency. This enables effective integration, real-time synchronization, and smooth collaborative presentation of handwriting input data in complex scenarios involving multiple venues and multiple users operating simultaneously. It solves the problems of content display errors, overlays, or omissions caused by the lack of integration mechanisms and real-time guarantees in existing technologies, improving the interactive experience and operational efficiency of remote collaboration. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating a multi-whiteboard data interaction method based on a cloud platform in one embodiment;

[0058] Figure 2 This is a schematic diagram of a meeting scenario illustrating a design for implementing data interaction between electronic whiteboards in one embodiment.

[0059] Figure 3 This is a schematic diagram of the structure of a cloud-based multi-whiteboard data interaction system in one embodiment. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] refer to Figure 1 The document presents a flowchart illustrating a multi-whiteboard data interaction method based on a cloud platform, as provided in this application. The method includes the following steps:

[0062] S1. Collect handwriting input data from multiple users through multi-node access to obtain a handwriting input data set.

[0063] Specifically, a multi-node distributed access architecture is constructed to achieve real-time collection of handwritten input data from multiple users, ultimately forming a handwritten input data set. This multi-node access architecture is compatible with various terminals such as electromagnetic induction whiteboards, capacitive touchscreens, and smart writing pens. Each terminal establishes a communication link with the cloud platform access gateway via a long-connection protocol, adapting to different network environments. The data collection process is completed collaboratively by the terminal's built-in sensing module and preprocessing unit: electromagnetic induction devices use coils to sense changes in the pen tip's magnetic field, capturing X / Y axis coordinate data and writing pressure values; capacitive touchscreen devices identify the touch position through changes in the panel's capacitance matrix, simultaneously collecting pressure parameters. The collected data must contain comprehensive information, specifically including real-time coordinate sequences, handwriting color parameters, line width attributes, writing timestamps accurate to milliseconds, and a unique user identifier. The user identifier is assigned through a two-way authentication mechanism between the terminal and the cloud platform, ensuring data traceability. After local verification to remove invalid values ​​caused by sensor malfunctions, the data collected by each terminal is encapsulated into data frames in a structured format and uploaded to the access gateway in real time via a long connection. The gateway summarizes and preliminarily classifies the data from multiple terminals to form a handwriting input data set containing data from multiple users and time periods. This set is temporarily stored in a cache database that supports high-concurrency read and write operations to provide data support for subsequent processing.

[0064] S2. Obtain the timestamp record information of each node based on the handwritten input data set. When the difference in the timestamp record information meets the preset time synchronization conditions, merge the handwritten input data sets to obtain the preliminary merged handwritten input dataset.

[0065] Specifically, the timestamp records of each node's data frame are extracted from the handwritten input dataset. Timestamp differences are verified based on preset time synchronization conditions. Once synchronization requirements are met, the dataset is merged to generate a preliminary merged handwritten input dataset. Due to potential clock discrepancies between different terminal systems, time calibration is first achieved via Network Time Protocol (NTP). An NTP time server is deployed on the cloud platform, automatically synchronizing the clocks upon terminal access. The preset time synchronization condition is that the difference between the timestamps of any two nodes is within a reasonable microsecond range. If the difference exceeds this range, a secondary calibration is triggered on the terminal, correcting the timestamps of the uploaded data. After time synchronization, the merging process begins, using user identification and writing timestamps as dual indexes. A timeline alignment and user partitioning strategy is employed: data frames are sorted in ascending order based primarily on timestamps, while each user is assigned an independent logical data partition to ensure the continuity of a single user's handwriting. The merging process requires two key operations: first, deduplication based on the unique identifier (UUID) of each data frame to eliminate duplicate uploads caused by network fluctuations; second, format standardization by converting coordinate data (relative / absolute) from different terminals to the cloud platform's standard absolute coordinate system (originating from the top left corner of the whiteboard, in pixels), and uniformly converting color parameters to hexadecimal format. After these processes, the multi-node data is integrated into a structured dataset and stored in a relational database. The data table fields include core information such as data ID, user ID, calibrated timestamp, X / Y coordinates, and stress value, facilitating rapid subsequent retrieval.

[0066] S3. Sort the data in the initially merged handwritten input dataset in order, determine whether there is a time conflict after sorting, and obtain the conflict judgment result.

[0067] Specifically, after the initially merged dataset is sorted by timestamp, time conflict detection, trajectory adjustment, and continuity verification are performed to finally output the conflict judgment result. First, the calibrated timestamps of each data item are extracted, and a data ID-timestamp key-value pair array is constructed. A stable sorting algorithm is used to achieve ascending time order, ensuring that the relative order of data items remains unchanged when timestamps are the same. Conflict detection is based on the time interval of each data item. The time interval of each data item is determined by the time difference between the collection timestamp and adjacent data. If the end point of the previous data interval is greater than the start point of the next data interval, a time conflict is determined, and the user ID, coordinate range, and other information of the conflicting data are recorded simultaneously. For conflicting data, a weighted fusion formula is used to adjust the trajectory, replacing the original data with the adjusted coordinates, while leaving non-conflicting data unchanged, forming an adjusted dataset. Finally, trajectory continuity verification is performed: the ratio of the distance between adjacent coordinate points to the time difference is calculated. If it exceeds the reasonable writing speed range, intermediate points are added through interpolation. Simultaneously, the dataset is expanded by combining terminal device status data. If the adjusted data satisfies both conflict-free and trajectory continuity, a judgment result indicating usable data is output; otherwise, the problem location is marked and further adjustments are suggested.

[0068] S4. When the conflict judgment result is that there is no time conflict, an ordered handwritten input sequence is generated, and the handwritten annotation trajectory information of multiple users is extracted from the ordered handwritten input sequence. The handwritten annotation trajectory information is integrated through the data fusion method to determine the unified handwritten data structure after fusion.

[0069] Specifically, assuming no conflict is detected, the sorted data is first converted into an ordered handwritten sequence. Then, multi-user handwritten annotation trajectories are extracted from this sequence, and a unified handwritten data structure is generated through data fusion. The ordered sequence is stored in a linked list, with timestamps used as pointers to associate each data frame for easy trajectory extraction. Trajectory extraction is grouped by user ID, with consecutive data frames from the same user forming candidate trajectories. Invalid mis-touch data is filtered out using coordinate change rate and time interval, ultimately forming a trajectory information set containing user ID, trajectory start and end times, coordinate sequence, handwriting features, and role information. Data fusion follows a feature alignment-weight allocation-information integration process: first, all trajectory coordinates are mapped to the cloud platform's standard coordinate system to eliminate terminal offset differences; then, basic weights are allocated based on a preset role system (host > presenter > ordinary user), and the weights are adjusted based on trajectory length and annotation frequency per unit time. During fusion, trajectory features are processed by weight, and multi-user trajectories are integrated by timestamp, forming a preliminary fusion structure containing a basic information layer (fusion time, version, etc.), a user trajectory layer (partitioned by ID), and a rendering information layer (anti-aliasing, transparency, etc.). The weight deviation is verified by checking the collaboration logs. If the difference between the trajectory contribution and the weight ratio exceeds the standard, a dynamic correction coefficient is introduced to adjust the weight. Finally, the consistency index of trajectory fusion distribution (spatial distribution rationality + temporal correlation) is extracted. If the index meets the standard, it is determined to be a unified handwritten data structure; otherwise, the weight is readjusted. This structure is serialized and stored in a distributed file system, supporting fast reading.

[0070] S5. Obtain the unified handwritten data structure after fusion, and distribute it to each collaborating device in real-time collaboration mode. When the distribution delay exceeds the preset network threshold, adjust the transmission priority strategy to obtain the optimized distribution data packet.

[0071] Specifically, a unified handwritten data structure is retrieved from distributed storage, and a basic data + incremental data transmission mode is used to distribute it to various devices in real-time collaboration scenarios. The distribution effect is optimized by dynamically adjusting the transmission priority. First, the collaboration identification information (session ID, device list, version number, etc.) in the data structure is extracted and encapsulated in the data header to achieve precise routing. Basic data is pushed once during session initialization, and a transmission control protocol is used to ensure reliability. Incremental data (newly generated handwriting) uses the User Datagram Protocol to reduce latency, and the distribution channel is dynamically switched according to the data type. The system calculates the distribution latency by sending timestamp - receiving acknowledgment timestamp. If it exceeds a preset threshold, priority adjustment is initiated: the data is broken down into real-time handwriting blocks (highest priority), historical supplement blocks (medium priority), and log information blocks (low priority). A weighted fair queue algorithm is used to sort them according to the weight / data length ratio, allocating more bandwidth to high-priority data. A segmented ACK (Acknowledge character) + data point mapping acknowledgment mechanism is constructed. Data packets are segmented and assigned unique identifiers. After receiving the data, the device reports the number of data points. The integrity is verified by comparing the number of sent and received data points. Missing segments trigger retransmission. After retransmission, a cyclic redundancy check is performed on the distribution packet: first, the structural integrity is verified, then the 32-bit checksum is calculated and compared with the header field. If they match, the optimized distribution packet is selected to ensure that the data is not tampered with or lost. The packet is distributed to nodes through the content delivery network to accelerate distribution and selects the optimal transmission path according to the geographical location of the device.

[0072] S6. Based on the optimized distribution data packet, the collaborative interface update is processed using a synchronous display method. The update status is confirmed through multi-node access feedback, and a confirmation signal for synchronous presentation is obtained.

[0073] Specifically, the optimized data packets are parsed to obtain the updated interface content, which is then distributed to target nodes via a combination of point-to-point and broadcast methods, forming an update queue indexed by device ID. A unified clock mechanism is used for synchronized display: each device is calibrated with the cloud platform's high-precision clock upon connection; rendering triggers require simultaneous clock signal matching and data parsing readiness. Handwriting is drawn in a background buffer using double-buffering technology, and the buffer is switched uniformly after all devices are ready to avoid flickering. After rendering, each device sends back an encrypted signal containing a rendering timestamp, interface hash value, and status code. The system verifies synchronization by comparing timestamp differences and verifies display consistency by comparing hash values. A preliminary confirmation identifier is generated based on the device-status bitmap, marking abnormal devices (timeout, verification failure, inconsistent status). Differentiated retries are performed for abnormal devices: timeout devices retransmit clock signals and updated content, verification failure devices add verification fields, and inconsistent status devices add standard rendering parameters. The retry interval is dynamically adjusted based on network conditions. Aggregate all final response signals from devices, remove duplicate and invalid data to generate intermediate signals, and then aggregate and cross-validate them by region: ensuring that all devices are within the response set (integrity), all hash values ​​are consistent (content consistency), and randomly select snapshots of the device upload interface to complete image comparison. After all the above verifications pass, generate a synchronous presentation confirmation signal containing session ID, device list, and global checksum, store it in the session log, and push it to the collaboration management module.

[0074] S7. Analyze the consistency of multi-user interaction from the synchronously presented confirmation signals. When the interaction consistency meets the preset collaboration requirements, update the shared display platform to obtain the final real-time integrated handwriting input presentation result.

[0075] Specifically, a dual-mode approach of device upload and platform retrieval is used to collect synchronous confirmation signals and extract interactive consistency data (operation timeline, trajectory association, response time, etc.). Collaboration status is determined based on quantitative indicators: user participation (number of effective trajectories / total time), operation synchronization rate (number of synchronized operations / total number of operations), and feedback consistency (hash value overlap) collectively form the evaluation criteria. These indicators are compared with preset collaboration requirements (time consistency threshold + content association threshold). If the requirements are not met, a microsecond-level comparison of timestamps is used to identify conflicting handwriting. The original coordinates of the user who performed the operation first are preserved, and visually distinguishable minor offset adjustments are made to the conflicting handwriting of the subsequent operation, generating a post-adjustment confirmation signal. Handwritten input is parsed from the adjusted signal, and data integrity (number of coordinate points matching the standard frame), format compatibility (parameters unified to the platform standard), and temporal continuity (reasonable time difference between adjacent trajectories) are verified. If the conditions are met, the data is arranged in ascending order by timestamp and associated with user roles to form an integrated input sequence. The shared display platform is updated using an incremental update + full verification model: new handwriting is partitioned by coordinate quadrant and written to the database. A version management module generates a handwriting presentation version with a unique version number. Hash values ​​of adjacent versions are compared to locate discrepancies. New handwriting is directly appended, and adjustments are made to overwrite the updated version, resulting in a resolution of discrepancies. If the resolution meets the criteria of no conflicts, unified parameters, and clear time, the handwriting from multiple users is displayed in a hierarchy based on role weight and in chronological order, generating a final presentation that supports interactive operations such as zooming in and out. A snapshot is synchronously stored in the distributed system, and operation logs are stored in the session record. If the criteria are not met, a readjustment signal is returned until the collaboration requirements are met.

[0076] In the aforementioned cloud-based multi-whiteboard data interaction method, the process begins by synchronously collecting user handwriting data from multiple nodes. This data is then aligned and merged based on timestamps to form a preliminary dataset. Subsequently, the dataset is globally sorted and conflict-checked. After confirming no conflicts, the handwriting trajectories of multiple users are extracted and fused to generate a unified data structure. During distribution, the transmission priority is dynamically adjusted based on real-time network latency. A synchronized display method and a multi-node feedback confirmation mechanism are used to update the collaboration interface and verification status. Finally, the overall interaction consistency is analyzed and the shared display platform is updated. This effectively integrates and synchronizes concurrent handwriting input from multiple sources in complex multi-venue access scenarios, avoiding display errors, content overlays, or omissions caused by data conflicts, transmission delays, or lack of coordination. This improves the fluency, interactivity, and overall efficiency of remote multi-party collaboration.

[0077] To further illustrate the solutions of the embodiments of this application, a specific example is provided below. For example... Figure 2 As shown, Figure 2 This is a schematic diagram of a meeting scenario for implementing data interaction between electronic whiteboards, used for:

[0078] After the terminals in meeting locations A and B establish communication via the cloud platform, the whiteboards of both parties will form a shared interactive state with a common data pool and synchronized operations, breaking the limitations of physical space and making remote collaboration feel as if they are in the same meeting room. Whether it is a cross-regional project discussion in a company, remote equipment parameter annotation in a factory, or collective lesson preparation in a school, or real-time Q&A in one-on-one online teaching, this function can accurately adapt to the needs of the scenario.

[0079] The conference terminals come in a variety of flexible forms, ranging from standalone conference machines deployed in large conference rooms to wall-mounted conference machines with integrated display functions. The former is suitable for mobility needs and can be flexibly placed according to the meeting location, while the latter saves space and is suitable for long-term use in fixed conference rooms. Both types of terminals can achieve seamless connection through the cloud platform.

[0080] Once a meeting is initiated and all parties have connected, the whiteboard will operate efficiently as an independent unit. Text and image data input by user A will be transmitted in real-time via the cloud platform and displayed on the whiteboard at user B in the same proportions, with identical stroke thickness, color, and layout. Crucially, the operation is synchronized: any writing, annotation, or partial erasing action on the whiteboard by user A will be instantly responded to by user B, without any delays or lag, thus bringing communication closer together.

[0081] In one embodiment, the data in the initially merged handwritten input dataset is sequentially arranged, and it is determined whether there are any time conflicts after the arrangement, thus obtaining a conflict determination result, including:

[0082] S11. Obtain the timestamp records of each data item from the initially merged handwriting input dataset, and sort the data order by comparing the timestamps to obtain the sorted handwriting input dataset.

[0083] Optionally, the timestamp records of each data item have been calibrated during the initial merging stage to ensure that the time of the cloud platform's network time protocol server is used as a unified benchmark. Timestamp comparison employs a high-precision time difference calculation method. By extracting the millisecond-level value of each data item's timestamp, a time-sorted array is constructed, where each array element is a key-value pair between the data item identifier and its corresponding timestamp. The sorting process uses a stable sorting algorithm to ensure that, even with identical timestamps, the relative order of data items remains consistent with the initial merging stage. The final output is a handwritten handwriting input dataset sorted in ascending order of timestamps, laying an ordered data foundation for subsequent conflict detection.

[0084] S12. For the sorted handwritten input dataset, use the timestamp comparison method to detect whether the timestamps of adjacent data items overlap. If the timestamps overlap, there is a time conflict, and time conflict detection information is obtained.

[0085] Optionally, timestamp comparison focuses on the time interval of each data item. The time interval for each data item consists of its collection timestamp and data duration. The data duration is calculated by the difference between the timestamps of adjacent data items; that is, the duration of the current data item is the difference between the timestamp of the next data item and the timestamp of the current data item. During detection, the time intervals of adjacent data items are compared sequentially. If the end point of the time interval of the preceding data item is greater than the start point of the time interval of the following data item, the two timestamps are determined to overlap. For data items with overlapping timestamps, the system records the user's identity, data item identifier, time overlap interval, and corresponding coordinate data range. This information collectively constitutes time conflict detection information, providing a clear target for subsequent trajectory adjustments.

[0086] S13. Based on the time conflict detection information, perform handwriting trajectory adjustment processing. The adjusted handwriting coordinates are obtained by weighted fusion correction of multiple conflicting coordinate points using the following formula:

[0087]

[0088] in, This indicates the adjusted coordinates of the handwriting. Represents the original coordinate point. Indicates the fusion weight coefficient. Indicates the first The weights of conflicting data items, Indicates the first Conflicting coordinate points This indicates the total number of conflicting data items.

[0089] In the above formula, This represents the adjusted handwriting coordinates obtained after weighted fusion correction. These coordinates are the final basis for subsequent interface rendering, ensuring that conflict trajectories are reasonably avoided in space. This represents the original coordinates of the data in the conflict data item. This parameter is retained to preserve the user's original writing intent during the adjustment process and to avoid handwriting distortion caused by over-correction. This represents the fusion weighting coefficient, and its value range is dynamically adjusted according to the degree of conflict. The larger the overlapping area of ​​the conflict region, the better. The smaller the value, the better it is to balance the need to preserve the original handwriting with the need to avoid conflicts. The optimal value range is usually determined through multiple experiments. Representing the The weight of each conflicting data item is calculated based on user operation priority, data integrity, and network transmission quality. For example, the operation weight of the host user is higher than that of ordinary participants, and the weight of the data transmission without packet loss is higher than that of the data transmission with packet loss. Representing the The conflict coordinates corresponding to each conflicting data item are the core location parameters within the conflict area and directly participate in the fusion calculation. This represents the total number of conflicting data items participating in this conflict adjustment, and its value is determined by the number of overlapping data items recorded in the time conflict detection information. In actual calculations, the weight of each conflicting data item is first determined based on preset rules. With fusion weight coefficient Then, substitute the coordinates into the formula to complete the weighted fusion of the coordinates, ensuring that the adjusted coordinates not only conform to the user's writing habits, but also effectively avoid display conflicts.

[0090] S14. Based on the adjusted handwriting coordinate points, generate an adjusted handwriting input dataset.

[0091] Optionally, based on the original relationships between conflicting data items, the adjusted coordinate points replace the original coordinate data in the corresponding data items, while retaining other attribute information of the data items, including color parameters, line width, user identification, etc. For non-conflicting data items, the original data after arrangement is directly used to ensure data integrity. After the replacement is completed, all data items are re-timestamped and verified to ensure that the adjusted dataset still maintains the order of time dimension, ultimately forming a structurally complete and time-conflicting adjusted handwritten input dataset.

[0092] S15. For the adjusted handwritten input dataset, obtain the trajectory continuity verification result, expand the dataset through multi-source data fusion, and obtain the conflict judgment result.

[0093] Optionally, trajectory continuity verification is achieved by calculating the distance difference and time difference between adjacent coordinate points. If the ratio of the distance difference to the time difference between adjacent coordinate points exceeds the preset writing speed range, the trajectory is determined to be broken, and intermediate coordinate points need to be supplemented using an interpolation algorithm; if it is within a reasonable range, the trajectory is determined to be continuous. Multi-source data fusion expansion supplements the dataset by combining device status data and network status data uploaded by various terminals. For example, information such as device resolution parameters and screen scaling ratios are associated with corresponding data items, providing more comprehensive parameter support for subsequent interface rendering. After verification and expansion, if the adjusted dataset meets the conditions of trajectory continuity and no time conflict, the conflict judgment result is that the conflict has been resolved and the data is usable; if there are still unresolved conflicts or trajectory breakage issues, the conflict judgment result is that further adjustment is needed, and the specific problem location information is output synchronously.

[0094] In one embodiment, when the conflict determination result indicates that there is no time conflict, an ordered handwritten input sequence is generated, and multi-user handwritten annotation trajectory information is extracted from the ordered handwritten input sequence. The handwritten annotation trajectory information is then integrated using a data fusion method to determine the unified handwritten data structure after fusion, including:

[0095] S21. By using an ordered sequence of handwritten inputs, extract handwritten annotation trajectory information involving multiple users to obtain the extracted trajectory information set.

[0096] Optionally, the ordered handwritten input sequence is stored in a linked list. The extraction process uses the user's identity identifier as the core index, traversing the linked list and grouping the data frames. Consecutive data frames corresponding to the same user identity identifier constitute the candidate trajectory for that user. The validity screening of candidate trajectories is based on writing behavior characteristics. By calculating the coordinate change rate and time interval of the data frames, scattered data frames generated by accidental touches are eliminated. Data frames with a coordinate change rate lower than a preset threshold or a time interval exceeding the writing action judgment duration are filtered out. The final extracted trajectory information set contains the valid handwritten annotation trajectories of each user. Each trajectory is associated with the user's identity identifier, trajectory start and end timestamps, complete coordinate sequence, handwriting feature parameters (color, line width, etc.), and user role information, providing structured data support for subsequent fusion processing.

[0097] S22. The extracted trajectory information set is integrated using a data fusion method, and user role weight adjustments are incorporated to determine the preliminary fused handwritten data structure.

[0098] Optionally, data fusion uses feature alignment, weight allocation, and information integration as its core process. First, trajectory feature alignment is performed, mapping all trajectory coordinates to the cloud platform's standard coordinate system. Coordinate offset correction eliminates display differences between different terminals. User role weights are determined based on a pre-defined role system for the collaborative scenario. This system includes levels such as host, speaker, and general participants, with different base weight values ​​for each role. The host has the highest base weight, followed by general participants. During weight allocation, the base weights are dynamically adjusted based on user activity levels, calculated using the effective trajectory length and annotation frequency per unit time. During fusion, the feature parameters of each trajectory are first weighted according to user role weights. Then, the weighted trajectory information is integrated in timestamp order to form a preliminary fused handwritten data structure containing multi-user trajectory relationships. A weight adjustment field is reserved in the structure for subsequent dynamic optimization.

[0099] S23. For the initially fused handwritten data structure, obtain the user weight records during the collaborative annotation process. When the user weight records show deviations, adjust the fusion weights to obtain the handwritten data structure after weight adjustment.

[0100] Optionally, user weight records are derived from real-time operation logs during the collaboration process. These logs include each user's annotation operation, operation duration, trajectory contribution, and other users' interactive feedback (such as referencing or modifying annotation content). Deviation is determined by comparing the weight records with the initial weight allocation results. If the difference between a user's trajectory contribution percentage and the current weight percentage exceeds a preset range, or if many other users reference that user's annotations but with low weights, a weight deviation is identified. A dynamic correction coefficient is introduced during adjustment. This coefficient is positively correlated with the contribution deviation value and is updated using the formula: Adjusted Weight = Initial Weight × (1 + Dynamic Correction Coefficient). Simultaneously, adjustment records are synchronized to the weight log, clearly specifying the reason for the adjustment, the weight values ​​before and after the adjustment, and the calculation basis. This ensures the traceability of weight adjustments, ultimately forming a handwritten data structure after weight adjustment, which better reflects each user's actual collaborative contribution.

[0101] S24. Based on the handwritten data structure after weight adjustment, obtain the consistency index from the trajectory fusion distribution, and determine the unified handwritten data structure after fusion by comparing the consistency index with the preset threshold.

[0102] Optionally, trajectory fusion distribution is achieved by constructing a trajectory spatial distribution matrix and a time series matrix. The spatial distribution matrix records the coverage area and density of each trajectory within the whiteboard coordinate range, while the time series matrix records the timestamp distribution and correlation of each trajectory. Consistency indicators include trajectory spatial distribution consistency and time series consistency. Spatial distribution consistency is calculated by assessing the reasonableness of overlap in the coverage areas of different trajectories; if the overlapping area corresponds to the coordinate range of key collaborative discussion content, consistency is improved. Time series consistency is calculated by analyzing the time interval and logical correlation of trajectories. Logical correlation is determined based on handwriting semantic recognition results; if subsequent trajectories supplement previous trajectories, correlation is improved. The two consistency indicators are weighted and summed to obtain a comprehensive consistency indicator. If this indicator reaches a preset threshold, it indicates that the fused trajectory information conforms to the collaborative logic in both spatial and temporal dimensions, and the handwritten data structure after weight adjustment can be used as the final unified handwritten data structure. If the threshold is not reached, the weights are readjusted until the consistency indicator meets the requirements. The unified handwritten data structure is converted into a binary data stream through data serialization and stored in the fusion data storage area of ​​the cloud platform. This storage area is implemented using a distributed file system, supporting persistent storage and fast retrieval of large-scale data.

[0103] In one embodiment, a unified handwritten data structure after fusion is obtained and distributed to each collaborating device in real-time collaboration mode. When the distribution delay exceeds a preset network threshold, the transmission priority strategy is adjusted to obtain an optimized distribution data packet, including:

[0104] S31. Extract collaboration identification information from the fused unified handwritten data structure, distribute it to each collaborative device through a preset network channel, determine whether the distribution delay exceeds the preset network threshold, and obtain the delay judgment result.

[0105] Optionally, collaboration identification information is the core basis for ensuring accurate data distribution. It includes a unique identifier for the collaboration session, a list of participating devices, a data version number, and a timestamp. This information is encapsulated in the data header, facilitating quick matching of collaborative devices to their respective sessions. The preset network channel is dynamically selected based on the data type. Incremental data for real-time handwriting primarily uses the User Datagram Protocol (UDP) channel to reduce transmission latency, while basic data and configuration information use the Transmission Control Protocol (TCP) channel to ensure reliability. Distribution latency is calculated based on the difference between the packet sending timestamp and the device receiving confirmation timestamp. The system has a built-in high-precision timer that records key time points. The latency judgment result is generated by comparing this difference with a preset network threshold. If the difference exceeds the threshold, it is determined that the latency exceeds the limit, and the duration of the excess latency, along with auxiliary information such as current network bandwidth and packet loss rate, is recorded simultaneously.

[0106] S32. When the delay judgment result is that the threshold is exceeded, the transmission priority strategy is adjusted for the unified handwritten data structure, the data packets are sorted by weighted queue, and the adjusted priority sequence is determined.

[0107] Optionally, the transmission priority strategy adjustment is based on the core principle of business value and real-time requirements. First, the unified handwritten data structure is decomposed into different types of data packets, including real-time handwriting data blocks, historical handwriting supplementation blocks, rendering configuration blocks, and log information blocks. The basic weights of each type of data packet are preset according to business priority, with the real-time handwriting data block having the highest weight and the log information block having the lowest weight. During weighted queue sorting, the weights are dynamically adjusted based on real-time network conditions. For example, when bandwidth utilization exceeds a preset value, the weight ratio of the real-time handwriting data block is further increased. The sorting algorithm adopts a weighted fair queue mechanism, determining the queue position by calculating the weight / data length ratio of each data packet. The larger the ratio, the higher the ranking. The final output is a sequence of data packets arranged in descending order of transmission priority, ensuring that high-priority data occupies network resources first.

[0108] S33. Construct a distribution confirmation mechanism based on the adjusted priority sequence, obtain the reception feedback information of each cooperating device, verify the completeness of the feedback by comparing the number of feedback data points, integrate the unified handwritten data structure, and obtain the confirmed distribution package.

[0109] Optionally, the distribution confirmation mechanism adopts a segmented ACK + data point mapping mode, dividing the data packets in the priority sequence into segments of fixed size, with each segment assigned a unique confirmation identifier. After receiving the segmented data, the cooperating device needs to send back feedback information including the confirmation identifier, reception status, and the number of data points. The number of data points is the total number of valid coordinate data points within that segment. After receiving the feedback information, the system verifies the completeness by comparing the difference between the total number of data points sent and the total number of data points received in feedback. If the difference is zero, the feedback is considered complete; if a difference exists, a missing segment is marked and a retransmission is triggered. After retransmission, all confirmed received segmented data are integrated according to the priority sequence, and the cooperation identifier and verification information in the data header are supplemented to form a structurally complete and data-free confirmed distribution packet.

[0110] S34. Perform packet integrity verification on the confirmed distribution packet, and use the cyclic redundancy check method to compare the checksum value to verify data consistency, and obtain the optimized distribution data packet.

[0111] Optionally, packet integrity verification consists of two steps. First, the structural integrity of the data packet is verified, checking for missing fields, abnormal lengths, or other issues. If structural abnormalities are found, the packet is directly deemed invalid and regenerated. After the structural verification passes, a cyclic redundancy check is initiated. The system calculates a 32-bit checksum value for the binary data of the distribution packet according to the standard algorithm for cyclic redundancy check. This value is compared with the checksum field reserved in the data packet header. If they match, it indicates that the data has not been tampered with or lost during transmission and integration, and the distribution packet is the optimized distribution data packet. If they do not match, the checksum is recalculated and compared again. If they do not match multiple times consecutively, a data re-integration process is triggered until the verification passes, ensuring that the final distributed data has extremely high consistency and reliability. The optimized distribution data packet will be distributed to various collaborating devices through the content delivery network acceleration nodes of the cloud platform. The content delivery network nodes select the optimal transmission path based on the geographical location of the devices, further reducing distribution latency.

[0112] In one embodiment, the collaborative interface update is processed using a synchronous display method based on the optimized distribution data packet. The update status is confirmed through multi-node access feedback, resulting in a confirmation signal for synchronous presentation, including:

[0113] S41. Obtain the updated content of the collaboration interface from the optimized distribution data packet, distribute the updated content to the target node through multi-node access, and obtain the updated queue after distribution.

[0114] Optionally, the optimized distribution data packet includes a data header and a core data area. The updated content for the collaboration interface is extracted from the core data area, specifically covering the coordinate sequence of handwritten strokes, rendering parameters (color, line width, etc.), interface layout configuration, and version identification information. This content is organized in XML format to ensure structured parsing. The target node refers to all collaborative devices currently connected in the collaboration session. The system determines the device list by querying the collaboration session registry, which is updated in real-time with device online status and network addresses. Distribution adopts a combined point-to-point and broadcast mode. Broadcast distribution is used for devices with stable online status, while point-to-point precise distribution is used for devices with significant network fluctuations. The updated queue after distribution is constructed using device identifiers as indexes. Each queue item contains the updated content, distribution timestamp, and retry count marker, providing an ordered data scheduling foundation for subsequent synchronization processing.

[0115] S42. For the updated queue after distribution, a synchronous display method is used to process the interface of each node. The synchronous display method uses a unified clock mechanism to render the interface elements simultaneously, obtain node feedback information, and determine the consistency of the update status.

[0116] Optionally, the unified clock mechanism uses the high-precision network clock of the cloud platform as a benchmark. Each collaborating device must synchronize and calibrate with this clock before receiving updated content, with the calibration error controlled within a very small range. Synchronous rendering is triggered through dual confirmation of a clock trigger signal and a data ready signal. When the clock trigger signal received by the device matches the local calibrated clock, and the updated content is parsed and a data ready signal is generated, interface rendering is initiated. The rendering process uses double buffering technology, completing handwriting drawing and interface layout adjustments in the background buffer. Once all devices have reached the rendering ready state, a unified buffer switch is executed. Node feedback information includes a rendering completion timestamp, an interface content hash value, and a device status code. The system determines time synchronization by comparing the difference in rendering completion timestamps of each node and verifies display consistency by comparing the interface content hash value. If both meet the preset standards, the update status is determined to be consistent.

[0117] S43. Based on the updated state consistency, the response signals of multiple nodes accessing the system are collected through a feedback confirmation mechanism to generate a preliminary confirmation identifier.

[0118] Optionally, the feedback confirmation mechanism adopts an immediate response + timeout retransmission mode. After each node completes rendering, it must send a response signal within a specified time. The response signal is transmitted in an encrypted format and includes the device identifier, preliminary confirmation code, and hash checksum. After receiving the response signal, the system first verifies the signal integrity through the hash checksum to eliminate the risk of transmission tampering. The preliminary confirmation identifier is generated based on the device-state mapping relationship and uses a bitmap marking method. Each bit corresponds to a cooperating device. A normal device response is marked as 1, and no response or abnormal response is marked as 0. At the same time, the specific information of the abnormal device is recorded to form the association data between the preliminary confirmation identifier and the abnormal device list.

[0119] S44. Based on the preliminary confirmation identifier, execute the node feedback loop to retry the abnormal response. The node feedback loop repeatedly sends updated content to the abnormal node until the response is normal, obtains the set of responses after retry, and determines the intermediate signal for synchronous presentation.

[0120] Optionally, abnormal responses include three types: no feedback after timeout, response signal verification failure, and inconsistent update status. Different retry strategies are adopted for different types: devices that do not respond after timeout are given priority to resend the update content and clock trigger signal; devices that fail verification are redistributed after adding a data verification field; devices with inconsistent status are resent with standard rendering parameters. The retry interval is dynamically adjusted according to the device's network status. Devices with high network latency use longer intervals to avoid network congestion, and the number of retries does not exceed a preset limit. The response set after retry integrates the final response signals of all devices, including the initial normal response and the response that recovers to normal after retry. The system performs deduplication and validity screening on the set, removing duplicate and invalid signals, and generating a synchronous intermediate signal containing the response status of all devices. The signal clearly marks the update result and processing process of each device.

[0121] S45. By combining the intermediate signals presented synchronously with the confirmation signal generation process, wherein the confirmation signal generation process adopts the aggregation of the response set after retry and verifies the overall consistency, and integrates the interface consistency guarantee, the final synchronously presented confirmation signal is obtained.

[0122] Optionally, the aggregated response set adopts a hierarchical aggregation + cross-validation approach. First, local aggregation is performed based on the device's region, generating a regional-level response report, which is then summarized into a global response set. Overall consistency verification is conducted from two dimensions: first, response integrity verification, confirming that all collaborating devices are included in the response set without omissions; second, content consistency verification, comparing the interface content hash values ​​of all devices to ensure complete consistency. Interface consistency is guaranteed by adding interface snapshot verification. Randomly selected snapshots of the uploaded interface renderings from some devices are used, and the system verifies the consistency between the snapshots and the standard interface using an image comparison algorithm. When the aggregated response set meets the integrity and consistency requirements, and the snapshot verification passes, a final synchronized confirmation signal is generated. This signal includes a collaboration session identifier, a full device confirmation list, a unified completion timestamp, and a global hash checksum. It is stored in the session log and simultaneously pushed to the cloud platform's collaboration management module, providing a basis for subsequent updates to the sharing platform.

[0123] In one embodiment, the consistency of multi-user interaction is analyzed from the synchronously presented confirmation signals. When the interaction consistency meets preset collaboration requirements, the shared display platform is updated to obtain the final real-time integrated handwriting input presentation result, including:

[0124] S51. Collect synchronization confirmation signals through multi-user devices, obtain interactive consistency data in the synchronization confirmation signals, and determine the collaborative status of multi-user participation.

[0125] Optionally, the acquisition of synchronization confirmation signals adopts a dual-mode approach: device-initiated uploading and platform-initiated retrieval. After generating a synchronization confirmation signal, multiple user devices immediately upload it to the cloud platform's collaboration management module. Simultaneously, the platform retrieves signal backups from online devices at fixed intervals to prevent signal loss. Interaction consistency data is extracted from the core data segment of the synchronization confirmation signal, specifically covering each user's handwritten operation timeline, trajectory spatial correlation data, operation response time, and interaction feedback records. This data is categorized and encapsulated according to user identity, forming a structured dataset. The determination of the collaboration status is based on quantitative indicators in the dataset, including user participation, operation synchronization rate, and feedback consistency. User participation is calculated as the ratio of the number of effective operation trajectories to the total time; the operation synchronization rate is the proportion of synchronized operations to the total number of operations; and feedback consistency is obtained by comparing the overlap of hash values ​​from each device's feedback. These three indicators together constitute the evaluation criteria for the collaboration status.

[0126] S52. For the collaborative status, analyze the interaction consistency. When the interaction consistency is lower than the preset collaboration requirements, use a conflict handwriting discrimination mechanism based on timestamp comparison to adjust the signal and obtain the adjusted confirmation signal.

[0127] Optionally, the interaction consistency analysis is based on preset collaboration requirements, which include a time consistency threshold and a content relevance threshold. Time consistency is calculated using the variance of the operation time interval, and content relevance is represented by a weighted sum of the intersection ratio of trajectory spaces and semantic relevance. If both indicators are below their respective thresholds, the interaction consistency is deemed unsatisfactory. The conflict handwriting discrimination mechanism is based on high-precision timestamp comparison, extracting the handwriting timestamps corresponding to the inconsistent interaction data. It distinguishes the order of operations by calculating the difference at the microsecond level, and simultaneously combines handwriting feature parameters (such as writing speed and line curvature) to distinguish the user's writing intention. During the adjustment process, the original handwriting coordinates of the user who operated first are retained, and the conflicting handwriting of the user who operated later is slightly offset. The offset is controlled within a visually distinguishable range that does not affect readability. After the adjustment is completed, a confirmation signal containing the corrected handwriting data is regenerated to ensure that the interaction data in the signal conforms to the collaboration logic.

[0128] S53. Extract handwritten input from the adjusted confirmation signal, determine the real-time integration possibility of handwritten input, and obtain the integrated input sequence.

[0129] Optionally, handwriting input extraction is based on data frame parsing, extracting each user's handwriting coordinate sequence, rendering parameters, and user identifier from the adjusted confirmation signal, and removing redundant fields generated during signal transmission. The feasibility of real-time integration is assessed from three dimensions: data integrity, format compatibility, and temporal continuity. Data integrity is verified by comparing the number of coordinate points with the standard frame structure; format compatibility is verified to ensure that handwriting parameters from different terminals are uniformly converted to the platform's standard format; and temporal continuity is verified by the timestamp difference between adjacent trajectories. Handwriting inputs that meet all three verification requirements are arranged in ascending order of timestamp, and simultaneously associated with user role identifiers and operation priorities, forming an integrated input sequence containing multi-user handwriting data. This sequence is stored using a linked list structure to support fast insertion and retrieval.

[0130] S54. Based on the integrated input sequence, update the shared display platform, obtain the handwriting presentation version on the platform, and determine the resolution results of the differences between the versions.

[0131] Optionally, the shared display platform updates using an incremental update + full verification mode. First, newly added handwriting data from the integrated input sequence is written to the platform database by partition, with partitioning based on the coordinate quadrants of the whiteboard interface, improving data read / write efficiency. Handwriting presentation versions are generated through the platform's built-in version management module. Each version is associated with a unique version number and update timestamp, and the version content contains complete handwriting data for all current users. Version difference detection is achieved by comparing the hash values ​​of handwriting data from adjacent versions. If hash values ​​differ, the difference area is located and the difference type is analyzed—whether it's newly added handwriting or adjusted conflicting handwriting. The difference resolution strategy is determined based on the difference type. Newly added handwriting is directly appended, while adjusted conflicting handwriting is overwritten and updated according to the adjusted coordinates. After resolution, the coordinates of the difference area, the resolution method, and the update time are recorded, forming the difference resolution result.

[0132] S55. When the difference resolution result meets the preset collaboration requirements, the handwriting of multiple users is integrated to obtain the final real-time integrated handwriting input presentation result.

[0133] Optionally, the verification of the difference resolution results is based on preset collaboration requirements. This verifies that the handwriting in the differing areas has no overlapping conflicts, rendering parameters are consistent, and the chronological order is clear. If the verification passes, the multi-user handwriting fusion process is initiated. The fusion strategy employs a dual rule of role weight and chronological order. Handwriting presentation levels are allocated according to user role weight, with the host and speaker's handwriting having a higher level than ordinary participants. Higher-level handwriting is displayed first in the overlapping area. Simultaneously, the handwriting presentation order is arranged chronologically, with later operations' handwriting displayed normally in non-conflicting areas. This ensures that the fused handwriting highlights the core user's contribution while fully preserving all collaborative content. The final presentation result supports multi-dimensional interactive operations. Users can zoom in, zoom out, move, and hide handwriting using gestures or shortcuts. The platform automatically generates a snapshot of the presentation result and an operation log. The snapshot is stored in a distributed file system, and the operation log is synchronized to the collaboration session record, providing data support for subsequent content tracing and review. If the difference resolution result does not meet the preset requirements, signal adjustments are performed until the collaboration requirements are met, at which point handwriting fusion is completed.

[0134] In the aforementioned cloud-based multi-whiteboard data interaction method, firstly, handwritten handwriting data from various users is collected synchronously through multi-node access, and merged and sorted according to their timestamp differences to construct a time-consistent input sequence. Then, after confirming no time-series conflicts, multi-user handwriting trajectory information is extracted from this sequence and integrated into a single, consistent structured data representation using data fusion technology. Subsequently, network latency is dynamically monitored during real-time distribution, and data transmission priority is adjusted to ensure the real-time delivery of critical data. Finally, optimized data packets drive the synchronized update of the interfaces of all collaborating devices, and node feedback confirmation and consistency verification ensure that consistent handwritten content is ultimately presented on all terminals. This solution effectively solves the problems of display errors and omissions caused by the difficulty in real-time integration and synchronization of multi-source handwritten data under multi-venue access by establishing a closed-loop technology chain from data collection, time-series alignment, conflict judgment, semantic fusion to adaptive network distribution and strong consistency synchronization. This achieves smooth, real-time collaborative presentation of multi-user handwritten handwriting, improving the interactive experience and operational reliability of remote collaboration.

[0135] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0136] Based on the same inventive concept, this application also provides a cloud-based multi-whiteboard data interaction system for implementing the aforementioned cloud-based multi-whiteboard data interaction method. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more cloud-based multi-whiteboard data interaction system embodiments provided below can be found in the above-described limitations of the cloud-based multi-whiteboard data interaction method, and will not be repeated here.

[0137] In one exemplary embodiment, such as Figure 3 As shown, a cloud platform-based multi-whiteboard data interaction system 200 is provided, including:

[0138] The multi-node handwriting synchronous acquisition module 201 is used to collect handwriting input data from multiple users through multi-node access, and obtain a set of handwriting input data.

[0139] The timing verification and data aggregation module 202 is used to obtain the timestamp record information of each node according to the handwritten input data set. When the difference in the timestamp record information meets the preset time synchronization conditions, the handwritten input data set is merged to obtain a preliminary merged handwritten input dataset.

[0140] The asynchronous conflict pre-detection module 203 is used to sort the data in the initially merged handwritten input dataset in order, determine whether there is a time conflict after sorting, and obtain the conflict judgment result.

[0141] The multi-trajectory semantic fusion module 204 is used to generate an ordered handwritten input sequence when the conflict judgment result is that there is no time conflict, extract handwritten annotation trajectory information involving multiple users from the ordered handwritten input sequence, integrate the handwritten annotation trajectory information through data fusion method, and determine the unified handwritten data structure after fusion.

[0142] The adaptive network distribution optimization module 205 is used to obtain the unified handwritten data structure after fusion and distribute it to each collaborative device in real-time collaboration mode. When the distribution delay exceeds the preset network threshold, the transmission priority strategy is adjusted to obtain the optimized distribution data packet.

[0143] The interface consistency synchronization module 206 is used to process the collaborative interface update according to the optimized distribution data packet using the synchronous display method, and obtain the confirmation signal of synchronous presentation by receiving feedback from multiple nodes to confirm the update status.

[0144] The collaborative presentation and consistency verification module 207 is used to analyze the interaction consistency of multiple users from the confirmation signals of synchronous presentation. When the interaction consistency meets the preset collaboration requirements, the shared display platform is updated to obtain the final real-time integrated handwriting input presentation result.

[0145] In one embodiment, the data in the initially merged handwritten input dataset is sequentially arranged, and it is determined whether there are any time conflicts after the arrangement, thus obtaining a conflict determination result, including:

[0146] S11. Obtain the timestamp records of each data item from the initially merged handwriting input dataset, and sort the data order by comparing the timestamps to obtain the sorted handwriting input dataset.

[0147] S12. For the sorted handwritten input dataset, the timestamp comparison method is used to detect whether the timestamps of adjacent data items overlap. If the timestamps overlap, there is a time conflict, and the time conflict detection information is obtained.

[0148] S13. Based on the time conflict detection information, perform handwriting trajectory adjustment processing. The adjusted handwriting coordinates are obtained by weighted fusion correction of multiple conflicting coordinate points using the following formula:

[0149]

[0150] in, This indicates the adjusted coordinates of the handwriting. Represents the original coordinate point. Indicates the fusion weight coefficient. Indicates the first The weights of conflicting data items, Indicates the first Conflicting coordinate points Indicates the total number of conflicting data items;

[0151] S14. Generate an adjusted handwriting input dataset based on the adjusted handwriting coordinate points.

[0152] S15. For the adjusted handwritten input dataset, obtain the trajectory continuity verification result, expand the dataset through multi-source data fusion, and obtain the conflict judgment result.

[0153] In one embodiment, when the conflict determination result indicates that there is no time conflict, an ordered handwritten input sequence is generated, and multi-user handwritten annotation trajectory information is extracted from the ordered handwritten input sequence. The handwritten annotation trajectory information is then integrated using a data fusion method to determine the unified handwritten data structure after fusion, including:

[0154] S21. By using an ordered sequence of handwritten inputs, extract handwritten annotation trajectory information involving multiple users to obtain the extracted trajectory information set.

[0155] S22. Use data fusion methods to integrate the extracted trajectory information set, incorporate user role weight adjustments, and determine the preliminary fused handwritten data structure.

[0156] S23. For the initially fused handwritten data structure, obtain the user weight records during the collaborative annotation process. When the user weight records show deviation, adjust the fusion weights to obtain the handwritten data structure after weight adjustment.

[0157] S24. Based on the handwritten data structure after weight adjustment, obtain the consistency index from the trajectory fusion distribution, and determine the unified handwritten data structure after fusion by comparing the consistency index with the preset threshold.

[0158] In one embodiment, a unified handwritten data structure after fusion is obtained and distributed to each collaborating device in real-time collaboration mode. When the distribution delay exceeds a preset network threshold, the transmission priority strategy is adjusted to obtain an optimized distribution data packet, including:

[0159] S31. Extract collaboration identification information from the fused unified handwritten data structure, distribute it to each collaborative device through a preset network channel, determine whether the distribution delay exceeds the preset network threshold, and obtain the delay judgment result.

[0160] S32. When the delay judgment result is that the threshold is exceeded, the transmission priority strategy is adjusted for the unified handwritten data structure, and the data packets are sorted by a weighted queue to determine the adjusted priority sequence.

[0161] S33. Construct a distribution confirmation mechanism based on the adjusted priority sequence, obtain the reception feedback information of each cooperating device, verify the completeness of the feedback by comparing the number of feedback data points, integrate the unified handwritten data structure, and obtain the confirmed distribution package.

[0162] S34. Perform packet integrity verification on the confirmed distribution packet, and use the cyclic redundancy check method to compare the checksum value to verify data consistency, and obtain the optimized distribution data packet.

[0163] In one embodiment, the collaborative interface update is processed using a synchronous display method based on the optimized distribution data packet. The update status is confirmed through multi-node access feedback, resulting in a confirmation signal for synchronous presentation, including:

[0164] S41. Obtain the updated content of the collaboration interface from the optimized distribution data packet, distribute the updated content to the target node through multi-node access, and obtain the distributed update queue.

[0165] S42. For the updated queue after distribution, the synchronous display method is used to process the interface of each node. The synchronous display method uses a unified clock mechanism to render the interface elements at the same time, obtain node feedback information, and determine the consistency of the update status.

[0166] S43. Based on the updated state consistency, collect response signals from multiple nodes accessing the system through a feedback confirmation mechanism to generate a preliminary confirmation identifier;

[0167] S44. Based on the preliminary confirmation identifier, execute the node feedback loop to retry the abnormal response. The node feedback loop repeatedly sends updated content to the abnormal node until the response is normal, obtains the set of responses after retry, and determines the intermediate signal for synchronous presentation.

[0168] S45. By combining the intermediate signals presented synchronously with the confirmation signal generation process, wherein the confirmation signal generation process adopts the aggregation of the response set after retry and verifies the overall consistency, and integrates the interface consistency guarantee, the final synchronously presented confirmation signal is obtained.

[0169] In one embodiment, the consistency of multi-user interaction is analyzed from the synchronously presented confirmation signals. When the interaction consistency meets preset collaboration requirements, the shared display platform is updated to obtain the final real-time integrated handwriting input presentation result, including:

[0170] S51. Collect synchronization confirmation signals through multi-user devices, obtain interactive consistency data in the synchronization confirmation signals, and determine the collaborative status of multi-user participation;

[0171] S52. For the collaboration status, analyze the interaction consistency. When the interaction consistency is lower than the preset collaboration requirements, use a conflict handwriting discrimination mechanism based on timestamp comparison to adjust the signal and obtain the adjusted confirmation signal.

[0172] S53. Extract handwritten input from the adjusted confirmation signal, determine the real-time integration possibility of handwritten input, and obtain the integrated input sequence;

[0173] S54. Based on the integrated input sequence, update the shared display platform, obtain the handwriting presentation version on the platform, and determine the resolution results of the differences between the versions;

[0174] S55. When the difference resolution result meets the preset collaboration requirements, the handwriting of multiple users is integrated to obtain the final real-time integrated handwriting input presentation result.

[0175] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the cloud-based multi-whiteboard data interaction method as described above.

[0176] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0177] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and 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 disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0178] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A multi-whiteboard data interaction method based on a cloud platform, characterized in that, The method includes: S1. Collect handwriting input data from multiple users through multi-node access to obtain a handwriting input data set; S2. Obtain the timestamp record information of each node according to the handwriting input data set. When the difference of the timestamp record information meets the preset time synchronization condition, merge the handwriting input data set to obtain a preliminary merged handwriting input dataset. S3. Arrange the data in the initially merged handwritten input dataset in order, determine whether there is a time conflict after the arrangement, and obtain the conflict judgment result. S4. When the conflict judgment result is that there is no time conflict, an ordered handwriting input sequence is generated, and handwriting annotation trajectory information involving multiple users is extracted from the ordered handwriting input sequence. The handwriting annotation trajectory information is integrated through a data fusion method to determine the unified handwriting data structure after fusion. S5. Obtain the fused unified handwritten data structure and distribute it to each collaborative device in real-time collaboration mode. When the distribution delay exceeds the preset network threshold, adjust the transmission priority strategy to obtain the optimized distribution data packet. S6. Based on the optimized distribution data packet, the collaborative interface update is processed using a synchronous display method. The update status is confirmed through multi-node access feedback, and a confirmation signal for synchronous presentation is obtained. S7. Analyze the consistency of multi-user interaction from the confirmation signals presented synchronously. When the consistency of interaction meets the preset collaboration requirements, update the shared display platform to obtain the final real-time integrated handwriting input presentation result.

2. The method according to claim 1, characterized in that, The step of sequentially arranging the data in the initially merged handwriting input dataset, determining whether there are time conflicts after the arrangement, and obtaining the conflict determination result includes: S11. Obtain the timestamp records of each data item from the preliminarily merged handwriting input dataset, and arrange the data order by comparing the timestamps to obtain the arranged handwriting input dataset. S12. For the arranged handwritten input dataset, a timestamp comparison method is used to detect whether the timestamps of adjacent data items overlap. If the timestamps overlap, there is a time conflict, and time conflict detection information is obtained. S13. Based on the time conflict detection information, perform handwriting trajectory adjustment processing. The adjusted handwriting coordinates are obtained by weighted fusion correction of multiple conflicting coordinate points using the following formula: in, This indicates the adjusted coordinates of the handwriting. Represents the original coordinate point. Indicates the fusion weighting coefficient. Indicates the first The weights of conflicting data items, Indicates the first Conflicting coordinate points Indicates the total number of conflicting data items; S14. Based on the adjusted handwriting coordinate points, generate an adjusted handwriting input dataset; S15. For the adjusted handwritten input dataset, obtain the trajectory continuity verification result, expand the dataset through multi-source data fusion, and obtain the conflict judgment result.

3. The method according to claim 1, characterized in that, When the conflict determination result indicates that there is no time conflict, an ordered handwritten input sequence is generated, and multi-user handwritten annotation trajectory information is extracted from the ordered handwritten input sequence. The handwritten annotation trajectory information is then integrated using a data fusion method to determine the unified handwritten data structure after fusion, including: S21. By using an ordered sequence of handwritten inputs, extract handwritten annotation trajectory information involving multiple users to obtain the extracted trajectory information set. S22. The extracted trajectory information set is integrated using a data fusion method, and user role weight adjustment is incorporated to determine the preliminary fused handwritten data structure. S23. For the initially fused handwritten data structure, obtain the user weight records during the collaborative annotation process. When the user weight records show deviation, adjust the fusion weights to obtain the handwritten data structure after weight adjustment. S24. Based on the handwritten data structure after weight adjustment, obtain a consistency index from the trajectory fusion distribution, and determine the unified handwritten data structure after fusion by comparing the consistency index with a preset threshold.

4. The method according to claim 1, characterized in that, The process involves acquiring the fused unified handwritten data structure, distributing it to various collaborating devices in real-time collaboration mode, and adjusting the transmission priority strategy when the distribution delay exceeds a preset network threshold to obtain an optimized distribution data packet, including: S31. Extract collaboration identification information from the fused unified handwritten data structure, distribute it to each collaboration device through a preset network channel, determine whether the distribution delay exceeds a preset network threshold, and obtain the delay judgment result. S32. When the delay judgment result exceeds the threshold, the transmission priority strategy is adjusted for the unified handwritten data structure, and the data packets are sorted by a weighted queue to determine the adjusted priority sequence. S33. Construct a distribution confirmation mechanism based on the adjusted priority sequence, obtain the reception feedback information of each cooperating device, verify the completeness of the feedback by comparing the number of feedback data points, integrate the unified handwritten data structure, and obtain the confirmed distribution package. S34. Perform packet integrity verification on the confirmed distribution packet, and use the cyclic redundancy check method to compare the checksum value to verify data consistency, and obtain the optimized distribution data packet.

5. The method according to claim 1, characterized in that, The process of updating the collaborative interface using a synchronous display method based on the optimized distribution data packet, and obtaining a confirmation signal for synchronous presentation through multi-node access feedback to confirm the update status, includes: S41. Obtain the updated content of the collaboration interface from the optimized distribution data packet, distribute the updated content to the target node through multi-node access, and obtain the distributed update queue. S42. For the distributed update queue, a synchronous display method is used to process the interface of each node. The synchronous display method uses a unified clock mechanism to render the interface elements simultaneously, obtain node feedback information, and determine the consistency of the update status. S43. Based on the consistency of the updated state, the response signals of multiple nodes accessing the system are collected through a feedback confirmation mechanism to generate a preliminary confirmation identifier; S44. Based on the preliminary confirmation identifier, execute the node feedback loop to retry the abnormal response. The node feedback loop repeatedly sends updated content to the abnormal node until the response is normal, obtains the set of responses after retry, and determines the intermediate signal for synchronous presentation. S45. Through the intermediate signal presented synchronously, combined with the confirmation signal generation process, wherein the confirmation signal generation process adopts the aggregation of the response set after retry and verification of overall consistency, and integrates interface consistency guarantee, to obtain the final synchronously presented confirmation signal.

6. The method according to claim 1, characterized in that, The process of analyzing the consistency of multi-user interaction from the synchronously presented confirmation signals, and updating the shared display platform when the consistency of interaction meets preset collaboration requirements, to obtain the final real-time integrated handwriting input presentation result, includes: S51. Collect synchronization confirmation signals through multi-user devices, obtain interaction consistency data in the synchronization confirmation signals, and determine the collaborative status of multi-user participation; S52. For the cooperation state, analyze the interaction consistency. When the interaction consistency is lower than the preset cooperation requirements, use a conflict handwriting discrimination mechanism based on timestamp comparison to adjust the signal and obtain the adjusted confirmation signal. S53. Extract handwritten input from the adjusted confirmation signal, determine the real-time integration possibility of the handwritten input, and obtain the integrated input sequence; S54. Based on the integrated input sequence, update the shared display platform, obtain the handwriting presentation version on the platform, and determine the result of resolving the differences between the versions; S55. When the difference resolution result meets the preset collaboration requirements, the handwriting of multiple users is integrated to obtain the final real-time integrated handwriting input presentation result.

7. A cloud-based multi-whiteboard data interaction system for implementing the method of any one of claims 1 to 6, characterized in that, The system includes: The multi-node handwriting synchronous acquisition module is used to collect handwriting input data from multiple users through multi-node access, and obtain a set of handwriting input data. The timing verification and data aggregation module is used to obtain the timestamp record information of each node according to the handwritten input data set. When the difference of the timestamp record information meets the preset time synchronization conditions, the handwritten input data set is merged to obtain a preliminary merged handwritten input dataset. The asynchronous conflict pre-detection module is used to arrange the data in the initially merged handwriting input dataset in sequence, determine whether there is a time conflict after the arrangement, and obtain the conflict judgment result. The multi-trajectory semantic fusion module is used to generate an ordered handwriting input sequence when the conflict judgment result is that there is no time conflict, extract handwriting annotation trajectory information involving multiple users from the ordered handwriting input sequence, integrate the handwriting annotation trajectory information through a data fusion method, and determine the unified handwriting data structure after fusion. An adaptive network distribution optimization module is used to obtain the fused unified handwritten data structure and distribute it to each collaborating device in real-time collaboration mode. When the distribution delay exceeds a preset network threshold, the transmission priority strategy is adjusted to obtain an optimized distribution data packet. The interface consistency synchronization module is used to process the collaborative interface update according to the optimized distribution data packet using a synchronous display method, and obtain a confirmation signal for synchronous presentation by receiving feedback from multiple nodes to confirm the update status. The collaborative presentation and consistency verification module is used to analyze the interaction consistency of multiple users from the confirmation signal of the synchronous presentation. When the interaction consistency meets the preset collaboration requirements, the shared display platform is updated to obtain the final real-time integrated handwriting input presentation result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

Images