A method and system for synchronously updating meeting materials in multi-meeting room collaboration

Abstract

This invention discloses a method and system for synchronously updating meeting materials in multi-meeting room collaboration, relating to the field of data processing technology. The method includes: during a multi-meeting room collaborative meeting, when a meeting material update instruction is issued, calling up the material usage progress of several meeting rooms on a central control platform; obtaining several predicted remaining times for the first use of updated materials in each meeting room based on the predicted material usage progress; using a material synchronization constraint that is less than several predicted remaining times, aiming to minimize the overall material synchronization timeliness deviation and maximize the overall material usage redundancy time, and combining updated material attribute information and network monitoring information of several meeting rooms, optimizing the updated material transmission scheme to determine the optimal material transmission scheme; and synchronously updating the updated materials to several meeting rooms according to the optimal material transmission scheme. This invention effectively improves the accuracy, adaptability, and efficiency of synchronously updating meeting materials in multi-meeting room collaborative scenarios.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for synchronously updating meeting materials in multi-meeting room collaboration. Background Technology

[0002] As cross-regional enterprise collaboration deepens, multi-meeting room interconnection scenarios are becoming increasingly common. Enterprises' demand for cross-space synchronization of meeting materials is growing rapidly. Existing technologies typically rely on centralized file servers or cloud storage for material distribution. When updates are made, the central control platform pushes the data uniformly or each meeting room terminal actively pulls the data to achieve basic data synchronization.

[0003] However, existing technologies have significant problems: First, static or manually triggered update mechanisms cannot detect the actual agenda and material usage progress in each meeting room, often leading to inappropriate update timing or disruptive delays during key presentations. Second, simple broadcast distribution or sequential transmission ignores the differences in network conditions in each meeting room and the urgency of the meeting itself, easily causing update delays at some nodes and data asynchrony among collaborating parties. Ultimately, existing technologies suffer from technical problems such as delayed meeting material updates, data asynchrony across multiple meeting rooms, and complex update mechanisms. Summary of the Invention

[0004] This invention provides a method and system for synchronously updating meeting materials in multi-meeting room collaboration, aiming to solve the technical problems of delayed meeting material updates, asynchronous data in multiple meeting rooms, and complex update mechanisms in the prior art.

[0005] In view of the above problems, the present invention provides a method and system for synchronously updating meeting materials in multi-meeting room collaboration.

[0006] In a first aspect, the present invention provides a method for synchronously updating meeting materials in multi-meeting room collaboration, comprising:

[0007] During collaborative meetings in multiple meeting rooms, when a meeting material update instruction is issued, the central control platform retrieves the usage progress of several materials from several meeting rooms.

[0008] Based on the aforementioned material usage progress predictions, several predicted remaining times for the first use of updated materials in the conference room are obtained;

[0009] Using less than the predicted remaining time as the material synchronization constraint, and aiming to minimize the overall material synchronization timeliness deviation and maximize the overall material usage redundancy time, the material transmission scheme is optimized by combining updated material attribute information and network monitoring information of several conference rooms to determine the optimal material transmission scheme.

[0010] The updated materials are synchronously updated to the aforementioned conference rooms according to the optimal material transfer scheme.

[0011] Secondly, the present invention provides a multi-meeting room collaborative meeting material synchronization and updating system, comprising:

[0012] The progress acquisition module is used to retrieve the usage progress of several materials from several conference rooms on the central control platform when a meeting material update instruction is issued during collaborative meetings in multiple conference rooms.

[0013] The duration prediction module is used to obtain several predicted remaining durations for the first use of updated materials in the conference room based on the predicted material usage progress.

[0014] The optimized transmission module is used to optimize the material transmission scheme by taking a material synchronization constraint that is less than the predicted remaining time, aiming to minimize the overall material synchronization time deviation and maximize the overall material usage redundancy time, and combining the updated material property information and the network monitoring information of several conference rooms to determine the optimal material transmission scheme.

[0015] The synchronous update module is used to synchronously update the updated materials to the plurality of conference rooms according to the optimal material transfer scheme.

[0016] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0017] This invention provides a method and system for synchronizing and updating meeting materials in multi-meeting room collaboration. It proposes an innovative solution to problems such as delayed updates, data asynchrony among multiple meeting rooms, and complex update mechanisms in existing technologies. In a multi-meeting room collaboration scenario, this invention uses a central control platform to access the material usage progress of each meeting room, predict the remaining time for the first use of updated materials, and uses this time as a synchronization constraint. Combining the updated material attributes and meeting room network information, it optimizes the transmission scheme with the goals of minimizing synchronization time deviation and maximizing material usage redundancy. The optimal scheme is then used to complete the material synchronization update, achieving precise matching of the actual meeting progress in each meeting room, reducing material synchronization delays and data asynchrony issues, and effectively improving the accuracy, adaptability, and efficiency of meeting material synchronization updates in multi-meeting room collaboration scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for synchronously updating meeting materials in a multi-meeting room collaboration, provided by an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of a multi-meeting room collaborative meeting material synchronization and updating system provided in an embodiment of the present invention;

[0021] The components represented by each number in the attached diagram are explained below:

[0022] Progress acquisition module 11, duration prediction module 12, transmission optimization module 13, and synchronization update module 14. Detailed Implementation

[0023] This invention provides a method and system for synchronizing and updating meeting materials across multiple meeting rooms, addressing the technical problems of delayed meeting material updates, data asynchrony across multiple meeting rooms, and complex update mechanisms in existing technologies.

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0026] Example 1, as Figure 1 As shown, the present invention provides a method for synchronously updating meeting materials in multi-meeting room collaboration, the method comprising:

[0027] S100: During collaborative meetings in multiple meeting rooms, when a meeting material update instruction is issued, the central control platform will call up the usage progress of several materials in several meeting rooms.

[0028] In this embodiment of the invention, during a multi-meeting collaborative meeting, when a meeting material update instruction is issued, the central control platform retrieves the material usage progress data from several meeting rooms. In multi-meeting collaborative meeting scenarios, the meeting progress in each meeting room naturally differs, resulting in inconsistent material usage progress. If the material usage status of each meeting room cannot be accurately and in real-time, subsequent material updates may result in either updating too early and consuming local resources or updating too late and failing to meet meeting needs. Therefore, it is necessary to first collect the material usage progress of each meeting room through dynamic sensing technology to provide accurate status information for subsequent update operations.

[0029] Step S100 in the method provided in this embodiment of the invention includes:

[0030] When multiple meeting rooms are used for collaborative meetings, the material usage ratio can be dynamically sensed in real time according to a preset monitoring time interval by deploying edge monitoring nodes in each meeting room.

[0031] When a meeting materials update instruction is issued, the edge monitoring node uploads a sequence of material usage ratios up to the current time point as several material usage progresses to the central control platform, where the central control platform is a cloud server.

[0032] First, when multiple meeting rooms are used for collaborative meetings, edge monitoring nodes deployed in each meeting room dynamically sense the proportion of materials used in real time according to a preset monitoring time interval. Edge monitoring nodes are small intelligent devices deployed locally in each meeting room, capable of data collection, storage, and initial transmission, allowing them to sense the usage status of local meeting materials locally. The preset monitoring time interval is a pre-set collection cycle, used to balance data real-time performance and device energy consumption, and is set to 5 minutes. The material usage ratio is a quantitative indicator used to represent the percentage of a set of meeting materials that has been viewed or presented in the current meeting room. Dynamic sensing technology means that the nodes automatically collect this ratio value according to the preset monitoring time interval, rather than acquiring it once or passively. For example, if one edge monitoring node is deployed in each of meeting rooms A and B, the node automatically collects the proportion of currently used materials to the total materials every 5 minutes, i.e., the material usage ratio. Meeting Room A began using materials at 14:00. The data collection results are as follows: 0% usage at 14:00, 20% usage at 14:05, 45% usage at 14:10, 70% usage at 14:15, and 90% usage at 14:20, forming a material usage percentage sequence: 0%-20%-45%-70%-90%. Meeting Room B began using materials at 14:00. The data collection results are as follows: 0% usage at 14:00, 15% usage at 14:05, 25% usage at 14:10, 50% usage at 14:15, and 75% usage at 14:20, forming a sequence: 0%-15%-25%-50%-75%.

[0033] Secondly, when a meeting material update instruction is issued, the edge monitoring nodes upload a sequence of material usage ratios up to the current time point as material usage progress data to the central control platform, which is a cloud server. The meeting material update instruction is triggered by the meeting speaker to notify the system that new materials need to be synchronized. The material usage progress is presented as a sequence of material usage ratios, reflecting the pace of material usage in the meeting room from the start of the meeting to the present. The central control platform (cloud server) is a cloud-based device that coordinates data from multiple meeting rooms and can receive and store information uploaded by each node. When an update instruction is received, the edge monitoring nodes of each meeting room package and upload the material usage ratio sequence up to the current time point to the central control platform in the form of a cloud server. For example, at 14:20, the speaker triggers the instruction to "update page 3 of the plan"; at this time, the edge node of meeting room A uploads the material usage ratio sequence 0%-20%-45%-70%-90% to the cloud server, and the node of meeting room B simultaneously uploads the material usage ratio sequence 0%-15%-25%-50%-75%, and the central control platform then obtains the material usage progress of the two meeting rooms.

[0034] In this embodiment of the invention, by dynamically and periodically sensing the edge monitoring nodes and combining the progress upload triggered by the update command, the central control platform can accurately and in real time grasp the material usage rhythm and current status of each conference room. This not only avoids the lag and error of traditional manual progress statistics, but also provides accurate basic data support for predicting the remaining time of the first use of updated materials.

[0035] S200: Based on the aforementioned material usage progress predictions, obtain several predicted remaining times for the first use of updated materials in the conference room.

[0036] In this embodiment of the invention, several predicted remaining times for the first use of updated materials in a meeting room are obtained based on the predicted material usage progress. In multi-meeting collaborative meeting scenarios, the material usage rhythm of each meeting room is heterogeneous, specifically manifested in differences in material delivery rates and varying degrees of fluctuation in usage rhythm. Simply obtaining static information about the current material usage progress is insufficient to determine the time node when each meeting room first calls for updated materials. Directly performing material synchronization operations will consume local storage and computing resources in the meeting room if synchronization is completed ahead of schedule; if synchronization is delayed, it will fail to match the actual meeting progress, resulting in the updated materials not being used in a timely manner. Therefore, it is necessary to quantitatively analyze the material usage rhythm of each meeting room based on the collected material usage progress data, and then predict the remaining time for its first use of updated materials, providing accurate time constraints for subsequent material synchronization operations.

[0037] Step S200 in the method provided in this embodiment of the invention includes:

[0038] Randomly select a first meeting room from the plurality of meeting rooms, and obtain a first material usage ratio sequence for the first meeting room;

[0039] In chronological order, the differences between adjacent first material usage ratios in the first material usage ratio sequence are calculated sequentially to generate a first material usage ratio deviation sequence.

[0040] The ratio of the standard deviation of the proportional deviation to the mean of the proportional deviation in the first material usage proportional deviation sequence is used as the first material usage fluctuation coefficient.

[0041] Based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence, the first predicted remaining time for the first meeting room to use updated materials for the first time is predicted and added to the plurality of predicted remaining times.

[0042] First, a first meeting room is randomly selected from the plurality of meeting rooms, and a first material usage ratio sequence for the first meeting room is obtained. The first meeting room refers to a meeting room to be analyzed randomly selected from multiple meeting rooms, used to calculate the predicted duration of each meeting room. The first material usage ratio sequence refers to the time series of the material usage ratio corresponding to the first meeting room, reflecting the material usage status at different time points. One meeting room is randomly selected from the meeting rooms of the collaborative meeting as the first meeting room, and the material usage ratio sequence of that meeting room obtained in S100 is retrieved. For example, if meeting room A is selected as the first meeting room, its first material usage ratio sequence is retrieved as: 0%-20%-45%-70%-90%, i.e., [0,0.2,0.45,0.7,0.9].

[0043] Secondly, following the chronological order, the differences between adjacent proportions of the first material usage ratio sequence are calculated sequentially to generate a first material usage ratio deviation sequence. The first material usage ratio deviation sequence is a sequence formed by subtracting the proportion from the previous proportion from the proportion at a later time step in the first material usage ratio sequence, reflecting the change in the material usage ratio within a unit monitoring interval. The differences between adjacent proportions in the first material usage ratio sequence are calculated sequentially, and these differences are arranged in order to obtain the deviation sequence. For example, for the proportion sequence of conference room A, the adjacent differences are calculated as follows: 0.2-0=0.2, 0.45-0.2=0.25, 0.7-0.45=0.25, 0.9-0.7=0.2; the final first material usage ratio deviation sequence is: [0.2, 0.25, 0.25, 0.2].

[0044] Furthermore, the ratio of the standard deviation of the proportional deviation to the mean of the proportional deviation in the first material usage proportional deviation sequence is used as the first material usage fluctuation coefficient. The first material usage fluctuation coefficient = standard deviation of the proportional deviation of the first material usage proportional deviation sequence / |mean of proportional deviation|, used to reflect the degree of fluctuation in the rhythm of material usage in the first meeting room. The larger the fluctuation coefficient value, the more unstable the rhythm. First, calculate the mean of the deviation sequence, then calculate its standard deviation, and finally divide the standard deviation by the absolute value of the mean to obtain the fluctuation coefficient. For example, for the deviation sequence [0.2, 0.25, 0.25, 0.2] of meeting room A: mean of proportional deviation = (0.2 + 0.25 + 0.25 + 0.2) / 4 = 0.225; calculate the standard deviation of proportional deviation: the sum of the squares of the differences between each deviation and the mean: Standard deviation = The fluctuation coefficient = 0.025 / 0.225≈0.111, indicating that the material usage rhythm in meeting room A is relatively stable.

[0045] Subsequently, based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence, the first predicted remaining time for the first meeting room to use updated materials for the first time is predicted and added to the plurality of predicted remaining times.

[0046] The method for predicting and obtaining the first remaining prediction time based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence includes:

[0047] Based on historical meeting operation records, a set of sample monitoring time intervals, a set of sample material usage fluctuation coefficients, and a set of sample material usage ratio sequences were collected. The historical remaining time of the old materials used up under different sample monitoring time intervals, sample material usage fluctuation coefficients, and sample material usage ratio sequences was collected as the sample remaining time, and the sample remaining time set was obtained.

[0048] The sample monitoring time interval set, sample material usage fluctuation coefficient set, sample material usage ratio sequence set, and sample remaining duration set are used as training data and divided into P training sets. The Long Short-Term Memory Network is supervised and trained until convergence, generating P duration predictors, where P is an integer greater than or equal to 5.

[0049] The ratio of the fluctuation coefficient of the first material to the fluctuation coefficient of the preset standard material is used as the first prediction compensation factor.

[0050] The first prediction compensation factor is multiplied by the initial predictor selection number and rounded to obtain the optimal predictor selection number L, where the initial predictor selection number is 3, and L is a positive integer greater than or equal to 1. If the calculated L is greater than P, then L is equal to P.

[0051] L duration predictors are randomly selected from the P duration predictors. Predictions are made based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence. The average of the L prediction results is calculated to obtain the first initial prediction of the remaining duration.

[0052] The first initial prediction remaining duration is corrected based on the first prediction compensation factor, and the first prediction remaining duration is output.

[0053] First, based on historical meeting operation records, a set of sample monitoring time intervals, a set of sample material usage fluctuation coefficients, and a set of sample material usage ratio sequences are collected. The historical remaining time after the old materials are used up under different sample monitoring time intervals, sample material usage fluctuation coefficients, and sample material usage ratio sequences are then collected as the sample remaining time, thus obtaining the sample remaining time set. Historical meeting operation records refer to archived records of material usage progress, monitoring intervals, and remaining time in past multi-meeting collaborative meetings. The sample monitoring time interval set refers to the set of different monitoring periods collected from historical records, such as 5 minutes, 10 minutes, and 15 minutes. The sample material usage fluctuation coefficient set is the set of material usage fluctuation coefficients for each meeting room in historical meetings. The sample material usage ratio sequence set is the set of time series of material usage ratios for each meeting room in historical meetings. The sample remaining time set refers to the set of historical remaining time before the old materials are used up, corresponding to the above sample parameters. By traversing historical meeting operation records, and extracting the remaining time after the old materials are used up according to the combination dimension of monitoring time interval + fluctuation coefficient + proportion sequence, four sample sets are constructed: sample monitoring time interval set, sample material usage fluctuation coefficient set, sample material usage proportion sequence set, and sample remaining time set.

[0054] For example, based on historical meeting operation records, 10,000 valid samples were collected: sample monitoring time interval set: {5min, 10min, 15min, ...}; sample material usage fluctuation coefficient set: {0.3, 0.4, 0.6, ...}; sample material usage ratio sequence set: {[0, 0.25, 0.5, 0.75], [0, 0.2, 0.45, 0.7, 0.9], ...}; sample remaining duration set: {10min, 8min, 12min, ...}.

[0055] Secondly, the sample monitoring time interval set, sample material usage fluctuation coefficient set, sample material usage ratio sequence set, and sample remaining duration set are used as training data and divided into P training sets. Supervised training of the Long Short-Term Memory (LSTM) network is performed until convergence, generating P duration predictors, where P is an integer greater than or equal to 5. Supervised training refers to the process of training the model to learn the mapping relationship using sample inputs and outputs as labels. Sample inputs refer to the sample monitoring time interval set, sample material usage fluctuation coefficient set, and sample material usage ratio sequence set; sample outputs refer to the sample remaining duration set. The Long Short-Term Memory (LSTM) network is a deep learning network that excels at capturing patterns in time-series data and is suitable for time series prediction scenarios. Convergence refers to the state where the root mean square error (RMSE) of the model prediction drops below a preset threshold, and training no longer significantly improves performance. The P training sets refer to randomly dividing the complete training data into P non-overlapping subsets (P≥5) for training multiple predictors. A duration predictor is a model that, after being trained, can take into account monitoring time intervals, material usage fluctuation coefficients, and material usage ratio sequences, and output the predicted duration.

[0056] For example, each duration predictor uses a single-branch LSTM network, containing 3 layers of LSTM units (64 neurons per layer) and 1 fully connected output layer. The input is a combination of features including the sample monitoring time interval, the sample material usage fluctuation coefficient, and the sample material usage ratio sequence. The output is the remaining duration of the corresponding sample. Training process: The four constructed sample sets are divided into P=5 training sets according to the ratio. Each set is input into 5 independent single-branch LSTM networks. The Adam optimizer (learning rate 0.001) is used to minimize the RMSE. Iterative training is performed until the RMSE of each network is stably below 0.5 minutes for 100 consecutive iterations. Training is then stopped and the model is saved, resulting in 5 duration predictors, denoted as duration predictor 1 to duration predictor 5. Training set partitioning: The 10,000 complete samples are divided into 5 training sets in an 8:2 ratio. Training set 1 contains 1,600 training samples and 400 validation samples; training set 2 contains 1,580 training samples and 395 validation samples, and so on. Input / output example: The input for a sample in training set 1 is 5 min (monitoring interval) + 0.3 (fluctuation coefficient) + [0, 0.25, 0.5, 0.75] (proportional sequence), and the output is 10 min (remaining time of the sample). Convergence judgment: When predictor 1 was trained to 800 iterations, the RMSE on the validation set dropped to 0.42 minutes, and the RMSE fluctuation range in the subsequent 100 iterations was ≤0.02 minutes, which met the convergence condition; similarly, predictors 2 to 5 converged at 750, 820, 780, and 850 iterations, respectively, with RMSE of 0.38 minutes, 0.45 minutes, 0.41 minutes, and 0.43 minutes, respectively, all of which were below the threshold of 0.5 minutes, and finally five duration predictors were obtained after training.

[0057] Then, the ratio of the first material usage fluctuation coefficient to the preset standard material usage fluctuation coefficient is used as the first prediction compensation factor. The preset standard material usage fluctuation coefficient refers to a pre-set benchmark fluctuation coefficient representing a stable material usage rhythm, and its value must conform to the actual scenario, for example, 0.1. The first prediction compensation factor reflects the degree of difference between the current meeting room usage rhythm fluctuation and the standard fluctuation. The greater the fluctuation, the larger the compensation factor, corresponding to a more complex prediction scenario and a higher error risk. The first prediction compensation factor = first material usage fluctuation coefficient / preset standard material usage fluctuation coefficient, used to quantify the complexity and error risk of the current prediction scenario. For example, if the preset standard material usage fluctuation coefficient = 0.1, the first material usage fluctuation coefficient ≈ 0.111; the first prediction compensation factor = 0.111 / 0.1 = 1.11. Since the fluctuation of meeting room A is greater than the standard value, the compensation factor > 1, indicating that the complexity of the prediction scenario is higher than the benchmark, and the error risk is slightly higher.

[0058] Further, the first prediction compensation factor is multiplied by the initial predictor selection number and rounded to obtain the optimal predictor selection number L, where the initial predictor selection number is 3, and L is a positive integer greater than or equal to 1. If the calculated L is greater than P, then L is set to P. The initial predictor selection number refers to the preset basic predictor call number, for example, fixed at 3. The optimal predictor selection number L refers to the predictor call number dynamically adjusted according to the compensation factor. This ensures a balance between prediction accuracy and computational efficiency. The optimal number of predictors, L, is calculated as: First prediction compensation factor × Initial number of predictors. The result is rounded to the nearest integer, ensuring that L is within the range of 1 to P. For example, if the first prediction compensation factor ≈ 1.11 and the initial number of predictors = 3, the optimal number of predictors, L = 1.11 × 3 = 3.33 ≈ 3. Since P = 5, Therefore, the optimal number of predictors is 3. If the calculated value is L=6 > P=5, then L=5 is chosen.

[0059] Subsequently, L duration predictors are randomly selected from the P duration predictors. Predictions are made based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence. The average of the L prediction results is calculated to obtain the first initial predicted remaining duration. The first initial predicted remaining duration refers to the preliminary remaining duration obtained by averaging the predictions made independently by the L predictors, reducing the random error of a single predictor. L duration predictors are randomly selected from the P duration predictors. The preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence are used as inputs to obtain L prediction results. The average of these results is then used as the initial predicted duration. For example, predictors 1, 2, and 3 (L=3) are randomly selected from duration predictors 1 to 5. The output results of the three predictors are 10.2 min, 9.8 min, and 10.5 min, respectively. The first initial predicted remaining duration = (10.2 + 9.8 + 10.5) / 3 ≈ 10.17 min.

[0060] Finally, the first initial prediction remaining duration is corrected according to the first prediction compensation factor, and the first prediction remaining duration is output.

[0061] The correction of the remaining duration of the first initial prediction based on the first prediction compensation factor includes:

[0062] Based on historical meeting operation records, the average historical remaining duration prediction error under the first prediction compensation factor is calculated as the first prediction error.

[0063] The first duration correction coefficient is obtained by subtracting the first prediction error from 1, and the product of the first duration correction coefficient and the first initial prediction remaining duration is taken as the first prediction remaining duration.

[0064] First, based on historical meeting records, the average historical remaining time prediction error under the first prediction compensation factor is calculated and used as the first prediction error. The average historical remaining time prediction error refers to the average ratio of the difference between the predicted remaining time and the actual remaining time to the actual remaining time in all scenarios with the same or similar first prediction compensation factor in historical meeting records. The first prediction error, i.e., the aforementioned average historical remaining time prediction error, quantifies the typical prediction error level corresponding to the current compensation factor. The historical meeting records are then iterated through to select historical scenarios where the deviation between the prediction compensation factor and the first prediction compensation factor is ≤ ±0.05, and the prediction error for each scenario is calculated. Prediction error = (historical predicted remaining time - historical actual remaining time) / historical actual remaining time. The average prediction error of all selected scenarios is then used as the first prediction error. For example, by filtering historical data for scenarios with compensation factors in the range of 1.06 to 1.16, a total of 20 sets of valid data were obtained. The prediction errors of each set were 4.8%, 5.2%, 4.9%, 5.1%, and so on. Finally, the first prediction error was obtained as 5%, or 0.05.

[0065] Secondly, a first duration correction coefficient is obtained by subtracting the first prediction error from 1. The product of the first duration correction coefficient and the first initial prediction remaining duration is taken as the first prediction remaining duration. The first duration correction coefficient is a correction coefficient obtained based on the first prediction error, used to offset the error in the initial prediction result. The smaller the absolute value of the error, the closer the correction coefficient is to 1. The first duration correction coefficient = 1 - first prediction error. The first duration correction coefficient is negatively correlated with the prediction error, which can specifically weaken the impact of the error on the prediction result. The first prediction remaining duration refers to the final prediction result after correction by the correction coefficient, which is more in line with the actual material usage rhythm of the conference room. The first prediction remaining duration = first duration correction coefficient × first initial prediction remaining duration. The result is the corrected first prediction remaining duration, which serves as the time constraint basis for subsequent material synchronization. For example, given that the first prediction error is 5% (0.05), the first duration correction coefficient is 1 - 0.05 = 0.95, and the first initial prediction remaining duration is 10.17 min. Therefore, the first prediction remaining duration for meeting room A is 10.17 min × 0.95 ≈ 9.66 min ≈ 580 s, and this result is finally output.

[0066] For example, following the above calculation logic, conference room B is selected as the second conference room. The calculation logic for the second predicted remaining time of conference room B is the same as that of conference room A. Similarly, the steps of obtaining the material usage ratio sequence, calculating the material usage ratio deviation sequence, deriving the material usage fluctuation coefficient, obtaining the initial predicted remaining time through multi-predictor fusion, and correcting with the prediction compensation factor are repeated. Finally, the second predicted remaining time of conference room B is approximately 10.9 minutes, or 654 seconds. Two predicted remaining times are obtained: the first predicted time of conference room A is 580 seconds, and the second predicted remaining time of conference room B is 654 seconds.

[0067] In this embodiment of the invention, by acquiring the material usage ratio sequence of the conference room and calculating the deviation sequence and fluctuation coefficient, the fluctuation characteristics of the material usage rhythm are quantified. Then, based on historical conference data, multiple single-branch LSTM duration predictors are trained. The number of optimal predictors is dynamically adjusted by combining the first prediction compensation factor, and multiple prediction results are fused to obtain the initial prediction duration. Finally, the correction coefficient is calculated by the historical error mean associated with the compensation factor to correct the initial result. This achieves accurate prediction of the remaining duration of the first use of updated materials in the conference room, effectively adapts to material usage scenarios with different fluctuation levels, reduces prediction errors, and provides a reliable and accurate time constraint basis for subsequent material synchronization scheme optimization, avoiding the problem of material synchronization being too early or too late due to prediction deviations.

[0068] S300: Using a material synchronization constraint that is less than the predicted remaining time, and aiming to minimize the overall material aging deviation and maximize the overall material usage redundancy time, the material transmission scheme is optimized by combining updated material attribute information and network monitoring information from several conference rooms to determine the optimal material transmission scheme.

[0069] In this embodiment of the invention, a material synchronization constraint is set at less than a certain number of predicted remaining times. The objectives are to minimize the overall material synchronization timeliness deviation and maximize the overall material usage redundancy time. By combining updated material attribute information and network monitoring information from several conference rooms, an optimal material transmission scheme is determined. During multi-conference material synchronization, it is necessary to simultaneously consider the time requirement for materials to complete synchronization within the predicted remaining time, the resource limitations of the central control platform, and the effect requirements for matching the synchronization rhythm of each conference room. If transmission resources are directly and randomly allocated, problems such as resource overload, synchronization timeouts in some conference rooms, or insufficient redundancy time after synchronization may occur. Therefore, it is necessary to combine material attributes, network status, and platform resource thresholds to achieve efficient and reliable material synchronization through constraint- and goal-oriented scheme optimization.

[0070] Step S300 in the method provided in this embodiment of the invention includes:

[0071] Obtain updated material property information, wherein the updated material property information includes data volume and data encryption method;

[0072] Obtain network monitoring information from several conference rooms, including basic performance data and channel quality data.

[0073] Obtain the concurrent transmission link capacity threshold of the central control platform;

[0074] The material transmission scheme is optimized based on the data volume, data encryption method, several network basic performance data, and several network channel quality data. The optimal material transmission scheme is output. The material synchronization constraint is less than the predicted remaining time, and the resource constraint is less than the concurrent transmission link capacity threshold. The goal is to minimize the overall material synchronization time deviation and maximize the overall material usage redundancy time.

[0075] First, the updated material attribute information is obtained, including data volume and data encryption method. Data volume refers to the file size of the updated material, determining the bandwidth and time required for transmission. Data encryption method (encryption transmission level) refers to the security encryption rules for the updated material transmission process; different levels correspond to different computational costs, such as Class A for symmetric encryption and Class B for asymmetric encryption. The file size and encryption transmission level of the updated material are retrieved from the supplementary information of the meeting material update instruction, serving as the basic parameters for the transmission scheme. For example, the attribute information of the updated material in this case is: data volume = 500MB, encryption transmission level = Class A, which is symmetric encryption with low computational cost.

[0076] Secondly, network monitoring information from several conference rooms is acquired. This information includes basic performance data and channel quality data. Basic performance data, a core indicator reflecting network transmission efficiency, includes round-trip time (RTT), available bandwidth, packet loss rate (PFR), and jitter. RTT refers to the time it takes for data to travel back and forth; available bandwidth refers to the bandwidth currently available for transmission; PFR refers to the percentage of packets lost during transmission; and jitter refers to the fluctuation in RTT. Channel quality data, an indicator reflecting network link stability, includes signal strength, signal-to-noise ratio (SNR), and bit error rate (BER). SNR refers to the signal power of the wireless link; SNR is the ratio of effective signal to noise; and BER refers to the percentage of erroneous symbols during transmission. The central control platform sends data requests to the edge monitoring nodes in each conference room to retrieve the current network basic performance and channel quality data in real time.

[0077] For example, the network monitoring information for conference room A is as follows: Basic performance: Round-trip latency = 20ms, available bandwidth = 100Mbps, packet loss rate = 0.2%, jitter = 5ms; Channel quality: Wi-Fi signal strength = -55dBm, signal-to-noise ratio = 30dB, bit error rate = 0.01%; The network monitoring information for conference room B is as follows: Basic performance: Round-trip latency = 25ms, available bandwidth = 80Mbps, packet loss rate = 0.3%, jitter = 6ms; Channel quality: Wi-Fi signal strength = -60dBm, signal-to-noise ratio = 28dB, bit error rate = 0.02%.

[0078] In addition, the concurrent transmission link capacity threshold of the central control platform is obtained. The concurrent transmission link capacity threshold refers to the maximum number of parallel data transmission sessions that the central control platform can effectively manage simultaneously under a given resource configuration, reflecting the upper limit of the platform's concurrent processing capability. The preset concurrent transmission link capacity threshold is retrieved from the resource configuration management module of the central control platform. For example, the concurrent transmission link capacity threshold of the central control platform corresponding to this collaborative meeting is 8, meaning it supports a maximum of 8 parallel data transmission sessions simultaneously.

[0079] Furthermore, with material synchronization constraints being less than the aforementioned number of predicted remaining times, and resource constraints being less than the aforementioned concurrent transmission link capacity threshold, and with the goal of minimizing the overall material synchronization timeliness deviation and maximizing the overall material usage redundancy time, the material transmission scheme is updated and optimized based on the aforementioned data volume, data encryption method, several network basic performance data, and several network channel quality data, and the optimal material transmission scheme is output.

[0080] The objective is to minimize the overall material aging deviation and maximize the overall material usage redundancy time. Based on the data volume, data encryption method, several network fundamental performance data, and several network channel quality data, the optimal material transmission scheme is optimized and output, including:

[0081] The material update order of the several meeting rooms is randomly combined and enumerated to generate multiple material update transmission schemes, and the first material update transmission scheme is randomly selected.

[0082] Within the conference material transmission simulation space, with resources constrained to be less than the concurrent transmission link capacity threshold, the material transmission simulation of the several conference rooms is performed based on the data volume, data encryption method, several network basic performance data, several network channel quality data and the first updated material transmission scheme, and the first simulated transmission duration set is output.

[0083] Determine whether the first simulated transmission duration in the first simulated transmission duration set is less than the predicted remaining duration corresponding to the same conference room. If so, the first updated material transmission scheme is taken as a qualified updated material transmission scheme, and the multiple updated material transmission schemes are sequentially screened to obtain multiple qualified updated material transmission schemes and multiple qualified simulated transmission duration sets.

[0084] With the goal of minimizing the overall synchronous material aging deviation and maximizing the overall material usage redundancy time, the optimal material transmission scheme is determined based on the evaluation of multiple qualified updated material transmission schemes and multiple qualified simulated transmission time sets.

[0085] First, the material update order of the several meeting rooms is randomly combined and enumerated to generate multiple material update transmission schemes, and the first material update transmission scheme is randomly selected. The material update order refers to the order in which each meeting room receives updated materials, including serial and parallel combinations. Random combination enumeration means performing all possible permutations of the meeting room update order to generate a set of schemes covering all transmission orders. For the several meeting rooms participating in the collaboration, all possible update order combinations are enumerated to generate multiple material update transmission schemes; one of them is randomly selected as the first material update transmission scheme. For example, the update order combinations of meeting rooms A and B include: Scheme 1: A transmits first, B transmits later (serial); Scheme 2: B transmits first, A transmits later (serial); Scheme 3: A and B transmit simultaneously (parallel); after generating the above three schemes, Scheme 1 is randomly selected as the first material update transmission scheme.

[0086] Secondly, within the conference material transmission simulation space, with a resource constraint of less than the concurrent transmission link capacity threshold, the material transmission simulation for the several conference rooms is performed based on the data volume, data encryption method, several network basic performance data, several network channel quality data, and the first updated material transmission scheme, outputting a first set of simulated transmission durations. The conference material transmission simulation space is a virtual computing space that recreates the actual transmission environment, simulating the impact of data volume, encryption method, and network status on transmission duration. The first set of simulated transmission durations refers to the time taken for each conference room to complete material transmission after executing the first updated material transmission scheme within the simulation space; that is, the time from transmission initiation to completion. Within the simulation space, with a concurrent transmission link capacity threshold as the resource constraint, updated material attributes, network data for each conference room, and the first updated material transmission scheme are input to simulate the transmission process, recording the completion transmission duration for each conference room to form the first set of simulated transmission durations. For example, for the first scheme (A first, B later): A's transmission is started first: Combining the network data of A, the simulation shows that the transmission time of A is 40s; After A's transmission is completed, B's transmission is started: The simulation shows that B's transmission time is 40s (A's time) + 50s (B's time) = 90s; Output the first simulated transmission time set: [40s (A), 90s (B)].

[0087] Then, it is determined whether all the simulated transmission durations in the first simulated transmission duration set are less than the predicted remaining duration corresponding to the same conference room. If so, the first updated material transmission scheme is designated as a qualified updated material transmission scheme, and the multiple updated material transmission schemes are sequentially filtered to obtain multiple qualified updated material transmission schemes and multiple qualified simulated transmission duration sets. Qualified updated material transmission schemes are those whose simulated transmission durations are all less than the predicted remaining duration of the corresponding conference room, i.e., schemes that satisfy the material synchronization constraint. The simulated transmission duration set of each transmission scheme is checked one by one to determine whether each duration is less than the predicted remaining duration of the corresponding conference room; if so, the scheme is marked as a qualified updated material transmission scheme, and its corresponding qualified simulated transmission duration set is retained.

[0088] For example, Scheme 1 (A first, B second): The simulation duration [40s, 90s] is less than the predicted remaining duration of A (580s) and the predicted remaining duration of B (654s), so it is considered qualified; Scheme 2 (B first, A second): The simulation duration [90s (A), 50s (B)] is less than the predicted remaining duration, so it is considered qualified; Scheme 3 (simultaneous transmission): The simulation duration [40s (A), 50s (B)] is less than the predicted remaining duration, so it is considered qualified; Finally, three qualified material transmission schemes are obtained, and the corresponding qualified simulation transmission duration sets are [40, 90], [90, 50], and [40, 50], respectively.

[0089] Based on this, with the goal of minimizing the overall synchronous material aging deviation and maximizing the overall material usage redundancy time, the optimal material transmission scheme is determined by evaluating multiple qualified updated material transmission schemes and multiple qualified simulated transmission time sets.

[0090] The optimal material transport scheme is determined based on multiple qualified updated material transport schemes and multiple qualified simulated transport duration sets, with the objectives of minimizing overall synchronous material aging deviation and maximizing overall material usage redundancy. This includes:

[0091] Several priority weights are set based on the meeting priorities of the aforementioned meeting rooms, wherein the priority weights are positively correlated with the meeting priorities;

[0092] Randomly select the first qualified updated material transport scheme and the first qualified simulated transport duration set;

[0093] The median of the first set of qualified simulated transmission durations is selected as the benchmark. The duration deviations of other first qualified simulated transmission durations are calculated respectively, and the first overall duration deviation is obtained by summing the multiple first duration deviations.

[0094] The first overall material usage redundancy time is calculated based on the aforementioned priority weights and the first qualified simulated transmission time set;

[0095] With the goal of minimizing the overall synchronous material aging deviation and maximizing the overall material usage redundancy time, the first scheme adaptation coefficient is calculated based on the first overall time deviation and the first overall material usage redundancy time.

[0096] The matching coefficients of multiple qualified material transport schemes are calculated sequentially, and the qualified material transport scheme with the largest matching coefficient is selected as the optimal material transport scheme.

[0097] First, several priority weights are assigned based on the meeting priorities of the various meeting rooms. These priority weights are positively correlated with the meeting priorities. Meeting priority refers to the importance of a meeting room in a collaborative meeting, such as main meeting room > sub-venues. Priority weights are weighting coefficients positively correlated with meeting priorities, used to quantify the importance proportion of different meeting rooms; the sum of all meeting room weights is 1. Based on the meeting priority of each meeting room, a corresponding priority weight is configured; the higher the priority, the greater the weight. For example, meeting room A (main meeting room) has a high priority, with a priority weight of 0.6; meeting room B (sub-venue) has a low priority, with a priority weight of 0.4, and 0.6 + 0.4 = 1.

[0098] Secondly, a first qualified material replacement transportation scheme and a first qualified simulated transportation duration set are randomly selected. The first qualified material replacement transportation scheme refers to a scheme randomly selected from multiple qualified schemes. The first qualified simulated transportation duration set refers to the transportation duration set for each conference room corresponding to the first qualified material replacement transportation scheme. One scheme is randomly selected from the qualified material replacement transportation schemes, and its corresponding qualified simulated transportation duration set is retrieved. For example, if scheme 1 is randomly selected as the first qualified scheme, its corresponding first qualified simulated transportation duration set is: [40s (A), 90s (B)].

[0099] Further, the median of the first qualified simulated transmission duration set is selected as the benchmark. Duration deviations are calculated for other first qualified simulated transmission durations, and the sum of these deviations is used to obtain the first overall duration deviation. The benchmark is the median of the first qualified simulated transmission duration set, serving as a reference value for measuring each duration deviation. The first overall duration deviation is the sum of the absolute values ​​of the deviations between the transmission durations of each conference room and the benchmark; a smaller value indicates a more balanced synchronization rhythm. First, the median of the duration set is calculated as the benchmark. Then, the absolute values ​​of the deviations of each duration from the benchmark are calculated sequentially, and finally, the summation is used to obtain the overall duration deviation. For example, the median of the duration set [40, 90] in Scheme 1 is (40 + 90) / 2 = 65s; the duration deviations are: |40 - 65| = 25s, |90 - 65| = 25s; the first overall duration deviation is 25 + 25 = 50s.

[0100] Then, based on the aforementioned priority weights and the first qualified simulated transmission duration set, the first overall material usage redundancy duration is calculated.

[0101] The first overall material usage redundancy duration is calculated based on the aforementioned priority weights and the first qualified simulated transmission duration set, including:

[0102] The difference between the predicted remaining duration and the first qualified simulated transmission duration corresponding to the same conference room in the first qualified simulated transmission duration set is used as the material usage redundancy duration, resulting in several material usage redundancy durations.

[0103] Based on the aforementioned priority weights, the weighted summation of the aforementioned material redundancy durations yields the first overall material redundancy duration.

[0104] First, the difference between the predicted remaining time and the first qualified simulated transmission time corresponding to the same meeting room in the first qualified simulated transmission time set is used as the material usage redundancy time, resulting in several material usage redundancy times. Material usage redundancy time refers to the difference between the predicted remaining time of a single meeting room's first use of updated materials and the qualified simulated transmission time of that meeting room, reflecting the buffer time from material synchronization completion to first use. Material usage redundancy time = predicted remaining time - first simulated transmission time. Several material usage redundancy times refer to the set of material usage redundancy times of all participating meeting rooms. For each meeting room, its corresponding predicted remaining time is subtracted from the transmission time of that meeting room in the first qualified simulated transmission time set to obtain a single material usage redundancy time, ultimately forming several material usage redundancy times. For example, if the predicted remaining time of meeting room A is 580s and the simulated transmission time is 40s, then the material usage redundancy time of meeting room A = 580 - 40 = 540s. Given that the predicted remaining time for conference room B is 720s, the simulated transmission time is 90s, and the material usage redundancy time for conference room B is 720-90=630s; we obtain two material usage redundancy times: [540s, 630s].

[0105] Secondly, based on the aforementioned priority weights, the redundant durations of material usage are weighted and summed to obtain the first overall redundant duration of material usage. The first overall redundant duration of material usage is the result of the weighted summation, quantifying the overall buffering capacity of all meeting rooms, with the redundant duration of higher-priority meeting rooms having a greater impact on the result. The redundant duration of material usage for each meeting room is multiplied by its corresponding priority weight, and then all products are summed to obtain the first overall redundant duration of material usage. For example, the first overall redundant duration of material usage = (540 × 0.6) + (564 × 0.4) = 550.8s.

[0106] Based on this, with the objectives of minimizing the overall synchronization material aging deviation and maximizing the overall material usage redundancy time, the first scheme adaptation coefficient is calculated according to the first overall time deviation and the first overall material usage redundancy time. The first scheme adaptation coefficient is an index that comprehensively measures the overall time deviation and the overall redundancy time; the larger the value, the better the scheme. Adaptation coefficient = overall redundancy time / (overall time deviation + 1). For example, the first scheme adaptation coefficient = 550.8 / (50 + 1) = 10.8.

[0107] Finally, the adaptation coefficients of multiple qualified material transport schemes are calculated sequentially, and the qualified material transport scheme with the largest adaptation coefficient is selected as the optimal material transport scheme. For all qualified material transport schemes, the above steps are repeated to calculate their respective adaptation coefficients, and the scheme with the largest adaptation coefficient is selected as the optimal material transport scheme. For example, calculate the adaptation coefficients of the other two qualified schemes: Scheme 2 (B first, A second, duration set [90, 50]): Overall duration deviation = |90-70|+|50-70|=20+20=40; Overall redundancy duration = (580-90)×0.6+(654-50)×0.4≈535.6; Adaptation coefficient = 535.6 / (40+1)≈13.06; Scheme 3 (simultaneous transmission, duration set [40, 50]): Overall duration deviation = |40-45|+|50-45|=5+5=10; Overall redundancy duration = (580-40)×0.6+(654-50)×0.4≈592; Adaptation coefficient = 592 / (10+1)≈51.42; Scheme 3 has the largest adaptation coefficient of 51.42, therefore Scheme 3 is determined to be the optimal material transmission scheme.

[0108] In this embodiment of the invention, by configuring priority weights for meeting rooms and combining the "overall duration deviation" and "weighted redundancy duration" to calculate the adaptation coefficient, the material usage buffer needs of high-priority meeting rooms are guaranteed, while also taking into account the balance of the synchronization rhythm of multiple meeting rooms. The final selected optimal solution can maximize the matching of the priority differences of actual meetings under the premise of meeting time and resource constraints, further improving the practicality and rationality of material synchronization in multiple meeting rooms.

[0109] S400: Update the materials synchronously to the plurality of meeting rooms according to the optimal material transfer scheme.

[0110] In this embodiment of the invention, the updated materials are synchronously updated to the plurality of conference rooms according to the optimal material transmission scheme. According to the optimal material transmission scheme determined in S300, the central control platform synchronously pushes the updated materials to each conference room. The central control platform first calls the corresponding transmission resources, sends out the updated materials and attribute information, and the edge monitoring nodes of each conference room collect and report the transmission progress, network stability, and data verification results in real time. For example, according to the optimal scheme of simultaneous transmission in conference rooms A and B (Scheme 3), the central control platform starts two parallel links to push 500MB of A-class encrypted materials. During transmission, node A reports "20% completed in 10 seconds, network stable," and node B reports "75% completed in 30 seconds, no data loss." When the edge node reports "transmission complete, data verification passed," the conference room terminal automatically replaces the old materials to complete the local update, and then returns an update-ready signal to the central control platform. The central control platform confirms that all conference rooms have completed the synchronous update. This ensures that the updated materials are delivered accurately within the predicted remaining time, without timeouts or data anomalies, guaranteeing the smooth progress of multi-conference collaborative meetings. For example, if A completes its transmission and update in 40 seconds and B completes its update in 50 seconds, both meeting their respective predicted remaining time requirements, the central control platform receives feedback from both parties that the update is ready, and the meeting can continue using the latest materials.

[0111] Through the specific implementation methods described above, the embodiments of the present invention achieve the following technical effects:

[0112] This invention provides a method and system for synchronizing and updating meeting materials across multiple meeting rooms. It dynamically senses the material usage ratio through edge monitoring nodes in each meeting room and uploads this information to a central control platform, accurately acquiring the material usage progress and laying a data foundation for subsequent operations. Based on the material usage ratio sequence, a fluctuation coefficient is calculated, and multiple LSTM duration predictors are trained using historical data. The number of predictors is dynamically adjusted and the results corrected using a compensation factor, accurately outputting the remaining time for each meeting room to use updated materials for the first time, providing a rigid time constraint. By integrating updated material attributes, network monitoring information, and meeting room priorities, and with the constraints of no timeout and no resource overrun, and the objectives of minimizing timeliness deviation and maximizing weighted redundancy duration, an optimal transmission scheme is sought to determine the optimal execution path. Parallel or ordered transmission is initiated according to the optimal scheme, and the transmission status is monitored in real time to complete local material updates. Ultimately, this achieves real-time, accurate, and secure synchronization of updated materials across multiple meeting rooms, reducing synchronization latency and maintenance costs, improving meeting collaboration efficiency, and effectively avoiding the problems of materials consuming resources too early or becoming unusable too late.

[0113] Example 2, as Figure 2 As shown, the present invention provides a multi-meeting room collaborative meeting material synchronization and updating system, the system comprising:

[0114] The progress acquisition module 11 is used to retrieve the usage progress of several materials in several conference rooms on the central control platform when a meeting material update instruction is issued during a collaborative meeting in multiple conference rooms.

[0115] The duration prediction module 12 is used to obtain several predicted remaining durations for the first use of updated materials in the conference room based on the predicted material usage progress.

[0116] The optimized transmission module 13 is used to optimize the material transmission scheme by taking the material synchronization constraint as less than the predicted remaining time, aiming to minimize the overall material synchronization time deviation and maximize the overall material usage redundancy time, and combining the updated material attribute information and the network monitoring information of several conference rooms to determine the optimal material transmission scheme.

[0117] The synchronous update module 14 is used to synchronously update the updated materials to the plurality of conference rooms according to the optimal material transfer scheme.

[0118] In one embodiment, the progress acquisition module 11 is further configured to:

[0119] When multiple meeting rooms are used for collaborative meetings, the material usage ratio can be dynamically sensed in real time according to a preset monitoring time interval by deploying edge monitoring nodes in each meeting room.

[0120] When a meeting materials update instruction is issued, the edge monitoring node uploads a sequence of material usage ratios up to the current time point as several material usage progresses to the central control platform, where the central control platform is a cloud server.

[0121] In one embodiment, the duration prediction module 12 is further configured to:

[0122] Randomly select a first meeting room from the plurality of meeting rooms, and obtain a first material usage ratio sequence for the first meeting room;

[0123] In chronological order, the differences between adjacent first material usage ratios in the first material usage ratio sequence are calculated sequentially to generate a first material usage ratio deviation sequence.

[0124] The ratio of the standard deviation of the proportional deviation to the mean of the proportional deviation in the first material usage proportional deviation sequence is used as the first material usage fluctuation coefficient.

[0125] Based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence, the first predicted remaining time for the first meeting room to use updated materials for the first time is predicted and added to the plurality of predicted remaining times.

[0126] The method for predicting and obtaining the first remaining prediction time based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence includes:

[0127] Based on historical meeting operation records, a set of sample monitoring time intervals, a set of sample material usage fluctuation coefficients, and a set of sample material usage ratio sequences were collected. The historical remaining time of the old materials used up under different sample monitoring time intervals, sample material usage fluctuation coefficients, and sample material usage ratio sequences was collected as the sample remaining time, and the sample remaining time set was obtained.

[0128] The sample monitoring time interval set, sample material usage fluctuation coefficient set, sample material usage ratio sequence set, and sample remaining duration set are used as training data and divided into P training sets. The Long Short-Term Memory Network is supervised and trained until convergence, generating P duration predictors, where P is an integer greater than or equal to 5.

[0129] The ratio of the fluctuation coefficient of the first material to the fluctuation coefficient of the preset standard material is used as the first prediction compensation factor.

[0130] The first prediction compensation factor is multiplied by the initial predictor selection number and rounded to obtain the optimal predictor selection number L, where the initial predictor selection number is 3, and L is a positive integer greater than or equal to 1. If the calculated L is greater than P, then L is equal to P.

[0131] L duration predictors are randomly selected from the P duration predictors. Predictions are made based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence. The average of the L prediction results is calculated to obtain the first initial prediction of the remaining duration.

[0132] The first initial prediction remaining duration is corrected based on the first prediction compensation factor, and the first prediction remaining duration is output.

[0133] The correction of the remaining duration of the first initial prediction based on the first prediction compensation factor includes:

[0134] Based on historical meeting operation records, the average historical remaining duration prediction error under the first prediction compensation factor is calculated as the first prediction error.

[0135] The first duration correction coefficient is obtained by subtracting the first prediction error from 1, and the product of the first duration correction coefficient and the first initial prediction remaining duration is taken as the first prediction remaining duration.

[0136] In one embodiment, the optimized transmission module 13 is further configured to:

[0137] Obtain updated material property information, wherein the updated material property information includes data volume and data encryption method;

[0138] Obtain network monitoring information from several conference rooms, including basic performance data and channel quality data.

[0139] Obtain the concurrent transmission link capacity threshold of the central control platform;

[0140] The material transmission scheme is optimized based on the data volume, data encryption method, several network basic performance data, and several network channel quality data. The optimal material transmission scheme is output. The material synchronization constraint is less than the predicted remaining time, and the resource constraint is less than the concurrent transmission link capacity threshold. The goal is to minimize the overall material synchronization time deviation and maximize the overall material usage redundancy time.

[0141] The objective is to minimize the overall material aging deviation and maximize the overall material usage redundancy time. Based on the data volume, data encryption method, several network fundamental performance data, and several network channel quality data, the optimal material transmission scheme is optimized and output, including:

[0142] The material update order of the several meeting rooms is randomly combined and enumerated to generate multiple material update transmission schemes, and the first material update transmission scheme is randomly selected.

[0143] Within the conference material transmission simulation space, with resources constrained to be less than the concurrent transmission link capacity threshold, the material transmission simulation of the several conference rooms is performed based on the data volume, data encryption method, several network basic performance data, several network channel quality data and the first updated material transmission scheme, and the first simulated transmission duration set is output.

[0144] Determine whether the first simulated transmission duration in the first simulated transmission duration set is less than the predicted remaining duration corresponding to the same conference room. If so, the first updated material transmission scheme is taken as a qualified updated material transmission scheme, and the multiple updated material transmission schemes are sequentially screened to obtain multiple qualified updated material transmission schemes and multiple qualified simulated transmission duration sets.

[0145] With the goal of minimizing the overall synchronous material aging deviation and maximizing the overall material usage redundancy time, the optimal material transmission scheme is determined based on the evaluation of multiple qualified updated material transmission schemes and multiple qualified simulated transmission time sets.

[0146] The optimal material transport scheme is determined based on multiple qualified updated material transport schemes and multiple qualified simulated transport duration sets, with the objectives of minimizing overall synchronous material aging deviation and maximizing overall material usage redundancy. This includes:

[0147] Several priority weights are set based on the meeting priorities of the aforementioned meeting rooms, wherein the priority weights are positively correlated with the meeting priorities;

[0148] Randomly select the first qualified updated material transport scheme and the first qualified simulated transport duration set;

[0149] The median of the first set of qualified simulated transmission durations is selected as the benchmark. The duration deviations of other first qualified simulated transmission durations are calculated respectively, and the first overall duration deviation is obtained by summing the multiple first duration deviations.

[0150] The first overall material usage redundancy time is calculated based on the aforementioned priority weights and the first qualified simulated transmission time set;

[0151] With the goal of minimizing the overall synchronous material aging deviation and maximizing the overall material usage redundancy time, the first scheme adaptation coefficient is calculated based on the first overall time deviation and the first overall material usage redundancy time.

[0152] The matching coefficients of multiple qualified material transport schemes are calculated sequentially, and the qualified material transport scheme with the largest matching coefficient is selected as the optimal material transport scheme.

[0153] The first overall material usage redundancy duration is calculated based on the aforementioned priority weights and the first qualified simulated transmission duration set, including:

[0154] The difference between the predicted remaining duration and the first qualified simulated transmission duration corresponding to the same conference room in the first qualified simulated transmission duration set is used as the material usage redundancy duration, resulting in several material usage redundancy durations.

[0155] Based on the aforementioned priority weights, the weighted summation of the aforementioned material redundancy durations yields the first overall material redundancy duration.

[0156] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0157] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0158] This specification and accompanying drawings are merely illustrative examples of the invention and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its scope. Therefore, if such modifications and modifications fall within the scope of the invention and its equivalents, the invention is intended to include these modifications and modifications.

Claims

1. A method for synchronously updating meeting materials in multi-meeting room collaboration, characterized in that, The methods include: During collaborative meetings in multiple meeting rooms, when a meeting material update instruction is issued, the central control platform retrieves the usage progress of several materials from several meeting rooms. Based on the aforementioned material usage progress predictions, several predicted remaining times for the first use of updated materials in the conference room are obtained; Using less than the predicted remaining time as the material synchronization constraint, and aiming to minimize the overall material synchronization timeliness deviation and maximize the overall material usage redundancy time, the material transmission scheme is optimized by combining updated material attribute information and network monitoring information of several conference rooms to determine the optimal material transmission scheme. The updated materials are synchronously updated to the aforementioned conference rooms according to the optimal material transfer scheme.

2. The method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 1, characterized in that, The central control platform retrieves the material usage progress data for several meeting rooms, including: When multiple meeting rooms are used for collaborative meetings, the material usage ratio can be dynamically sensed in real time according to a preset monitoring time interval by deploying edge monitoring nodes in each meeting room. When a meeting materials update instruction is issued, the edge monitoring node uploads a sequence of material usage ratios up to the current time point as several material usage progresses to the central control platform, where the central control platform is a cloud server.

3. The method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 2, characterized in that, Based on the aforementioned material usage progress predictions, several predicted remaining times for the first use of updated materials in the conference room are obtained, including: Randomly select a first meeting room from the plurality of meeting rooms, and obtain a first material usage ratio sequence for the first meeting room; In chronological order, the differences between adjacent first material usage ratios in the first material usage ratio sequence are calculated sequentially to generate a first material usage ratio deviation sequence. The ratio of the standard deviation of the proportional deviation to the mean of the proportional deviation in the first material usage proportional deviation sequence is used as the first material usage fluctuation coefficient. Based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence, the first predicted remaining time for the first meeting room to use updated materials for the first time is predicted and added to the plurality of predicted remaining times.

4. The method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 3, characterized in that, Based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence, the first prediction remaining duration is predicted, including: Based on historical meeting operation records, a set of sample monitoring time intervals, a set of sample material usage fluctuation coefficients, and a set of sample material usage ratio sequences were collected. The historical remaining time of the old materials used up under different sample monitoring time intervals, sample material usage fluctuation coefficients, and sample material usage ratio sequences was collected as the sample remaining time, and the sample remaining time set was obtained. The sample monitoring time interval set, sample material usage fluctuation coefficient set, sample material usage ratio sequence set, and sample remaining duration set are used as training data and divided into P training sets. The Long Short-Term Memory Network is supervised and trained until convergence, generating P duration predictors, where P is an integer greater than or equal to 5. The ratio of the fluctuation coefficient of the first material to the fluctuation coefficient of the preset standard material is used as the first prediction compensation factor. The first prediction compensation factor is multiplied by the initial predictor selection number and rounded to obtain the optimal predictor selection number L, where the initial predictor selection number is 3, and L is a positive integer greater than or equal to 1. If the calculated L is greater than P, then L is equal to P. L duration predictors are randomly selected from the P duration predictors. Predictions are made based on the preset monitoring time interval, the first material usage fluctuation coefficient, and the first material usage ratio sequence. The average of the L prediction results is calculated to obtain the first initial prediction of the remaining duration. The first initial prediction remaining duration is corrected based on the first prediction compensation factor, and the first prediction remaining duration is output.

5. The method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 4, characterized in that, The remaining duration of the first initial prediction is corrected according to the first prediction compensation factor, including: Based on historical meeting operation records, the average historical remaining duration prediction error under the first prediction compensation factor is calculated as the first prediction error. The first duration correction coefficient is obtained by subtracting the first prediction error from 1, and the product of the first duration correction coefficient and the first initial prediction remaining duration is taken as the first prediction remaining duration.

6. The method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 1, characterized in that, Using a material synchronization constraint less than a certain number of predicted remaining times, and aiming to minimize the overall material aging deviation and maximize the overall material usage redundancy time, the material transmission scheme is optimized by combining updated material attribute information and network monitoring information from several conference rooms. This includes: Obtain updated material property information, wherein the updated material property information includes data volume and data encryption method; Obtain network monitoring information from several conference rooms, including basic performance data and channel quality data. Obtain the concurrent transmission link capacity threshold of the central control platform; The material transmission scheme is optimized based on the data volume, data encryption method, several network basic performance data, and several network channel quality data. The optimal material transmission scheme is output. The material synchronization constraint is less than the predicted remaining time, and the resource constraint is less than the concurrent transmission link capacity threshold. The goal is to minimize the overall material synchronization time deviation and maximize the overall material usage redundancy time.

7. The method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 6, characterized in that, With the goal of minimizing overall material aging deviation and maximizing overall material usage redundancy, the material transmission scheme is optimized based on the data volume, data encryption method, several network basic performance data, and several network channel quality data, and the optimal material transmission scheme is output, including: The material update order of the several meeting rooms is randomly combined and enumerated to generate multiple material update transmission schemes, and the first material update transmission scheme is randomly selected. Within the conference material transmission simulation space, with resources constrained to be less than the concurrent transmission link capacity threshold, the material transmission simulation of the several conference rooms is performed based on the data volume, data encryption method, several network basic performance data, several network channel quality data and the first updated material transmission scheme, and the first simulated transmission duration set is output. Determine whether the first simulated transmission duration in the first simulated transmission duration set is less than the predicted remaining duration corresponding to the same conference room. If so, the first updated material transmission scheme is taken as a qualified updated material transmission scheme, and the multiple updated material transmission schemes are sequentially screened to obtain multiple qualified updated material transmission schemes and multiple qualified simulated transmission duration sets. With the goal of minimizing the overall synchronous material aging deviation and maximizing the overall material usage redundancy time, the optimal material transmission scheme is determined based on the evaluation of multiple qualified updated material transmission schemes and multiple qualified simulated transmission time sets.

8. The method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 7, characterized in that, With the objectives of minimizing overall synchronous material aging deviation and maximizing overall material usage redundancy, the optimal material transport scheme is determined based on the evaluation of multiple qualified updated material transport schemes and multiple qualified simulated transport duration sets, including: Several priority weights are set based on the meeting priorities of the aforementioned meeting rooms, wherein the priority weights are positively correlated with the meeting priorities; Randomly select the first qualified updated material transport scheme and the first qualified simulated transport duration set; The median of the first set of qualified simulated transmission durations is selected as the benchmark. The duration deviations of other first qualified simulated transmission durations are calculated respectively, and the first overall duration deviation is obtained by summing the multiple first duration deviations. The first overall material usage redundancy time is calculated based on the aforementioned priority weights and the first qualified simulated transmission time set; With the goal of minimizing the overall synchronous material aging deviation and maximizing the overall material usage redundancy time, the first scheme adaptation coefficient is calculated based on the first overall time deviation and the first overall material usage redundancy time. The matching coefficients of multiple qualified material transport schemes are calculated sequentially, and the qualified material transport scheme with the largest matching coefficient is selected as the optimal material transport scheme.

9. A method for synchronously updating meeting materials in multi-meeting room collaboration according to claim 8, characterized in that, The first overall material usage redundancy time is calculated based on the aforementioned priority weights and the first qualified simulated transmission time set, including: The difference between the predicted remaining duration and the first qualified simulated transmission duration corresponding to the same conference room in the first qualified simulated transmission duration set is used as the material usage redundancy duration, resulting in several material usage redundancy durations. Based on the aforementioned priority weights, the weighted summation of the aforementioned material redundancy durations yields the first overall material redundancy duration.

10. A multi-meeting room collaborative meeting material synchronization and update system, characterized in that, For implementing the method for synchronously updating meeting materials in multi-meeting room collaboration as described in any one of claims 1-9, the system comprises: The progress acquisition module is used to retrieve the usage progress of several materials from several conference rooms on the central control platform when a meeting material update instruction is issued during collaborative meetings in multiple conference rooms. The duration prediction module is used to obtain several predicted remaining durations for the first use of updated materials in the conference room based on the predicted material usage progress. The optimized transmission module is used to optimize the material transmission scheme by taking a material synchronization constraint that is less than the predicted remaining time, aiming to minimize the overall material synchronization time deviation and maximize the overall material usage redundancy time, and combining the updated material property information and the network monitoring information of several conference rooms to determine the optimal material transmission scheme. The synchronous update module is used to synchronously update the updated materials to the plurality of conference rooms according to the optimal material transfer scheme.

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