A digital conference information management system

Abstract

The application discloses a kind of digital conference information management systems, it is related to data management technical field, including dynamic analysis module, estimation intercept module, monitoring rating module, conference evolution module, conference efficiency analysis module;The application is synergized by dynamic analysis module and estimation intercept module, and accurate resource requirement list is intelligently generated, by real-time semantic analysis technology, the subject relevance between conference discussion content and established agenda is actively monitored and quantified, when detecting that discussion seriously deviates, the system will immediately issue a warning to the host, based on historical data to establish efficiency evaluation model, current conference key performance indicators can be quantitatively calculated and evaluated in real time, when efficiency value is lower than preset threshold, the system will automatically generate and push specific optimization suggestions, and personal meeting participation performance degree is automatically calculated according to the set algorithm model, to effectively stimulate the enthusiasm of participants, improve team collaboration quality.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a digital conference information management system. Background Technology

[0002] With the rapid development of information technology, digital meetings have gradually become an important way for various organizations and enterprises to communicate, collaborate, and make decisions. A digital meeting information management system is a software system that uses modern information technology to comprehensively and efficiently manage meeting-related information. It aims to improve the efficiency and quality of meeting organization, execution, and follow-up. It supports multiple people participating in meetings online, but it is not ideal in terms of collaborative functions such as real-time editing of meeting documents and joint annotation of meeting key points.

[0003] Existing meeting information management systems, relying on traditional methods of information notification and manual scheduling, generally operate in a "passive recording" state during meetings, lacking the ability to "actively guide" the process. While existing systems can record audio and video and share screens, they are completely unable to understand the real-time content of the meeting. This results in a lack of any mechanism to alert or intervene when the meeting discussion deviates from the core agenda, leading to widespread problems of low meeting efficiency and wasted time. Furthermore, meeting security measures are limited, and there is a lack of a comprehensive credit rating system for participants, making it difficult to effectively evaluate and manage participants' behavior and past performance. To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to: dynamically analyze meeting levels to implement differentiated management, predict resource needs to avoid waste; ensure security through encryption, and comprehensively monitor and evaluate to improve meeting quality.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a digital conference information management system, including a dynamic parsing module, a prediction and interception module, a monitoring and rating module, a conference evolution module, and a conference effectiveness analysis module; The dynamic parsing module is used to acquire meeting schedules in real time. The meeting schedule consists of a dataset of meeting topics, participant information, and meeting time, and the meeting level is dynamically classified according to the job level in the participant information. The estimation and capture module is used to obtain the meeting level type and meeting arrangement dataset. Based on the meeting arrangement dataset, it estimates the meeting arrangement resource requirement list. Based on the estimated meeting arrangement resource requirement list, it generates a resource adjustment application instruction and adjusts the meeting resources through the data control terminal. The meeting evolution module is used to perform topic deviation analysis based on the text data collected in real time by the meeting monitoring module, and dynamically adjust and push updated meeting agendas according to the analysis results. The meeting performance analysis module is used to build a performance evaluation model based on historical meeting data and calculate the time utilization rate and agenda completion rate of the current meeting in real time. The monitoring and rating module collects audio, image, and text data in real time during the meeting through audio and video acquisition devices. Based on the meeting monitoring data and the meeting level type, it calculates the individual's meeting participation performance.

[0006] The meeting data includes various aspects such as voice, images, text communication, and behavioral data, as well as an anti-fraud module, which is used to acquire meeting data for fraud detection. By analyzing data from participants’ voice, images, text communication, and behavior, the system uses speech recognition technology to determine if there are traces of machine synthesis in the speech. Image recognition technology is used to detect the facial expressions and movements of participants, and to perform real-time detection of video and image multimedia content involved in the meeting to identify the authenticity of the images. By analyzing the detailed features of the images and the frame rate changes of the video, if an image is identified as incorrect, it is determined to be an unreal image.

[0007] Furthermore, it also includes an encryption trigger module, which is used to obtain the meeting level type, comprehensively evaluate the nature of the meeting based on the meeting level type and the sensitivity of the meeting, and automatically trigger the corresponding encryption level to obtain the encryption level of the meeting arrangement.

[0008] Furthermore, the types of meetings are categorized based on the information of the meeting participants, specifically including the following: S100. Obtain specific company personnel information and construct a personnel structure based on personnel level, dividing them into ordinary employees, management, senior executives, and leadership personnel levels, and labeling personnel level information; S102. Based on different personnel levels, the corresponding meeting types are divided: meetings at the management level and above are senior meetings; meetings at the senior management level and below are group meetings; and meetings at the management level and below are ordinary meetings.

[0009] Furthermore, the estimated resource requirements for the meeting arrangements are listed below: S200. Obtain the scheduling dataset. Based on the meeting topics, attendee information, and meeting times in the scheduling dataset, analyze the departments to which the attendees belong based on the attendee information to obtain a list of attendees' departments. Analyze the meeting content and direction through the meeting topics to identify the relevant business departments. S201. Obtain the departmental resources related to the meeting content based on the personnel department list, summarize and integrate the required resources to form a demand data set, and plan the acquisition of resources according to the meeting schedule, and arrange the time nodes for data collection, sorting and review in stages. S202. Arrange the demand data set according to the relevance of the meeting topic, and then arrange the demands according to the importance of the resources and the order of meeting usage to form an estimated list of meeting arrangement resource requirements.

[0010] Furthermore, identity verification will be conducted based on participant information, specifically including the following: S300: Obtain the authorized list of persons allowed to attend the meeting during the meeting arrangement process, obtain the meeting arrangement dataset, obtain real-time information on persons attending the meeting, compare the collected information on persons attending the meeting with the pre-set legal identity database, and compare it again with the authorized list of persons attending the meeting to obtain the personnel identity verification result. S301. If the personnel verification result does not match, it is determined that the personnel authorization list does not match the information of the personnel attending the meeting in real time. The personnel authorization list is then verified, and the manager is notified to mark the information and modify the authorized personnel list. S302. Based on the personnel identity verification results, formulate rules for generating resource control permission instructions based on the identity verification results, generate corresponding resource control permission instructions, and the instructions are in the form of electronic authorization codes, access credentials, and permission setting information, which are used to instruct the resource management system to allocate and restrict resource access, and send the generated resource control permission instructions to the corresponding data control terminal.

[0011] Furthermore, the comprehensive evaluation, based on the nature of the meeting, specifically includes the following: S400: Obtain the original agenda of the meeting, parse each agenda item into a set of key keywords, receive voice data in the meeting in real time and convert it into text, and extract keywords from the real-time discussion content; S401: Calculate the semantic reasonableness of the keywords in the real-time discussion content with the set of keywords for the current agenda item. If the semantic reasonableness is lower than the first threshold, it is determined that the meeting discussion has deviated from the current agenda and a reminder message is sent to the host terminal. S402: If the semantic reasonableness of the discussion content and the topic set of a subsequent agenda item is higher than the second threshold, the subsequent agenda item will be automatically moved to the current agenda, and an agenda adjustment suggestion will be generated and pushed to all participants.

[0012] Furthermore, a comprehensive evaluation is conducted to calculate individual meeting participation performance, specifically including the following: S500, meeting data includes real-time performance data of participants in the meeting, compliance with meeting rules, lateness, early departure, statistics on the number and proportion of on-time login, lateness, and no login, statistics on the number of times participants actively speak in the meeting, the number of discussion sessions they participate in, and the frequency of responding to others' speeches, as evaluation indicators; S501. Based on the collected data, assign different weights to each indicator, and calculate the score of each indicator according to the set evaluation criteria. The calculation formula is as follows: , In the above formula, A is the number of times on time, Z is the total number of meetings, C is the number of times late, T is the number of times leaving early, F is the number of times actively speaking, L is the number of times participating in discussions, f is the standard number of meetings, D is the number of violations recognized by the company, and R is the total score obtained. S502. Based on the calculated comprehensive score, analyze the individual's meeting participation performance and set different score ranges to define the performance level. When R is greater than 90, it is an excellent level, and when R is less than 90 but greater than 80, it is a good level.

[0013] Furthermore, an effectiveness evaluation model is established based on historical meeting data, specifically including the following: Structured raw performance data was collected from historical meeting minutes. This structured data was used as input data for the performance evaluation model. Time-related feature analysis, topic-related feature analysis, and participant feature analysis were conducted. A linear regression model was selected for model training and execution. The input consists of the aforementioned features, which are dynamically updated. The output is a continuous value. Real-time features are input into the trained model to predict the final time utilization rate and agenda completion rate. Meeting effectiveness is categorized into high, medium, and low levels. The input is features, and the output is category labels. Time utilization rate: U = Item completion rate: R= .

[0014] Furthermore, it also includes a resource tracking module, which is used to determine the meeting resources controlled by the data control terminal. Acquire meeting resources controlled by the data control terminal, automatically add data watermarks to the meeting resources, including meeting topic, time, organizer, and assign copyright labels to the meeting resources; During the dissemination of conference resources, the transmission path of the materials is recorded. Based on network communication protocols and related data monitoring mechanisms, the system automatically records the information of each node in the transmission when the materials are transmitted between different devices or platforms. The system continuously monitors each stage of the conference resource dissemination process to assess resource usage and quickly pinpoint usage paths.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This digital meeting information management system, through the collaborative work of dynamic analysis and predictive interception modules, can automatically and dynamically identify the meeting level based on the job level of the participants. Based on this level, meeting topic, and participating department information, it intelligently generates a precise resource requirement list. Through real-time semantic analysis technology, it proactively monitors and quantifies the topic relevance between meeting discussion content and the established agenda. When a discussion deviates significantly from the agenda, the system immediately alerts the moderator. When discussion content unexpectedly aligns with subsequent topics, it intelligently suggests adjusting the agenda order. Deeply integrating artificial intelligence technology into meeting process control, it establishes a performance evaluation model based on historical data, enabling real-time quantitative calculation and evaluation of key performance indicators for the current meeting. When the performance value falls below a preset threshold, the system automatically generates and pushes specific optimization suggestions and automatically calculates individual meeting participation performance based on a set algorithm model. This provides a fair and transparent data basis for employee performance evaluation, team collaboration ability analysis, and talent development, thereby effectively motivating participants and improving the quality of team collaboration. Attached Figure Description

[0016] Figure 1 A schematic diagram of the overall system structure of the present invention is shown. Detailed Implementation

[0017] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: like Figure 1 As shown, a digital conference information management system includes a dynamic parsing module, a prediction and interception module, a monitoring and rating module, a conference evolution module, and a conference effectiveness analysis module. The dynamic parsing module is used to acquire meeting schedules in real time. The meeting schedule consists of a dataset of meeting topics, participant information, and meeting time, and the meeting level is dynamically classified according to the job level in the participant information. The estimation and capture module is used to obtain the meeting level type and meeting arrangement dataset. Based on the meeting arrangement dataset, it estimates the meeting arrangement resource requirement list. Based on the estimated meeting arrangement resource requirement list, it generates a resource adjustment application instruction and adjusts the meeting resources through the data control terminal. The meeting evolution module is used to perform topic deviation analysis based on the text data collected in real time by the meeting monitoring module, and dynamically adjust and push updated meeting agendas according to the analysis results. The meeting performance analysis module is used to build a performance evaluation model based on historical meeting data and calculate the time utilization rate and agenda completion rate of the current meeting in real time. The monitoring and rating module collects audio, image, and text data in real time during the meeting through audio and video acquisition devices. Based on the meeting monitoring data and the meeting level type, it calculates the individual's meeting participation performance.

[0019] The meeting data includes various aspects such as voice, images, text communication, and behavioral data, as well as an anti-fraud module, which is used to acquire meeting data for fraud detection. By analyzing data from participants’ voice, images, text communication, and behavior, the system uses speech recognition technology to determine if there are traces of machine synthesis in the speech. Image recognition technology is used to detect the facial expressions and movements of participants, and to perform real-time detection of video and image multimedia content involved in the meeting to identify the authenticity of the images. By analyzing the detailed features of the images and the frame rate changes of the video, if an image is identified as incorrect, it is determined to be an unreal image.

[0020] It also includes an encryption trigger module, which is used to obtain the meeting level type, comprehensively evaluate the nature of the meeting based on the meeting level type and the sensitivity of the meeting, and automatically trigger the corresponding encryption level to obtain the encryption level of the meeting arrangement.

[0021] The meetings were categorized based on attendee information, specifically including the following: S100. Obtain specific company personnel information and construct a personnel structure based on personnel level, dividing them into ordinary employees, management, senior executives, and leadership personnel levels, and labeling personnel level information; S102. Based on different personnel levels, the corresponding meeting types are divided: meetings at the management level and above are senior meetings; meetings at the senior management level and below are group meetings; and meetings at the management level and below are ordinary meetings.

[0022] The estimated resource requirements for the meeting are as follows: S200. Obtain the scheduling dataset. Based on the meeting topics, attendee information, and meeting times in the scheduling dataset, analyze the departments to which the attendees belong based on the attendee information to obtain a list of attendees' departments. Analyze the meeting content and direction through the meeting topics to identify the relevant business departments. S201. Obtain the departmental resources related to the meeting content based on the personnel department list, summarize and integrate the required resources to form a demand data set, and plan the acquisition of resources according to the meeting schedule, and arrange the time nodes for data collection, sorting and review in stages. S202. Arrange the demand data set according to the relevance of the meeting topic, and then arrange the demands according to the importance of the resources and the order of meeting usage to form an estimated list of meeting arrangement resource requirements.

[0023] Identity verification will be conducted based on participant information, specifically including the following: S300: Obtain the authorized list of persons allowed to attend the meeting during the meeting arrangement process, obtain the meeting arrangement dataset, obtain real-time information on persons attending the meeting, compare the collected information on persons attending the meeting with the pre-set legal identity database, and compare it again with the authorized list of persons attending the meeting to obtain the personnel identity verification result. S301. If the personnel verification result does not match, it is determined that the personnel authorization list does not match the information of the personnel attending the meeting in real time. The personnel authorization list is then verified, and the manager is notified to mark the information and modify the authorized personnel list. S302. Based on the personnel identity verification results, formulate rules for generating resource control permission instructions based on the identity verification results, generate corresponding resource control permission instructions, and the instructions are in the form of electronic authorization codes, access credentials, and permission setting information, which are used to instruct the resource management system to allocate and restrict resource access, and send the generated resource control permission instructions to the corresponding data control terminal.

[0024] The comprehensive evaluation, based on the nature of the meeting, specifically includes the following: S400: Obtain the original agenda of the meeting, parse each agenda item into a set of key keywords, receive voice data in the meeting in real time and convert it into text, and extract keywords from the real-time discussion content; S401: Calculate the semantic reasonableness of the keywords in the real-time discussion content with the set of keywords for the current agenda item. If the semantic reasonableness is lower than the first threshold, it is determined that the meeting discussion has deviated from the current agenda and a reminder message is sent to the host terminal. S402: If the semantic reasonableness of the discussion content and the topic set of a subsequent agenda item is higher than the second threshold, the subsequent agenda item will be automatically moved to the current agenda, and an agenda adjustment suggestion will be generated and pushed to all participants.

[0025] A comprehensive evaluation was conducted to calculate individual meeting participation performance, including the following: S500, meeting data includes real-time performance data of participants in the meeting, compliance with meeting rules, lateness, early departure, statistics on the number and proportion of on-time login, lateness, and no login, statistics on the number of times participants actively speak in the meeting, the number of discussion sessions they participate in, and the frequency of responding to others' speeches, as evaluation indicators; S501. Based on the collected data, assign different weights to each indicator, and calculate the score of each indicator according to the set evaluation criteria. The calculation formula is as follows: , In the above formula, A is the number of times on time, Z is the total number of meetings, C is the number of times late, T is the number of times leaving early, F is the number of times actively speaking, L is the number of times participating in discussions, f is the standard number of meetings, D is the number of violations recognized by the company, and R is the total score obtained. S502. Based on the calculated comprehensive score, analyze the individual's meeting participation performance and set different score ranges to define the performance level. When R is greater than 90, it is an excellent level, and when R is less than 90 but greater than 80, it is a good level.

[0026] The effectiveness evaluation model is established based on historical meeting data, specifically including the following: Structured raw performance data was collected from historical meeting minutes. This structured data was used as input data for the performance evaluation model. Time-related feature analysis, topic-related feature analysis, and participant feature analysis were conducted. A linear regression model was selected for model training and execution. The input is the above features and dynamic feature updates are performed. The output is a continuous value. The real-time features are input into the trained model to predict the final time utilization rate and topic completion rate. The time utilization rate and topic completion rate are obtained, and the meeting effectiveness is divided into high, medium and low levels. The input is features and the output is category labels. Time utilization rate: U= Effective discussion time is obtained by filtering out unproductive periods such as silence and off-topic remarks; Issue completion rate: R= ; For topics that are not completed but have clear follow-up plans, a performance score can be generated for each historical meeting according to preset rules. When the estimated performance score is lower than the preset threshold set for the current meeting type, the optimization suggestion generation process is triggered immediately. If U is too low, it is suggested that "the current discussion has deviated from the topic time and the moderator should guide the discussion." If R is lagging behind, it is suggested that "at the current pace, it is not expected that all topics can be completed. It is suggested that low-priority topics be moved to the next meeting." If E is too low, it is suggested that "the current topic discussion has exceeded the time limit and no resolution has been reached. It is suggested that the decision-maker be identified or the topic be changed to a pending task."

[0027] It also includes a resource tracking module, which is used to determine the meeting resources controlled by the data control terminal. Acquire meeting resources controlled by the data control terminal, automatically add data watermarks to the meeting resources, including meeting topic, time, organizer, and assign copyright labels to the meeting resources; During the dissemination of conference resources, the transmission path of the materials is recorded. Based on network communication protocols and related data monitoring mechanisms, the system automatically records the information of each node in the transmission when the materials are transmitted between different devices or platforms. The system continuously monitors each stage of the conference resource dissemination process to assess resource usage and quickly pinpoint usage paths.

[0028] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0029] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the above formulas can be set by those skilled in the art according to the actual situation. In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; furthermore, the coupling or direct coupling or communication connection between the shown or discussed mutuals can be through some interfaces, and the indirect coupling or communication connection between the apparatus or modules can be electrical, mechanical or other forms. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A digital conference information management system, characterized in that, It includes a dynamic parsing module, a prediction and extraction module, a monitoring and rating module, a meeting evolution module, and a meeting effectiveness analysis module; The dynamic parsing module is used to acquire meeting schedules in real time. The meeting schedule consists of a dataset of meeting topics, participant information, and meeting time, and the meeting level is dynamically classified according to the job level in the participant information. The estimation and capture module is used to obtain the meeting level type and meeting arrangement dataset. Based on the meeting arrangement dataset, it estimates the meeting arrangement resource requirement list. Based on the estimated meeting arrangement resource requirement list, it generates a resource adjustment application instruction and adjusts the meeting resources through the data control terminal. The meeting evolution module is used to perform topic deviation analysis based on the text data collected in real time by the monitoring and rating module, and dynamically adjust and push updated meeting agendas according to the analysis results. The meeting effectiveness analysis module is used to build an effectiveness evaluation model based on historical meeting data and to calculate the time utilization rate and agenda completion rate of the current meeting in real time. The effectiveness evaluation model based on historical meeting data includes the following: Structured raw performance data was collected from historical meeting minutes. This structured data was used as input data for the performance evaluation model. Time-related feature analysis, topic-related feature analysis, and participant feature analysis were conducted. A linear regression model was selected for model training and execution. The input consists of the aforementioned features, which are dynamically updated. The output is a continuous value. Real-time features are input into the trained model to predict the final time utilization rate and agenda completion rate. Meeting effectiveness is categorized into high, medium, and low levels. The input is features, and the output is category labels. Time utilization rate: U = Issue completion rate: R= ; The monitoring and rating module collects real-time audio, image, and text data during the meeting using audio and video capture devices. Based on the meeting monitoring data and the meeting level type, it calculates the individual's meeting participation performance and conducts a comprehensive evaluation, including the following: S500, meeting data includes real-time performance data of participants in the meeting, statistics on the number of times they log in on time, the number of times they are late, the number of times they leave early, the total number of meetings, and the standard number of meetings. It also includes statistics on the number of times participants actively speak, participate in discussions, and the number of violations identified by the company, as evaluation indicators. S501. Based on the collected data, assign different weights to each indicator, and calculate the score of each indicator according to the set evaluation criteria. The calculation formula is as follows: ， In the above formula, A is the number of times you log in on time, Z is the total number of meetings, C is the number of times you are late, T is the number of times you leave early, F is the number of times you speak up, L is the number of times you participate in discussions, f is the standard number of meetings, D is the number of violations recognized by the company, and R is the total score obtained. S502. Based on the calculated comprehensive score, analyze the individual's meeting participation performance and set different score ranges to define the performance level, including excellent level and good level.

2. The digital conference information management system according to claim 1, characterized in that, The meeting data includes various aspects such as voice, images, text communication, and behavioral data, as well as an anti-fraud module, which is used to acquire meeting data for fraud detection. By analyzing data from participants’ voice, images, text communication, and behavior, the system uses speech recognition technology to determine if there are traces of machine synthesis in the speech. Image recognition technology is used to detect the facial expressions and movements of participants, and to perform real-time detection of video and image multimedia content involved in the meeting to identify the authenticity of the images. By analyzing the detailed features of the images and the frame rate changes of the video, if an image is identified as incorrect, it is determined to be an unreal image.

3. The digital conference information management system according to claim 1, characterized in that, It also includes an encryption trigger module, which is used to obtain the meeting level type, comprehensively evaluate the nature of the meeting based on the meeting level type and the sensitivity of the meeting, and automatically trigger the corresponding encryption level to obtain the encryption level of the meeting arrangement.

4. The digital conference information management system according to claim 1, characterized in that, The meetings were categorized based on attendee information, specifically including the following: S100. Obtain specific company personnel information and construct a personnel structure based on personnel level, dividing them into ordinary employees, management, senior executives, and leadership personnel levels, and labeling personnel level information; S102. Based on different personnel levels, the corresponding meeting types are divided: meetings at the management level and above are senior meetings; meetings at the senior management level and below are group meetings; and meetings at the management level and below are ordinary meetings.

5. The digital conference information management system according to claim 1, characterized in that, The estimated resource requirements for the meeting are as follows: S200. Obtain the scheduling dataset. Based on the meeting topics, attendee information, and meeting times in the scheduling dataset, analyze the departments to which the attendees belong based on the attendee information to obtain a list of attendees' departments. Analyze the meeting content and direction through the meeting topics to identify the relevant business departments. S201. Obtain the departmental resources related to the meeting content based on the personnel department list, summarize and integrate the required resources to form a demand data set, and plan the acquisition of resources according to the meeting schedule, and arrange the time nodes for data collection, sorting and review in stages. S202. Arrange the demand data set according to the relevance of the meeting topic, and then arrange the demands according to the importance of the resources and the order of meeting usage to form an estimated list of meeting arrangement resource requirements.

6. The digital conference information management system according to claim 1, characterized in that, Identity verification will be conducted based on participant information, specifically including the following: S300: Obtain the authorized list of persons allowed to attend the meeting during the meeting arrangement process, obtain the meeting arrangement dataset, obtain real-time information on persons attending the meeting, compare the collected information on persons attending the meeting with the pre-set legal identity database, and compare it again with the authorized list of persons attending the meeting to obtain the personnel identity verification result. S301. If the personnel verification result does not match, it is determined that the personnel authorization list does not match the information of the personnel attending the meeting in real time. The personnel authorization list is then verified, and the manager is notified to mark the information and modify the authorized personnel list. S302. Based on the personnel identity verification results, formulate rules for generating resource control permission instructions based on the identity verification results, generate corresponding resource control permission instructions, and the instructions are in the form of electronic authorization codes, access credentials, and permission setting information, which are used to instruct the resource management system to allocate and restrict resource access, and send the generated resource control permission instructions to the corresponding data control terminal.

7. The digital conference information management system according to claim 1, characterized in that, The comprehensive evaluation, based on the nature of the meeting, specifically includes the following: S400: Obtain the original agenda of the meeting, parse each agenda item into a set of key keywords, receive voice data in the meeting in real time and convert it into text, and extract keywords from the real-time discussion content; S401: Calculate the semantic reasonableness of the keywords in the real-time discussion content with the set of keywords for the current agenda item. If the semantic reasonableness is lower than the first threshold, it is determined that the meeting discussion has deviated from the current agenda and a reminder message is sent to the host terminal. S402: If the semantic reasonableness of the discussion content and the topic set of a subsequent agenda item is higher than the second threshold, the subsequent agenda item will be automatically moved to the current agenda, and an agenda adjustment suggestion will be generated and pushed to all participants.

8. The digital conference information management system according to claim 1, characterized in that, It also includes a resource tracking module, which is used to determine the meeting resources controlled by the data control terminal. Acquire meeting resources controlled by the data control terminal, automatically add data watermarks to the meeting resources, including meeting topic, time, organizer, and assign copyright labels to the meeting resources; During the dissemination of conference resources, the transmission path of the materials is recorded. Based on network communication protocols and related data monitoring mechanisms, the system automatically records the information of each node in the transmission when the materials are transmitted between different devices or platforms. The system continuously monitors each stage of the conference resource dissemination process to assess resource usage and quickly pinpoint usage paths.

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