Paperless conference management system and method for power plant office

Abstract

The invention discloses a paperless conference management system and method for power plant office, and the system can give an early warning in time when a network goes wrong by collecting the network state information in real time and calculating the network quality degradation index, thereby achieving the active monitoring and response to the network abnormality, and reducing the risk of conference interruption. In the conference process, the communication module continuously monitors the network performance, and the resource analysis module optimizes the conference network prediction model through the machine learning technology, thereby effectively improving the audio and video synchronization and resource management capability of the conference. And the conference management module dynamically generates a cold start optimization strategy and a cache preheating strategy according to the conference performance evaluation coefficient, so that the conference start speed and the resource utilization efficiency are improved. And an audio and video synchronization mechanism is adjusted according to the real-time jitter information, so that high-quality synchronization of the audio and the video is ensured, and the usability and experience of the conference are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of conference management, and particularly to a paperless conference management system and method for power plant office work. Background Art

[0002] With the development of information technology, traditional office methods are gradually transforming towards digitalization and intelligence. The paperless office system is an important part of this transformation, aiming to reduce the use of paper documents and improve office efficiency and environmental friendliness. As an important facility for energy production, a power plant involves a large number of meetings in its daily operation and management, especially in cases where coordination is required with multiple remote locations such as the company headquarters, basin power plants, or suppliers. The traditional paper-based conference method not only consumes a large amount of resources but also has problems such as low efficiency and poor information circulation. In addition, with the popularization of video conferencing technology, power plants have gradually introduced remote video conferencing systems to achieve real-time communication and decision-making across regions and departments. However, the traditional video conferencing system still faces many challenges in the actual application of power plants and urgently needs further optimization.

[0003] First, the traditional video conferencing system has deficiencies in the stability of remote connections. The geographical location of power plants is usually relatively remote, and the network conditions are relatively complex, prone to problems such as insufficient network bandwidth, high latency, and packet loss. These network problems will lead to a decline in the quality of video conferences and even interruptions, affecting the normal progress of meetings. The traditional technology often relies on manual intervention to handle these problems, lacking an automated monitoring and optimization mechanism, and unable to prevent problems before they occur or respond quickly when problems occur.

[0004] Secondly, the traditional conference system lacks the ability of intelligent management and scheduling of resources. When the conference system starts, it usually loads a large number of conference files and audio-visual data, but the loading of these resources is often statically configured, lacking a dynamic adjustment mechanism for different network conditions and different user needs. This situation may lead to waste or shortage of some conference resources, thereby affecting the meeting experience. For example, in the case of poor network conditions, if the video resolution, frame rate, and audio-visual synchronization strategy cannot be flexibly adjusted, it may lead to meeting interruptions or audio-visual asynchronization problems. When the network condition is good, too conservative resource scheduling strategies will waste bandwidth and computing resources. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems in the related art to some extent.

[0006] The present invention provides a paperless meeting management system for power plant office work. By collecting network status information in real time and calculating the network quality degradation index, the system can issue early warnings in a timely manner when network problems occur, thereby achieving active monitoring and response to network anomalies and reducing the risk of meeting interruption.

[0007] Another object of the present invention is to provide a paperless meeting management method for power plant office work.

[0008] To achieve the above object, on the one hand, the present invention provides a paperless meeting management system for power plant office work, including an online meeting establishment module, a network information monitoring module, a communication module, a resource analysis module, and a meeting management module;

[0009] The online meeting establishment module is used to establish a remote video conferencing system inside and outside the power plant, realize remote meetings with the company's headquarters, basin power plants, or suppliers, and deploy network monitoring devices at each remote point of each power plant;

[0010] The network information monitoring module is used to collect the network status information of each remote point of each power plant online, generate a first data set, calculate the network quality degradation index, compare the network quality degradation index with the degradation threshold, and if the network quality degradation index is greater than the degradation threshold, send a warning instruction to the outside;

[0011] The communication module is used to monitor the network communication performance status of each remote point in real time after the start of the meeting video, generate communication status information to form a second data set according to the actual communication quality, and at the same time monitor and report communication anomalies or interruption events occurring in the network;

[0012] The resource analysis module is used to extract characteristic parameters from the second data set after information processing, obtain a cold start optimization coefficient by correlating the data memory leakage rate, network path switching frequency, meeting document dynamic editing conflict rate, and meeting system acoustic echo suppression delay in the characteristic parameters, obtain a cache warm-up coefficient by correlating the pre-loaded resource compression ratio, historical cache hit rate, temporary resource cache allocation amount, and parallel data preprocessing amount at startup in the characteristic parameters, establish and train a meeting network prediction model based on machine learning, and obtain a meeting performance evaluation coefficient through association analysis;

[0013] The meeting management module is used to evaluate the meeting performance evaluation coefficient to obtain a performance evaluation level, and generate corresponding cold start optimization strategies and cache warm-up strategies according to the performance evaluation level.

[0014] The paperless meeting management system for power plant office work according to the embodiments of the present invention may further have the following additional technical features:

[0015] In an embodiment of the present invention, the online meeting establishment module includes a remote connection unit and a marking unit;

[0016] The remote connection unit is used to establish a remote meeting connection between the power plant and the company headquarters, basin power plants or suppliers, configure user accounts and permissions for each participant in the meeting, and set options for access control, video and audio devices of the meeting;

[0017] The marking unit is used to deploy meeting hardware devices at the power plant and remote points, divide the remote points inside and outside the power plant according to geographical location, network status or business function, generate several sub-regions, and respectively mark them as multiple remote points.

[0018] In an embodiment of the present invention, the meeting performance evaluation coefficient is obtained by calculating according to the following formula:

[0019]

[0020] In the formula, SPEc is the meeting performance evaluation coefficient, Avsjr represents the audio-video synchronization jitter rate, and α, β and δ respectively represent the preset proportionality coefficients of the audio-video synchronization jitter rate Avsjr, cold start optimization coefficient Ltxs and cache preheating coefficient Cylxs.

[0021] In an embodiment of the present invention, the network information monitoring module includes a network status monitoring unit, a communication status difference calculation unit and a first calculation unit;

[0022] The network status monitoring unit is used to obtain the packet loss rate Dbl, network bandwidth Ttlz, network delay Wscz, average transmission delay Mtd, packet loss recovery time PLr and timing offset value Sxpy in real time by deploying network monitoring devices at each power plant and its remote points, generate a network parameter set, clean and process the network parameter set, and perform standardization processing on the data through dimensionless processing technology;

[0023] The communication status difference calculation unit is used to calculate the difference degree between the network status of each power plant and its remote points and the reference status, and obtain the communication status difference Xtxz by calculating according to the following formula:

[0024]

[0025] In the formula, Dbl dq represents the currently monitored packet loss rate, Dbl jz represents the reference packet loss rate, a1 represents the preset proportionality coefficient of the difference between the current packet loss rate and the reference packet loss rate, Ttlz dq represents the currently monitored network bandwidth, Ttlz jzdenotes the reference network bandwidth, a2 denotes a preset proportionality coefficient of the difference between the current network bandwidth and the reference network bandwidth, Wscz dq denotes the current network latency, Wscz jz denotes the reference network latency, a3 denotes a preset proportionality coefficient of the difference between the current network latency and the reference network latency;

[0026] The first calculation unit is used to extract the average transmission delay Mtd, packet loss recovery time PLr, timing offset value Sxpy, and communication status difference Xtxz in the network parameter set. After dimensionless processing, the network quality degradation index Tczs is calculated and obtained through the following formula:

[0027]

[0028] In the formula, a4, a5, a6, and a7 respectively denote preset proportionality coefficients of the average transmission delay Mtd, packet loss recovery time PLr, timing offset value Sxpy, and communication status difference Xtxz, and C1 denotes the first correction constant.

[0029] In an embodiment of the present invention, the network information monitoring module further includes a first evaluation unit and a first warning unit;

[0030] The first evaluation unit is used to preset a degradation threshold X, and compare and evaluate the network quality degradation index Tczs with the degradation threshold X to obtain an evaluation result, including:

[0031] If the network quality degradation index Tczs is greater than the degradation threshold X, it indicates that the network quality of the current remote point is unqualified and there is a degradation problem, and a warning instruction is generated through the first warning unit;

[0032] If the network quality degradation index Tczs is equal to the degradation threshold X, it indicates that the network quality of the current remote point is at the critical point of unqualified, increases the cache prediction amount, and prepares standby bandwidth;

[0033] If the network quality degradation index Tczs is less than the degradation threshold X, it indicates that the network quality of the current remote point is qualified, there is no need to adjust resource allocation, and the current network state is maintained.

[0034] In an embodiment of the present invention, the resource analysis module includes a model establishment unit and a feature extraction unit;

[0035] The model establishment unit is used to establish a conference network prediction model through machine learning technology, and after dividing the second data set into a training set and a test set for training and testing, it is applied to network prediction;

[0036] The feature extraction unit is used to extract feature parameters from the second data set after information processing, and calculate and obtain the data memory leakage rate Mlxl, network path switching frequency Ljqhz, conference document dynamic editing conflict rate Bjcl, conference system acoustic echo suppression delay Hsyc, preloaded resource compression ratio Ysbz, historical cache hit rate Lsmz, temporary resource cache allocation amount Lscf, and parallel data preprocessing amount Bqcl at startup based on the conference network prediction model through the following formulas:

[0037]

[0038] In the formula, M currentl is the current memory usage, M baselinel is the baseline memory usage, T duratio is the observation time period, M leak is the memory leakage amount, M total represents the total memory usage;

[0039]

[0040] In the formula, P i is the frequency of the i-th path switch, W i is the weight value of the i-th path switch, T i is the time window of the i-th path switch, and N is the total number of switches;

[0041]

[0042] In the formula, C i is the number of conflicts in the i-th edit, D i is the weight value of the conflict, E i is the total number of edits in the i-th edit, and M is the total number of edits;

[0043]

[0044] In the formula, Sxy i is the i-th acoustic processing delay value, p i is the weight of the acoustic processing delay value, Sj i is the total processing time of the i-th time, and F is the number of processes;

[0045]

[0046] In the formula, Zyys i is the compressed size of the i-th resource, Zyw i is the uncompressed size of the i-th resource, K i represents the weight value of the i-th resource, and H is the total number of resources;

[0047]

[0048] Wherein, Hcmz i is the number of cache hits for the i-th time, Hcqq i is the total number of cache requests for the i-th time, hc i is the weight value for the i-th time, and G is the total number of cache requests;

[0049]

[0050] Wherein, Lshc i is the amount of temporary cache allocated for the i-th time, Zhcl i is the total cache amount for the i-th time, L i is the weight value of the amount of temporary cache allocated for the i-th time, and V represents the total cache allocation amount;

[0051]

[0052] Wherein, Qdcl i is the amount of data processed when starting the conference video for the i-th time, Qdsx i is the total amount of data required when starting the conference video for the i-th time, Q i is the weight value of the amount of data processed when starting the conference video for the i-th time, and X is the total amount of data.

[0053] In an embodiment of the present invention, the resource analysis module further includes a first correlation unit and a second correlation unit;

[0054] The first correlation unit is used to correlate the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, and the conference system acoustic echo suppression delay Hsyc through the following formula to calculate and obtain the cold start optimization coefficient Ltxs:

[0055]

[0056] Wherein, b1, b2, b3, and b4 respectively represent the preset proportionality coefficients of the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, and the conference system acoustic echo suppression delay Hsyc, and C2 represents the second correction constant;

[0057] The second correlation unit is used to correlate the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf, and the parallel data preprocessing amount Bqcl at startup through the following formula to calculate and obtain the cache warm-up coefficient Cylxs:

[0058]

[0059] In the formula, b5, b6, b7, and b8 respectively represent the preset proportionality coefficients of the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf, and the parallel data preprocessing amount Bqcl at startup, and C3 represents the third correction constant.

[0060] In an embodiment of the present invention, the resource analysis module further includes an audio - video jitter monitoring unit, and the audio - video jitter monitoring unit is used to collect the audio - video synchronization jitter information during the conference video in real time, and obtain the audio - video synchronization jitter rate Avsjr through the following formula:

[0061]

[0062]

[0063] ΔA i represents the time difference between the i - th audio frame and video frame, represents the timestamp of the i - th audio frame, represents the timestamp of the video frame corresponding to the i - th audio frame; B is the total number of frames.

[0064] In an embodiment of the present invention, the conference management module includes a performance evaluation unit, a loading cache policy unit, and an audio synchronization unit;

[0065] The performance evaluation unit is used to evaluate the conference performance evaluation coefficient SPEc and the startup performance threshold Xn to obtain the performance evaluation level, including:

[0066] When the conference performance evaluation coefficient SPEc is greater than the startup performance threshold Xn, it indicates that the conference performance is poor, generates a first performance level, and generates a first optimization strategy through the loading cache policy unit;

[0067] When the conference performance evaluation coefficient SPEc is equal to the startup performance threshold Xn, it indicates that the conference performance is within the range, generates a second performance level, and generates a second optimization strategy through the loading cache policy unit;

[0068] When the conference performance evaluation coefficient SPEc is less than the startup performance threshold Xn, it indicates that the conference performance is normal, generates a third performance level, and generates a third optimization strategy through the loading cache policy unit;

[0069] The audio synchronization unit is used to dynamically adjust the audio - video synchronization mechanism according to the audio - video synchronization jitter rate Avsjr, preset an evaluation threshold y, and compare and analyze the evaluation threshold y with the audio - video synchronization jitter rate Avsjr to determine the audio - video synchronization level status, including:

[0070] If the audio - video synchronization jitter rate Avsjr is greater than the evaluation threshold y, adjust the buffer and reduce the video frame rate;

[0071] If the audio - video synchronization jitter rate Avsjr is equal to the evaluation threshold y, set the audio buffer and reduce the video frame rate;

[0072] If the audio - video synchronization jitter rate Avsjr is less than the evaluation threshold y, the audio - video synchronization strategy is executed according to the standard mechanism.

[0073] To achieve the above object, another aspect of the present invention proposes a paperless meeting management method for power plant office, including:

[0074] Set up a remote video conferencing system inside and outside the power plant, configure meeting access control, allocate user accounts and permissions, and deploy network monitoring devices for each remote point, divide sub - regions and mark the remote points;

[0075] Collect the network status information of each remote point of each power plant, generate the first data group, and construct a network quality degradation index for evaluation. If the network quality degradation index is greater than the degradation threshold, send a warning instruction to the outside;

[0076] Real - time monitor the network communication performance of each remote point, generate the second data group, and monitor communication anomalies. Extract feature parameters from the second data group, establish and train a meeting network prediction model through machine learning technology, obtain the audio - video synchronization jitter rate, cold - start optimization coefficient, and cache pre - heating coefficient, and obtain the meeting performance evaluation coefficient by correlation;

[0077] Evaluate the meeting performance evaluation coefficient to obtain the performance evaluation level, and generate corresponding cold - start optimization strategies and cache pre - heating strategies according to the performance evaluation level;

[0078] Compare the evaluation threshold with the audio - video synchronization jitter rate, and dynamically adjust the audio - video synchronization mechanism according to the comparison result.

[0079] The paperless meeting management method and system for power plant office in the embodiments of the present invention continuously monitor the network performance during the meeting, optimize the meeting network prediction model through machine learning technology, effectively improve the audio - video synchronization and resource management capabilities of the meeting. Dynamically generate cold - start optimization strategies and cache pre - heating strategies according to the meeting performance evaluation coefficient SPEc, improve the meeting start - up speed and resource utilization efficiency. Adjust the audio - video synchronization mechanism according to real - time jitter information, ensure high - quality synchronization of audio and video, and further improve the usability and experience of the meeting.

[0080] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. Brief Description of the Drawings

[0081] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, where:

[0082] Figure 1 is a structural diagram of a paperless meeting management system for power plant office according to an embodiment of the present invention;

[0083] Figure 2 is a flowchart of a paperless meeting management method for power plant office according to an embodiment of the present invention. Detailed Embodiments

[0084] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0085] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0086] The paperless meeting management system and method for power plant office according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

[0087] Figure 1 is a structural diagram of a paperless meeting management system for power plant office according to an embodiment of the present invention, as Figure 1 shown, the system includes an online meeting establishment module, a network information monitoring module, a communication module, a resource analysis module, and a meeting management module;

[0088] The online meeting establishment module is used to establish a remote video conferencing system inside and outside the power plant, realize remote meetings with the company's headquarters, basin power plants or suppliers, and deploy network monitoring devices at each remote point of each power plant;

[0089] The network information monitoring module is used to collect the network status information of each remote point of each power plant online, generate a first data group, transmit it to the network database for storage. The first data group includes but is not limited to the packet loss rate Dbl, network bandwidth Ttlz, network delay Wscz, and communication status difference Xtxz, and then calculate the network quality degradation index Tczs, and compare the network quality degradation index Tczs with the degradation threshold X. If the network quality degradation index Tczs > degradation threshold X, an early warning instruction will be sent to the outside;

[0090] The communication module is used to monitor the network communication performance status of each remote point in real time after the start of the conference video, generate communication status information according to the actual communication quality, form a second data group, and transmit this data group to the network database. At the same time, it monitors and reports communication anomalies or interruption events occurring in the network;

[0091] The resource analysis module is used to extract characteristic parameters from the second data group after information processing, including but not limited to data memory leakage rate Mlxl, network path switching frequency Ljqhz, conference document dynamic editing conflict rate Bjcl, conference system acoustic echo suppression delay Hsyc, preloaded resource compression ratio Ysbz, historical cache hit rate Lsmz, temporary resource cache allocation amount Lscf, and parallel data preprocessing amount Bqcl at startup; by correlating the data memory leakage rate Mlxl, network path switching frequency Ljqhz, conference document dynamic editing conflict rate Bjcl, and conference system acoustic echo suppression delay Hsyc, the cold start optimization coefficient Ltxs is obtained. By correlating the preloaded resource compression ratio Ysbz, historical cache hit rate Lsmz, temporary resource cache allocation amount Lscf, and parallel data preprocessing amount Bqcl at startup, the cache warm-up coefficient Cylxs is obtained. Combining machine learning techniques, a conference network prediction model is established and trained. After dimensionless processing, the conference performance evaluation coefficient SPEc is obtained through correlation analysis. The conference performance evaluation coefficient SPEc is calculated through the following formula:

[0092]

[0093] In the formula, Avsjr represents the audio-visual synchronization jitter rate, and α, β, and δ respectively represent the preset proportionality coefficients of the audio-visual synchronization jitter rate Avsjr, cold start optimization coefficient Ltxs, and cache warm-up coefficient Cylxs; the higher the cache warm-up coefficient Cylxs, the better. While the higher the audio-visual synchronization jitter rate Avsjr and cold start optimization coefficient Ltxs, the worse the performance. Therefore, the positive and negative proportional relationships are different. So, in the formula, the cache warm-up coefficient Cylxs is subtracted by subtraction; the better the cache warm-up effect, the overall performance is improved. Therefore, its impact on SPEc is positive. The cache warm-up coefficient Cylxs is subtracted because its positive impact contrasts with the other two negative impacts.

[0094] The conference management module is used to evaluate the conference performance evaluation coefficient SPEc to obtain a performance evaluation level, and generate corresponding cold start optimization strategies and cache warm-up strategies based on the performance evaluation level.

[0095] In an embodiment of the present invention, the system collects the network status information of each remote point of the power plant in real time through the network information monitoring module, and generates a network quality degradation index Tczs. When it is detected that the network quality degradation index exceeds the preset threshold, the system can automatically send a warning instruction, so as to take countermeasures in advance and avoid the interruption of the meeting caused by network problems. This automated monitoring and response mechanism greatly reduces the dependence on manual intervention and improves the stability and reliability of the video conference. The resource analysis module of the present invention can dynamically adjust the loading strategy of the conference resources and perform intelligent scheduling based on different network conditions and user requirements. By extracting and correlating multiple characteristic parameters (such as data memory leakage rate, network path switching frequency, etc.), the system can generate a cold start optimization coefficient Ltxs and a cache preheating coefficient Cylxs, and combine machine learning technology to predict the conference network. This mechanism ensures that when the network condition is poor, the system can flexibly adjust the video resolution, frame rate and audio-visual synchronization strategy to avoid the problems of meeting interruption or out-of-sync; when the network condition is good, the system can also effectively utilize the bandwidth and computing resources to reduce waste. Through the calculation and evaluation of the conference performance evaluation coefficient SPEc, the system can generate a performance evaluation level for each meeting and automatically generate corresponding cold start optimization strategies and cache preheating strategies according to the level. This data-driven optimization method significantly improves the overall experience of the meeting and can ensure a high meeting quality both at startup and during operation.

[0096] In an embodiment of the present invention, the online meeting establishment module includes a remote connection unit and a marking unit;

[0097] The remote connection unit is responsible for establishing a remote meeting connection between the power plant and the company headquarters, basin power plants or suppliers, configuring the user accounts and permissions of each participant in the meeting, and setting the access control, video and audio device options of the meeting;

[0098] The marking unit is responsible for deploying meeting hardware devices at the power plant and remote points, including video cameras, microphones, speakers, network interface devices and network monitoring devices, and dividing the remote points inside and outside the power plant according to geographical location, network status or business function to generate several sub-regions, which are respectively marked as the first remote point R1, the second remote point R2, the third remote point R3,..., the u-th remote point Ru.

[0099] In this embodiment, the remote connection unit is responsible for establishing a stable connection for remote meetings of power plants and related units, and configuring the accounts and permissions of each user. This mechanism allows administrators to manage meeting participants in a refined manner, ensuring that only authorized personnel can participate in the meeting. By setting access control for the meeting and configuring options for video and audio devices, the system not only improves the security of the meeting, but also enhances the customization and flexibility of the meeting experience, meeting the meeting requirements of different scenarios and needs.

[0100] The marking unit is responsible for deploying necessary meeting hardware devices at power plants and remote points, and dividing and marking these remote points according to geographical location, network status or business function. This way of dividing and marking can help administrators manage and schedule meeting resources more effectively. For example, by preferentially arranging remote points with better network status as the main venue, or aggregating remote points with similar business functions into the same sub-region, the organization efficiency of the meeting can be improved, and the meeting quality problems caused by differences in network conditions can be reduced.

[0101] The sub-region markings (such as R1, R2, R3, etc.) generated by the marking unit enable the system to flexibly adjust the structure and form of the meeting according to different scenarios or needs. Whether it is the company's headquarters, basin power plants or meeting points of suppliers, they can be dynamically configured and managed according to the actual situation, avoiding the limitations brought by a fixed structure. This flexibility not only improves the adaptability of the system, but also can better handle emergencies or special meeting requirements.

[0102] In an embodiment of the present invention, the network information monitoring module includes a network status monitoring unit, a communication status difference calculation unit and a first calculation unit;

[0103] The network status monitoring unit is used to obtain the packet loss rate Dbl, network bandwidth Ttlz, network delay Wscz, average transmission delay Mtd, packet loss recovery time PLr and timing offset value Sxpy in real time by deploying network monitoring devices at each power plant and its remote points, generate a network parameter set, and clean and process the network parameter set, including removing outliers in the data, filling in missing data, and standardizing the data through dimensionless processing techniques;

[0104] The communication status difference calculation unit is used to calculate the degree of difference between the network status of each power plant and its remote points and the reference status, and obtain the communication status difference Xtxz through the following formula:

[0105]

[0106] In the formula, Dbl eq represents the currently monitored packet loss rate, Dbl jzdenotes the baseline packet loss rate, a1 denotes a preset proportionality coefficient of the difference between the current packet loss rate and the baseline packet loss rate, Ttlz dq denotes the currently monitored network bandwidth, Ttlz jz denotes the baseline network bandwidth, a2 denotes a preset proportionality coefficient of the difference between the current network bandwidth and the baseline network bandwidth, Wscz dq denotes the current network latency, Wscz jz denotes the baseline network latency, a3 denotes a preset proportionality coefficient of the difference between the current network latency and the baseline network latency, which is adjusted by the user, 0 < a1 < 1, 0 < a2 < 1, 0 < a3 < 1, and a1 + a2 + a3 = 1.

[0107] In this embodiment, the calculation formula of the communication state difference Xtxz comprehensively considers the differences of multiple key network parameters, including packet loss rate, network bandwidth, network latency, etc. These parameters directly affect the quality and stability of the video conference. By calculating the differences between these parameters and their baseline values, the deviation degree between the current network state and the ideal state can be more accurately reflected. In this way, the system can detect potential problems earlier, so as to take measures in time to prevent the decline of the conference quality. When calculating the communication state difference, multiple preset proportionality coefficients (such as etc.) are introduced into the formula. These coefficients allow the system to flexibly adjust the weights of different network parameters in the calculation according to actual needs. This means that in some application scenarios that are more sensitive to packet loss rate, the system can appropriately increase the weight of, while in other scenarios, the weights of other coefficients can be adjusted. This flexibility enables the system to better adapt to different conference requirements and network environments. By comparing the differences between the current network state and the baseline state, the system can quickly locate and diagnose problems. For example, if the network latency of a certain remote point is significantly higher than the baseline value and the communication state difference Xtxz increases significantly, the system can quickly identify this problem and send an alarm to the administrator, thus improving the efficiency of conference management and troubleshooting.

[0108] In an embodiment of the present invention, the first calculation unit is used to extract the average transmission delay Mtd, packet loss recovery time PLr, timing offset value Sxpy, and communication state difference Xtxz from the network parameter set. After dimensionless processing, the network quality degradation index Tczs is calculated through the following formula:

[0109]

[0110] Wherein, a4, a5, a6, and a7 respectively represent preset proportionality coefficients of the average transmission delay Mtd, packet loss recovery time PLr, timing offset value Sxpy, and communication state difference Xtxz, which are adjusted and set by the user, and C1 represents the first correction constant. 0 < a4 < 1, 0 < a5 < 1, 0 < a6 < 1, 0 < a7 < 1, and a4 + a5 + a6 + a7 = 1.

[0111] The network information monitoring module further includes a first evaluation unit and a first warning unit;

[0112] The first evaluation unit is used to preset a deterioration threshold X, and compare and evaluate the network quality deterioration index Tczs with the deterioration threshold X to obtain an evaluation result, including:

[0113] When the network quality deterioration index Tczs > the deterioration threshold X, it indicates that the network quality of the remote point is unqualified and there are deterioration problems, and a warning instruction is generated through the first warning unit, including reducing the conference video resolution by 25% - 50%, reducing the frame rate by 10% - 20%, and increasing the bandwidth by 20% - 30%, sending a network quality report to the IT support team, and setting the current conference video to high priority;

[0114] When the network quality deterioration index Tczs = the deterioration threshold X, it indicates that the network quality of the remote point is at the critical point of unqualified, increasing the cache prediction amount by 10% - 20%, and preparing 10% - 15% of the backup bandwidth;

[0115] When the network quality deterioration index Tczs < the deterioration threshold X, it indicates that the network quality of the remote point is qualified, there is no need to adjust the resource allocation, maintain the current network state, and maintain the regular monitoring frequency at once per hour.

[0116] In this embodiment, by extracting key parameters such as the average transmission delay Mtd, packet loss recovery time PLr, timing offset value Sxpy, and communication status difference Xtxz, and calculating the network quality degradation index Tczs after dimensionless processing, the present invention can comprehensively reflect the changes in the overall network state. This multi-dimensional parameter combination effectively improves the accuracy and sensitivity of the degradation index, enabling the system to more accurately monitor the degradation of the network state. The first evaluation unit performs real-time comparison and evaluation on the network quality degradation index Tczs through a preset degradation threshold X, and generates corresponding countermeasures according to different evaluation results. For example, when Tczs exceeds the degradation threshold X, the system automatically takes a series of countermeasures, such as reducing the conference video resolution, adjusting the frame rate and bandwidth, etc., thereby effectively alleviating the impact of network degradation on the conference quality. This real-time response ability ensures the continuity and stability of the conference. Through the design of the first warning unit, the system can not only issue warning instructions when the network quality deteriorates, but also automatically adjust the conference resource allocation according to the specific situation. For example, when the degradation index reaches the critical point, the system will increase the cache prediction amount and prepare standby bandwidth to prevent potential network interruptions. In this way, the system can take preventive measures before network problems occur, greatly reducing the risk of conference interruptions caused by network problems. When the network quality is qualified or at the critical point of unqualified, the system can maintain or fine-tune the resource allocation strategy and adjust the monitoring frequency according to the actual situation. This not only improves the resource utilization efficiency, but also ensures the continuity and effectiveness of network monitoring, thereby providing a more stable and efficient network support for the entire conference system.

[0117] In an embodiment of the present invention, the resource analysis module includes a model establishment unit and a feature extraction unit;

[0118] The model establishment unit is used to establish a conference network prediction model through machine learning techniques, including linear regression and support vector machine, and after dividing the second data set into a training set and a test set for training and testing, apply it to network prediction;

[0119] The feature extraction unit is used to extract feature parameters from the second data set after information processing, and calculate the data memory leakage rate Mlxl, network path switching frequency Ljqhz, conference document dynamic editing conflict rate Bjcl, conference system acoustic echo suppression delay Hsyc, pre-loaded resource compression ratio Ysbz, historical cache hit rate Lsmz, temporary resource cache allocation amount Lscf, and parallel data preprocessing amount Bqcl at startup through the conference network prediction model according to the following formula:

[0120]

[0121] where M currentl is the current memory usage, Mbaselinel is the baseline memory usage, T duratio is the observation time period, M leak is the memory leakage, M total represents the total memory usage; the meaning of calculating the data memory leakage rate Mlxl is to measure the abnormal increase in memory usage during the meeting process to help identify potential memory leakage problems;

[0122]

[0123] In the formula, P i is the frequency of the i-th path switch, W i is the weight value of the i-th path switch, T i is the time window of the i-th path switch, and N is the total number of switches; evaluate the frequency of network path switches during the meeting process and their impact on network performance;

[0124]

[0125] In the formula, C i is the number of editing conflicts for the i-th time, D i is the weight value of the conflict, E i is the total number of edits for the i-th time, and M is the total number of edits; measure the frequency of conflicts generated during the editing of the meeting document, which affects the synchronization and collaboration efficiency of the document;

[0126]

[0127] In the formula, Sxy i is the i-th acoustic processing delay value, p i is the weight of the acoustic processing delay value, Sj i is the i-th total processing time, and F is the number of processes; quantify the echo suppression delay during the acoustic processing, which affects the meeting voice quality;

[0128]

[0129] In the formula, Zyys i is the size of the i-th resource after compression, Zyw i is the size of the i-th resource before compression, K i represents the weight value of the i-th resource, and H is the total number of resources; evaluate the compression efficiency of resource preloading to optimize resource transmission and storage;

[0130]

[0131] In the formula, Hcmz i is the number of cache hits for the i-th time, Hcqq i is the total number of cache requests for the i-th time, hc iis the weight value for the i-th time, G is the total number of cache requests; calculate the hit rate of the historical cache to improve data access efficiency;

[0132]

[0133] In the formula, Lshc i is the temporarily allocated cache amount for the i-th time, Zhcl i is the total cache amount for the i-th time, L i is the weight value of the temporarily allocated cache amount for the i-th time, V represents the total cache allocation amount; monitor the allocation of the temporary cache and optimize the cache strategy;

[0134]

[0135] In the formula, Qdcl i is the amount of data processed when starting the conference video for the i-th time, Qdsx i is the total amount of data required when starting the conference video for the i-th time, Q i is the weight value of the amount of data processed when starting the conference video for the i-th time, X is the total amount of data. Quantify the data preprocessing ability at startup and optimize the startup performance.

[0136] The resource analysis module further includes a first correlation unit and a second correlation unit;

[0137] The first correlation unit is used to correlate the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, and the conference system acoustic echo suppression delay Hsyc through the following formula to calculate and obtain the cold start optimization coefficient Ltxs:

[0138]

[0139] In the formula, b1, b2, b3, and b4 respectively represent the preset proportional coefficients of the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, and the conference system acoustic echo suppression delay Hsyc, which are adjusted and set by the user, 0 < b1 < 1, 0 < b2 < 1, 0 < b3 < 1, 0 < b4 < 1, and b1 + b2 + b3 + b4 = 1. C2 represents the second correction constant;

[0140] The second correlation unit is used to correlate the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf, and the parallel data preprocessing amount Bqcl at startup through the following formula to calculate and obtain the cache warm-up coefficient Cylxs:

[0141]

[0142] Wherein, b5, b6, b7, and b8 respectively represent the preset proportionality coefficients of the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf, and the parallel data preprocessing amount Bqcl at startup, which are adjusted and set by the user, 0 < b5 < 1, 0 < b6 < 1, 0 < b7 < 1, 0 < b8 < 1, and b5 + b6 + b7 + b8 = 1. C3 represents the third correction constant.

[0143] In this embodiment, the cold start optimization coefficient Ltxs obtained by calculation through the first correlation unit can comprehensively consider key parameters such as the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, and the acoustic echo suppression delay Hsyc of the conference system. Such comprehensive evaluation can more accurately reflect the performance bottleneck of the conference system during the cold start process, and then formulate targeted optimization strategies, such as preloading resources with higher priorities and reducing unnecessary memory occupation. Such optimization strategies can effectively reduce the delay during the startup of the conference system, improve the startup efficiency, and ensure that the conference can proceed quickly and stably. The cache warm-up coefficient Cylxs obtained by calculation through the second correlation unit organically combines parameters such as the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf, and the parallel data preprocessing amount Bqcl at startup, and can comprehensively improve the intelligent level of cache management. Specifically, the cache warm-up coefficient Cylxs can guide the system to reasonably allocate and warm up cache resources at startup, ensure that key resources can be loaded in advance, improve the response speed during the conference startup, and at the same time optimize the resource usage efficiency. Such intelligent cache management reduces performance problems caused by insufficient cache or low cache hit rate, and improves the fluency and stability of the overall conference experience. Through the above two correlation units, the present invention realizes the collaborative optimization of the cold start and cache management of the conference system. The cold start optimization coefficient Ltxs and the cache warm-up coefficient Cylxs complement each other and jointly act on the system startup and resource management processes to ensure that the conference system can achieve the best performance under different network conditions. Such comprehensive performance improvement not only improves the response speed and stability of the system, but also reduces resource waste and optimizes the overall efficiency of the system.

[0144] In an embodiment of the present invention, the resource analysis module further includes an audio-video jitter monitoring unit, and the audio-video jitter monitoring unit is used to collect real-time audio-video synchronization jitter information during the conference video, and obtain the audio-video synchronization jitter rate Avsjr through the following formula:

[0145]

[0146] ΔA i represents the time difference between the i-th audio frame and video frame, represents the timestamp of the i-th audio frame, represents the timestamp of the video frame corresponding to the i-th audio frame; B is the total number of frames.

[0147] In this embodiment, by effectively monitoring and managing the audio-visual synchronization jitter, the fluency and consistency of audio and video during the meeting can be ensured. This is especially important for remote meetings, which helps to improve the participants' sense of participation and satisfaction and enhance the overall effect of the meeting. The audio-visual jitter monitoring unit calculates the audio-visual synchronization jitter rate Avsjr, which can accurately measure the time difference between each pair of audio frames and video frames. This detailed measurement helps to identify and quantify the problem of audio-visual desynchronization, thus ensuring the synchronization of audio and video in the meeting and enhancing the overall meeting experience. By monitoring and analyzing the audio-visual synchronization jitter rate Avsjr, the system can adjust the audio-visual synchronization strategy in real time. For example, when a high jitter rate is detected, the system can automatically adjust the processing mode of the audio-visual stream, optimize the buffering and synchronization algorithms to reduce the impact of jitter on the user experience. When the audio-visual synchronization jitter rate exceeds the preset threshold, the system can automatically generate a warning and adjust the relevant resource configuration (such as video resolution, frame rate, etc.) to mitigate the impact of jitter. This automated response mechanism improves the intelligence level of the system and reduces the need for manual intervention.

[0148] In an embodiment of the present invention, the meeting management module includes a performance evaluation unit and a loading cache strategy unit;

[0149] The performance evaluation unit is used to evaluate the meeting performance evaluation coefficient SPEc with the startup performance threshold Xn to obtain a performance evaluation level, including:

[0150] When the meeting performance evaluation coefficient SPEc > the startup performance threshold Xn, it indicates that the meeting performance is poor, generates a first performance level, and generates a first optimization strategy through the loading cache strategy unit, including: only loading 5MB of meeting files and 100MB of audio-visual data cache for this remote point;

[0151] When the meeting performance evaluation coefficient SPEc = the startup performance threshold Xn, it indicates that the meeting performance is within the range, generates a second performance level, and generates a second optimization strategy through the loading cache strategy unit, including: loading 10MB of meeting files and 200MB of video data cache for this remote point;

[0152] When the meeting performance evaluation coefficient SPEc < the startup performance threshold Xn, it indicates that the meeting performance is normal, generates a third performance level, and generates a third optimization strategy through the loading cache strategy unit, including: preloading 15MB of meeting files and 300MB of video data cache.

[0153] In this embodiment, the performance evaluation unit can evaluate the overall performance of the conference in real time by comparing the conference performance evaluation coefficient SPEc with the startup performance threshold Xn. This dynamic evaluation helps to identify performance issues in a timely manner, enabling targeted measures to be taken. Based on the performance evaluation results, the loading cache policy unit generates different optimization strategies to adjust the resource allocation for different performance levels. This allows for the reasonable allocation of resources according to actual needs, avoiding resource waste or insufficiency, and ensuring the stability and smoothness of the conference. For remote points with poor performance (the first performance level), by restricting the amount of conference file and audio-video data cached, the network burden and system pressure are reduced, which helps to avoid conference interruptions or quality degradation caused by resource overload. When the conference performance is within the normal range (the second performance level), appropriately increasing the amount of conference file and video data cached helps to improve data transmission efficiency and reduce latency, further enhancing the conference quality. For remote points with normal performance (the third performance level), preloading a larger amount of conference file and video data cached can significantly reduce the conference startup time, enhance the experience of participants, and ensure the smooth progress of the conference. The automated performance evaluation and optimization strategy generation mechanism reduces the dependence on manual intervention, improves the intelligence level of the system, and reduces the management cost.

[0154] In an embodiment of the present invention, the conference management module further includes an audio synchronization unit, which is used to dynamically adjust the audio-video synchronization mechanism according to the audio-video synchronization jitter rate Avsjr, pre-set an evaluation threshold y, and compare and analyze the evaluation threshold y with the audio-video synchronization jitter rate Avsjr to determine the audio-video synchronization level status, including:

[0155] If the audio-video synchronization jitter rate Avsjr > the evaluation threshold y, adjust to a buffer of more than 100 ms and reduce the video frame rate from the original 30 frames per second to 15 - 20 frames per second;

[0156] If the audio-video synchronization jitter rate Avsjr = the evaluation threshold y, set the audio buffer to 75 ms and moderately reduce the video frame rate from the original 30 frames per second to 21 - 25 frames per second to balance the audio-video synchronization effect;

[0157] If the audio-video synchronization jitter rate Avsjr < the evaluation threshold y, execute the audio-video synchronization strategy according to the standard mechanism and set the audio buffer to 50 ms.

[0158] In this embodiment, based on the comparative analysis of the audio - video synchronization jitter rate Avsjr and the evaluation threshold y, the audio synchronization unit can adjust the audio - video synchronization mechanism in real - time. This dynamic adjustment can effectively reduce the problem of audio - video asynchronization, ensure the fluency and consistency of audio - video in the meeting, and improve the experience of participants. When the audio - video synchronization jitter rate is detected to exceed or be lower than the evaluation threshold, the audio synchronization unit can automatically adjust the audio buffer and the video frame rate. This automatic response mechanism enhances the flexibility and adaptability of the system and reduces the need for manual intervention. By increasing the buffer and reducing the video frame rate when the audio - video synchronization jitter rate exceeds the evaluation threshold, the system can effectively alleviate the problem of audio - video asynchronization and improve the overall quality of audio - video. When the audio - video synchronization jitter rate is within the normal range, the audio buffer and the video frame rate are moderately adjusted to ensure the balance of the synchronization effect. According to different audio - video synchronization level states, the size of the audio buffer and the video frame rate are adjusted, making the system more efficient in resource utilization. For example, when the synchronization jitter rate is high, increasing the buffer can improve the synchronization effect, but the video frame rate also needs to be reduced to adapt to the change of resources, which can avoid unnecessary resource waste while ensuring the quality of audio - video.

[0159] The beneficial effects of the present invention are as follows:

[0160] (1) The paperless meeting management system and method of the present invention provide an efficient and reliable meeting solution in the power plant office environment. First, through the online meeting establishment module, the system can accurately configure meeting access control and user permissions, and deploy network monitoring devices at each remote point. This measure significantly improves the overall stability and security of the meeting system. Second, by real - time collecting network status information and calculating the network quality degradation index Tczs, the system can issue early warnings in a timely manner when network problems occur, thereby realizing active monitoring and response to network anomalies and reducing the risk of meeting interruption. Third, during the meeting, the communication module continuously monitors network performance, and the resource analysis module optimizes the meeting network prediction model through machine learning technology, effectively improving the audio - video synchronization and resource management capabilities of the meeting. This intelligent data analysis and optimization can provide the best meeting experience under different network conditions. Fourth, the meeting management module dynamically generates cold - start optimization strategies and cache pre - heating strategies based on the meeting performance evaluation coefficient SPEc, improving the meeting startup speed and resource utilization efficiency. Finally, the audio - video jitter monitoring unit adjusts the audio - video synchronization mechanism according to real - time jitter information, ensuring high - quality synchronization of audio - video and further improving the usability and experience of the meeting. In summary, the system and method provided by the present invention significantly improve the efficiency and quality of meeting management in the power plant office environment and reduce the negative impact caused by network problems.

[0161] (2) The system collects the network status information of each remote point in the power plant in real time through the network information monitoring module, and generates a network quality deterioration index Tczs. When it detects that the network quality deterioration index exceeds the preset threshold, the system can automatically send a warning instruction, so as to take countermeasures in advance and avoid meeting interruption caused by network problems. This automated monitoring and response mechanism greatly reduces the dependence on manual intervention and improves the stability and reliability of the video conference.

[0162] (3) The resource analysis module of the present invention can dynamically adjust the loading strategy of conference resources and perform intelligent scheduling based on different network conditions and user requirements. By extracting and correlating multiple characteristic parameters (such as data memory leakage rate, network path switching frequency, etc.), the system can generate a cold start optimization coefficient Ltxs and a cache preheating coefficient Cylxs, and combine machine learning technology to predict the conference network. This mechanism ensures that when the network condition is poor, the system can flexibly adjust the video resolution, frame rate and audio-video synchronization strategy to avoid meeting interruption or out-of-sync problems; when the network condition is good, the system can also effectively utilize bandwidth and computing resources to reduce waste. Through the calculation and evaluation of the conference performance evaluation coefficient SPEc, the system can generate a performance evaluation level for each conference and automatically generate corresponding cold start optimization strategies and cache preheating strategies according to the level. This data-driven optimization method significantly improves the overall experience of the conference and can ensure a high conference quality both at startup and during operation.

[0163] The paperless conference management system for power plant office according to the embodiment of the present invention can issue a warning in time when the network has problems by collecting network status information in real time and calculating the network quality deterioration index, so as to realize the active monitoring and response to network anomalies and reduce the risk of meeting interruption. During the meeting, continuously monitor the network performance, optimize the conference network prediction model through machine learning technology, and effectively improve the audio-video synchronization and resource management capabilities of the conference. Dynamically generate cold start optimization strategies and cache preheating strategies according to the conference performance evaluation coefficient, improve the conference startup speed and resource utilization efficiency. Adjust the audio-video synchronization mechanism according to the real-time jitter information to ensure high-quality synchronization of audio and video, and further improve the usability and experience of the conference.

[0164] To implement the above embodiment, as Figure 2 shown, the present embodiment also provides a paperless conference management method for power plant office, including:

[0165] S1, set up remote video conference systems inside and outside the power plant, configure conference access control, allocate user accounts and permissions, and deploy network monitoring devices for each remote point, divide sub-regions and mark remote points;

[0166] S2. Collect the network status information of each remote point of each power plant, generate the first data set, and construct a network quality degradation index for evaluation. If the network quality degradation index is greater than the degradation threshold, send a warning instruction to the outside;

[0167] S3. Monitor the network communication performance of each remote point in real time, generate the second data set, and monitor communication anomalies. Extract characteristic parameters from the second data set, and establish and train a conference network prediction model through machine learning technology to obtain the audio-video synchronization jitter rate, cold start optimization coefficient, and cache preheating coefficient, and obtain the conference performance evaluation coefficient through association;

[0168] S4. Evaluate the conference performance evaluation coefficient to obtain the performance evaluation level, and generate corresponding cold start optimization strategies and cache preheating strategies according to the performance evaluation level;

[0169] S5. Compare the evaluation threshold with the audio-video synchronization jitter rate, and dynamically adjust the audio-video synchronization mechanism according to the comparison result.

[0170] Specifically, through the online conference establishment module, set up the remote video conference system inside and outside the power plant, configure conference access control, allocate user accounts and permissions, and deploy network monitoring devices for each remote point, divide sub-areas and mark remote points; collect the network status information of each remote point of each power plant, generate the first data set, and construct a network quality degradation index Tczs for evaluation. If the network quality degradation index Tczs > degradation threshold X, send a warning instruction to the outside; after the conference starts, the communication module monitors the network communication performance of each remote point in real time, generates the second data set, and monitors communication anomalies. The resource analysis module extracts characteristic parameters from the second data set, and establishes and trains a conference network prediction model through machine learning technology to obtain the audio-video synchronization jitter rate Avsjr, cold start optimization coefficient Ltxs, and cache preheating coefficient Cylxs, and obtain the conference performance evaluation coefficient SPEc; the conference management module is used to evaluate the conference performance evaluation coefficient SPEc to obtain the performance evaluation level, and generate corresponding cold start optimization strategies and cache preheating strategies according to the performance evaluation level; the audio synchronization unit dynamically adjusts the audio-video synchronization mechanism according to the comparison between the evaluation threshold y and the audio-video synchronization jitter rate Avsjr.

[0171] It can be understood that the setting of the threshold size is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the base quantity set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0172] The paperless meeting management method for power plant offices according to the embodiments of the present invention can issue early warnings in a timely manner when network problems occur by collecting network status information in real time and calculating the network quality degradation index, thereby realizing active monitoring and response to network anomalies and reducing the risk of meeting interruption. During the meeting, the network performance is continuously monitored, and the meeting network prediction model is optimized through machine learning technology, effectively improving the audio and video synchronization and resource management capabilities of the meeting. According to the meeting performance evaluation coefficient, cold start optimization strategies and cache preheating strategies are dynamically generated, improving the meeting startup speed and resource utilization efficiency. The audio and video synchronization mechanism is adjusted according to the real-time jitter information to ensure high-quality synchronization of audio and video, further improving the usability and experience of the meeting.

[0173] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0174] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Claims

1. A paperless conference management system for power plant offices, characterized in that: It includes online conference establishment module, network information monitoring module, communication module, resource analysis module and conference management module; The online conference establishment module is used to establish a remote video conference system inside and outside the power plant, realize remote conferences with the company headquarters, basin power plants or suppliers, and deploy network monitoring equipment at each remote point in each power plant; The network information monitoring module is used to collect network status information of each remote point of each power plant online, generate a first data group, and calculate a network quality degradation index, compare the network quality degradation index with a degradation threshold, and send an early warning instruction to the outside if the network quality degradation index is greater than the degradation threshold; The communication module is used to monitor the network communication performance of each remote point in real time after the conference video starts, and generate communication status information to form a second data group according to the actual communication quality, and monitor and report communication anomalies or interruption events occurring in the network; The resource analysis module is used to extract characteristic parameters from the second data group after information processing, obtain a cold start optimization coefficient by correlating the data memory leakage rate, network path switching frequency, conference document dynamic editing conflict rate, and conference system acoustic echo suppression delay in the characteristic parameters, obtain a cache warm-up coefficient by correlating the preloaded resource compression ratio, historical cache hit rate, temporary resource cache allocation amount, and startup parallel data preprocessing amount in the characteristic parameters, establish and train a conference network prediction model based on machine learning, and obtain a conference performance evaluation coefficient through correlation analysis; The conference management module is used to evaluate the conference performance evaluation coefficient to obtain a performance evaluation level, and generate a corresponding cold start optimization strategy and a cache warm-up strategy according to the performance evaluation level.

2. The system according to claim 1, characterized in that The online conference establishment module includes a remote connection unit and a marking unit; The remote connection unit is used to establish a remote conference connection between the power plant and the company headquarters, basin power plants or suppliers, and configure user accounts and permissions for each participant in the conference, and set access control, video and audio equipment options for the conference; The marking unit is used to deploy conference hardware equipment in the power plant and remote points, and divide the remote points inside and outside the power plant according to geographical location, network status or business function to generate several sub-areas, which are marked as multiple remote points respectively.

3. The system according to claim 1, characterized in that The conference performance evaluation coefficient is obtained by calculating the following formula: Wherein, SPEc is the conference performance evaluation coefficient, Avsjr is the audio and video synchronization jitter rate, α, β and δ are the preset proportional coefficients of the audio and video synchronization jitter rate Avsjr, the cold start optimization coefficient Ltxs and the cache warm-up coefficient Cylxs, respectively.

4. The system according to claim 1, characterized in that The network information monitoring module includes a network status monitoring unit, a communication status difference calculation unit and a first calculation unit; The network status monitoring unit is used to monitor and obtain the data packet loss rate Dbl, network bandwidth Ttlz, network delay Wscz, average transmission delay Mtd, packet loss recovery time PLr and timing offset value Sxpy in real time by deploying network monitoring equipment in each power plant and its remote points, and generate a network parameter set, clean up and process the network parameter set, and standardize the data through dimensionless processing technology; The communication state difference calculation unit is used to calculate the difference between the network state of each power plant and each remote point and the reference state, and obtain the communication state difference Xtxz by the following formula: Where Dbl eq Indicates the currently monitored packet loss rate, Dbl jz represents the benchmark packet loss rate, a1 represents the preset proportional coefficient of the difference between the current packet loss rate and the benchmark packet loss rate, Ttlz dq Indicates the currently monitored network bandwidth, Ttlz jz Indicates the baseline network bandwidth, a2 indicates the preset ratio coefficient of the difference between the current network bandwidth and the baseline network bandwidth, Wscz dq Indicates the current network delay, Wscz jz represents the benchmark network delay, and a3 represents the preset proportional coefficient of the difference between the current network delay and the benchmark network delay; The first calculation unit is used to extract the average transmission delay Mtd, the packet loss recovery time PLr, the timing offset value Sxpy and the communication state difference Xtxz from the network parameter set, and after dimensionless processing, calculate the network quality degradation index Tczs by the following formula: Wherein, a4, a5, a6 and a7 represent the preset proportional coefficients of the average transmission delay Mtd, the packet loss recovery time PLr, the timing offset value Sxpy and the communication state difference value Xtxz respectively, and C1 represents the first correction constant.

5. The system according to claim 4, characterized in that The network information monitoring module also includes a first evaluation unit and a first early warning unit; The first evaluation unit is used to preset a degradation threshold value X, and compare and evaluate the network quality degradation index Tczs with the degradation threshold value X to obtain an evaluation result, including: If the network quality degradation index Tczs is greater than the degradation threshold value X, it means that the current remote point network quality is unqualified and there is a degradation problem, and an early warning instruction is generated through the first early warning unit; If the network quality degradation index Tczs is equal to the degradation threshold X, it means that the current remote point network quality is at the critical point of being unqualified, and the cache prediction amount is increased and spare bandwidth is prepared; If the network quality degradation index Tczs is less than the degradation threshold X, it means that the current remote point network quality is qualified, there is no need to adjust resource allocation, and the current network status is maintained.

6. The system according to claim 1, characterized in that The resource analysis module includes a model building unit and a feature extraction unit; The model building unit is used to build a conference network prediction model through machine learning technology, and divide the second data group into a training set and a test set for training and testing, and then apply it to network prediction; The feature extraction unit is used to extract feature parameters from the second data group after information processing, and calculate the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, the conference system acoustic echo suppression delay Hsyc, the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf and the startup parallel data preprocessing amount Bqcl based on the conference network prediction model through the following formula: Where M currentl is the current memory usage, M baselinel is the baseline memory usage, T duratio is the observation period, M leak is the amount of memory leak, M total Indicates the total memory usage; Where P i is the frequency of the i-th path switching, W i is the weight value of the i-th path switching, T i is the time window of the i-th path switching, and N is the total number of switching times; In the formula, C i is the number of edit conflicts for the i-th time, D i is the weight of the conflict, E i is the total number of edits for the ith time, and M is the total number of edits; In the formula, Sxy i is the delay value of the ith acoustic treatment, p i is the weight of the acoustic treatment delay value, Sj i is the total processing time for the ith time, and F is the number of processing times; Where Zyys i is the compressed size of the i-th resource, Zyw i is the uncompressed size of the i-th resource, K i represents the weight value of the i-th resource, and H is the total number of resources; In the formula, Hcmz i is the number of buffer hits for the i-th time, Hcqq i is the total number of cache requests for the ith time, hc i is the weight value of the ith time, G is the total number of cache requests; Where, Lshc i is the temporary cache amount allocated for the i-th time, Zhcl i is the total cache size for the ith time, L i is the weight value of the temporary cache amount allocated for the i-th time, and V represents the total cache allocation amount; Where, Qdcl i Qdsx is the amount of data processed when starting the conference video for the i-th time. i is the total amount of data required to start the conference video for the i-th time, Q i is the weight value of the amount of data processed when starting the conference video for the i-th time, and X is the total amount of data.

7. The system according to claim 6, characterized in that The resource analysis module further includes a first correlation unit and a second correlation unit; The first associating unit is used to associate the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, and the conference system acoustic echo suppression delay Hsyc through the following formula to calculate and obtain the cold start optimization coefficient Ltxs: Wherein, b1, b2, b3 and b4 represent the preset proportional coefficients of the data memory leakage rate Mlxl, the network path switching frequency Ljqhz, the conference document dynamic editing conflict rate Bjcl, and the conference system acoustic echo suppression delay Hsyc, respectively, and C2 represents the second correction constant; The second associating unit is used to associate the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf and the startup parallel data preprocessing amount Bqcl through the following formula to calculate and obtain the cache preheating coefficient Cylxs: Wherein, b5, b6, b7 and b8 represent the preset proportional coefficients of the preloaded resource compression ratio Ysbz, the historical cache hit rate Lsmz, the temporary resource cache allocation amount Lscf and the startup parallel data preprocessing amount Bqcl respectively, and C3 represents the third correction constant.

8. The system according to claim 7, characterized in that The resource analysis module further includes an audio and video jitter monitoring unit, which is used to collect audio and video synchronization jitter information in real time during the conference video, and obtain the audio and video synchronization jitter rate Avsjr by calculating the following formula: ΔA i represents the time difference between the i-th audio frame and the video frame, represents the timestamp of the i-th audio frame, Indicates the timestamp of the video frame corresponding to the i-th audio frame; B is the total number of frames.

9. The system according to claim 8, characterized in that The conference management module includes a performance evaluation unit, a loading cache strategy unit and an audio synchronization unit; wherein, The performance evaluation unit is used to evaluate the conference performance evaluation coefficient SPEc and the start performance threshold Xn to obtain a performance evaluation level, including: When the conference performance evaluation coefficient SPEc is greater than the startup performance threshold Xn, it indicates that the conference performance is poor, a first performance level is generated, and a first optimization strategy is generated by loading a cache strategy unit; When the conference performance evaluation coefficient SPEc is equal to the startup performance threshold Xn, it means that the conference performance is within the range, a second performance level is generated, and a second optimization strategy is generated by loading the cache strategy unit; When the conference performance evaluation coefficient SPEc is less than the startup performance threshold Xn, it indicates that the conference performance is normal, a third performance level is generated, and a third optimization strategy is generated by loading a cache strategy unit; The audio synchronization unit is used to dynamically adjust the audio and video synchronization mechanism according to the audio and video synchronization jitter rate Avsjr, pre-set the evaluation threshold y, and compare and analyze the evaluation threshold y with the audio and video synchronization jitter rate Avsjr to determine the audio and video synchronization level state, including: If the audio and video synchronization jitter rate Avsjr is greater than the evaluation threshold y, the buffer is adjusted and the video frame rate is reduced; If the audio and video synchronization jitter rate Avsjr is equal to the evaluation threshold y, the audio buffer is set and the video frame rate is reduced; If the audio and video synchronization jitter rate Avsjr is less than the evaluation threshold y, the audio and video synchronization strategy is executed according to the standard mechanism.

10. A paperless conference management method for power plant offices, applied to the paperless conference management system for power plant offices as claimed in any one of claims 1 to 9, characterized in that: include: Set up remote video conferencing systems inside and outside the power plant and configure conference access control, assign user accounts and permissions, and deploy network monitoring equipment for each remote point, divide sub-areas and mark remote points; Collect network status information of each remote point of each power plant to generate a first data group, and construct a network quality degradation index for evaluation. If the network quality degradation index is greater than a degradation threshold, send an early warning instruction to the outside; Monitor the network communication performance of each remote point in real time to generate a second data group, monitor communication anomalies, extract feature parameters from the second data group, establish and train a conference network prediction model through machine learning technology, obtain the audio and video synchronization jitter rate, cold start optimization coefficient and cache warm-up coefficient, and associate them to obtain a conference performance evaluation coefficient; Evaluate the conference performance evaluation coefficient to obtain a performance evaluation level, and generate a corresponding cold start optimization strategy and cache warm-up strategy based on the performance evaluation level; The evaluation threshold is compared with the audio and video synchronization jitter rate, and the audio and video synchronization mechanism is dynamically adjusted according to the comparison result.

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