Video conference quality optimization method and device, equipment, storage medium and product

Abstract

The invention relates to the technical field of communication, in particular to a video conference quality optimization method and device, equipment, a storage medium and a product. Through monitoring network equipment interface data in real time, video quality and network quality in a transmission process are further evaluated according to the data; and determining an optimal transmission path and an adjustment scheme of network parameters according to evaluation results of the two, and completing transmission quality optimization of the video conference after the transmission parameters are updated. In addition, after adjustment, network transmission quality can be continuously monitored, and an evaluation report is generated, so that conference experience is continuously optimized. The scheme has the beneficial effects of improving the stability and quality of the video conference, adapting to network fluctuation through dynamic adjustment, reducing delay and packet loss, ensuring that better conference experience can be maintained even if the network condition is poor, reducing manual intervention through the automatic monitoring and adjusting process of the scheme, and improving the video conference experience. The conference interruption risk caused by network problems can be reduced, and the user satisfaction is improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to methods, apparatus, devices, storage media, and products for optimizing video conferencing quality. Background Technology

[0002] With the rise of remote work and online education, video conferencing has become an important way for people to communicate and collaborate. However, the quality of video conferencing is often severely affected by network conditions. Traditional video conferencing systems typically rely on manual tuning to address these network challenges, which is inefficient and struggles to cope with complex network environments. Specifically, based on quality assessment results, maintenance personnel manually adjust the network configuration of the video conferencing system, or manual intervention from maintenance personnel is required. For example, when automated tuning algorithms fail to effectively optimize network quality, manual intervention is needed to diagnose the problem and take appropriate measures.

[0003] While existing video conferencing systems can perform network quality monitoring and optimization to a certain extent, the high cost and inefficiency of manual intervention often prevents them from responding promptly to network quality fluctuations, leading to a decline in user experience. Therefore, there is an urgent need for an automated system to assess network quality and perform corresponding optimizations to improve the stability and user experience of video conferencing systems.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, storage medium, and product for optimizing video conferencing quality. It aims to solve the technical problem in the prior art of how to achieve automated evaluation of video conferencing network quality and automatically adjust network configuration based on the evaluation results to improve the quality and stability of video conferencing.

[0006] To achieve the above objectives, the present invention provides a video conferencing quality optimization method, the method comprising the following steps: Acquire real-time network transmission quality data, which includes network parameters and video transmission quality index data; Based on the video transmission quality index data, a similarity score for the standard transmission quality index is obtained; Determine the target transmission path based on the network parameters; Based on the similarity score and the target transmission path, video transmission parameters are determined, and the video conferencing data transmission quality is optimized based on the video transmission parameters.

[0007] In some embodiments, obtaining real-time network transmission quality data includes: Data is collected from the network devices associated with the video conference to obtain the interface data of the network devices; Based on the interface data, real-time network transmission quality data is obtained, wherein the interface data includes interface transmission rate, bandwidth utilization, signal-to-noise ratio, transmission delay, and packet loss rate.

[0008] In some embodiments, the video transmission quality index data includes actual resolution, video compression ratio, transmission latency, latency jitter, and packet loss rate. The step of obtaining a similarity score for standard transmission quality indicators based on the video transmission quality index data includes: Based on the actual resolution and video compression ratio, the sharpness evaluation result is obtained; Based on the transmission delay and latency jitter, the smoothness evaluation result is obtained; Based on the packet loss rate, the integrity assessment result is obtained; Based on the clarity assessment results, fluency assessment results, and integrity assessment results, a similarity score for the standard transmission quality index is obtained.

[0009] In some embodiments, determining the target transmission path based on the network parameters includes: Based on the transmission path selection rules, determine the types of transmission path evaluation indicators and their corresponding indicator weights. Obtain the transmission path evaluation index data from the network parameters; Based on the transmission path evaluation index data and its corresponding index weights, the weighted score results of each transmission path are obtained. The target transmission path is determined based on the weighted score results corresponding to each of the transmission paths.

[0010] In some embodiments, determining video transmission parameters based on the similarity score and the target transmission path, and optimizing video conferencing data transmission quality based on the video transmission parameters, includes: Based on the similarity score, determine the parameter adjustment ratio; Determine the parameter adjustment type based on the target transmission path; The video transmission parameters are determined based on the parameter adjustment type, the parameter adjustment ratio, and the video transmission parameters at the current moment. Optimize the quality of video conferencing data transmission based on the video transmission parameters.

[0011] In some embodiments, after adjusting the video transmission parameters based on the similarity score and the target transmission path, the method further includes: Obtain network transmission quality data after adjusting video transmission parameters; Based on the network transmission quality data, transmission quality assessment data and transmission quality change trends are obtained; Based on the transmission quality assessment data and the trend of transmission quality changes, a network quality assessment report is generated.

[0012] Furthermore, to achieve the above objectives, the present invention also proposes a video conferencing quality optimization device, the video conferencing quality optimization device comprising: The transmission quality monitoring module is used to acquire real-time network transmission quality data, which includes network parameters and video transmission quality index data. The data processing module is used to obtain a similarity score of the standard transmission quality index based on the video transmission quality index data. The data processing module is also used to determine the target transmission path based on the network parameters; The automatic parameter adjustment module is used to determine video transmission parameters based on the similarity score and the target transmission path, and to optimize the video conferencing data transmission quality based on the video transmission parameters.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a video conferencing quality optimization device, which includes: a memory, a processor, and a video conferencing quality optimization program stored in the memory and executable on the processor, wherein the video conferencing quality optimization program is configured to implement the steps of the video conferencing quality optimization method described above.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a video conferencing quality optimization program, which, when executed by a processor, implements the steps of the video conferencing quality optimization method described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the video conferencing quality optimization method described above.

[0016] The one or more technical solutions proposed in this application have at least the following technical effects: This application monitors network device interface data in real time, obtains network transmission quality data and video transmission quality indicators based on this data, further evaluates the clarity, smoothness, and integrity of the video, calculates a similarity score, and determines the optimal transmission path based on network parameters. When preset optimization conditions are met, the video transmission parameters are dynamically adjusted to optimize the transmission quality of the video conference. Furthermore, after adjustment, network transmission quality is continuously monitored, and an evaluation report is generated to continuously optimize the meeting experience. Based on this solution, its beneficial effects can be foreseen, including improving the stability and quality of video conferences, adapting to network fluctuations through dynamic adjustment, reducing latency and packet loss, ensuring a good meeting experience even under poor network conditions, and reducing manual intervention through automated monitoring and adjustment processes, thus helping to reduce the risk of meeting interruptions due to network problems, improving user satisfaction, optimizing network resource utilization, and improving cost-effectiveness. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the video conferencing quality optimization method of the present invention; Figure 2 This is a flowchart illustrating the second embodiment of the video conferencing quality optimization method of the present invention; Figure 3 This is a flowchart illustrating the third embodiment of the video conferencing quality optimization method of the present invention; Figure 4 This is a structural block diagram of the first embodiment of the video conferencing quality optimization device of the present invention; Figure 5 This is a schematic diagram of the structure of a video conferencing quality optimization device for the hardware operating environment involved in the embodiments of the present invention.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is: to obtain real-time network transmission quality data, wherein the real-time network transmission quality data includes network parameters and video transmission quality index data; to obtain a similarity score based on the video transmission quality index data; to determine the target transmission path based on the network parameters; and to adjust the video transmission parameters based on the similarity score and the target transmission path when the similarity score and the target transmission path meet preset optimization conditions, so as to achieve video conferencing data transmission quality optimization.

[0024] Because the quality of video conferencing is often severely affected by network conditions in existing technologies, including but not limited to bandwidth limitations, network latency, packet loss rate, signal-to-noise ratio, etc., manual optimization is usually adopted when facing these different types of network fluctuations, which is inefficient and difficult to cope with complex network environments.

[0025] In traditional technologies, when faced with sudden network fluctuations, the network configuration of the video conferencing system is typically adjusted manually by operations and maintenance personnel, or manual intervention is required. For example, when automated tuning algorithms fail to effectively optimize network quality, manual intervention is needed to diagnose the problem and take corresponding measures. While this method can achieve a certain degree of network quality monitoring and tuning, it is cumbersome and prone to errors, has high system complexity and maintenance costs, and can only monitor some network parameters, such as latency and packet loss rate, lacking a comprehensive evaluation and automatic tuning mechanism, thus failing to fully reflect the network condition.

[0026] This application provides a solution that monitors network device interface data in real time, further evaluates video and network quality during transmission based on this data, determines the optimal transmission path and network parameter adjustment scheme based on the evaluation results, and optimizes video conferencing transmission quality after updating the transmission parameters. Furthermore, it continuously monitors network transmission quality after adjustments, generating evaluation reports to continuously optimize the meeting experience. The beneficial effects of this solution include improved video conferencing stability and quality, dynamically adjusting to network fluctuations, reducing latency and packet loss, ensuring a good meeting experience even under poor network conditions, and the automated monitoring and adjustment process reduces manual intervention, helping to reduce the risk of meeting interruptions due to network problems and improving user satisfaction.

[0027] Based on this, embodiments of the present invention provide a method for optimizing video conferencing quality, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of a video conferencing quality optimization method according to the present invention.

[0028] In this embodiment, the video conferencing quality optimization method includes the following steps: Step S10: Obtain real-time network transmission quality data, which includes network parameters and video transmission quality index data.

[0029] It should be noted that the executing entity of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device or cloud server that can realize the above functions. The following uses a cloud server as an example to describe this embodiment and the following embodiments.

[0030] It should be noted that real-time network transmission quality data includes two aspects: network parameters and video transmission quality indicators. Network parameters are used to evaluate the network transmission quality at the current moment, while video transmission quality indicators refer to the actual video quality displayed at the current moment. Since network quality is the foundation of video conferencing data transmission, and video quality is the result of data transmission, evaluating both aspects of data can provide a comprehensive optimization solution for video conferencing.

[0031] Understandably, network parameters reflect the efficiency and stability of data transmission within the network, such as transmission rate, bandwidth utilization, signal-to-noise ratio, transmission latency, and packet loss rate. These are fundamental elements for video conferencing data transmission. Meanwhile, video transmission quality metrics, such as actual resolution, video compression ratio, transmission latency, jitter, and packet loss rate, directly affect the user's actual experience. By combining data from both aspects, problems in video conferencing can be diagnosed more accurately, allowing for targeted optimization measures. For example, if network parameters show high bandwidth utilization but high transmission latency, routing adjustments or increased bandwidth may be necessary; conversely, if video transmission quality metrics show low resolution or high compression ratio, encoding settings or optimized video stream transmission strategies may be required.

[0032] In one embodiment, obtaining real-time network transmission quality data includes: collecting data from network devices associated with the video conference to obtain interface data of the network devices; and obtaining real-time network transmission quality data based on the interface data, wherein the interface data includes interface transmission rate, bandwidth utilization, signal-to-noise ratio, transmission delay, and packet loss rate.

[0033] Understandably, by using the Simple Network Management Protocol (SNMP) to automatically collect network device data related to video conferencing, the real-time status of the current network can be obtained efficiently. In this embodiment, SNMPv3 is selected as the SNMP version for the monitoring model. By enabling the SNMP agent service on the network device and configuring the SNMP agent with read permissions and access control, interface information related to video conferencing data transmission in the network device can be obtained. For example, by combining the interface's input / output rate with the interface's bandwidth information, the bandwidth utilization rate can be calculated to assess network bandwidth usage; by obtaining the signal strength and noise level of the wireless network device, the signal-to-noise ratio can be calculated to assess signal quality; by using the routing information between network devices, the round-trip time of data packets can be calculated to assess network latency; and by using the interface statistics of the network device, the packet loss rate can be calculated to assess network packet loss.

[0034] It should be understood that through these monitoring activities, the system can understand the network status in real time and promptly identify problems that may affect the quality of video conferencing, such as insufficient bandwidth, signal interference, network latency, and packet loss, providing data support for subsequent optimization steps.

[0035] Step S20: Obtain the similarity score of the standard transmission quality index based on the video transmission quality index data.

[0036] It should be noted that in this step, video transmission quality metrics include, but are not limited to, actual resolution, video compression ratio, transmission latency, jitter, and packet loss rate. These metrics reflect the clarity, smoothness, and integrity of video transmission and are key factors in evaluating video transmission quality. The similarity score is a quantified value that integrates these metrics and measures the degree of similarity between the actual video transmission quality and the ideal state.

[0037] As we understand it, the similarity score refers to the similarity to transmission under ideal conditions. It's a dynamically changing value, varying with network conditions and video transmission quality. This score guides whether video transmission parameters need adjustment to optimize video quality. For example, if the similarity score falls below the threshold corresponding to standard transmission quality, it indicates the need for measures such as adjusting encoding settings, increasing bandwidth, or changing the transmission path to improve video transmission quality. The similarity score also helps network management systems make automated, data-driven decisions to ensure high-quality video transmission services are provided across various network environments.

[0038] Step S30: Determine the target transmission path based on the network parameters.

[0039] It should be noted that, in order to improve the reliability and fault tolerance of the network, the network is often designed with redundant paths. In this way, if one transmission path fails, data can be transmitted through other paths to ensure uninterrupted network communication. Therefore, network quality analysis of the available transmission paths can be performed by real-time transmission demand and network parameters of each transmission path to determine the optimal transmission path.

[0040] It is understandable that data transmission needs change in real time, meaning that the optimal transmission path at any given moment will also change according to actual needs. This is because the network parameters of the current transmission path change over time, for example, increasing during peak user activity periods (such as working hours), leading to congestion on some paths. At this time, the original optimal path may no longer be suitable. On the other hand, new participants may join during video conferences, meaning that more bandwidth is needed to process additional video and audio streams, which may lead to congestion on the current transmission network, especially in a shared network environment. Therefore, the process of selecting the optimal transmission path in this embodiment is actually a dynamic adjustment process.

[0041] Step S40: Determine video transmission parameters based on the similarity score and the target transmission path, and optimize the video conferencing data transmission quality based on the video transmission parameters.

[0042] It should be noted that the preset optimization conditions refer to the conditions under which the optimization process will be started. For example, the optimization process will not be started if the quality of the current video conference meets the requirements and there is no better network transmission path available at the moment.

[0043] In one embodiment, determining video transmission parameters based on the similarity score and the target transmission path, and optimizing video conferencing data transmission quality based on the video transmission parameters, includes: determining a parameter adjustment ratio based on the similarity score; determining a parameter adjustment type based on the target transmission path; determining video transmission parameters based on the parameter adjustment type, the parameter adjustment ratio, and the current video transmission parameters; and optimizing video conferencing data transmission quality based on the video transmission parameters.

[0044] Understandably, when setting parameter adjustment rules based on similarity scores and selected transmission paths, the adjustment type of video transmission parameters is first determined, such as the bandwidth increase / decrease range and compression ratio adjustment range. Then, based on a pre-set similarity score threshold, which is used for the adjustment ratio of video transmission parameters, a parameter adjustment scheme is formulated and new video transmission parameters are generated based on the similarity scores and selected transmission paths. The new video transmission parameters include two aspects: when to adjust the parameter type and the parameter adjustment ratio.

[0045] It should be understood that by calculating a similarity score for video quality using similarity metrics, formulating parameter adjustment rules based on the score and the selected transmission path, and automatically adjusting video transmission parameters according to these rules, dynamic optimization and adaptive adjustment of video transmission quality can be achieved, improving user experience. Such an automated tuning system can continuously monitor and adjust parameters during video conferencing, ensuring that video quality remains at its optimal level at all times.

[0046] Furthermore, after adjusting the video transmission parameters based on the similarity score and the target transmission path, the process further includes: obtaining network transmission quality data after adjusting the video transmission parameters; obtaining transmission quality assessment data and transmission quality change trends based on the network transmission quality data; and generating a network quality assessment report based on the transmission quality assessment data and transmission quality change trends.

[0047] Understandably, the process begins by establishing scoring rules and weights based on the importance of the monitoring indicators. Then, network parameters and transmission quality indicators are continuously monitored during the video conference. Finally, a score for each indicator is calculated according to the established scoring rules and weights. By continuously monitoring network parameters and transmission quality indicators during the video conference, calculating the scores of each indicator in real time, and recording their trends, potential problems can be identified and resolved promptly, ensuring continuous optimization of video conference quality.

[0048] This embodiment monitors network device interface data in real time, further evaluating video and network quality during transmission. Based on the evaluation results, it determines the optimal transmission path and network parameter adjustment scheme, optimizing video conferencing transmission quality after updating the transmission parameters. Furthermore, it continuously monitors network transmission quality after adjustments, generating evaluation reports to continuously optimize the meeting experience. The beneficial effects of this solution include improved video conferencing stability and quality, dynamically adjusting to network fluctuations, reducing latency and packet loss, ensuring a good meeting experience even under poor network conditions, and the automated monitoring and adjustment process reduces manual intervention, helping to lower the risk of meeting interruptions due to network problems and improving user satisfaction.

[0049] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S20 includes: Step S201: Obtain the clarity evaluation result based on the actual resolution and video compression ratio.

[0050] It should be noted that, in this embodiment, the formula for calculating the sharpness assessment when evaluating video transmission quality is as follows:

[0051] It's understandable that actual resolution refers to the resolution of a video or image displayed on a display device, i.e., the actual number of pixels displayed on the screen. Ideal resolution refers to the maximum possible resolution that the device can process or display; this is the upper limit of the device's capabilities and is not always the same as the actual resolution. For example, a display device that supports 4K video might have an ideal resolution of 3840x2160, even if the currently playing content is at a lower resolution, such as 1080p. Video compression ratio refers to the ratio between the size of the compressed video and its original size. It is an important indicator of video encoding efficiency, reflecting how much the video data can be reduced to after compression. In this embodiment, assuming an actual resolution of 1280x720, an ideal resolution of 1920x1080, a video compression ratio of 0.5, a latency of 100ms, a jitter of 20ms, and a packet loss rate of 2%, the sharpness score calculated using the above formula is 37.04%, or a sharpness evaluation result of 0.37. As can be seen from the formula, the higher the actual smoothness or the lower the video compression ratio, the higher the sharpness score.

[0052] Step S202: Obtain the smoothness evaluation result based on the transmission delay and latency jitter.

[0053] It should be noted that, in this embodiment, the formula for calculating smoothness evaluation when assessing video transmission quality is as follows:

[0054] As is understandable, latency refers to the time required for data to travel from its source to its destination, usually measured in milliseconds (ms). Latency includes multiple components, such as processing latency, queuing latency, transmission latency, and propagation latency. In this embodiment, latency refers to the overall latency result. Latency jitter refers to the instability of network latency, describing the variation in latency between data packets. In an ideal network environment, the latency between data packets should be consistent. However, in reality, due to factors such as routing changes and network congestion, the latency of each data packet may differ. This variation in latency is jitter. High jitter usually affects the performance of real-time video conferencing, potentially causing call interruptions, video stuttering, and other problems. Using the example from the previous steps, the fluency score of 4.76% can be calculated using the above formula. As can be seen from the formula, the greater the latency and latency jitter, the lower the fluency score.

[0055] Step S203: Obtain the integrity assessment result based on the packet loss rate.

[0056] It should be noted that packet loss rate refers to the proportion of data packets lost during network communication due to various reasons. In this embodiment, integrity score = 100% - packet loss rate, meaning that integrity assessment is only related to the packet loss rate.

[0057] Step S204: Based on the clarity assessment results, fluency assessment results, and integrity assessment results, obtain the similarity score of the standard transmission quality index.

[0058] It should be noted that by evaluating clarity, fluency, and integrity, a comprehensive understanding of video transmission quality can be obtained. In the specific implementation, the scores of clarity, fluency, and integrity of the video are normalized, and a similarity score is calculated. The obtained similarity score can reflect the degree of similarity between the actual transmission effect and the ideal transmission effect.

[0059] Understandably, when performing normalization to calculate similarity, the score for each evaluation item is divided by the maximum possible score, making it between 0 and 1, and then the three are weighted to obtain the final similarity score.

[0060] It should be understood that by normalizing the scores for sharpness, fluency, and integrity, they can be unified onto the same scale for comprehensive evaluation. Combined with preset weighting parameters, a similarity score for the video transmission effect can be obtained. This score can intuitively reflect the degree of similarity between the actual transmission effect and the ideal transmission effect, providing a comprehensive indicator for actual video transmission quality evaluation.

[0061] This embodiment calculates a similarity score to an ideal transmission scenario by evaluating three aspects: video clarity, smoothness, and integrity, thereby obtaining a general video quality assessment result. This method quantifies the actual quality of video transmission, providing quality feedback and guidance for subsequent optimization of video conferencing.

[0062] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S30 includes: Step S301: Determine the type of transmission path evaluation index and its corresponding index weight according to the transmission path selection rules.

[0063] It should be noted that transmission path selection rules refer to a series of standards or criteria used in a network to select the optimal transmission path. These rules are used to evaluate and compare different transmission paths and determine which path is best suited for the current data transmission requirements. Selection rules typically consider multiple factors to ensure that the selected path provides the best performance and reliability. In this embodiment, the transmission path selection rules mainly include the types of transmission path evaluation indicators and their corresponding indicator weights.

[0064] Understandably, transmission path evaluation metrics include network parameters such as signal-to-noise ratio and packet loss rate. The type and quantity of metric data can be customized in different evaluation algorithms, and their weights can also be adaptively set. For each transmission path, a weighted score is calculated based on the collected network parameter values ​​and the set weights. The formula for calculating the weighted score is: Weighted score = w1*p1 + w2*p2 + ... + w n *p n In the formula, w n It is the weight of the nth network parameter; p n It is the nth network parameter value.

[0065] It should be understood that this formula allows different network parameters to be assigned different weights based on their impact on transmission quality. For example, if the signal-to-noise ratio (SNR) is considered more important than the packet loss rate, a higher weight can be assigned to the SNR. Furthermore, the types of transmission path evaluation metrics are not limited to SNR and packet loss rate; other parameters related to network transmission can also be set. In this way, the performance of each transmission path can be evaluated more accurately, and the optimal transmission path can be determined.

[0066] Step S302: Obtain the transmission path evaluation index data from the network parameters.

[0067] It should be noted that in this embodiment, the SNMP tool is used to collect network parameters of each transmission path. Before the collection operation, the SNMP tool is first installed and configured, and the SNMP agent is configured on the network device to allow the SNMP tool to query device parameters. The network parameters of each available transmission path can be obtained through this tool.

[0068] Step S303: Based on the transmission path evaluation index data and its corresponding index weights, obtain the weighted score results for each transmission path.

[0069] It should be noted that when selecting a transmission path, a weighted scoring method is used, which assigns corresponding weights according to the importance of each network parameter, and then comprehensively evaluates the score of each available transmission path, selecting the path with the highest score as the target transmission path to switch to.

[0070] Understandably, the weighted score is not static; it needs to be adjusted based on changes in the network environment and updates in business requirements. For example, if network bandwidth suddenly becomes strained, the weight of the bandwidth metric may need to be increased.

[0071] Step S304: Determine the target transmission path based on the weighted score results corresponding to each of the transmission paths.

[0072] Understandably, suppose there are two transmission paths, and signal-to-noise ratio (SNR) and packet loss rate (SNR) are the two metrics we are interested in, with weights of 0.7 and 0.3 respectively. Specifically, transmission path A has an SNR of 25 and a SNR of 0.02; transmission path B has an SNR of 30 and a SNR of 0.01. The weighted score for transmission path A, calculated using the above formula, is 17.506, and the weighted score for transmission path B, calculated using the same formula, is 21.003. Therefore, transmission path B has a higher weighted score and is selected as the optimal path.

[0073] This embodiment determines the type of transmission path evaluation index and its corresponding index weight by using transmission path selection rules; obtains the transmission path evaluation index data in the network parameters; obtains the weighted score result of each transmission path based on the transmission path evaluation index data and its corresponding index weight; and determines the target transmission path based on the weighted score result of each transmission path.

[0074] By employing the weighted scoring method described above, the importance of various network parameters can be comprehensively considered, thereby more accurately selecting the optimal transmission path. Setting appropriate weights makes the evaluation process more flexible and targeted, ensuring that the selected transmission path meets actual needs and maximizes video transmission quality.

[0075] This application also provides a video conferencing quality optimization device; please refer to [reference needed]. Figure 4 The video conferencing quality optimization device includes: Transmission quality monitoring module 10 is used to acquire real-time network transmission quality data, which includes network parameters and video transmission quality index data. The data processing module 20 is used to obtain a similarity score of the standard transmission quality index based on the video transmission quality index data. The data processing module 20 is further configured to determine the target transmission path based on the network parameters; The parameter automatic adjustment module 30 is used to determine video transmission parameters based on the similarity score and the target transmission path, and to optimize the video conferencing data transmission quality based on the video transmission parameters.

[0076] In one embodiment, the transmission quality monitoring module 10 is further configured to collect data from the network devices associated with the video conference to obtain interface data of the network devices; and to obtain real-time network transmission quality data based on the interface data, wherein the interface data includes interface transmission rate, bandwidth utilization, signal-to-noise ratio, transmission delay, and packet loss rate.

[0077] In one embodiment, the data processing module 20 is further configured to obtain a sharpness evaluation result based on the actual resolution and video compression ratio; obtain a smoothness evaluation result based on the transmission delay and latency jitter; obtain an integrity evaluation result based on the packet loss rate; and obtain a similarity score for standard transmission quality indicators based on the sharpness evaluation result, the smoothness evaluation result, and the integrity evaluation result.

[0078] In one embodiment, the data processing module 20 is further configured to: determine the type of transmission path evaluation index and its corresponding index weight according to the transmission path selection rule; obtain the transmission path evaluation index data in the network parameters; obtain the weighted score result of each transmission path according to the transmission path evaluation index data and its corresponding index weight; and determine the target transmission path according to the weighted score result of each transmission path.

[0079] In one embodiment, the automatic parameter adjustment module 30 is further configured to obtain parameter adjustment rules based on the target transmission path, the parameter adjustment rules including parameter adjustment type and parameter adjustment range; and adjust the video transmission parameters based on the similarity score and the parameter adjustment rules to optimize the video conferencing data transmission quality.

[0080] In one embodiment, the transmission quality monitoring module 10 is further configured to acquire network transmission quality data after adjusting video transmission parameters; obtain transmission quality assessment data and transmission quality change trend based on the network transmission quality data; and generate a network quality assessment report based on the transmission quality assessment data and transmission quality change trend.

[0081] This embodiment monitors network device interface data in real time, further evaluating video and network quality during transmission. Based on the evaluation results, it determines the optimal transmission path and network parameter adjustment scheme, optimizing video conferencing transmission quality after updating the transmission parameters. Furthermore, it continuously monitors network transmission quality after adjustments, generating evaluation reports to continuously optimize the meeting experience. The beneficial effects of this solution include improved video conferencing stability and quality, dynamically adjusting to network fluctuations, reducing latency and packet loss, ensuring a good meeting experience even under poor network conditions, and the automated monitoring and adjustment process reduces manual intervention, helping to lower the risk of meeting interruptions due to network problems and improving user satisfaction.

[0082] The video conferencing quality optimization device provided in this application, employing the video conferencing quality optimization method described in the above embodiments, can solve the technical problem of video conferencing quality optimization. Compared with the prior art, the beneficial effects of the video conferencing quality optimization device provided in this application are the same as those of the video conferencing quality optimization method described in the above embodiments, and other technical features in the video conferencing quality optimization device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0083] This application provides a video conferencing quality optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the video conferencing quality optimization method in Embodiment 1 above.

[0084] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a video conferencing quality optimization device suitable for implementing embodiments of this application. The video conferencing quality optimization device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The video conferencing quality optimization device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0085] like Figure 5As shown, the video conferencing quality optimization device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the video conferencing quality optimization device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the video conferencing quality optimization equipment to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows video conferencing quality optimization equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0086] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0087] The video conferencing quality optimization device provided in this application, employing the video conferencing quality optimization method described in the above embodiments, can solve the technical problem of video conferencing quality optimization. Compared with the prior art, the beneficial effects of the video conferencing quality optimization device provided in this application are the same as those of the video conferencing quality optimization method described in the above embodiments, and other technical features of this video conferencing quality optimization device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0088] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0089] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0090] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the video conferencing quality optimization method described in the above embodiments.

[0091] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0092] The aforementioned computer-readable storage medium may be included in the video conferencing quality optimization device; or it may exist independently and not assembled into the video conferencing quality optimization device.

[0093] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the video conferencing quality optimization device, cause the video conferencing quality optimization device to perform video conferencing quality optimization.

[0094] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0096] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0097] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described video conferencing quality optimization method, and is capable of solving the technical problem of video conferencing quality optimization. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the video conferencing quality optimization method provided in the above embodiments, and will not be repeated here.

[0098] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the video conferencing quality optimization method described above.

[0099] The computer program product provided in this application can solve the technical problem of video conferencing quality optimization. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the video conferencing quality optimization method provided in the above embodiments, and will not be repeated here.

[0100] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for optimizing video conferencing quality, characterized in that, The video conferencing quality optimization method includes: Acquire real-time network transmission quality data, which includes network parameters and video transmission quality index data; Based on the video transmission quality index data, a similarity score for the standard transmission quality index is obtained; Determine the target transmission path based on the network parameters; Based on the similarity score and the target transmission path, video transmission parameters are determined, and the video conferencing data transmission quality is optimized based on the video transmission parameters.

2. The video conferencing quality optimization method as described in claim 1, characterized in that, The acquisition of real-time network transmission quality data includes: Data is collected from the network devices associated with the video conference to obtain the interface data of the network devices; Based on the interface data, real-time network transmission quality data is obtained, wherein the interface data includes interface transmission rate, bandwidth utilization, signal-to-noise ratio, transmission delay, and packet loss rate.

3. The video conferencing quality optimization method as described in claim 1, characterized in that, The video transmission quality index data includes actual resolution, video compression ratio, transmission latency, latency jitter, and packet loss rate. The similarity score for standard transmission quality indicators is obtained based on the video transmission quality index data, including: Based on the actual resolution and video compression ratio, the sharpness evaluation result is obtained; Based on the transmission delay and latency jitter, the smoothness evaluation result is obtained; Based on the packet loss rate, the integrity assessment result is obtained; Based on the clarity assessment results, fluency assessment results, and integrity assessment results, a similarity score for the standard transmission quality index is obtained.

4. The video conferencing quality optimization method as described in claim 1, characterized in that, Determining the target transmission path based on the network parameters includes: Based on the transmission path selection rules, determine the types of transmission path evaluation indicators and their corresponding indicator weights. Obtain the transmission path evaluation index data from the network parameters; Based on the transmission path evaluation index data and its corresponding index weights, the weighted score results of each transmission path are obtained. The target transmission path is determined based on the weighted score results corresponding to each of the transmission paths.

5. The video conferencing quality optimization method as described in claim 1, characterized in that, The step of determining video transmission parameters based on the similarity score and the target transmission path, and optimizing video conferencing data transmission quality based on the video transmission parameters, includes: Based on the similarity score, determine the parameter adjustment ratio; Determine the parameter adjustment type based on the target transmission path; The video transmission parameters are determined based on the parameter adjustment type, the parameter adjustment ratio, and the video transmission parameters at the current moment. Optimize the quality of video conferencing data transmission based on the video transmission parameters.

6. The video conferencing quality optimization method according to any one of claims 1 to 5, characterized in that, After adjusting the video transmission parameters based on the similarity score and the target transmission path, the process further includes: Obtain network transmission quality data after adjusting video transmission parameters; Based on the network transmission quality data, transmission quality assessment data and transmission quality change trends are obtained; Based on the transmission quality assessment data and the trend of transmission quality changes, a network quality assessment report is generated.

7. A video conferencing quality optimization device, characterized in that, The video conferencing quality optimization device includes: The transmission quality monitoring module is used to acquire real-time network transmission quality data, which includes network parameters and video transmission quality index data. The data processing module is used to obtain a similarity score of the standard transmission quality index based on the video transmission quality index data. The data processing module is also used to determine the target transmission path based on the network parameters; The automatic parameter adjustment module is used to determine video transmission parameters based on the similarity score and the target transmission path, and to optimize the video conferencing data transmission quality based on the video transmission parameters.

8. A video conferencing quality optimization device, characterized in that, The video conferencing quality optimization device includes: a memory, a processor, and a video conferencing quality optimization program stored in the memory and executable on the processor, the video conferencing quality optimization program being configured to implement the video conferencing quality optimization method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a video conferencing quality optimization program, which, when executed by a processor, implements the video conferencing quality optimization method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the video conferencing quality optimization method as described in any one of claims 1 to 6.

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