Conference quality determination method and device, electronic equipment and storage medium

Abstract

The invention provides a conference quality determination method and device, electronic equipment and a storage medium, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring first data of participants based on first equipment; acquiring second data of the participants based on second equipment; wherein the data acquisition principles of the first device and the second device are different; determining at least one piece of first feature information of the participants according to the first data; wherein the first feature information is used for representing emotional information and / or conference participation degree of participants; second feature information of the participants is determined according to the second data, and the second feature information is used for representing the fatigue degree of the participants; and calculating a quality evaluation result of the current conference based on the first feature information and the second feature information.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and more specifically, to a conference quality determination method, device, electronic device and storage medium. Background Art

[0002] With the increase in modern corporate meetings, meeting quality and efficiency have become particularly important. Traditional meeting quality evaluation methods usually rely on questionnaires or verbal feedback from participants after the meeting to obtain meeting quality evaluation results. These methods are not only time-consuming, but also often subjective and lagging, and cannot reflect the true feelings of participants and the actual effect of the meeting in real time.

[0003] Therefore, there is an urgent need for a method that can monitor and analyze meeting satisfaction in real time to improve the quality and efficiency of meetings. Summary of the invention

[0004] In view of this, the present disclosure provides a conference quality determination method, device, electronic device and storage medium for improving the timeliness and accuracy of conference quality evaluation.

[0005] One aspect of the present disclosure provides a method for determining conference quality, the method comprising: acquiring first data of a conference participant based on a first device; acquiring second data of the conference participant based on a second device; determining at least one first feature information of the conference participant based on the first data; wherein the first feature information is used to characterize the emotional information and / or conference participation of the conference participant; determining second feature information of the conference participant based on the second data, wherein the second feature information is used to characterize the fatigue level of the conference participant; and calculating a quality evaluation result of the current conference based on the first feature information and the second feature information.

[0006] According to an embodiment of the present disclosure, the method further includes: generating prompt information according to the quality evaluation result; wherein the prompt information is used to guide the conference organizer to optimize the subsequent process of the current conference.

[0007] According to an embodiment of the present disclosure, obtaining first data of the current conference scene based on the first device includes: obtaining at least one conference scene picture in real time based on the first device, wherein the conference scene picture includes images of all participants; identifying the conference scene picture, and obtaining first data corresponding to each participant from the conference scene picture, wherein the first data only includes single-person images of participants.

[0008] According to an embodiment of the present disclosure, first characteristic information of a conference participant is determined based on first information, including: using a pre-trained image processing model to process the first data to obtain facial information, motion information, and eye information of the conference participant; determining the first characteristic information of the conference participant based on the facial information, motion information, and eye information, wherein the first characteristic information is used to characterize the emotional information and meeting participation of the conference participant.

[0009] According to an embodiment of the present disclosure, the second data includes at least the heart rate information and body surface temperature distribution information of the conference participants.

[0010] According to an embodiment of the present disclosure, second characteristic information of conference participants is determined based on second data, including: calculating the heart rate variability of the conference participants based on the heart rate information of the conference participants; obtaining the body surface temperature changes of the conference participants based on the body surface temperature distribution information of the conference participants; determining the second characteristic information of the conference participants based on the heart rate variability and the body surface temperature changes; wherein the second characteristic information is used to characterize the fatigue level of the conference participants.

[0011] According to an embodiment of the present disclosure, a quality evaluation result of the current meeting is calculated based on the first feature information and the second feature information, including: preprocessing the first feature information and the second feature information to obtain input features; and determining the quality evaluation result of the current meeting based on the input features by a pre-trained meeting quality evaluation model.

[0012] Another aspect of the present disclosure provides a conference quality determination device, including: a first acquisition module, used to acquire first data of a conference participant based on a first device; a second acquisition module, used to acquire second data of the conference participant based on a second device; a first determination module, used to determine at least one first feature information of the conference participant based on the first data; wherein the first feature information is used to characterize the emotional information and / or conference participation of the conference participant; a second determination module, used to determine second feature information of the conference participant based on the second data, wherein the second feature information is used to characterize the fatigue level of the conference participant; and a calculation module, used to calculate a quality evaluation result of the current conference based on the first feature information and the second feature information.

[0013] Another aspect of the present disclosure provides an electronic device, including: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0014] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0015] Another aspect of the present disclosure provides a computer program product, comprising computer executable instructions, which are used to implement the method described above when the instructions are executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 The application scenario diagram of the conference quality determination method, apparatus, device, medium and program product according to the embodiment of the present disclosure is schematically shown;

[0018] Figure 2 A flowchart of a method for determining conference quality according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 3 Schematically shows a flow chart of acquiring first data of a current conference scene based on a first device according to an embodiment of the present disclosure;

[0020] Figure 4 A flowchart of determining first characteristic information of a conference participant according to first information according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 5 A flowchart of determining second characteristic information of a conference participant according to second data according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 6 A flowchart schematically illustrates a method of calculating a quality evaluation result of a current meeting based on first feature information and second feature information according to an embodiment of the present disclosure;

[0023] Figure 7 The structure block diagram of the conference quality determination device according to the embodiment of the present disclosure is schematically shown;

[0024] Figure 8 A block diagram of an electronic device suitable for implementing a conference quality determination method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] It should be noted that in the technical solution of the present invention, the collection, use, storage, sharing and transfer of data (for example, including but not limited to user personal information) involved are in compliance with the provisions of relevant laws and regulations, and it is necessary to inform the user and obtain the user's consent or authorization. When applicable, the user's personal information is de-identified and / or anonymized and / or encrypted.

[0030] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security, and national security.

[0031] In the embodiments of the present disclosure, before obtaining or collecting user personal information, the user has been informed and the user's consent or authorization has been obtained.

[0032] An embodiment of the present disclosure provides a method for determining conference quality, the method comprising: acquiring first data of a conference participant based on a first device; acquiring second data of the conference participant based on a second device; wherein the principles of data acquisition of the first device and the second device are different; determining at least one first feature information of the conference participant based on the first data; wherein the first feature information is used to characterize the emotional information and / or conference participation of the conference participant; determining second feature information of the conference participant based on the second data, wherein the second feature information is used to characterize the fatigue level of the conference participant; and calculating a quality evaluation result of the current conference based on the first feature information and the second feature information.

[0033] Figure 1 The following schematically illustrates an application scenario diagram of the conference quality determination method, apparatus, device, medium, and program product according to an embodiment of the present disclosure. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0034] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0035] The user may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only for example).

[0036] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0037] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0038] It should be noted that the conference quality determination method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the conference quality determination system provided in the embodiment of the present disclosure can generally be set in the server 105. The conference quality determination method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Correspondingly, the conference quality determination system provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the conference quality determination method provided in the embodiment of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or by other terminal devices that are different from the first terminal device 101, the second terminal device 102 or the third terminal device 103. Accordingly, the conference quality determination system provided by the embodiment of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0039] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0040] The following will be based on Figure 1 The scene described by Figure 2~Figure 6 The data processing method of the disclosed embodiment is described in detail.

[0041] Figure 2 The flowchart of the conference quality determination method according to the embodiment of the present disclosure is schematically shown.

[0042] like Figure 2 As shown, the conference quality determination method of this embodiment includes operations S210 to S250.

[0043] In operation S210, first data of a conference participant is acquired based on a first device.

[0044] In some embodiments, the first device may be, for example, a camera, a video camera, or other image acquisition device that can capture the current conference scene. The first device is used to capture the current conference scene in real time, and the first data of each participant in the current conference scene is acquired based on the capture result of the current conference scene.

[0045] In operation S220, second data of the conference participant is acquired based on the second device; wherein the first device and the second device have different principles for acquiring data.

[0046] In some embodiments, the data acquisition principle of the second device is different from that of the first device, and the second device is used to acquire the second data of the participants. The second device may be, for example, a wearable device, a biosensor, an infrared thermal imaging device, etc. The second data is used to reflect the physiological information of the participants at the current moment, such as heart rate information and body surface temperature distribution information.

[0047] In the specific implementation process, if the second device is a wearable device or a biosensor, the second device needs to be distributed to each participant before the meeting starts and the participant wears the second device, so as to obtain the second data of the participant in time during the meeting.

[0048] In operation S230, at least one first characteristic information of the conference participant is determined according to the first data; wherein the first characteristic information is used to characterize the emotional information and / or conference participation of the conference participant.

[0049] In some embodiments, the first data is a single image of a conference participant, and the single image of each conference participant is analyzed by image recognition and analysis technology to determine the first characteristic information of the conference participant.

[0050] The first characteristic information is used to reflect the emotional information and / or conference participation of the conference participants, and the conference participation is determined based at least on the action information and eye information of the conference participants.

[0051] In operation S240, second characteristic information of the conference participant is determined according to the second data, wherein the second characteristic information is used to characterize the fatigue level of the conference participant.

[0052] In some embodiments, the fatigue level of the conference participants at the current moment can be determined by analyzing the physiological information of the conference participants.

[0053] In operation S250, a quality evaluation result of the current conference is calculated based on the first feature information and the second feature information.

[0054] In some embodiments, the first feature information and the second feature information are preprocessed to obtain input features, and the input features are input into a pre-trained conference quality evaluation model, and the conference quality evaluation model determines the quality evaluation result of the conference at the current stage based on the input features. The quality evaluation result can reflect the satisfaction of the participants with the conference at the current stage.

[0055] exist Figure 2 Based on the provided conference quality determination method, the data processing method of this embodiment may further include: in operation S260, generating prompt information according to the quality evaluation result; wherein the prompt information is used to guide the conference organizer to optimize the subsequent process of the current conference.

[0056] Specifically, for example, during the meeting, the quality evaluation results at the current stage and the generated prompt information can be fed back to the meeting organizer through a visual interface, and the meeting organizer can optimize the meeting process (such as adjusting the agenda, changing the discussion method, etc.) according to the received information to improve the quality of the meeting.

[0057] In the specific implementation process, the pre-trained conference quality model can be, for example, a random forest model. The conference quality evaluation model includes multiple decision trees, and the quality evaluation result of the current conference is obtained by analyzing the preprocessed input features.

[0058] The conference quality determination method provided by the present disclosure can be executed at irregular intervals during the conference to timely determine the quality of the conference at each stage, so that the conference organizer can timely adjust the content and form of the conference according to the conference quality evaluation results, which helps to adjust the conference quality in real time.

[0059] Furthermore, the embodiments of the present disclosure acquire relevant data through different devices to obtain feature information of different dimensions, and determine the conference quality based on the multi-dimensional feature information, which helps to improve the objectivity and accuracy of the conference quality evaluation results.

[0060] Figure 3 A flowchart of obtaining first data of a current conference scene based on a first device according to an embodiment of the present disclosure is schematically shown.

[0061] like Figure 3 As shown, the method of acquiring the first data of the current conference scene based on the first device in this embodiment includes operations S310 to S320.

[0062] In operation S310, at least one conference scene picture is acquired in real time based on the first device, wherein the conference scene picture includes images of all conference participants.

[0063] In operation S320, the conference scene picture is identified, and first data corresponding to each participant is obtained from the conference scene picture, wherein the first data only includes a single image of the participant.

[0064] In some embodiments, the current conference scene is photographed in real time based on the first device to obtain a conference scene picture, wherein the conference scene picture should include images of all participants. For example, a first device with multiple cameras can be arranged at the conference site to perform multi-camera photography of the conference scene. After obtaining the conference scene picture, portrait recognition is performed from the conference scene picture based on a pre-trained portrait recognition model to obtain a single person image of each participant (i.e., first data).

[0065] Figure 4 The flowchart of determining the first characteristic information of a conference participant based on the first information according to an embodiment of the present disclosure is schematically shown.

[0066] like Figure 4 As shown, the method of determining the first characteristic information of the conference participant according to the first information in this embodiment includes operations S410 to S420.

[0067] In operation S410, the first data is processed using a pre-trained image processing model to obtain facial information, action information, and eye information of the conference participant.

[0068] In operation S420, first feature information of the conference participant is determined based on the facial information, the action information, and the eye information, wherein the first feature information is used to characterize the emotion information and conference participation of the conference participant.

[0069] In some embodiments, target information of a conference participant is identified from the first data based on a pre-trained image processing model, wherein the target information includes facial information, motion information, eye information, etc. of the conference participant.

[0070] In the specific implementation process, the identity and emotions of the participants are analyzed based on their facial information, and the first data and second data of the participants are matched in time to determine the multi-dimensional feature information of the participants. The emotions of the participants may include excitement, happiness, surprise, curiosity, doubt, anger, pain, contempt, etc. Different emotions can reflect the atmosphere of the meeting to a certain extent, thereby assisting in evaluating the quality of the meeting.

[0071] By analyzing the body movements of the participants, the movement information of the participants is determined, and the movement information can reflect the meeting participation of the participants to a certain extent. Among them, the movement information can include, for example, the frequency of speaking, nodding, shaking head, bowing head, recording, and leaving the meeting. When the participants frequently bow their heads and leave the meeting, it means that the participants have a low degree of participation in the meeting. When the participants speak, nod, record, etc., it means that the participants have a high degree of participation in the meeting. Meeting participation can reflect the satisfaction of the participants with the meeting to a certain extent, thereby indirectly reflecting the quality of the meeting.

[0072] It should be noted that in addition to determining the motion information of the participants based on the first data, the motion information of the participants can also be obtained based on the second device. For example, when the second device is a smart bracelet, the body movements of the participants can be directly detected by the second device. Technical personnel in this field can choose the method of obtaining the motion information of the participants according to actual conditions, and this disclosure is not limited here.

[0073] And, based on the first data, the line of sight of the participant is tracked to determine the line of sight direction (i.e., eye information) of the participant, and further determine the participant's attention to the content of the meeting and the degree of participation in the meeting. The line of sight information can be combined with the action information to jointly determine the meeting participation of the participant. For example, when the participant's action is lying down, but the line of sight is always following the large screen, the person's meeting participation is also high.

[0074] The disclosed embodiment analyzes the first data of the conference participants to obtain evaluation indicators of multiple different dimensions for reflecting the emotions and conference participation of the conference participants, and determines the first characteristic information of the conference participants based on these evaluation indicators.

[0075] Figure 5 The flowchart of determining the second characteristic information of the conference participant according to the second data according to an embodiment of the present disclosure is schematically shown.

[0076] like Figure 5 As shown, the method of determining the second characteristic information of the conference participant according to the second data in this embodiment includes operations S510 to S530.

[0077] In operation S510, the heart rate variability of the conference participants is calculated based on the heart rate information of the conference participants.

[0078] In operation S520, the body surface temperature change of the conference participant is obtained based on the body surface temperature distribution information of the conference participant.

[0079] In operation S530, second characteristic information of the conference participant is determined according to the heart rate variability and the body surface temperature change; wherein the second characteristic information is used to characterize the fatigue degree of the conference participant.

[0080] In some embodiments, the heart rate information and body surface temperature distribution information of the participants are obtained in real time based on the second device. The second device may be, for example, a wearable device, a biosensor, an infrared thermal imaging device, etc. Based on the heart rate information at continuous moments, the heart rate variability of the participants is determined, wherein heart rate variability refers to the slight changes in the time intervals between consecutive heart beats, which is negatively correlated with fatigue status and psychological pressure. When the human body is in a state of fatigue or increased psychological pressure, heart rate variability may decrease. Similarly, changes in body surface temperature are also closely related to the physiological state of the human body, and can indirectly reflect the degree of fatigue of the human body. Therefore, in addition to the first characteristic information, the present disclosure also introduces second characteristic information, which is used to determine the current physiological state of the participants, thereby evaluating the fatigue level of the participants and further improving the accuracy of subsequent meeting satisfaction evaluations of the participants.

[0081] Figure 6 A flowchart for calculating a quality evaluation result of a current meeting based on first feature information and second feature information according to an embodiment of the present disclosure is schematically shown.

[0082] like Figure 6 As shown, in this embodiment, the quality evaluation result of the current conference is calculated based on the first feature information and the second feature information, including operations S610 to S620.

[0083] In operation S610, the first feature information and the second feature information are preprocessed to obtain input features.

[0084] In some embodiments, preprocessing may include at least one of data cleaning, denoising, and standardization. By preprocessing the first feature information and the second feature, the data quality can be effectively improved, thereby improving the accuracy of the conference quality assessment results.

[0085] In the specific implementation process, data cleaning includes: removing invalid data (deleting data records that cannot be identified or are incorrectly identified), timestamp correction (ensuring that the timestamp of the data is accurate, and correcting the data when necessary), and outlier detection (checking whether the results are within a reasonable range, and deleting or correcting records with obvious anomalies). Denoising includes: smoothing (using sliding average and other methods to remove noise from continuously changing data), pattern recognition (identifying and correcting misjudged data labels through machine learning algorithms), behavioral pattern analysis (removing behavioral data that encourages or does not conform to regular patterns through behavioral sequence analysis), and trend analysis (smoothing the second feature information through trend analysis to remove instantaneous fluctuations). Standardization includes: normalization (converting the emotion recognition results in the first feature information into a unified numerical range to facilitate comparative modeling), classification and coding (converting the behavior type in the first feature information into a categorical variable and coding it), grading standards (establishing fatigue grading standards, converting continuous data into categorical data), and segmented analysis (segmented processing and analysis of the first and second feature information according to different stages or themes of the meeting).

[0086] In operation S620, a quality evaluation result of the current conference is determined based on the input features by the pre-trained conference quality evaluation model.

[0087] In some embodiments, a meeting quality evaluation model is established based on a random forest algorithm, and participants' satisfaction scores on each stage of the meeting and the entire meeting are collected online or offline. The collected satisfaction scores are used as target variables for model training, and the model is optimized by adjusting the weight distribution to obtain a pre-trained meeting quality evaluation model.

[0088] When predicting the quality evaluation result of a conference based on the pre-trained conference quality evaluation model, the input features may include the historical evaluation data of the participants in addition to the first feature information and the second feature information. In the prediction process, different input features have different weights.

[0089] In the specific implementation process, the weight proportions of different input features can be, for example, the weight of emotion is 0.3, the weight of behavior is 0.25, the weight of fatigue is 0.2, the weight of meeting participation is 0.15, and the weight of historical evaluation data is 0.1. Among them, emotion is a key factor that directly affects the participants' evaluation of meeting quality, and positive emotions are usually associated with higher satisfaction. The behavior of participants can reflect their level of involvement and interest in the meeting. Long meetings may cause participants to be in a state of fatigue, thereby affecting the participants' evaluation of the quality of the meeting, which is a relatively important negative factor. The participation of participants in the meeting can reflect their enthusiasm for participating in the discussion and the degree of acceptance of the content of the meeting, thereby indirectly affecting the participants' evaluation of the quality of the meeting. Historical evaluation data can provide clues about the participant's personal preferences and expectations for the evaluation of meeting quality, thereby improving the accuracy of the meeting quality evaluation results.

[0090] The pre-trained conference quality evaluation model determines the quality evaluation result of the current conference based on the above input features. It should be noted that the result output by the conference quality evaluation model is a continuous value, which needs to be converted into a score between 1 and 10 by scaling (using minimum-maximum normalization) to obtain the quality evaluation result of the current conference. For example, if the minimum value of the predicted value output by the conference quality evaluation is -1 and the maximum value is 1, the predicted value can be converted into a score between 1 and 10 by the method of (predicted value + 1) * 5, and the mapping rules between the score and the quality evaluation result are defined to map each score to a specific quality evaluation result. For example, "very high quality" is mapped to 10 points, "high quality" is mapped to 8 points, and so on. Among them, the conference quality evaluation result is positively correlated with the satisfaction of the participants with the conference. When the participant's evaluation result of the conference quality is high, it means that the participant is relatively satisfied with the conference.

[0091] After obtaining the meeting quality evaluation results of all participants in the current meeting, the final quality evaluation result of the meeting is determined based on the meeting quality evaluation result of each participant, and meeting optimization suggestions are put forward based on the quality evaluation result. The quality evaluation results and meeting optimization suggestions are fed back to the meeting organizer through a visual interface, so that the meeting organizer can make real-time adjustments to subsequent meetings, such as adjusting the agenda, changing the discussion method, etc.

[0092] Optionally, when making conference optimization suggestions based on the quality evaluation results, in addition to providing overall optimization based on the final quality evaluation results, the conference quality evaluation results of each participant can be clustered, and based on the area where each participant is located, regional optimization suggestions for different conference areas can be provided to further improve the optimization effect. After the meeting is over, the quality evaluation results of the entire meeting are comprehensively analyzed to generate a comprehensive quality evaluation report for the meeting.

[0093] The conference quality determination method provided by the disclosed embodiment obtains the first data and the second data of the participants in real time, timely detects and feedbacks the participants' evaluation and satisfaction of the conference quality at the current stage, so that the conference organizer can adjust the content and form of the conference in a timely manner. And the conference quality evaluation result in the disclosed embodiment is obtained based on multi-dimensional data analysis, which can effectively improve the comprehensiveness, accuracy and objectivity of the conference quality evaluation result and reduce the influence of subjective factors.

[0094] Based on the above conference quality determination method, the present disclosure also provides a conference quality determination device. Figure 7 The device is described in detail.

[0095] Figure 7 The structural block diagram of the conference quality determination device according to an embodiment of the present disclosure is schematically shown.

[0096] like Figure 7 As shown, the conference quality determination device 700 of this embodiment includes a first acquisition module 710 , a second acquisition module 720 , a first determination module 730 , a second determination module 740 and a calculation module 750 .

[0097] The first acquisition module 710 is used to acquire the first data of the conference participant based on the first device. In one embodiment, the first acquisition module 710 can be used to perform the operation S210 described above, which will not be described in detail here.

[0098] The second acquisition module 720 is used to acquire the second data of the conference participant based on the second device. In one embodiment, the second acquisition module 720 can be used to perform the operation S220 described above, which will not be described in detail here.

[0099] The first determination module 730 is used to determine at least one first characteristic information of the conference participant according to the first data; wherein the first characteristic information is used to characterize the emotional information and / or conference participation of the conference participant. In one embodiment, the first determination module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0100] The second determination module 740 is used to determine the second characteristic information of the conference participant according to the second data, wherein the second characteristic information is used to characterize the fatigue degree of the conference participant. In one embodiment, the second determination module 740 can be used to perform the operation S240 described above, which will not be described in detail here.

[0101] The calculation module 750 is used to calculate the quality evaluation result of the current meeting based on the first feature information and the second feature information. In one embodiment, the calculation module 750 can be used to perform the operation S250 described above, which will not be described in detail here.

[0102] According to an embodiment of the present disclosure, any multiple modules of the first acquisition module 710, the second acquisition module 720, the first determination module 730, the second determination module 740 and the calculation module 750 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first acquisition module 710, the second acquisition module 720, the first determination module 730, the second determination module 740 and the calculation module 750 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the first acquisition module 710, the second acquisition module 720, the first determination module 730, the second determination module 740 and the calculation module 750 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0103] Figure 8 A block diagram of an electronic device suitable for implementing a conference quality determination method according to an embodiment of the present disclosure is schematically shown.

[0104] like Figure 8 As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0105] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0106] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the input / output (I / O) interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output (I / O) interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that the computer program read therefrom is installed into the storage portion 808 as needed.

[0107] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0108] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 802 and / or RAM 803 described above and / or one or more memories other than ROM 802 and RAM 803.

[0109] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the conference quality determination method provided by the embodiment of the present disclosure.

[0110] The above functions defined in the system / device of the embodiment of the present disclosure are executed when the computer program is executed by the processor 801. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0111] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0112] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0113] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0114] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0116] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for determining conference quality, characterized in that: The method comprises: Acquire first data of the conference participant based on the first device; Acquiring second data of the conference participant based on the second device; wherein the first device and the second device have different principles for acquiring data; Determine at least one first characteristic information of the conference participant according to the first data; wherein the first characteristic information is used to characterize the emotional information and / or conference participation of the conference participant; Determining second characteristic information of the conference participant according to the second data, wherein the second characteristic information is used to characterize the fatigue level of the conference participant; A quality evaluation result of the current conference is calculated based on the first feature information and the second feature information.

2. The conference quality determination method according to claim 1, characterized in that: The method further comprises: Prompt information is generated according to the quality evaluation result; wherein the prompt information is used to guide the conference organizer to optimize the subsequent process of the current conference.

3. The conference quality determination method according to claim 1, characterized in that: The acquiring first data of the current conference scene based on the first device includes: Acquiring at least one conference scene picture in real time based on the first device, wherein the conference scene picture includes images of all participants; The conference scene picture is identified, and first data corresponding to each conference participant is obtained from the conference scene picture, wherein the first data only includes a single image of the conference participant.

4. The conference quality determination method according to claim 3, characterized in that: The determining the first characteristic information of the conference participant according to the first information includes: Processing the first data using a pre-trained image processing model to obtain facial information, action information, and eye information of the participant; The first characteristic information of the conference participant is determined based on the facial information, action information, and eye information, wherein the first characteristic information is used to characterize the emotional information and conference participation of the conference participant.

5. The conference quality determination method according to claim 1, characterized in that: The second data at least includes the heart rate information and body surface temperature distribution information of the conference participant.

6. The conference quality determination method according to claim 5, characterized in that: The determining the second characteristic information of the conference participant according to the second data includes: Calculating the heart rate variability of the conference participant based on the heart rate information of the conference participant; Obtaining the change of the body surface temperature of the participant based on the body surface temperature distribution information of the participant; The second characteristic information of the conference participant is determined according to the heart rate variability and the body surface temperature change; wherein the second characteristic information is used to characterize the fatigue degree of the conference participant.

7. The conference quality determination method according to claim 1, characterized in that: The calculating the quality evaluation result of the current conference based on the first feature information and the second feature information includes: Preprocessing the first feature information and the second feature information to obtain input features; The pre-trained conference quality assessment model determines the quality assessment result of the current conference based on the input features.

8. A conference quality determination device, characterized in that: The device comprises: A first acquisition module, used for acquiring first data of a conference participant based on a first device; A second acquisition module, used for acquiring second data of the conference participant based on the second device; A first determination module, configured to determine at least one first characteristic information of the conference participant according to the first data; wherein the first characteristic information is used to characterize the emotional information and / or conference participation of the conference participant; A second determination module, configured to determine second characteristic information of the conference participant according to the second data, wherein the second characteristic information is used to characterize the fatigue level of the conference participant; A calculation module is used to calculate a quality evaluation result of the current meeting based on the first characteristic information and the second characteristic information.

9. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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