Conference seat arrangement method and device, electronic equipment and storage medium

Abstract

The invention provides a conference seat arrangement method and device, electronic equipment and a storage medium. Relates to the technical field of conference seat arrangement. The method comprises the following steps: acquiring participant information of a target conference; determining a conference layout type corresponding to the target conference; inputting the participant information and the attribute information of the conference layout type into a conference seat arrangement model to obtain a seat arrangement result of the target conference; the conference seat arrangement model is obtained by training a preset machine learning model through historical conference data; the conference seat arrangement model can determine corresponding weight information according to different participant information, and determine the seat arrangement result according to the weight information and the attribute information of the conference layout type. By means of the conference seat arrangement method and device, the technical problems that an existing conference seat arrangement method is low in seat arrangement efficiency and low in flexibility are solved, the seat arrangement efficiency is improved, and the conference seat arrangement flexibility is improved.

Description

Technical Field

[0001] This application relates to the technical field of conference seat arrangement, and particularly to a conference seat arrangement method, device, electronic device, and storage medium. Background Art

[0002] Current conference seat arrangement methods often rely on manual operations, such as using statistical tables for seat arrangement. This method not only takes time and effort, but also due to human factors, the information of participants cannot be synchronized in a timely manner, and situations such as missed seat arrangements often occur, lacking flexibility. Therefore, the current conference seat arrangement methods have the problems of low seat arrangement efficiency and low flexibility. Summary of the Invention

[0003] Embodiments of this application provide a conference seat arrangement method, device, electronic device, and storage medium.

[0004] According to the first aspect of this application, a conference seat arrangement method is provided. The method includes:

[0005] Obtain the information of the participants in the target conference;

[0006] Determine the conference layout type corresponding to the target conference;

[0007] Input the participant information and the attribute information of the conference layout type into a conference seat arrangement model to obtain the seat arrangement result of the target conference; the conference seat arrangement model is obtained by training a preset machine learning model with historical conference data; the conference seat arrangement model can determine the corresponding weight information according to different participant information, and determine the seat arrangement result according to the weight information and the attribute information of the conference layout type.

[0008] According to an embodiment of this application, the determining the conference layout type corresponding to the target conference includes:

[0009] Display a conference layout selection interface; the conference layout selection interface includes a plurality of controls, and the plurality of controls respectively correspond to different conference layout types;

[0010] Receive a selection instruction for the controls in the conference layout selection interface;

[0011] Based on the selection instruction, determine the conference layout type corresponding to the control.

[0012] According to an embodiment of this application, the determining the conference layout type corresponding to the target conference includes:

[0013] Obtain the seat information of the venue corresponding to the target conference;

[0014] Determine the meeting layout type corresponding to the target meeting according to the venue seat information and the participant information.

[0015] According to an embodiment of the present application, inputting the participant information and the attribute information of the meeting layout type into a meeting seat arrangement model to obtain the seat arrangement result of the target meeting includes:

[0016] The meeting seat arrangement model determines the weight information corresponding to the participants based on the participant information;

[0017] Based on the attribute information of the meeting layout type, determine the seat dot matrix corresponding to the meeting layout type;

[0018] Based on the weight information and the seat dot matrix, determine the seat of each participant in the seat dot matrix;

[0019] Generate the seat arrangement result of the target meeting based on the seats of each participant.

[0020] According to an embodiment of the present application, before generating the seat arrangement result of the target meeting based on the seats of each participant, the method further includes:

[0021] Display the seats of each participant;

[0022] Receive an adjustment instruction for the seats;

[0023] Adjust the seats of the corresponding participants based on the adjustment instruction.

[0024] According to an embodiment of the present application, determining the seat of each participant in the seat dot matrix based on the weight information and the seat dot matrix includes:

[0025] The weight information at least includes industry weight, position weight, level weight, meeting role weight, and historical meeting seat weight;

[0026] Obtain the company type information included in the participant information;

[0027] Establish an association relationship between the company type information, the seat dot matrix, and the meeting layout type;

[0028] Based on the association relationship and the weight information, determine the seat of each participant in the seat dot matrix.

[0029] According to an embodiment of the present application, training a preset machine learning model with historical meeting data includes:

[0030] Extract features from the historical meeting data to obtain meeting feature parameters; the meeting feature parameters at least include: seat feature parameters, line-of-sight clarity parameters, and communication efficiency parameters;

[0031] Based on the meeting feature parameters, train the preset machine learning model to obtain the meeting seat arrangement model.

[0032] According to the second aspect of the present application, there is provided a meeting seat arrangement device, the device includes:

[0033] An acquisition module, configured to acquire information of participants in a target meeting;

[0034] A determination module, configured to determine the meeting layout type corresponding to the target meeting;

[0035] A seat arrangement module, configured to input the participant information and the attribute information of the meeting layout type into the meeting seat arrangement model to obtain the seat arrangement result of the target meeting; the meeting seat arrangement model is obtained by training a preset machine learning model with historical meeting data; the meeting seat arrangement model can determine corresponding weight information according to different participant information, and determine the seat arrangement result according to the weight information and the attribute information of the meeting layout type.

[0036] According to the third aspect of the present application, there is provided an electronic device, including:

[0037] At least one processor; and

[0038] A memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the present application.

[0040] According to the fourth aspect of the present application, there is provided a non-transitory computer-readable storage medium storing computer instructions, and the computer instructions are used to cause a computer to execute the method described in the present application.

[0041] The method of the embodiment of the present application includes: obtaining the information of the participants in the target meeting; determining the type of meeting layout corresponding to the target meeting; inputting the participant information and the attribute information of the meeting layout type into a meeting seat arrangement model to obtain the seat arrangement result of the target meeting; the meeting seat arrangement model is obtained by training a preset machine learning model with historical meeting data; the meeting seat arrangement model can determine the corresponding weight information according to different participant information, and determine the seat arrangement result according to the weight information and the attribute information of the meeting layout type. Through the present application, the seat arrangement efficiency is improved and the flexibility of meeting seat arrangement is enhanced.

[0042] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0043] By referring to the drawings and reading the detailed description below, the above and other purposes, features, and advantages of the exemplary embodiments of the present application will become easily understandable. In the drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, where:

[0044] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0045] Figure 1 Shows the processing flow diagram of the meeting seat arrangement method provided by the embodiment of the present application Figure 1 ;

[0046] Figure 2 Shows the processing flow diagram of the meeting seat arrangement method provided by the embodiment of the present application Figure 2 ;

[0047] Figure 3 Shows the processing flow diagram of the meeting seat arrangement method provided by the embodiment of the present application Figure 3 ;

[0048] Figure 4 Shows the processing flow diagram of the meeting seat arrangement method provided by the embodiment of the present application Figure 4 ;

[0049] Figure 5 Shows the processing flow diagram of the meeting seat arrangement method provided by the embodiment of the present application Figure 5 ;

[0050] Figure 6 Shows the processing flow diagram of the meeting seat arrangement method provided by the embodiment of the present application Figure 6 ;

[0051] Figure 7 An optional schematic diagram of the meeting seat arrangement device provided by the embodiments of the present application is shown;

[0052] Figure 8 An optional schematic diagram of an electronic device provided by the embodiments of the present application is shown. Detailed implementation manners

[0053] To make the objectives, features, and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0054] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0055] In the following description, the terms "first / second" only distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second" can be interchanged with a specific order or sequence when allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0057] The processing flow in the meeting seat arrangement method provided by the embodiments of the present application will be described. Refer to Figure 1 , Figure 1 is the schematic diagram of the processing flow of the meeting seat arrangement method provided by the embodiments of the present application Figure 1 , and will be described in conjunction with Figure 1 the steps S101 - S103 shown.

[0058] Step S101, obtain the information of the participants in the target meeting.

[0059] In some embodiments, the information of the participants may include attribute information such as the industry, position, level, meeting role, and historical meeting seats of the participants. The target meeting may include: a meeting for which meeting seat arrangement is required.

[0060] Step S102, determine the meeting layout type corresponding to the target meeting.

[0061] In some embodiments, the meeting layout type may include: the physical layout method of the meeting room. Specific meeting layout types may include: desk type, ladder type, fan type, U type, loop type, classic meeting, round table meeting, bilateral meeting and other layout types. The embodiments of the present application do not limit the specific meeting layout type.

[0062] Step S103, input the information of the participants and the attribute information of the meeting layout type into the meeting seat arrangement model to obtain the seat arrangement result of the target meeting; the meeting seat arrangement model is obtained by training a preset machine learning model with historical meeting data; the meeting seat arrangement model can determine the corresponding weight information according to different participant information, and determine the seat arrangement result according to the weight information and the attribute information of the meeting layout type.

[0063] In some embodiments, the meeting seat arrangement model may include: a preset machine learning model that determines corresponding weight information according to different attribute information of the participants and combines the attribute information of the meeting layout type to determine the seat arrangement result. The meeting seat arrangement model can be obtained by training a preset machine learning model with historical meeting data. The machine learning model may include: clustering analysis algorithm, classification algorithm and neural network model. The historical meeting data may include: historical meeting basic information, historical participant information and historical meeting check-in situation. The trained meeting seat arrangement model can generate the best seat layout patterns corresponding to different meeting layout types. The seat arrangement result may include: the seat layout plan output by the model. Among them, the seat layout plan may include: the seats of each participant in the seat matrix. The weight information may include the weight values calculated according to the participant information. The weight information can be used to represent the relative importance of each participant in the target meeting.

[0064] The method of the embodiments of the present application analyzes the information of the participants in the target meeting and the determined meeting layout type, and inputs the participant information and the meeting layout type into the machine learning model trained with historical meeting data. It can automatically determine the weight information of each participant, and can also intelligently generate the seat arrangement result according to the weight information and the specific requirements of the meeting layout. In this way, it can automatically generate the seat arrangement result, reducing the time and possible errors of manual operation. At the same time, since the meeting seat arrangement model can flexibly adapt to different meeting scales and layouts and adjust the seat arrangement according to different situations, the seat arrangement efficiency is improved and the flexibility of the meeting seat arrangement is enhanced.

[0065] In some embodiments, the processing flow diagram of the meeting seat arrangement method Figure 2 , such as Figure 2As shown, determining the meeting layout type corresponding to the target meeting in step S102 may include:

[0066] Step S201, display a meeting layout selection interface; the meeting layout selection interface includes multiple controls, and the multiple controls respectively correspond to different meeting layout types.

[0067] Step S202, receive a selection instruction for a control within the meeting layout selection interface.

[0068] Step S203, based on the selection instruction, determine the meeting layout type corresponding to the control.

[0069] In this embodiment, the meeting layout selection interface may allow the user to select a layout suitable for the target meeting from multiple preset meeting layout types. The meeting layout selection interface may include multiple controls. The controls may include: controls such as buttons, icons, and lists. Each control represents a meeting layout type. The selection instruction may be generated by the user clicking on the control. The selection instruction may also be generated by other interactions between the user and the control, which is not limited in the embodiments of the present application.

[0070] As an example, the online seat arrangement system may include a meeting layout selection interface. The meeting layout selection interface may be displayed in the online seat arrangement system. The meeting layout selection interface may include multiple buttons, and each button represents a different meeting layout type. For example, button 1 corresponds to a U-shaped layout, and button 2 corresponds to a round-table meeting. A text description of the meeting layout type may be attached next to the button. When the user clicks button 2 according to the meeting requirements, the online seat arrangement system may receive the selection instruction for button 2 and determine that the meeting layout type corresponding to the target meeting is a round-table meeting.

[0071] The method of the embodiments of the present application, by analyzing the participant information of the target meeting and the determined meeting layout type, inputs the participant information and the meeting layout type into a machine learning model trained with historical meeting data. It can automatically determine the weight information of each participant, and can also intelligently generate a seat arrangement result according to the weight information and the specific requirements of the meeting layout. In this way, it can automatically generate a seat arrangement result, reducing the time of manual operation and possible errors. At the same time, since the meeting seat arrangement model can flexibly adapt to different meeting scales and layouts and adjust the seat arrangement according to different situations, the seat arrangement efficiency is improved and the flexibility of the meeting seat arrangement is increased.

[0072] In some embodiments, a schematic diagram of the processing flow of the meeting seat arrangement method Figure 3 , as Figure 3 shown, determining the meeting layout type corresponding to the target meeting in step S102 may include:

[0073] Step S301: Obtain the venue seat information corresponding to the target meeting.

[0074] Step S302: Determine the meeting layout type corresponding to the target meeting according to the venue seat information and the information of the participants.

[0075] In this embodiment, the venue seat information may include: the specific seat arrangement information of the actual meeting room corresponding to the target meeting. Specifically, the venue seat information may include information such as the total number of seats, the distribution of seats, seat numbers, and the relative positions of seats and meeting room facilities. The embodiments of the present application do not limit the specific meeting seat information. The meeting room facilities may include: projectors and podiums.

[0076] As an example, according to the total number of seats and the distribution of seats included in the venue seat information of Meeting Room 1, it is determined that the meeting layout types that Meeting Room 1 can accommodate include the classroom type and the bilateral meeting type. According to the information of the participants, it is determined that the speakers included in the target meeting are Speaker A and Speaker B, and Speaker A and Speaker B represent different companies respectively. Therefore, the bilateral meeting is more suitable for the target meeting. It is determined that the meeting layout type corresponding to the target meeting is the bilateral meeting type.

[0077] In some embodiments, the processing flow diagram of the meeting seat arrangement method Figure 4 , as Figure 4 shown, inputting the information of the participants and the attribute information of the meeting layout type into the meeting seat arrangement model in Step S103 to obtain the seat arrangement result of the target meeting may include:

[0078] Step S401: The meeting seat arrangement model determines the weight information corresponding to the participants based on the information of the participants.

[0079] In this embodiment, the weight information may include industry weight, position weight, level weight, meeting role weight, and historical meeting seat weight. Calculate the weight values corresponding to the industry weight, position weight, level weight, meeting role weight, and historical meeting seat weight of each participant according to the information of the participants. Perform weight summation based on the calculated weight values, and the total weight value can reflect the relative importance of the participants in the target meeting.

[0080] In some embodiments, it is assumed that the ratio of each weight information in the meeting seating arrangement model is: industry weight: position weight: level weight: meeting role weight: historical meeting seat weight = 1:1:2:1.5:0.5. Specifically, in implementation, first, according to the influence and relevance of the industry to which the attendee belongs, determine its industry weight value. For example, the weight of the financial industry is 10 points, and the weight of the Internet industry is 8 points, etc. Secondly, based on the position level of the attendee, such as the general manager's weight is 10 points, the department manager's weight is 7 points, etc., determine the position weight value. Then, according to the level of the attendee, such as the weight of high-level personnel is 15 points, the weight of mid-level personnel is 10 points, etc., determine the level weight value. Next, according to the role of the attendee in the meeting, such as the weight of the keynote speaker is 12 points, the weight of the attendee is 8 points, etc., determine the meeting role weight value. Finally, according to the seating situation of the attendee in previous meetings, such as the weight of those who often sit in the front row is 5 points, and the weight of those in the back row is 3 points, etc., determine the historical meeting seat weight value. Then, perform weighted calculation on each weight value according to the above ratio. For example, for a certain attendee, the industry weight is 10 points, the position weight is 7 points, the level weight is 10 points, the meeting role weight is 8 points, and the historical meeting seat weight is 3 points. Its total weight value is 10×1 / 6 + 7×1 / 6 + 10×2 / 6 + 8×1.5 / 6 + 3×0.5 / 6 = 10.5 points. Calculate the total weight value of each attendee in this way.

[0081] Step S402: Based on the attribute information of the meeting layout type, determine the seat dot matrix corresponding to the meeting layout type.

[0082] As an example, the meeting seating arrangement model defines the structure of the seat dot matrix according to the attribute information of the meeting layout type. Then, divide the dot matrix diagram on the two-dimensional plane corresponding to the actual meeting room, assign numbers to each seat point in the two-dimensional plane, and reserve seats corresponding to special facility requirements. Verify whether the dot matrix diagram conforms to the actual space layout of the actual meeting room, and finally output the seat dot matrix diagram. The attribute information of the meeting layout type may include: the total number of seats, the layout shape, and special facility requirements. The seat dot matrix may include seats corresponding to each attendee.

[0083] Step S403: Based on the weight information and the seat dot matrix, determine the seat of each attendee in the seat dot matrix.

[0084] Step S404: Based on the seats of each attendee, generate the seat arrangement result of the target meeting.

[0085] As an example, the meeting seating arrangement model sorts according to the total weight value included in the weight information of the participants. The meeting seating arrangement model will give priority to the participants with larger total weight values and arrange them in more important seats in the seating matrix, such as near the podium or the central area of the meeting room. The meeting seating arrangement model will continue to allocate seats in the seating matrix based on the remaining seating matrix and the weight values of the remaining participants until all participants are arranged in seats. Based on the seats of each participant, the meeting seating arrangement model will generate the seating arrangement result of the target meeting. The seating arrangement result may include a seating chart and a list of participants. Through the seating arrangement result, the name and corresponding seat number of each participant can be determined. The total weight value of the participants with special needs is higher than that of other participants, and it can be preset which participants in the target meeting have special needs.

[0086] In some embodiments, before step S404, the meeting seating arrangement method further includes: displaying the seat of each participant; receiving an adjustment instruction for the seat; and adjusting the seat of the corresponding participant based on the adjustment instruction.

[0087] As an example, the online seating arrangement system displays the name and corresponding seat number of each participant on the seat preview interface, receives seat adjustment requests submitted by the participants or other management personnel through the online seating arrangement system, and generates corresponding adjustment instructions according to the seat adjustment requests. The online seating arrangement system automatically reallocates seats based on the adjustment instructions and updates the seats of the participants in the seating matrix.

[0088] The method of the embodiment of the present application inputs the participant information of the target meeting and the determined meeting layout type into a machine learning model trained with historical meeting data by analyzing the participant information of the target meeting and the determined meeting layout type. It can automatically determine the weight information of each participant, and can also intelligently generate a seating arrangement result according to the weight information and the specific requirements of the meeting layout. In this way, it can automatically generate a seating arrangement result, reducing the time and possible errors of manual operations. At the same time, since the meeting seating arrangement model can flexibly adapt to different meeting scales and layouts and adjust the seating arrangement according to different situations, the seating arrangement efficiency is improved and the flexibility of the meeting seating arrangement is enhanced.

[0089] In some embodiments, the processing flow diagram of the meeting seating arrangement method Figure 5 , such as Figure 5 shown, based on the weight information and the seating matrix in step S403, determining the seat of each participant in the seating matrix may include:

[0090] Step S501, obtaining the company type information included in the participant information.

[0091] Step S502: Establish the association relationship between the company type information, seat matrix, and meeting layout type.

[0092] Step S503: Determine the seats of each participant in the seat matrix based on the association relationship and weight information.

[0093] In this embodiment, the company type information may include type information such as the industry, scale, and market positioning of the company to which the participants belong. The embodiments of the present application do not limit the specific company type information. To establish the association relationship between the company type information, seat matrix, and meeting layout type, first determine the meeting purpose of the target meeting and the characteristics of the participating companies according to the company type information. Then, establish the association relationship based on the determined meeting purpose of the target meeting, the characteristics of the participating companies, the seat matrix, and the meeting layout type.

[0094] As an example, the target meeting may be an industry meeting, and the meeting layout type may be a classic layout. Determine the company type information corresponding to the participants according to the participant information. Establish the association relationship between the company type information, seat matrix, and meeting layout type. According to the association relationship, arrange the personnel of the same company in the same row. For example, the companies participating in the target meeting in the industry include Company A and Company B. Arrange all the participants from Company A in the fifth row and all the participants from Company B in the sixth row. Moreover, the influence of Company A in the industry is higher than that of Company B in the industry, and so on. At the same time, according to the weight information and association relationship of each participant, determine the seats relatively closer to the central position in each row for the participants with relatively larger total weight values. Determine the seats relatively farther from the central position in each row for the participants with relatively smaller total weight values.

[0095] The method of the embodiments of the present application, by analyzing the participant information of the target meeting and the determined meeting layout type, inputs the participant information and the meeting layout type into a machine learning model trained with historical meeting data. It can automatically determine the weight information of each participant and can also intelligently generate the seat arrangement result according to the weight information and the specific requirements of the meeting layout. In this way, it can automatically generate the seat arrangement result, reducing the time of manual operation and possible errors. At the same time, since the meeting seat arrangement model can flexibly adapt to different meeting scales and layouts and adjust the seat arrangement according to different situations, it improves the seat arrangement efficiency and the flexibility of the meeting seat arrangement.

[0096] In some embodiments, the processing flow diagram of the meeting seat arrangement method Figure 6 , as Figure 6 shown, training the preset machine learning model with historical meeting data in step S103 may include:

[0097] Step S601: Extract features from historical meeting data to obtain meeting feature parameters.

[0098] Step S602: Based on the meeting feature parameters, train a preset machine learning model to obtain a meeting seat arrangement model.

[0099] In this embodiment, the meeting feature parameters may include: seat feature parameters, line-of-sight clarity parameters, and communication efficiency parameters. The seat feature parameters may include the physical features of each seat (such as the distance from the podium). The line-of-sight clarity may include whether the seat is directly facing the screen and whether there are visual obstructions for the seat. The communication efficiency parameters may include the distance and angle between seats that are convenient for communication. Then, based on the seat feature parameters, line-of-sight clarity parameters, and communication efficiency parameters, train a preset machine learning model to obtain a meeting seat arrangement model. The trained meeting seat arrangement model can determine the corresponding weight information according to different participant information, and automatically allocate the corresponding seats according to the weight information and the attribute information of the meeting layout type, and finally output the seat arrangement result.

[0100] In some embodiments, the process of determining the machine learning model corresponding to the meeting layout type is as follows: According to the meeting layout type (such as circular, rectangular, classroom style, etc.), collect the feature parameters related to the seats, including the physical features of the seats, line-of-sight clarity, and communication efficiency. Combine the historical meeting data to label the effect of seat arrangement, such as the satisfaction and feedback parameters of the participants, etc., as the labels for model training. Use the collected seat feature parameters and the labeling results as inputs to train the preset machine learning model. Through training, the model learns the relationship between seat features and meeting effects, and forms a meeting seat arrangement model. According to the information such as the positions and roles included in the participant information, the meeting seat arrangement model can automatically determine the weight information of each feature parameter. For example, the seats of important guests pay more attention to line-of-sight clarity, while the seats for group discussions pay more attention to communication efficiency.

[0101] In some embodiments, the meeting seat arrangement method may further include: After generating the seat arrangement result of the target meeting, the online seat arrangement system can send the seat information to the participants corresponding to the target meeting via email, text message, or mobile application. After the target meeting reaches the meeting start time, the participants can confirm their arrival through the check-in function of the online seat arrangement system, and the online seat arrangement system can update the seat status in real time and provide a seat query service on site.

[0102] The method of the embodiment of the present application inputs the participant information of the target meeting and the determined meeting layout type into a machine learning model trained with historical meeting data. It can automatically determine the weight information of each participant, and can also intelligently generate a seat arrangement result according to the weight information and the specific requirements of the meeting layout. In this way, it can automatically generate a seat arrangement result, reducing the time and possible errors of manual operations. At the same time, since the meeting seat arrangement model can flexibly adapt to different meeting scales and layouts and adjust the seat arrangement according to different situations, the seat arrangement efficiency is improved and the flexibility of the meeting seat arrangement is enhanced.

[0103] Next, the exemplary structure of the software modules included in the meeting seat arrangement device 90 provided by the embodiment of the present application will be further described. In some embodiments, as Figure 7 shown, the meeting seat arrangement device 90 may include: an acquisition module 901, which can be used to acquire the participant information of the target meeting; a determination module 902, which can be used to determine the meeting layout type corresponding to the target meeting; a seat arrangement module 903, which can be used to input the participant information and the attribute information of the meeting layout type into the meeting seat arrangement model to obtain the seat arrangement result of the target meeting; the meeting seat arrangement model is obtained by training a preset machine learning model with historical meeting data; the meeting seat arrangement model can determine the corresponding weight information according to different participant information, and determine the seat arrangement result according to the weight information and the attribute information of the meeting layout type.

[0104] In some embodiments, the determination module 902 can be used to: display a meeting layout selection interface; the meeting layout selection interface includes a plurality of controls, and the plurality of controls respectively correspond to different meeting layout types; receive a selection instruction for the controls in the meeting layout selection interface; and determine the meeting layout type corresponding to the control based on the selection instruction.

[0105] In some embodiments, the determination module 902 can be used to: acquire the venue seat information corresponding to the target meeting; and determine the meeting layout type corresponding to the target meeting according to the venue seat information and the participant information.

[0106] In some embodiments, the seat arrangement module 903 can be used to: the meeting seat arrangement model determines the corresponding weight information of the participants based on the participant information; determines the seat matrix corresponding to the meeting layout type based on the attribute information of the meeting layout type; determines the seat of each participant in the seat matrix based on the weight information and the seat matrix; and generates the seat arrangement result of the target meeting based on the seats of each participant.

[0107] In some embodiments, the seat arrangement module 903 can be used to: display the seats of each participant; receive an adjustment instruction for the seat; and adjust the seat of the corresponding participant based on the adjustment instruction.

[0108] In some embodiments, the seat arrangement module 903 can be used to: the weight information at least includes industry weight, position weight, level weight, meeting role weight, and historical meeting seat weight; obtain the company type information included in the participant information; establish an association relationship between the company type information, the seat lattice, and the meeting layout type; and determine the seat of each participant in the seat lattice based on the association relationship and the weight information.

[0109] In some embodiments, the meeting seat arrangement device 90 may further include a training module, and the training module can be used to: extract features from historical meeting data to obtain meeting feature parameters; the meeting feature parameters at least include: seat feature parameters, line-of-sight clarity parameters, and communication efficiency parameters; and train a preset machine learning model based on the meeting feature parameters to obtain a meeting seat arrangement model.

[0110] It should be noted that the description of the device in the embodiments of the present application is similar to the description of the above method embodiments, and has similar beneficial effects to the method embodiments, so details will not be repeated. For the technical details not described in the meeting seat arrangement device provided in the embodiments of the present application, they can be understood according to Figures 1 to 7 the description of any one of the drawings.

[0111] According to the embodiments of the present application, the present application also provides an electronic device and a non-transitory computer-readable storage medium.

[0112] Figure 8 FIG. shows a schematic block diagram of an exemplary electronic device 800 that can be used to implement the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present application described herein and / or claimed.

[0113] As Figure 8As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0114] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disc, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0115] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 executes the various methods and processes described above, such as the meeting seat arrangement method. For example, in some embodiments, the meeting seat arrangement method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the meeting seat arrangement method described above can be executed. Alternatively, in other embodiments, the computing unit 801 can be configured to execute the meeting seat arrangement method by any other appropriate means (e.g., by means of firmware).

[0116] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0117] The program code for implementing the methods of this application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0118] In the context of this application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0120] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0121] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server incorporating a blockchain.

[0122] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved, and no limitations are imposed herein.

[0123] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In the description of this application, "a plurality of" means two or more unless otherwise specifically defined.

[0124] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A method for arranging conference seats, characterized in that: The method comprises: Get the participant information of the target meeting; Determine a conference layout type corresponding to the target conference; The information of the participants and the attribute information of the conference layout type are input into a conference seating model to obtain a seating arrangement result for the target conference; the conference seating model is obtained by training a preset machine learning model through historical conference data; the conference seating model can determine corresponding weight information according to different information of the participants, and determine the seating arrangement result according to the weight information and the attribute information of the conference layout type.

2. The method according to claim 1, characterized in that Determining the conference layout type corresponding to the target conference includes: Displaying a conference layout selection interface; the conference layout selection interface includes a plurality of controls, and the plurality of controls respectively correspond to different conference layout types; Receiving a selection instruction for a control in the conference layout selection interface; Based on the selection instruction, a conference layout type corresponding to the control is determined.

3. The method according to claim 1, characterized in that Determining the conference layout type corresponding to the target conference includes: Obtaining the venue seat information corresponding to the target meeting; The conference layout type corresponding to the target conference is determined according to the conference venue seat information and the conference participant information.

4. The method according to claim 1, characterized in that: The step of inputting the information of the participants and the attribute information of the conference layout type into the conference seating model to obtain the seating arrangement result of the target conference includes: The conference seating model determines weight information corresponding to the conference participants based on the conference participant information; Determining a seat dot matrix corresponding to the conference layout type based on the attribute information of the conference layout type; Based on the weight information and the seat matrix, determine the seat of each participant in the seat matrix; Based on the seat of each participant, a seat arrangement result of the target meeting is generated.

5. The method according to claim 4, characterized in that Before generating the seat arrangement result of the target conference based on the seat of each participant, the method further includes: Display the seat of each participant; receiving an adjustment instruction for the seat; Based on the adjustment instruction, the seats of the corresponding conference participants are adjusted.

6. The method according to claim 4, characterized in that The step of determining the seat of each participant in the seat matrix based on the weight information and the seat matrix includes: The weight information includes at least industry weight, position weight, level weight, meeting role weight and historical meeting seat weight; Obtain company type information included in the participant information; Establishing an association relationship between the company type information, the seat matrix and the conference layout type; Based on the association relationship and the weight information, the seat of each participant in the seat matrix is ​​determined.

7. The method according to claim 1, characterized in that The training of the preset machine learning model by using the historical meeting data includes: Extracting features from the historical meeting data to obtain meeting feature parameters; the meeting feature parameters at least include: seat feature parameters, sight clarity parameters, and communication efficiency parameters; Based on the conference characteristic parameters, the preset machine learning model is trained to obtain the conference seating model.

8. A conference seating arrangement device, characterized in that: The device comprises: The acquisition module is used to obtain the participant information of the target conference; A determination module, used to determine the conference layout type corresponding to the target conference; A seating arrangement module is used to input the participant information and the attribute information of the conference layout type into a conference seating model to obtain a seating arrangement result for the target conference; the conference seating model is obtained by training a preset machine learning model with historical conference data; the conference seating model can determine corresponding weight information according to different participant information, and determine the seating arrangement result according to the weight information and the attribute information of the conference layout type.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1-7.

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