CN111178566A - Method and system for reserving meeting room - Google Patents
Method and system for reserving meeting room Download PDFInfo
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- CN111178566A CN111178566A CN201911345244.2A CN201911345244A CN111178566A CN 111178566 A CN111178566 A CN 111178566A CN 201911345244 A CN201911345244 A CN 201911345244A CN 111178566 A CN111178566 A CN 111178566A
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Abstract
The invention discloses a method and a system for reserving a meeting room, wherein the method comprises the following steps: acquiring identity information of a subscriber, and automatically generating configuration conditions for the subscriber to select through a predetermined authority and a historical predetermined model of the subscriber; and generating conference rooms with different time periods according to the preset entrance where the subscriber is located and the configuration condition selected by the subscriber and providing the conference rooms with the subscriber for selection. The embodiment actually changes the original single predetermined flow, adds the recommendation reservation, classifies different flows required by the reservation through subdividing the predetermined categories, and each category provides a final result scheme. The method for selecting the conference room based on the historical reservation information of the subscribers and the one-key configuration conditions of the subscribers can remarkably reduce the scheduled time of the subscribers, and can improve the expectation and the scheduling result of accurately scheduling the conference room according to the deep learning of the historical reservation information of the subscribers.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a method and a system for reserving a meeting room.
Background
At present, many employees of a large enterprise need to book in advance when applying for using a conference room, but booking a conference usually needs to fill in many configuration items, such as a conference time period, participants, booking places, and the like. Because the preset flow is single and the preset result needs to be adjusted by human intervention, the original single preset system is inconvenient to use and has a long preset time period. The other conference recommendation in the existing conference room reservation system takes each reservation condition as a parameter item, only the allocation of the existing conference room resource belongs to single optimization, and the recommended conference room is often not suitable for the actual requirements of subscribers and often not suitable for the reservation habits of the subscribers on the basis of the use habits of users.
For example, the patent numbers are: 201610632877.1 discloses a self-service application system for conference rooms in companies, which comprises: the meeting room application module is used for editing and sending a meeting room application request form; an address book module; the conference room data storage module is used for storing the room numbers of all the conference rooms and the number of the accommodated persons in the conference rooms; the data extraction module is used for receiving and analyzing a meeting room use application form and acquiring key information in the meeting room use application form; the address book comparison module is used for analyzing the application form and judging whether a meeting room distribution instruction is sent or not; the conference room distribution module is used for receiving the conference distribution instruction of the address list comparison module and distributing conference rooms; the two-dimension code generating module is used for encoding the two-dimension code and sending the two-dimension code to the mobile phone; the user finds out and opens the entrance guard of the conference room by means of the room number received on the mobile phone and the obtained two-dimensional code. Although the conference room reservation application system can reserve the conference room by self, the operation is complicated, the input configuration condition and the provided conference room are more mechanical, and the reservation time is slow.
Disclosure of Invention
In order to solve the problems in the prior art, embodiments of the present invention provide a method and a system for reserving a conference room, which can quickly provide the best choice of a reservation party for the reservation party by reserving information of the conference room through the history of the reservation party, thereby improving reservation efficiency.
In order to solve the technical problems, the invention adopts the technical scheme that:
in a first aspect, an embodiment of the present invention provides a method for reserving a conference room, including the following steps:
acquiring identity information of a subscriber, and automatically generating configuration conditions for the subscriber to select through a predetermined authority and a historical predetermined model of the subscriber;
and generating conference rooms with different time periods according to the preset entrance where the subscriber is located and the configuration condition selected by the subscriber and providing the conference rooms with the subscriber for selection.
Further, the historical scheduling models include a conference subject model, a conference person model, a conference time model, and a conference room model based on the historical scheduling information of the subscribers.
Furthermore, the conference theme model judges the department, project, conference type and conference holding time frequency of the historical conference theme by carrying out intelligent context analysis on the historical conference theme, and carries out high-low sequencing on the probability classification of the historical conference theme through a naive Bayes classifier.
Further, the predetermined entries include a group predetermined entry of the subscriber and a micro-application predetermined entry, the group predetermined entry automatically associates information data in a group, and the micro-application predetermined entry configures conference members and time periods through the subscriber and then automatically associates information data in a micro-application.
Further, the conference person model ranks the participation frequency of the persons in the historical conference according to the group reservation entry or the micro-application reservation entry.
Further, providing the subscriber with a selected conference room includes: current time meeting rooms, other time period meeting rooms, custom meeting rooms, and unavailable meeting rooms.
Further, the meeting time model is used for counting and sequencing the times of occurrence in the same meeting time period in different time periods.
On the other hand, an embodiment of the present invention further provides a system for reserving a conference room, including:
the condition configuration module is used for acquiring the identity information of a subscriber and automatically generating configuration conditions for the subscriber to select through the predetermined authority and the historical predetermined model of the subscriber;
and the conference room generating module is used for generating conference rooms in different time periods according to the preset entrance where the subscriber is located and the configuration condition selected by the subscriber and providing the conference rooms for the subscriber to select.
Further, the condition configuration module includes a history model unit that sets a conference subject model, a conference person model, a conference time model, and a conference room model through history predetermined information of the subscriber.
Furthermore, the conference theme model judges the department, project, conference type and conference holding time frequency of the historical conference theme by carrying out intelligent context analysis on the historical conference theme, and carries out high-low sequencing on the probability classification of the historical conference theme through a naive Bayes classifier.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the embodiment of the invention discloses a method and a system for reserving a conference room, wherein in the process of reserving the conference room by the method, personal information of a reservation person is firstly acquired so as to determine the permission of reserving the conference room, then configuration conditions are generated by acquiring the historical information of the reservation person for reserving the conference room for the reservation person to select, and then the selection of the reservation person of the conference room worker in different time periods is automatically generated by combining the selected configuration conditions based on a group entrance or a micro application entrance of the reservation person. The embodiment actually changes the original single predetermined flow, adds the recommendation reservation, classifies different flows required by the reservation through subdividing the predetermined categories, and each category provides a final result scheme. The method for selecting the conference room based on the historical reservation information of the subscribers and the one-key configuration conditions of the subscribers can remarkably reduce the scheduled time of the subscribers, and can improve the expectation and the scheduling result of accurately scheduling the conference room according to the deep learning of the historical reservation information of the subscribers.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for reserving a meeting room according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a reservation logic of a method for reserving a conference room according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating reservation through a group entry in the method for reserving a conference room according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in a first aspect, as shown in fig. 1, the present embodiment provides a method for reserving a conference room, including the following steps:
s1: acquiring identity information of a subscriber, and automatically generating configuration conditions for the subscriber to select through a predetermined authority and a historical predetermined model of the subscriber;
s2: and generating conference rooms with different time periods according to the preset entrance where the subscriber is located and the configuration condition selected by the subscriber and providing the conference rooms with the subscriber for selection.
Specifically, as shown in the logic diagram of the booking conference room shown in fig. 2, first, initial information of a booker, including personal information such as ID, name, job number and the like, is obtained from a user login information table, then, according to the personal information, the booking authority of the booker is known, which conference rooms allow the booker to book, which conference room bookers are not qualified to book, and resources (conference rooms) are divided according to the authority of the booker; automatically generating configuration conditions for a subscriber to select according to historical preset data of the subscriber, automatically recommending an optimal conference room to present to the subscriber after machine learning according to the historical data, simultaneously presenting various configuration conditions to the subscriber, further recommending the conference room according to a group entrance or a micro-application entrance of the subscriber, and automatically bringing group members to join in a preset conference when the group entrance is formed; when accessing from the micro-application, a plurality of sets of named member information in the form of past subjects are brought in from the backup history configuration item data and provided to the subscriber for selection. The secondarily recommended conference room generally has three predetermined routes, and the conference room selected by the subscriber is provided with: current time meeting rooms, other time period meeting rooms, custom meeting rooms, and unavailable meeting rooms. The timeliness meeting rooms in the graph are matched with the current user positioning information through meeting rooms in the standby historical record configuration item data, and the current available meeting rooms are arranged through a historical preset model. The custom conference room is actually a traditional reservation method that a reservation person performs a reservation configuration condition by himself, that is, when there is no history data, or the history data is less, or the reservation person performs manual reservation by himself without following a history reservation model, for example, when the current reservation person position cannot be located or a reservation conference place is not fixed, the reservation person is required to manually select a reservation position such as Nanjing-headquarters, that is, the reservation person performs manual input; if no meeting room is available, i.e. if the result item is empty, no suitable meeting room is available for the subscriber to choose from, the configuration conditions need to be changed to regenerate a meeting room, or the subscriber cancels the subscription.
Through the predetermined logic and the specific predetermined path, the present embodiment actually changes the original single predetermined flow, adds the recommended reservation, classifies the different flows required by the reservation by subdividing the predetermined categories, and each category provides a final result solution. The method has the advantages that the original historical records are subjected to parameter classification and purification treatment, time period rule extraction and same field frequency characteristic extraction, and the method has a huge effect in processing the incidence relation of mass data. The context analysis method of machine learning is used, and original information is separated from parameter items through the constructed parameters. Under the same historical information, the information has internal relevance, and a whole highly refined rule value is formed after the time, personnel, conference subject and conference room purification result are obtained and is associated with a reservation person. The purified information is shown by way of example as follows: a certain (reservation person) -every monday afternoon 2: 00-6: 00 (time period) -a certain department (personnel) -work reporting meeting (conference subject) -A01 (conference room), and after the rule is obtained, the reservation information is provided to automatically bring out information. Realize "one-key reservation". The traditional report filling mode is converted into intelligent reservation, unseen association relation is handed to a system program, and a result which best meets the expectation of a reservation user is intelligently calculated through a large amount of data matching. The method for selecting the conference room based on the historical reservation information of the subscribers and the one-key configuration conditions of the subscribers can remarkably reduce the scheduled time of the subscribers, and can improve the expectation and the scheduling result of accurately scheduling the conference room according to the deep learning of the historical reservation information of the subscribers.
Furthermore, the timely conference room matches the current time period, the conference room matched to the current suitable conference room is recommended to the reservation person as a recommended conference room, the conference rooms which can be used are listed out according to time sequence and are not matched to the suitable conference room and are selected by the reservation person, and in addition, the custom conference room needs to be used by the reservation person according to a custom setting item result. No available meeting room is available to prompt the subscriber to select other modes of meeting such as web conferencing. The setting condition items can be changed for all three types of conferences, and the setting item results can be updated again, so that the prescheders find meeting rooms which meet the requirements of the prescheders. In addition, the timeliness meeting room and the self-defined meeting room enable a reservation person to finish reservation after confirming the reservation information, and the reservation record enters the original historical record.
Preferably, the historical scheduling models include a conference subject model, a conference person model, a conference time model, and a conference room model based on the historical scheduling information of the subscribers. The four models are direct tools for finally recommending meeting rooms, statistics of the four dimensions are carried out on historical meetings through the four models, weights of proper proportions are divided through the four dimensions, and the optimal proportion weights are found through continuous fitting, so that optimal recommendation is carried out. The data model structure of the preset historical record is directly called when the standby historical configuration item data is called in the process, and the filtered and purified data is more humanized and intelligent. By utilizing the historical booking model associated data rule, a long series of filling of meeting rooms, time, participants and the like in the traditional booking meeting process is simplified into the fact that the system directly recommends the best meeting room according to the historical booking behaviors of the booker, the booker only needs to select the meeting room which is not the booker, and the working efficiency can show the effect in a multiplied way from the booked complexity.
Specifically, the conference theme model is used for judging the department, project, conference type and conference holding time frequency of the historical conference theme by performing intelligent context analysis on the historical conference theme, and performing high-low sequencing on the probability classification of the historical conference theme through a naive Bayes classifier. For example, by performing intelligent context analysis on the conference theme, it is determined which words belong to departments, projects, conference types, and time frequencies; if the requirement review of a certain product is carried out, the fact that the certain product belongs to the project is analyzed, and the requirement review belongs to the conference type. The "first quarter" in the "first quarter project exchange report" belongs to the time frequency, and the "exchange report" belongs to the conference type. Adopting a naive Bayes classifier to perform probability classification on various words of the subject of the conference subject sentence, and preparing to determine attribute characteristic attributes (department, project, conference type and time frequency) to obtain a historical subject of a training sample; and then calculating the conditional probability of the characteristic attribute of each conference theme, and finally sorting the theme probability according to the calculated conditional probability, wherein the conditional probability is used as the basis for finally distributing the weight of the conference room.
Further, the conference person model ranks the participation frequency of the persons in the historical conference according to the group reservation entry or the micro-application reservation entry. Further, the meeting time model is used for counting and sequencing the times of occurrence in the same meeting time period in different time periods. The conference time model may be divided into regular time and irregular time for the time period of the historical conference. The regular time law characteristic refers to that the regular time is scheduled in the same time period within a time node (the minimum unit is a month), such as scheduled every week (weekly regular meeting). x1 ═ [ (a 1-b 1), i1], range (00: 00-24: 00), a1 is predetermined starting time, b1 is predetermined ending time, and the sequence is based on the occurrence frequency of i1 in the time period; y1 ═ c1, j1, c1 is the day of the week, by the number of occurrences of j 1. And counting the specific data of x1 and y1, and performing basis of weight distribution of the conference room.
Preferably, the predetermined entries include a group predetermined entry and a micro-application predetermined entry of the subscriber, the group predetermined entry automatically associates information data in a group, and the micro-application predetermined entry configures conference members and time periods through the subscriber and then automatically associates information data in a micro-application. As shown in the schematic flow chart of fig. 3 for booking through the group portal, by directly obtaining the data associated with the group information from the backup history configuration data, the booker is in the group, and the information associated with the members in the group, such as the number of members in the group, the conference room in which the members in the group book most frequently, the members in the group who attend the conference most frequently, and the like, for example, the information of the member associated with the group of a certain product group is associated with the group of "members" in the history, and when the information is matched that the booker is in the product group three times per month (time period frequency), the requirement review (conference type in the conference theme) is brought into the conference theme "a certain product 2 month requirement review meeting" in the matching conference room, wherein more than 90% of the meeting rooms are a101 meeting rooms three times per month, and the a101 meeting room in the real-time data is vacant in a predetermined time period, and the matching becomes the. The subscriber confirms the next key reservation without error after seeing the matched information. The real-time data match currently enumerates the free meeting rooms in other time periods in the week, and the prescheduler can reset the scheduled time to select the meeting room.
Example two:
the embodiment provides a system for reserving a meeting room, which comprises:
the condition configuration module is used for acquiring the identity information of a subscriber and automatically generating configuration conditions for the subscriber to select through the predetermined authority and the historical predetermined model of the subscriber;
and the conference room generating module is used for generating conference rooms in different time periods according to the preset entrance where the subscriber is located and the configuration condition selected by the subscriber and providing the conference rooms for the subscriber to select.
Specifically, a subscriber acquires initial information of the subscriber from a user login information table through the system for reserving the conference room, wherein the initial information comprises personal information such as ID, name, job number and the like, then knows the reservation authority of the subscriber according to the personal information, namely which conference rooms allow the subscriber to reserve, which conference rooms are not qualified to reserve, and divides resources (conference rooms) according to the authority of the subscriber; automatically generating configuration conditions for a subscriber to select according to historical preset data of the subscriber, automatically recommending an optimal conference room to present to the subscriber after machine learning according to the historical data, simultaneously presenting various configuration conditions to the subscriber, further recommending the conference room according to a group entrance or a micro-application entrance of the subscriber, and automatically bringing group members to join in a preset conference when the group entrance is formed; when accessing from the micro-application, a plurality of sets of named member information in the form of past subjects are brought in from the backup history configuration item data and provided to the subscriber for selection. The secondarily recommended conference room generally has three predetermined routes, and the conference room selected by the subscriber is provided with: current time meeting rooms, other time period meeting rooms, custom meeting rooms, and unavailable meeting rooms. The timeliness meeting rooms in the graph are matched with the current user positioning information through meeting rooms in the standby historical record configuration item data, and the current available meeting rooms are arranged through a historical preset model. The custom conference room is actually a traditional reservation method that a reservation person performs a reservation configuration condition by himself, that is, when there is no history data, or the history data is less, or the reservation person performs manual reservation by himself without following a history reservation model, for example, when the current reservation person position cannot be located or a reservation conference place is not fixed, the reservation person is required to manually select a reservation position such as Nanjing-headquarters, that is, the reservation person performs manual input; if no meeting room is available, i.e. if the result item is empty, no suitable meeting room is available for the subscriber to choose from, the configuration conditions need to be changed to regenerate a meeting room, or the subscriber cancels the subscription.
The original single preset flow is changed through the condition configuration module and the meeting room generation module, recommendation reservation is added, different flows required by a reservation person are classified through subdividing preset categories, and each category provides a final result scheme. The method has the advantages that the original historical records are subjected to parameter classification and purification treatment, time period rule extraction and same field frequency characteristic extraction, and the method has a huge effect in processing the incidence relation of mass data. The context analysis method of machine learning is used, and original information is separated from parameter items through the constructed parameters. Under the same historical information, the information has internal relevance, and a whole highly refined rule value is formed after the time, personnel, conference subject and conference room purification result are obtained and is associated with a reservation person. The purified information is shown by way of example as follows: a certain (reservation person) -every monday afternoon 2: 00-6: 00 (time period) -a certain department (personnel) -work reporting meeting (conference subject) -A01 (conference room), and after the rule is obtained, the reservation information is provided to automatically bring out information. Realize "one-key reservation". The traditional report filling mode is converted into intelligent reservation, unseen association relation is handed to a system program, and a result which best meets the expectation of a reservation user is intelligently calculated through a large amount of data matching. The method for selecting the conference room based on the historical reservation information of the subscribers and the one-key configuration conditions of the subscribers can remarkably reduce the scheduled time of the subscribers, and can improve the expectation and the scheduling result of accurately scheduling the conference room according to the deep learning of the historical reservation information of the subscribers.
Preferably, the condition configuration module includes a history model unit that sets a conference subject model, a conference person model, a conference time model, and a conference room model through history predetermined information of the subscriber. The four models are direct tools for finally recommending meeting rooms, statistics of the four dimensions are carried out on historical meetings through the four models, weights of proper proportions are divided through the four dimensions, and the optimal proportion weights are found through continuous fitting, so that optimal recommendation is carried out. The data model structure of the preset historical record is directly called when the standby historical configuration item data is called in the process, and the filtered and purified data is more humanized and intelligent. By utilizing the historical booking model associated data rule, a long series of filling of meeting rooms, time, participants and the like in the traditional booking meeting process is simplified into the fact that the system directly recommends the best meeting room according to the historical booking behaviors of the booker, the booker only needs to select the meeting room which is not the booker, and the working efficiency can show the effect in a multiplied way from the booked complexity.
Preferably, the conference theme model is used for judging the department, project, conference type and conference holding time frequency of the historical conference theme by performing intelligent context analysis on the historical conference theme, and performing high-low sequencing on the probability classification of the historical conference theme through a naive Bayes classifier. Further, the meeting time model is used for counting and sequencing the times of occurrence in the same meeting time period in different time periods.
Preferably, the conference room generation module includes an entrance selection unit, the entrance selection unit selects a group reservation entrance and a micro application reservation entrance of a subscriber, the group reservation entrance automatically associates information data in a group, and the micro application reservation entrance configures conference members and a time period through the subscriber and then automatically associates information data in a micro application.
Preferably, the providing to the subscriber a selected conference room comprises: current time meeting rooms, other time period meeting rooms, custom meeting rooms, and unavailable meeting rooms.
All the above-mentioned optional technical solutions can be combined arbitrarily to form the optional embodiments of the present invention, and are not described herein again.
It should be noted that: in the system for reserving a conference room provided in the above embodiment, when the conference room is reserved, only the division of the above functional modules is exemplified, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the system for reserving a conference room is divided into different functional modules to complete all or part of the above described functions. In addition, the system for reserving a conference room provided in the above embodiment and the method embodiment for reserving a conference room belong to the same concept, and specific implementation processes thereof are described in the method embodiment and are not described herein again.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A method of reserving a conference room, comprising the steps of:
acquiring identity information of a subscriber, and automatically generating configuration conditions for the subscriber to select through a predetermined authority and a historical predetermined model of the subscriber;
and generating conference rooms with different time periods according to the preset entrance where the subscriber is located and the configuration condition selected by the subscriber and providing the conference rooms with the subscriber for selection.
2. The method of reserving a conference room as claimed in claim 1, wherein the historical reservation models include a conference subject model, a conference person model, a conference time model and a conference room model based on the historical reservation information of the subscribers.
3. The method of reserving a meeting room as claimed in claim 2, wherein the meeting topic model is obtained by performing intelligent context analysis on a historical meeting topic, determining the department, project, meeting type and meeting holding time frequency of the historical meeting topic, and ranking the topic probability classification of the historical meeting through a naive Bayes classifier.
4. The method of reserving a meeting room according to claim 2, wherein the reservation entries include a group reservation entry of the reservation and a micro application reservation entry, the group reservation entry automatically associates the information data in the group, and the micro application reservation entry automatically associates the information data in the micro application after configuring the meeting members and the time period by the reservation.
5. The method of booking a meeting room of claim 4 wherein the meeting people model ranks the frequency of people participating in the historical meeting according to the group booking entry or the micro-application booking entry.
6. The method of reserving a conference room as claimed in claim 1, wherein providing the subscriber with the selected conference room comprises: current time meeting rooms, other time period meeting rooms, custom meeting rooms, and unavailable meeting rooms.
7. The method of reserving a meeting room of claim 2 wherein the meeting time model is created by counting and ordering the number of occurrences in the same meeting time period over different time periods.
8. A system for reserving a conference room, comprising:
the condition configuration module is used for acquiring the identity information of a subscriber and automatically generating configuration conditions for the subscriber to select through the predetermined authority and the historical predetermined model of the subscriber;
and the conference room generating module is used for generating conference rooms in different time periods according to the preset entrance where the subscriber is located and the configuration condition selected by the subscriber and providing the conference rooms for the subscriber to select.
9. The system of reserving a meeting room of claim 8, wherein the condition configuration module comprises a history model unit which sets a meeting subject model, a meeting person model, a meeting time model and a meeting room model through history reservation information of the subscriber.
10. The system for reserving a meeting room as claimed in claim 9, wherein the meeting topic model is obtained by performing intelligent context analysis on a historical meeting topic, determining the department, project, meeting type and meeting holding time frequency of the historical meeting topic, and ranking the probability classification of the topics of the historical meeting through a naive bayes classifier.
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| CN111818045A (en) * | 2020-07-07 | 2020-10-23 | 全时云商务服务股份有限公司 | Conference reservation method, device, equipment and storage medium |
| CN112734068A (en) * | 2021-01-13 | 2021-04-30 | 腾讯科技（深圳）有限公司 | Conference room reservation method, conference room reservation device, computer equipment and storage medium |
| CN114239888A (en) * | 2021-11-30 | 2022-03-25 | 珠海大横琴科技发展有限公司 | Conference room reservation method and device |
| CN114692914A (en) * | 2022-03-31 | 2022-07-01 | 中国建设银行股份有限公司 | Room state monitoring reservation method, device, equipment and computer storage medium |
| CN114970911A (en) * | 2022-04-15 | 2022-08-30 | 江西省天轴通讯有限公司 | A studio intelligent reservation method, system, storage medium and device |
| CN119398791A (en) * | 2024-12-31 | 2025-02-07 | 福建省万物智联科技有限公司 | A management method and related equipment for intelligent reception |
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| CN111818045A (en) * | 2020-07-07 | 2020-10-23 | 全时云商务服务股份有限公司 | Conference reservation method, device, equipment and storage medium |
| CN112734068A (en) * | 2021-01-13 | 2021-04-30 | 腾讯科技（深圳）有限公司 | Conference room reservation method, conference room reservation device, computer equipment and storage medium |
| CN114239888A (en) * | 2021-11-30 | 2022-03-25 | 珠海大横琴科技发展有限公司 | Conference room reservation method and device |
| CN114692914A (en) * | 2022-03-31 | 2022-07-01 | 中国建设银行股份有限公司 | Room state monitoring reservation method, device, equipment and computer storage medium |
| CN114970911A (en) * | 2022-04-15 | 2022-08-30 | 江西省天轴通讯有限公司 | A studio intelligent reservation method, system, storage medium and device |
| CN119398791A (en) * | 2024-12-31 | 2025-02-07 | 福建省万物智联科技有限公司 | A management method and related equipment for intelligent reception |
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Application publication date: 20200519 |