A shared conference room reservation management system based on artificial intelligence

Abstract

The application relates to the technical field of office appointment management, in particular to a shared conference room appointment management system based on artificial intelligence, which comprises an information collection module, which is used to collect conference room information and user information; a data analysis module, which dynamically generates an assignable conference room list by comparing the coincidence state of an appointment and a real-time occupation time period; a resource management module, which comprises: an early departure prediction unit, which is used to determine early departure prediction for a conducted conference based on the elastic fluctuation amount and the duration of the conducted conference in the use conference room; an early departure reminding unit, which is used to determine early departure reminding for an appointment individual based on the volume of the conducted conference with early departure prediction and the individual disappearance tendency value; and a resource reallocation unit, which is used to determine whether a conference room is an assignable conference room or to reduce the appointment priority of the conference room and trigger a maintenance warning according to the early departure reminding feedback result of the appointment individual and the vacancy duration of the conference room.

Description

Technical Field

[0001] This invention relates to the field of office management system technology, and in particular to a shared meeting room reservation management system based on artificial intelligence. Background Technology

[0002] In modern office environments, efficient management of shared meeting rooms is crucial. Traditional shared meeting room reservation management systems have several shortcomings. Firstly, limited information collection methods make it difficult to comprehensively and in real-time obtain meeting room reservation, usage, and user information, resulting in incomplete and untimely data updates that impact subsequent management decisions. Secondly, the lack of intelligent data analysis methods for determining meeting room status makes it impossible to accurately determine whether a meeting room is available or in use based on the relationship between the reserved and requested time slots, often leading to reservation conflicts. Furthermore, the system's ability to control meeting progress is weak, failing to effectively predict early departures and thus hindering the efficient use of meeting room resources. Moreover, system parameter settings are often fixed and cannot be dynamically adjusted based on actual usage, such as preset end-of-meeting flexibility and vacancy periods, making it difficult to adapt to complex and ever-changing meeting scenarios. Based on these shortcomings, there is an urgent need for an AI-based shared meeting room reservation management system to improve management efficiency and optimize resource allocation.

[0003] For example, Chinese patent application publication number CN110728388A discloses a shared meeting room reservation management system, including an operation platform module, an IoT gateway, a web server, a message queuing service module, a data analysis module, and a monitoring and alarm module. Meeting hosts can flexibly log in to the system interface through various methods, conveniently reserve meeting rooms according to meeting needs, and flexibly customize the hardware and software equipped in the meeting rooms. In addition, the reservation of meeting rooms can preset the permissions of the participants. Those who meet the requirements for participation and those who are invited can pass through the access control through identity verification, providing a channel for other people to apply for participation and approval. Leading IoT smart hardware covers access control, temperature and humidity air quality sensors, smart lighting control, smart air conditioning, and audio and video remote conferencing equipment, realizing the organic integration of hardware and software in the meeting room and greatly enriching the user experience.

[0004] However, existing technologies suffer from inflexible and inaccurate resource allocation, inaccurate prediction of meeting status, and a lack of dynamic adjustment mechanisms, resulting in low utilization of meeting room resources and frequent scheduling conflicts. Summary of the Invention

[0005] To address this, the present invention provides an AI-based shared meeting room reservation management system to overcome the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms in existing technologies, which lead to low utilization of meeting room resources and frequent reservation conflicts.

[0006] To achieve the above objectives, the present invention provides an artificial intelligence-based shared meeting room reservation management system, comprising:

[0007] The information collection module is used to collect meeting room reservation information, meeting room usage information, and user information;

[0008] The data analysis module, which is connected to the information collection module, is used to determine whether the meeting room is an allocable meeting room or a meeting room in use based on whether the meeting room's reservation time period and the reservation application time period overlap.

[0009] The resource management module, which is connected to the data analysis module, includes:

[0010] An early departure prediction unit is used to determine whether to issue an early departure prediction for the meetings already held in the meeting room based on the end elasticity fluctuation of the meetings already held and the duration of the meetings already held.

[0011] An early departure reminder unit, which is connected to the early departure prediction unit, is used to determine whether to issue an early departure reminder to the scheduled individual based on the volume of the already conducted meeting that issued the early departure prediction and the disappearance tendency value of the individuals in the already conducted meeting.

[0012] A resource reallocation unit, connected to the early departure reminder unit, is used to determine whether a reserved meeting room is an allocable meeting room based on the early departure reminder feedback type of the reserved individual and the vacancy duration of the reserved meeting room, or to reduce the reservation priority of the meeting room and trigger a maintenance warning.

[0013] The adjustment module, which is connected to the information collection module, the data analysis module, and the resource management module, is used to determine whether to adjust the preset end elastic fluctuation amount or the preset vacancy period based on the proportion of individual conflict events occurring in the allocable meeting rooms within a preset period and the proportion of early departure events occurring in the early departure prediction.

[0014] Furthermore, the data analysis module determines whether a meeting room is an allocable meeting room or is in use by including:

[0015] If the scheduled time slot for the meeting room overlaps with the time slot requested for the reservation, the meeting room is determined to be in use.

[0016] Alternatively, if the scheduled time slot for the meeting room does not overlap with the time slot requested for the reservation, the meeting room is determined to be an allocable meeting room.

[0017] Furthermore, the early departure prediction unit determines whether to issue an early departure prediction for the already conducted meeting by including:

[0018] If the end elastic fluctuation of an already conducted meeting is less than or equal to the preset elastic fluctuation or the duration of an already conducted meeting is greater than the preset duration, an early termination prediction is issued for the already conducted meeting.

[0019] If the end elastic fluctuation of an already conducted meeting is greater than the preset elastic fluctuation and the duration of the already conducted meeting is less than the preset duration, it is determined not to issue an early termination prediction for the already conducted meeting.

[0020] Furthermore, the elastic fluctuation amount is determined based on the ratio of the average actual duration of several meetings of the same type to the total scheduled duration, the preset elastic fluctuation amount is determined based on the ratio of the historical average actual duration of meetings of the same type to the total scheduled duration, and the preset duration is determined based on the historical average actual duration of meetings of the same type.

[0021] Furthermore, the early departure reminder unit determines whether to issue an early departure reminder to the individual who made the appointment by including:

[0022] If the volume of an ongoing meeting that has issued an early departure prediction is less than or equal to the preset volume and the individual's tendency to disappear from the ongoing meeting is greater than the preset tendency value, then an early departure reminder will be issued to the scheduled individual.

[0023] If the volume of an ongoing meeting that has issued an early departure prediction is greater than the preset volume, or if the individual's tendency to disappear during the ongoing meeting is less than or equal to the preset tendency value, then it is determined not to issue an early departure reminder to the scheduled individual.

[0024] Furthermore, the preset volume is determined based on the average volume of several meetings of the same type, the individual disappearance tendency value is determined based on the ratio of the number of people who have been detected leaving to the total number of participants in the meeting room, and the preset tendency value is determined based on the average of the individual disappearance tendency values ​​of several meetings of the same type that show early departure.

[0025] Furthermore, the resource reallocation unit determines whether the reserved meeting room is an allocable meeting room or lowers the reservation priority of the meeting room and triggers a maintenance warning, including:

[0026] The meeting room is determined to be an allocable meeting room if the early departure reminder feedback type of the individual is early departure, or if the early departure reminder feedback type of the individual is stagnation and the vacancy time of the meeting room reserved by the individual is greater than the preset vacancy time.

[0027] If the early departure reminder feedback type for an individual is "in use" and the vacancy time of the reserved meeting room is greater than the preset vacancy time, the reservation priority of that meeting room will be reduced and a maintenance warning will be triggered.

[0028] Furthermore, the preset idle time is determined based on the remaining time of the already conducted meeting.

[0029] Furthermore, the adjustment module determines whether to adjust the preset elastic fluctuation amount or the preset idle time by including:

[0030] If the percentage of individual conflict events in the allocable meeting rooms within a preset period is greater than the preset percentage of conflict events, then the preset vacancy period will be adjusted.

[0031] If the proportion of early termination events in the early termination prediction issued within the preset period is less than the preset early termination proportion, the preset end elastic fluctuation amount will be adjusted.

[0032] Furthermore, the adjustment amount of the preset vacancy period is positively correlated with the proportion of individual conflict events occurring in the allocable meeting rooms within the preset period, and the adjustment amount of the preset elastic fluctuation amount is positively correlated with the proportion of early departure events occurring in the early departure prediction.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention determines whether a meeting room is available for allocation or in use by detecting whether the reservation time period and the reservation application time period overlap. Under the condition that the reservation time period and the reservation application time period overlap, the present invention accurately filters out the meeting rooms in use, thereby improving the efficiency of resource utilization. Under the condition that the reservation time period and the reservation application time period do not overlap, the present invention accurately filters out the meeting rooms available for allocation, thereby improving the efficiency of resource allocation. The above method solves the problems of inflexible and inaccurate resource allocation, inaccurate prediction of meeting status, and lack of dynamic adjustment mechanism, which lead to low utilization rate of meeting room resources and frequent reservation conflicts.

[0034] Furthermore, this invention determines whether to predict early termination of meetings by comparing the end elastic fluctuation amount of the meeting with the preset elastic fluctuation amount and the duration of the meeting that has already started with the preset duration. This quickly identifies meeting scenarios that may end early, thereby optimizing the dynamic allocation efficiency of meeting room resources. The above method solves the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanism, which lead to low utilization of meeting room resources and frequent scheduling conflicts.

[0035] Furthermore, this invention determines to issue early departure reminders to scheduled individuals by comparing the predicted volume of an ongoing meeting with a preset volume and the individual tendency value of an ongoing meeting with a preset tendency value. If the predicted volume of an ongoing meeting is less than or equal to the preset volume and the individual tendency value of an ongoing meeting is greater than the preset tendency value, it indicates a decrease in meeting room activity and significant staff turnover. In this case, an early departure reminder is precisely issued to the scheduled individual. If the predicted volume of an ongoing meeting is greater than the preset volume or the individual tendency value of an ongoing meeting is less than or equal to the preset tendency value, it indicates the meeting is in progress, and no early departure reminder is issued to the scheduled individual. This method solves the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms, which lead to low meeting room resource utilization and frequent scheduling conflicts.

[0036] Furthermore, this invention addresses the issues of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms, which lead to low meeting room resource utilization and frequent booking conflicts. These issues arise either from early departure feedback (e.g., early departure, or no early departure reminder but a longer vacancy period for the reserved meeting room), indicating the individual agreed to leave early or did not provide such a reminder. In this case, the meeting room is determined to be available for allocation. If the early departure reminder feedback indicates the meeting room is in use and the vacancy period exceeds the preset vacancy period, it indicates equipment malfunction. Therefore, the priority of the meeting room reservation is lowered, and a maintenance warning is triggered. This method solves the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms, which result in low meeting room resource utilization and frequent booking conflicts.

[0037] Furthermore, this invention determines the time when multiple individuals with reservations clash due to a short preset control duration occurs, based on the fact that the proportion of individual conflict events occurring in the allocable meeting room within a preset period is greater than the preset conflict proportion. In this case, the preset vacancy period is increased by a first adjustment coefficient to reduce the occurrence of conflict events. If the proportion of early departure events in the early departure prediction within the preset period is less than the preset event proportion, it indicates that the preset elastic fluctuation amount is set unreasonably, leading to inaccurate early departure predictions. In this case, the preset end elastic fluctuation amount is reduced by a second adjustment coefficient to improve the accuracy and efficiency of early departure predictions. Through the above method, the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms, which lead to low utilization of meeting room resources and frequent reservation conflicts, are solved. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the structure of the shared meeting room reservation management system based on artificial intelligence according to an embodiment of the present invention;

[0039] Figure 2This is a schematic diagram of the resource management module of the shared meeting room reservation management system based on artificial intelligence, according to an embodiment of the present invention.

[0040] Figure 3 This is a flowchart illustrating the early departure reminder unit of the shared meeting room reservation management system based on artificial intelligence, as described in an embodiment of the present invention.

[0041] Figure 4 This is a flowchart illustrating the workflow of the adjustment module of the AI-based shared meeting room reservation management system according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0043] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0044] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0045] Please see Figures 1-4 As shown, Figure 1 This is a schematic diagram of the structure of the shared meeting room reservation management system based on artificial intelligence according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the resource management module of the shared meeting room reservation management system based on artificial intelligence, according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the early departure reminder unit of the shared meeting room reservation management system based on artificial intelligence, as described in an embodiment of the present invention. Figure 4 This is a flowchart illustrating the workflow of the adjustment module of the AI-based shared meeting room reservation management system according to an embodiment of the present invention.

[0046] This invention provides an artificial intelligence-based shared meeting room reservation management system, comprising:

[0047] The information collection module is used to collect meeting room reservation information, meeting room usage information, and user information;

[0048] The data analysis module, which is connected to the information collection module, is used to determine whether the meeting room is an allocable meeting room or a meeting room in use based on whether the meeting room's reservation time period and the reservation application time period overlap.

[0049] The resource management module, which is connected to the data analysis module, includes:

[0050] An early departure prediction unit is used to determine whether to issue an early departure prediction for the meetings already held in the meeting room based on the end elasticity fluctuation of the meetings already held and the duration of the meetings already held.

[0051] An early departure reminder unit, which is connected to the early departure prediction unit, is used to determine whether to issue an early departure reminder to the scheduled individual based on the volume of the already conducted meeting that issued the early departure prediction and the disappearance tendency value of the individuals in the already conducted meeting.

[0052] A resource reallocation unit, connected to the early departure reminder unit, is used to determine whether a reserved meeting room is an allocable meeting room based on the early departure reminder feedback type of the reserved individual and the vacancy duration of the reserved meeting room, or to reduce the reservation priority of the meeting room and trigger a maintenance warning.

[0053] The adjustment module, which is connected to the information collection module, the data analysis module, and the resource management module, is used to determine whether to adjust the preset end elastic fluctuation amount or the preset vacancy period based on the proportion of individual conflict events occurring in the allocable meeting rooms within a preset period and the proportion of early departure events occurring in the early departure prediction.

[0054] In this embodiment of the invention, the meeting room reservation information includes, but is not limited to, "reservation time, reservation duration, and name of the person making the reservation"; the meeting room usage information includes, but is not limited to, "actual start time, actual end time, and number of users"; and the user information includes, but is not limited to, "user ID, user contact information, and user's department".

[0055] Specifically, the data analysis module determines whether a meeting room is available for allocation or in use based on whether the reservation time period and the reservation application time period overlap.

[0056] If the reserved time slot of the meeting room coincides with the time slot requested for the reservation, the data analysis module determines that the meeting room is in use.

[0057] If the scheduled time slot for the meeting room does not overlap with the time slot requested for the reservation, the data analysis module determines that the meeting room is an allocable meeting room.

[0058] In this embodiment of the invention, determining whether the reservation time slot and the reservation application time slot of the meeting room overlap includes that the reservation time slot and the reservation application time slot do not overlap at any time point. For example, if the existing reservation time slot of the meeting room is known to be from 9:00 AM to 11:00 AM, and there is now a reservation application time slot from 10:00 AM to 12:00 PM, and the reservation time slot and the reservation application time slot do not overlap, then the meeting room is an allocable meeting room.

[0059] This invention determines whether a meeting room is available for allocation or in use by detecting whether the reserved time slot overlaps with the requested time slot. If the reserved time slot overlaps with the requested time slot, meeting rooms in use are precisely selected, improving resource utilization efficiency. If the reserved time slot does not overlap with the requested time slot, available meeting rooms are precisely selected, improving the efficiency of self-selection allocation. This method solves the problems of inflexible and inaccurate resource allocation, inaccurate prediction of meeting status, and lack of dynamic adjustment mechanisms, which lead to low meeting room resource utilization and frequent reservation conflicts.

[0060] Specifically, the early departure prediction unit determines whether to issue an early departure prediction for the already-conducted meeting based on the comparison results of the end elastic fluctuation amount of the already-conducted meeting with the preset elastic fluctuation amount and the comparison results of the duration of the already-conducted meeting with the preset duration.

[0061] If the end elastic fluctuation of an already conducted meeting is less than or equal to a preset elastic fluctuation or the duration of an already conducted meeting is greater than a preset duration, the early termination prediction unit determines to issue an early termination prediction for the already conducted meeting.

[0062] If the end elastic fluctuation of an ongoing meeting is greater than the preset elastic fluctuation and the duration of the ongoing meeting is less than the preset duration, the early termination prediction unit determines not to issue an early termination prediction for the ongoing meeting.

[0063] In this embodiment of the invention, the elastic fluctuation amount is the ratio of the average actual duration of several meetings of the same type to the total scheduled duration. The preset elastic fluctuation amount is the ratio of the historical average actual duration of meetings of the same type to the total scheduled duration. The preset duration is determined by the historical average actual duration of meetings of the same type. For example, if the average actual duration of several meetings of the same type is 15 minutes and the total scheduled duration is 20 minutes, the elastic fluctuation amount is 0.75. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0064] This invention determines whether to predict early termination of an ongoing meeting by comparing the end-of-meeting elastic fluctuation with a preset elastic fluctuation and the duration of the ongoing meeting with a preset duration. This quickly identifies meeting scenarios that may end early, thereby optimizing the dynamic allocation efficiency of meeting room resources. The above method solves the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms, which lead to low utilization of meeting room resources and frequent scheduling conflicts.

[0065] Specifically, the early departure reminder unit determines to issue an early departure reminder to the scheduled individual based on the comparison between the volume of the already conducted meeting predicted for early departure and the preset volume, and the comparison between the individual's tendency value of the already conducted meeting and the preset tendency value.

[0066] If the volume of the already-conducted meeting that issued the early departure prediction is less than or equal to the preset volume and the individual's tendency to disappear from the already-conducted meeting is greater than the preset tendency value, the early departure reminder unit determines to issue an early departure reminder to the scheduled individual.

[0067] If the volume of an ongoing meeting that has issued an early departure prediction is greater than a preset volume, or if the individual's tendency to disappear from the ongoing meeting is less than or equal to a preset tendency value, the early departure reminder unit determines not to issue an early departure reminder to the scheduled individual.

[0068] In this embodiment of the invention, the preset volume is the average volume of several meetings of the same type. For example, the preset volume range is set to 45-55dB, and the preferred value of the preset volume is 50dB. This setting is based on experience and the assessment of the sound level of a normal meeting, so as to more accurately judge the status of the meeting and the participation of personnel through the volume. The individual disappearance tendency value is determined according to the ratio of the number of people who have been detected leaving to the total number of participants in the meeting room. The preset tendency value is the average of the individual disappearance tendency values ​​of several meetings of the same type in which early departure occurs. For example, if 3 people have been detected leaving and the actual total number of participants is 10, the individual disappearance tendency value is 0.3. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0069] This invention determines whether to issue an early departure reminder to a scheduled individual by comparing the predicted volume of an ongoing meeting with a preset volume, and by comparing the individual's tendency value for the ongoing meeting with a preset tendency value. If the predicted volume of an ongoing meeting is less than or equal to the preset volume, and the individual's tendency value for disappearing from the meeting is greater than the preset tendency value, it indicates a decrease in meeting room activity and significant staff turnover. In this case, an early departure reminder is precisely issued to the scheduled individual. If the predicted volume of an ongoing meeting is greater than the preset volume, or the individual's tendency value for disappearing from the meeting is less than or equal to the preset tendency value, it indicates the meeting is in progress, and no early departure reminder is issued to the scheduled individual. This method solves the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms, which lead to low meeting room resource utilization and frequent scheduling conflicts.

[0070] Specifically, the resource reallocation unit, under the condition that it determines whether the reserved meeting room is an allocable meeting room or lowers the reservation priority of the meeting room and triggers a maintenance warning, determines whether the reserved meeting room is an allocable meeting room or lowers the reservation priority of the meeting room and triggers a maintenance warning based on the early departure feedback of the reservation individual or the comparison result of the vacancy time of the reserved meeting room of the reservation individual with the preset vacancy time.

[0071] If the early departure reminder feedback type for the individual who made the reservation is "early departure", or if the early departure reminder feedback type for the individual who made the reservation is "stagnation" and the vacancy time of the reserved meeting room for the individual is greater than the preset vacancy time, the resource reallocation unit determines that the meeting room is an allocable meeting room.

[0072] If the early departure reminder feedback type of the individual who made the reservation is "in use" and the vacancy time of the reserved meeting room is greater than the preset vacancy time, the resource reallocation unit determines to reduce the reservation priority of the meeting room and triggers a maintenance warning.

[0073] In this embodiment of the invention, the early departure reminder feedback type is "early departure," meaning the individual who made the reservation has indicated that they are leaving early; the early departure reminder feedback type is "stagnation," meaning the individual who made the reservation has not provided any feedback; and the early departure reminder feedback type is "use," meaning the individual has indicated that they are using the service. The vacancy time of the reserved meeting room for the individual who made the reservation is the remaining duration of the already conducted meeting. The preset vacancy time is one-fifth of the vacancy time of the reserved meeting room for the individual who made the reservation. For example, if the vacancy time of the reserved meeting room for the individual who made the reservation is 20 minutes, the preset vacancy time is 4 minutes. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0074] This invention addresses the issues of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and a long vacancy period for the reserved meeting room exceeding a preset vacancy time. This indicates either early departure by the individual or no early departure reminder but a prolonged vacancy period for the reserved meeting room. In this case, the meeting room is determined to be available for allocation. Furthermore, if the early departure reminder is confirmed as "used" and the vacancy period exceeds a preset vacancy time, it indicates equipment malfunction in the meeting room. This leads to a reduction in the meeting room's reservation priority and triggers a maintenance warning. This method solves the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and a lack of dynamic adjustment mechanisms, resulting in low meeting room resource utilization and frequent reservation conflicts.

[0075] Specifically, the adjustment module, under the condition of determining whether to adjust the preset elastic fluctuation amount or the preset idle time, determines whether to adjust the preset end elastic fluctuation amount or the preset idle time based on the comparison results of the proportion of individual conflict events occurring in the allocable meeting room within the preset period and the preset conflict proportion, and the comparison results of the proportion of early departure events occurring in the early departure prediction and the preset event proportion.

[0076] If the proportion of individual conflict events occurring in the allocable meeting room within a preset period is greater than the preset conflict occurrence proportion, the adjustment module determines to adjust the preset vacancy time using a first adjustment coefficient;

[0077] If the proportion of early departure events in the early departure prediction issued within the preset period is less than the preset early departure proportion, the adjustment module determines to adjust the preset end elastic fluctuation amount with the second adjustment coefficient.

[0078] If the percentage of individual conflict events occurring in the allocable meeting room within a preset period is less than or equal to the preset conflict occurrence percentage, and the percentage of early departure events occurring in the early departure prediction issued within the preset period is greater than or equal to the preset early departure percentage, the adjustment module determines that there is no need to adjust the preset end elastic fluctuation amount and the preset idle time.

[0079] In this embodiment of the invention, the percentage of individual conflict events occurring in the allocable meeting rooms within a preset period is the ratio of events where several individuals book the same meeting room to the number of meeting room booking events within the preset period. The preset period is set to a range of 5-10 days, with a preferred value of 7 days, as 7 days aligns with the weekly meeting schedule and covers the entire work cycle. The preset conflict percentage is 3%. Several empirical studies have shown that user satisfaction significantly decreases when the conflict rate is greater than 3%, and computational resources significantly increase when the conflict rate is less than 3%. The preset early departure percentage is 20% to avoid excessive sensitivity affecting users. The first adjustment coefficient is set to a value range of 1.03-1.19, with a preferred value of 1.11. The second adjustment coefficient is set to a value range of 0.82-0.96, with a preferred value of 0.89. The adjustment amount of the preset vacancy time is positively correlated with the proportion of individual conflict events occurring in the allocable meeting rooms within the preset period. The adjustment amount of the preset elastic fluctuation amount is positively correlated with the proportion of early departure events occurring in the early departure prediction. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the values ​​according to actual needs.

[0080] This invention determines the time when multiple individuals with reservations clash due to a short preset control duration occurs, based on the fact that the proportion of individual conflict events occurring in the allocable meeting room within a preset period is greater than the preset conflict proportion. In this case, a first adjustment coefficient is used to increase the preset vacancy time to reduce the occurrence of conflict events. If the proportion of early departure events in the early departure prediction within the preset period is less than the preset event proportion, it indicates that the preset elastic fluctuation amount is set unreasonably, leading to inaccurate early departure predictions. In this case, a second adjustment coefficient is used to reduce the preset end elastic fluctuation amount to improve the accuracy and efficiency of early departure predictions. The above method solves the problems of inflexible and inaccurate resource allocation, inaccurate meeting status prediction, and lack of dynamic adjustment mechanisms, resulting in low meeting room resource utilization and frequent reservation conflicts.

[0081] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based shared conference room reservation management system, characterized by, The application comprises: an information collection module configured to collect reservation information of a conference room, usage information of the conference room, and user information; a data analysis module connected to the information collection module and configured to determine whether the conference room is a distributable conference room or is in use based on whether a reservation time period of the conference room coincides with a reservation application time period; a resource management module connected to the data analysis module and comprising: an early departure prediction unit configured to determine whether to issue an early departure prediction for an ongoing meeting in the conference room in use based on an end flexibility fluctuation amount of the ongoing meeting and a duration of the ongoing meeting; an early departure reminding unit connected to the early departure prediction unit and configured to determine whether to issue an early departure reminder for a reservation individual based on a volume of the ongoing meeting for which the early departure prediction is issued and an individual disappearance tendency value of the ongoing meeting; a resource reallocation unit connected to the early departure reminding unit and configured to determine that a reservation conference room is a distributable conference room or to reduce a reservation priority of the conference room and trigger a maintenance warning according to a type of early departure reminder feedback of the reservation individual and an idle duration of the reservation conference room; an adjustment module connected to the information collection module, the data analysis module, and the resource management module respectively and configured to determine whether to adjust a preset end flexibility fluctuation amount or to adjust a preset idle duration based on a proportion of individual conflict events in distributable conference rooms in a preset period and a proportion of early departure events in early departure predictions in the preset period; the early departure prediction unit determining whether to issue an early departure prediction for the ongoing meeting comprises: determining to issue the early departure prediction for the ongoing meeting on a condition that the end flexibility fluctuation amount of the ongoing meeting is less than or equal to a preset flexibility fluctuation amount or the duration of the ongoing meeting is greater than a preset duration; determining not to issue the early departure prediction for the ongoing meeting on a condition that the end flexibility fluctuation amount of the ongoing meeting is greater than the preset flexibility fluctuation amount and the duration of the ongoing meeting is less than the preset duration; the end flexibility fluctuation amount is determined according to a ratio of an average of actual durations of a plurality of same type meetings to a total reservation duration, the preset flexibility fluctuation amount is determined according to a ratio of a historical average of actual durations of the same type meetings to the total reservation duration, and the preset duration is determined according to a historical average of actual durations of the same type meetings; the adjustment module determining whether to adjust the preset flexibility fluctuation amount or to adjust the preset idle duration comprises: determining to adjust the preset idle duration on a condition that the proportion of individual conflict events in the distributable conference rooms in the preset period is greater than a preset conflict occurrence proportion; determining to adjust the preset end flexibility fluctuation amount on a condition that the proportion of early departure events in the early departure predictions in the preset period is less than a preset early departure proportion; an adjustment amount of the preset idle duration is positively correlated with the proportion of individual conflict events in the distributable conference rooms in the preset period, and an adjustment amount of the preset flexibility fluctuation amount is positively correlated with the proportion of early departure events in the early departure predictions. 2.The AI-based shared conference room reservation management system of claim 1, wherein the data analysis module determining whether the conference room is a distributable conference room or is in use comprises: determining the conference room as in use under the condition that the reservation time period of the conference room coincides with the reservation application time period; or determining the conference room as assignable under the condition that the reservation time period of the conference room does not coincide with the reservation application time period. 3.The AI-based shared conference room reservation management system of claim 2, wherein, The early departure reminding unit determines whether to send an early departure reminder to the reservation individual, including: determining to send an early departure reminder to the reservation individual under the condition that the volume of the ongoing conference where the early departure prediction is sent is less than or equal to a preset volume and the disappearance tendency value of the individual of the ongoing conference is greater than a preset tendency value; determining not to send an early departure reminder to the reservation individual under the condition that the volume of the ongoing conference where the early departure prediction is sent is greater than the preset volume or the disappearance tendency value of the individual of the ongoing conference is less than or equal to the preset tendency value. 4.The AI-based shared conference room reservation management system of claim 3, wherein, The preset volume is determined according to the average value of the volume in several same type of conferences, the individual disappearance tendency value is determined according to the ratio of the number of detected departures to the total number of participants in the conference room, and the preset tendency value is determined according to the average value of the individual disappearance tendency values in several same type of conferences where early departure phenomenon occurs. 5.The AI-based shared conference room reservation management system of claim 4, wherein, The resource reallocation unit determines whether the reserved conference room is assignable or reduces the reservation priority of the conference room and triggers maintenance warning, including: determining the conference room as assignable under the condition that the early departure reminder feedback type of the reservation individual is early departure, or the early departure reminder feedback type of the reservation individual is stagnation and the vacancy time of the conference room reserved by the reservation individual is greater than a preset vacancy duration; determining to reduce the reservation priority of the conference room and trigger maintenance warning under the condition that the early departure reminder feedback type of the reservation individual is use and the vacancy time of the conference room reserved by the reservation individual is greater than the preset vacancy duration. 6.The AI-based shared conference room reservation management system of claim 5, wherein, The preset vacancy duration is determined according to the remaining duration of the ongoing conference.

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