CN113128963A - Intelligent conference schedule suggestion method and system, electronic equipment and storage medium - Google Patents
Intelligent conference schedule suggestion method and system, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113128963A CN113128963A CN202110419175.6A CN202110419175A CN113128963A CN 113128963 A CN113128963 A CN 113128963A CN 202110419175 A CN202110419175 A CN 202110419175A CN 113128963 A CN113128963 A CN 113128963A
- Authority
- CN
- China
- Prior art keywords
- conference
- time
- objective
- participants
- maximized
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 33
- 239000012634 fragment Substances 0.000 claims abstract description 29
- 238000002922 simulated annealing Methods 0.000 claims abstract description 26
- 238000005457 optimization Methods 0.000 claims abstract description 23
- 230000006870 function Effects 0.000 claims description 48
- 238000010276 construction Methods 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 12
- 238000013467 fragmentation Methods 0.000 claims description 8
- 238000006062 fragmentation reaction Methods 0.000 claims description 8
- 239000000463 material Substances 0.000 abstract description 3
- 230000008520 organization Effects 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000012552 review Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
- G06Q10/1093—Calendar-based scheduling for persons or groups
- G06Q10/1095—Meeting or appointment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Abstract
The invention discloses an intelligent conference schedule suggesting method, a system, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring the time arrangement of a conference and participants; constructing a multi-objective optimization objective function consisting of a minimized fragment time objective and a maximized conference interval objective; and obtaining the optimal value of the objective function by using a simulated annealing algorithm according to the time arrangement of the conference and the participants, and rearranging the conference time according to the optimal value. The invention realizes intelligent arrangement of daily conferences of a company, can save a large amount of manpower and material resources on the premise of considering personal time arrangement of participants, improves organization efficiency and meets requirements of multiple parties.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to an intelligent conference schedule suggestion method, an intelligent conference schedule suggestion system, electronic equipment and a storage medium.
Background
During the daily operations of a company, many meetings are generated, and the meetings usually involve multiple departments and multiple participants, and if the meetings are not arranged reasonably, time conflicts can be caused between the meetings, and the meetings cannot be held according to the original plan. Imagine such a scenario:
the project group A needs to hold a demand review conference, a conference organizer sequentially consults people for free time, the time is set to be 15:00-16:30 after the determination of the whole group of people is obtained, and the member plumes suddenly realize that the member plumes have a 14:00-15:30 conference and are very important shortly before the conference is held, so that the member plumes cannot participate in the 15:00-16:30 demand review conference on time. In this case, the meeting organizer for project group a needs to consult everybody for additional free time and schedule it. Assume that the holding time for the re-agreed project group a requirement review meeting is 13:30-15:00 the next day, which is the free time that all meeting participants can attend the meeting on time. But the member queen would have to attend another project conference at 15:00 and prepare the story reporting work so that there is no free time between the two conferences and there is no more sufficient preparation for the story of the next conference. For small wu, he had other conferences at 16:00 pm, who took only one hour to attend another conference, and half an hour of idle time before 13:30 pm, split the half-hour idle time into two small time slots, and could not have a large block of reality to do other things. For another member, a meeting more than a little in the afternoon is not used, and some other work is more desirable at this time.
From the scenario, it can be seen that if a manual method is used to schedule the conference, the following problems may occur:
(1) the labor and time are greatly spent, and the efficiency is low. Each time a meeting is encountered, the meeting organizer is required to collect information of idle time from the participants, which consumes a lot of time and energy.
(2) The conference time decision modification cost is high. Because manual statistics is adopted, the situation that some conferences are forgotten or statistical information is wrong is inevitable, and the time and energy are needed again for re-statistics, so that the conference modification cost is very high.
(3) The meeting schedule is too tight. For some conference-intensive participants (especially sales, or leadership level people, conferences are often intensive), there is little or no rest time between the conference and the conference, and it is at fatigue.
(4) Time is fragmented. So that a large period of time becomes small pieces and cannot have a long time to do a thing that needs to be done with concentration.
(5) Meeting personnel preferences are not considered. Assuming that two alternative time periods are present, employee satisfaction can be greatly improved if a more acceptable time period can be selected in view of meeting time preferences of the meeting participants.
Disclosure of Invention
The invention provides an intelligent conference schedule suggestion method, a system, electronic equipment and a storage medium, aiming at the technical problem that the arrangement of conference time is unreasonable.
In a first aspect, an embodiment of the present application provides an intelligent conference schedule suggestion method, including:
an information acquisition step: acquiring the time arrangement of a conference and participants;
and an objective function construction step: constructing a multi-objective optimization objective function consisting of a minimized fragment time objective and a maximized conference interval objective;
a conference scheduling step: and obtaining the optimal value of the objective function by using a simulated annealing algorithm according to the time arrangement of the conference and the participants, and rearranging the conference time according to the optimal value.
The intelligent conference schedule suggestion method comprises the following steps of:
and (3) target construction: constructing the minimized fragment time target and the maximized conference interval target according to the levels of the participants and the time arrangement of the conference and the participants;
a total target construction step: and constructing the objective function according to the minimized fragment time objective, the maximized conference interval objective and the weights respectively corresponding to the minimized fragment time objective and the maximized conference interval objective.
The intelligent conference schedule suggestion method comprises the following steps:
a common idle time obtaining step: obtaining a plurality of public idle time periods of the conference and the participants according to the time arrangement of the conference and the participants;
and (3) searching a simulated annealing algorithm: respectively searching in each section of public idle time by using a simulated annealing algorithm according to the target function;
and returning the optimal value to the step: and returning the optimal value in each segment of the public idle time to the user, and rearranging the conference according to the optimal value.
The intelligent conference schedule suggesting method comprises the step of distributing the weight of the minimum fragment time target and the maximum conference interval target in the objective function according to the time arrangement of the conference and the participants.
In a second aspect, an embodiment of the present application provides an intelligent meeting schedule suggestion system, including:
an information acquisition unit: acquiring the time arrangement of a conference and participants;
an objective function construction unit: constructing a multi-objective optimization objective function consisting of a minimized fragment time objective and a maximized conference interval objective;
a conference scheduling unit: and obtaining the optimal value of the objective function by using a simulated annealing algorithm according to the time arrangement of the conference and the participants, and rearranging the conference time according to the optimal value.
The intelligent conference schedule suggestion system comprises an objective function construction unit and a conference schedule recommendation unit, wherein the objective function construction unit comprises:
a target construction module: constructing the minimized fragment time target and the maximized conference interval target according to the levels of the participants and the time arrangement of the conference and the participants;
a general target construction module: and constructing the objective function according to the minimized fragment time objective, the maximized conference interval objective and the weights respectively corresponding to the minimized fragment time objective and the maximized conference interval objective.
The intelligent conference schedule suggestion system comprises the conference scheduling unit and a conference scheduling unit, wherein the conference scheduling unit comprises:
a common idle time acquisition module: obtaining a plurality of public idle time periods of the conference and the participants according to the time arrangement of the conference and the participants;
the simulated annealing algorithm searching module comprises: respectively searching in each section of public idle time by using a simulated annealing algorithm according to the target function;
an optimal value returning module: and returning the optimal value in each segment of the public idle time to the user, and rearranging the conference according to the optimal value.
In the intelligent conference schedule suggestion system, the total objective construction module allocates the weight of the minimized fragment time objective and the maximized conference interval objective in the objective function according to the time arrangement of the conference and the participants.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the intelligent meeting schedule suggestion method according to the first aspect is implemented.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the intelligent meeting schedule suggestion method according to the first aspect.
Compared with the prior art, the invention has the advantages and positive effects that:
the invention relates to the technical field of operational research optimization, and provides a multi-objective optimization method for selecting optimal meeting time in a time period in which a meeting can be scheduled, so that intelligent scheduling of daily meetings generated by a company is realized. A large amount of manpower and material resources can be saved on the premise of considering the personal time arrangement of the participants, the organization efficiency is improved, and the requirements of multiple parties are met.
Drawings
FIG. 1 is a schematic diagram illustrating steps of an intelligent method for suggesting a meeting schedule according to the present invention;
FIG. 2 is a flowchart based on step S2 in FIG. 1 according to the present invention;
FIG. 3 is a flowchart based on step S3 in FIG. 1 according to the present invention;
FIG. 4 is a schematic diagram of a simulated annealing algorithm provided by the present invention;
FIG. 5 is a flow chart of a simulated annealing algorithm provided by the present invention;
FIG. 6 is a frame diagram of an intelligent conference schedule suggestion system provided by the present invention;
fig. 7 is a block diagram of a computer device according to an embodiment of the present application.
Wherein the reference numerals are:
1. an information acquisition unit; 2. an objective function construction unit; 21. a target construction module; 22. a total target construction module; 3. a conference scheduling unit; 31. a common idle time acquisition module; 32. a simulated annealing algorithm searching module; 33. an optimal value returning module; 81. a processor; 82. a memory; 83. a communication interface; 80. a bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
Before describing in detail the various embodiments of the present invention, the core inventive concepts of the present invention are summarized and described in detail by the following several embodiments.
In the invention, a multi-objective optimized objective function consisting of a minimum fragment time objective and a maximum conference interval objective is solved by using a simulated annealing algorithm in a plurality of idle time periods of the conference and the participants to obtain an optimal value of the objective function, and the conference time is rearranged according to the optimal value.
The first embodiment is as follows:
fig. 1 is a schematic step diagram of an intelligent conference schedule suggestion method provided by the present invention. As shown in fig. 1, this embodiment discloses a specific implementation of a method for intelligently suggesting a meeting schedule (hereinafter referred to as "method").
The invention comprehensively considers several factors of time and labor consumption, too tight arrangement, time fragmentation, different preferences of participants and the like in meeting arrangement and modification, informationizes the meeting arrangement, saves manpower and material resources and improves the organization efficiency. When time arrangement is carried out, an operation optimization algorithm is needed to select a proper time, so that the fragmentization time of the participants is shortest or the interval between the conference and the conference is longest, and the conference can be better prepared. This creates two goals, minimizing fragmentation time and maximizing meeting interval, which are often conflicting, and thus involves a balance between the two goals, i.e., a two-goal optimization problem. The invention mainly provides how to reasonably arrange meeting time by using double-target optimization.
Multi-objective optimization usually has multiple objectives, which are often conflicting, and therefore balanced between the objectives. Two common methods for the multi-objective problem are provided, one method is to convert the multi-objective optimization problem into a single-objective optimization problem, carry out weighted summation on a plurality of objectives, give different weights to the objectives according to the importance degrees of the objectives, and then use the single-objective optimization method for optimization; the other is to use a multi-objective evolutionary algorithm to solve, so as to obtain a group of pareto optimal solution sets, and to select from the optimal solution sets according to actual problems. The method mainly considers the conversion of a multi-objective optimization problem into a single-objective optimization problem, and then obtains an optimal solution.
Specifically, the method disclosed in this embodiment mainly includes the following steps:
step S1: acquiring the time arrangement of a conference and participants;
step S2: constructing a multi-objective optimization objective function consisting of a minimized fragment time objective and a maximized conference interval objective;
referring to fig. 2, step S2 specifically includes the following steps:
step S21: constructing the minimized fragment time target and the maximized conference interval target according to the levels of the participants and the time arrangement of the conference and the participants;
step S22: and constructing the objective function according to the minimized fragment time objective, the maximized conference interval objective and the weights respectively corresponding to the minimized fragment time objective and the maximized conference interval objective.
Specifically, the constructed minimum fragmentation time objective and maximum conference interval objective and the overall objective function are as follows:
the first target is: minimize fragmentation time:
and a second target: maximizing the meeting interval:
overall objective:
Obj＝w1*Obj1+w2*Obj2
wherein, PiThe grade of the participant; siFor the start of the idle period of the participant, EiThe end time of the idle time period of the participants; s0For the start time of a new meeting to be scheduled, E0The end time for a new meeting desired to be scheduled; o isjOther idle time periods of the day; w is aiThe weights (importance levels) of goal one and goal two.
Specifically, the weights of two targets are allocated according to the time arrangement of the conference and the participants, wherein the first target is the minimized fragmentation time, the second target is the maximized conference interval, if a common idle time is shorter, and if the common idle time is shorter, the two conference intervals are indicated to be shorter, the target tends to select the maximized conference interval so as to ensure better rest between the two conferees; if the common idle time is long, meaning that the conference interval is long, the goal tends to choose to minimize fragmentation time to guarantee a long time period.
Step S3: and obtaining the optimal value of the objective function by using a simulated annealing algorithm according to the time arrangement of the conference and the participants, and rearranging the conference time according to the optimal value.
Referring to fig. 3, step S3 specifically includes the following steps:
step S31: obtaining a plurality of public idle time periods of the conference and the participants according to the time arrangement of the conference and the participants;
step S32: respectively searching in each section of public idle time by using a simulated annealing algorithm according to the target function;
step S33: and returning the optimal value in each segment of the public idle time to the user, and rearranging the conference according to the optimal value.
Specifically, referring to fig. 4, a simulated annealing algorithm is used to search in each common idle time, and the optimal value in each common idle time is returned to the user, so that the user can decide in which time slot to rearrange the meeting.
Referring to fig. 5, the generation and acceptance of the new solution of the simulated annealing algorithm can be divided into the following four steps:
the first step is to generate a new solution located in the solution space from the current solution by a generating function; in order to facilitate subsequent calculation and acceptance and reduce the time consumption of the algorithm, a method which can generate a new solution from a current new solution through simple transformation is usually selected.
The second step is to calculate the difference of the objective function corresponding to the new solution. Since the objective function difference is generated only by the transform part, the calculation of the objective function difference is preferably calculated in increments.
And thirdly, judging whether the new solution is accepted or not according to an acceptance criterion, wherein the most common acceptance criterion is a Metropolis criterion, if the delta T is less than 0, S' is accepted as the new current solution S, and otherwise, the probability exp (-delta T/T) is accepted as the new current solution S.
The fourth step is to replace the current solution with the new solution when the new solution is determined to be accepted, which is achieved by only modifying the transformation portion of the current solution corresponding to when the new solution is generated, and at the same time, modifying the objective function value. At this point, the current solution achieves one iteration. On this basis the next round of testing can be started. And when the new solution is judged to be abandoned, continuing the next round of test on the basis of the original current solution. The simulated annealing algorithm is independent of the initial value, and the solution obtained by the algorithm is independent of the initial solution state S (which is the starting point of the algorithm iteration).
The simulated annealing algorithm has asymptotic convergence and has been theoretically proved to be a global optimization algorithm which converges on a global optimal solution with a probability of 1.
The invention models the intelligent meeting arrangement suggestion problem as an operation and research optimization problem, aims at minimizing fragment time and maximizing meeting interval, and provides a multi-objective optimization algorithm for solving the optimization problem.
Example two:
in combination with the intelligent conference schedule suggestion method disclosed in the first embodiment, the first embodiment discloses a specific implementation example of an intelligent conference schedule suggestion system (hereinafter referred to as "system").
Referring to fig. 6, the system includes:
the information acquisition unit 1: acquiring the time arrangement of a conference and participants;
the objective function construction unit 2: constructing a multi-objective optimization objective function consisting of a minimized fragment time objective and a maximized conference interval objective;
conference scheduling unit 3: and obtaining the optimal value of the objective function by using a simulated annealing algorithm according to the time arrangement of the conference and the participants, and rearranging the conference time according to the optimal value.
Specifically, the objective function constructing unit 2 includes:
the object building block 21: constructing the minimized fragment time target and the maximized conference interval target according to the levels of the participants and the time arrangement of the conference and the participants;
the overall goal building block 22: and constructing the objective function according to the minimized fragment time objective, the maximized conference interval objective and the weights respectively corresponding to the minimized fragment time objective and the maximized conference interval objective.
Specifically, the conference schedule unit 3 includes:
the common idle time obtaining module 31: obtaining a plurality of public idle time periods of the conference and the participants according to the time arrangement of the conference and the participants;
simulated annealing algorithm search module 32: respectively searching in each section of public idle time by using a simulated annealing algorithm according to the target function;
the optimal value return module 33: and returning the optimal value in each segment of the public idle time to the user, and rearranging the conference according to the optimal value.
Specifically, the overall goal building module 22 assigns the weights of the minimized fragmentation time goal and the maximized meeting interval goal in the objective function according to the schedules of the meeting and the participants.
Please refer to the description of the first embodiment, which will not be repeated herein.
Example three:
referring to FIG. 7, the embodiment discloses an embodiment of a computer device. The computer device may comprise a processor 81 and a memory 82 in which computer program instructions are stored.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.
The processor 81 reads and executes the computer program instructions stored in the memory 82 to implement any one of the intelligent meeting schedule suggesting methods in the above embodiments.
In some of these embodiments, the computer device may also include a communication interface 83 and a bus 80. As shown in fig. 7, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
In addition, in combination with the intelligent conference schedule suggestion method in the foregoing embodiments, embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any one of the intelligent meeting schedule suggestion methods in the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An intelligent conference schedule suggestion method is characterized by comprising the following steps:
an information acquisition step: acquiring the time arrangement of a conference and participants;
and an objective function construction step: constructing a multi-objective optimization objective function consisting of a minimized fragment time objective and a maximized conference interval objective;
a conference scheduling step: and obtaining the optimal value of the objective function by using a simulated annealing algorithm according to the time arrangement of the conference and the participants, and rearranging the conference time according to the optimal value.
2. The intelligent conference schedule suggestion method according to claim 1, wherein the objective function construction step comprises the following steps:
and (3) target construction: constructing the minimized fragment time target and the maximized conference interval target according to the levels of the participants and the time arrangement of the conference and the participants;
a total target construction step: and constructing the objective function according to the minimized fragment time objective, the maximized conference interval objective and the weights respectively corresponding to the minimized fragment time objective and the maximized conference interval objective.
3. The intelligent meeting schedule suggestion method of claim 1, wherein the meeting scheduling step comprises:
a common idle time obtaining step: obtaining a plurality of public idle time periods of the conference and the participants according to the time arrangement of the conference and the participants;
and (3) searching a simulated annealing algorithm: respectively searching in each section of public idle time by using a simulated annealing algorithm according to the target function;
and returning the optimal value to the step: and returning the optimal value in each segment of the public idle time to the user, and rearranging the conference according to the optimal value.
4. The intelligent meeting schedule suggestion method of claim 2, wherein the weights of the minimized fragmentation time objective and the maximized meeting interval objective in the objective function are distributed according to the time schedule of the meeting and the participants.
5. An intelligent meeting schedule suggestion system, comprising:
an information acquisition unit: acquiring the time arrangement of a conference and participants;
an objective function construction unit: constructing a multi-objective optimization objective function consisting of a minimized fragment time objective and a maximized conference interval objective;
a conference scheduling unit: and obtaining the optimal value of the objective function by using a simulated annealing algorithm according to the time arrangement of the conference and the participants, and rearranging the conference time according to the optimal value.
6. The intelligent meeting schedule suggestion system of claim 5, wherein the objective function construction unit comprises:
a target construction module: constructing the minimized fragment time target and the maximized conference interval target according to the levels of the participants and the time arrangement of the conference and the participants;
a general target construction module: and constructing the objective function according to the minimized fragment time objective, the maximized conference interval objective and the weights respectively corresponding to the minimized fragment time objective and the maximized conference interval objective.
7. The intelligent meeting schedule suggestion system of claim 5, wherein the meeting scheduling unit comprises:
a common idle time acquisition module: obtaining a plurality of public idle time periods of the conference and the participants according to the time arrangement of the conference and the participants;
the simulated annealing algorithm searching module comprises: respectively searching in each section of public idle time by using a simulated annealing algorithm according to the target function;
an optimal value returning module: and returning the optimal value in each segment of the public idle time to the user, and rearranging the conference according to the optimal value.
8. The intelligent meeting schedule suggestion system of claim 6, wherein the overall goal building module assigns the weight of the minimized shard time goal and the maximized meeting interval goal in the objective function according to the schedule of the meeting and the participants.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the intelligent meeting schedule suggestion method of any one of claims 1-4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for intelligent suggestion of a meeting schedule according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110419175.6A CN113128963A (en) | 2021-04-19 | 2021-04-19 | Intelligent conference schedule suggestion method and system, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110419175.6A CN113128963A (en) | 2021-04-19 | 2021-04-19 | Intelligent conference schedule suggestion method and system, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113128963A true CN113128963A (en) | 2021-07-16 |
Family
ID=76778104
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110419175.6A Pending CN113128963A (en) | 2021-04-19 | 2021-04-19 | Intelligent conference schedule suggestion method and system, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113128963A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030204474A1 (en) * | 2002-04-25 | 2003-10-30 | International Business Machines Corporation | Event scheduling with optimization |
| US20090132329A1 (en) * | 2007-11-20 | 2009-05-21 | International Business Machines Corporation | Meeting Scheduling to Minimize Inconvenience of Meeting Participants |
| US20180107987A1 (en) * | 2016-10-14 | 2018-04-19 | Microsoft Technology Licensing, Llc | Meeting service with meeting time and location optimization |
-
2021
- 2021-04-19 CN CN202110419175.6A patent/CN113128963A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030204474A1 (en) * | 2002-04-25 | 2003-10-30 | International Business Machines Corporation | Event scheduling with optimization |
| US20090132329A1 (en) * | 2007-11-20 | 2009-05-21 | International Business Machines Corporation | Meeting Scheduling to Minimize Inconvenience of Meeting Participants |
| US20180107987A1 (en) * | 2016-10-14 | 2018-04-19 | Microsoft Technology Licensing, Llc | Meeting service with meeting time and location optimization |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Verma et al. | Two sides of a coin: Optimizing the schedule of mapreduce jobs to minimize their makespan and improve cluster performance | |
| Dong et al. | Scheduling mixed real-time and non-real-time applications in mapreduce environment | |
| Xu et al. | A multiple priority queueing genetic algorithm for task scheduling on heterogeneous computing systems | |
| Nakahira et al. | Smoothed least-laxity-first algorithm for EV charging | |
| CN110908782B (en) | Genetic algorithm optimization-based packaging type distributed job task scheduling method and system | |
| CN111666179A (en) | Intelligent replication system and server for multi-point data disaster tolerance | |
| Jeong et al. | Minimizing total tardiness in a two-machine re-entrant flowshop with sequence-dependent setup times | |
| US20100125479A1 (en) | Meeting scheduler | |
| Park et al. | Practical Human Resource Allocation in Software Projects Using Genetic Algorithm. | |
| CN113128963A (en) | Intelligent conference schedule suggestion method and system, electronic equipment and storage medium | |
| CN114139730A (en) | Dynamic pricing and deployment method for machine learning task in edge cloud network | |
| CN110097200A (en) | Meeting room preordering method, device, equipment and storage medium | |
| CN116226104A (en) | Data table maintenance method and device, storage medium and electronic equipment | |
| CN111124672A (en) | Data distribution method based on Redis cluster and related equipment | |
| Whitt | A multi-class fluid model for a contact center with skill-based routing | |
| CN115599522A (en) | Task scheduling method, device and equipment for cloud computing platform | |
| Rokicki et al. | Just in time: Controlling temporal performance in crowdsourcing competitions | |
| Lee et al. | Scheduling jobs with stochastic holding costs | |
| CN113010310A (en) | Job data processing method and device and server | |
| CN113077235A (en) | Conference schedule conflict management method and system, electronic equipment and storage medium | |
| Honnappa et al. | Strategic arrivals into queueing networks | |
| CN106056231B (en) | Automatic matching recommendation method and device based on multi-party position and time availability | |
| Cordeiro | The impact of cooperation on new high performance computing platforms | |
| US20230306375A1 (en) | Automatic notification generation | |
| Elghirani et al. | Performance enhancement through hybrid replication and genetic algorithm co-scheduling in data grids |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |