Seat allocation method based on greedy and heuristic search strategy and related device
By employing a greedy and heuristic search strategy for seating allocation, the problem of low efficiency in traditional manual allocation is solved, enabling fast and reasonable seating allocation, improving meeting efficiency and user satisfaction, and demonstrating flexibility.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2024-11-28
- Publication Date
- 2026-05-29
AI Technical Summary
Traditional meeting seating arrangements rely on manual operation, resulting in low efficiency, unreasonable allocation, and difficulty in responding to temporary changes, which affects meeting efficiency and atmosphere.
A greedy and heuristic search strategy is adopted, combining greedy and heuristic algorithms. By obtaining information on attendees and meeting rooms, efficient and reasonable seat allocation results are generated. The greedy algorithm selects the best or second-best seats, while the heuristic algorithm guides the search process, reducing the amount of computation.
It enables rapid and efficient seat allocation, reduces manual operation steps, improves allocation speed and accuracy, provides a personalized experience, has the ability to flexibly respond to temporary changes, and ensures the efficient use of seat resources.
Smart Images
Figure CN122114413A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent seating allocation technology, specifically to a seating allocation method and related equipment based on greedy and heuristic search strategies. Background Technology
[0002] In the process of assigning seats in traditional meetings, the reliance on manual operation often reveals many shortcomings.
[0003] First, efficiency is a particularly prominent issue: manual operation is not only time-consuming and labor-intensive, but also becomes especially lengthy and cumbersome when there are a large number of participants, seriously affecting the overall organizational efficiency.
[0004] Secondly, due to the lack of scientific allocation strategies and reasonable planning, this traditional method often leads to unreasonable seat allocation. Sometimes, some participants even feel uncomfortable because they are arranged in an unsuitable position, which in turn affects the atmosphere and effectiveness of the entire meeting.
[0005] Furthermore, this fixed, manual allocation method has significant limitations in the face of temporary changes or unexpected events. It is difficult to respond flexibly and can easily lead to chaos in seating arrangements, thus causing unnecessary disruption to the normal conduct of the meeting.
[0006] Therefore, finding a more efficient, scientific, and highly adaptable seating arrangement scheme for meetings has become an urgent task. Summary of the Invention
[0007] The purpose of this invention is to provide a seating allocation method and related equipment based on greedy and heuristic search strategies, so as to solve the technical problem of how to intelligently allocate seating in the prior art.
[0008] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a seating allocation method based on a greedy and heuristic search strategy, comprising: Obtain information on attendees and meeting rooms; Based on the obtained information on attendees and meeting rooms, seat allocation results are generated using a logical algorithm based on greedy and heuristic algorithms. Output the seat allocation results to complete the seat allocation work based on greedy and heuristic search strategies.
[0009] Preferably, in the steps of obtaining participant information and meeting room information, the participant information includes the participant's name, identity information, job information, and department information; the meeting room information includes the size of the meeting room, seating layout, seat type, number of seats, and facilities in the meeting room.
[0010] Preferably, in the step of generating seat allocation results logically based on the obtained participant and meeting room information using greedy and heuristic algorithms, the specific process is as follows: Read and parse participant information and meeting room information; Create a participant list and a meeting room seating matrix based on the read and parsed participant and meeting room information; Based on the priority order of attendees' information and the meeting room information, the attendees' list is sorted on the meeting room seating matrix using both greedy and heuristic algorithms.
[0011] Furthermore, the specific process of sorting using the greedy algorithm is as follows: S1, Set the list of attendees P ={ p 1, p 2,…, p n},in n This refers to the total number of attendees; setting the meeting room seating matrix. M ,in M [ i ][ j ] indicates the first i Line number j The seating status of the column; S2, for each participant p k Based on the objective function of the greedy algorithm S ( p k , M )= s Seating arrangements were made; among them S It is a selection function. p k These are the participants currently being considered. M It is a meeting room seating matrix. s It is the selected seat; S3, based on the participants p k The personnel information is used to search for available seats in the conference room seating matrix M. If the searched seat meets the seat status conditions, S4 is executed; otherwise, S5 is executed. S4. Assign the found optimal seats to the corresponding attendees. p k And update the state of the meeting room seating matrix M, marking the assigned seats as unavailable; S5. Find the nearest available seat to the optimal seat and assign it to the corresponding attendee. pk And update the state of the meeting room seating matrix M, marking the assigned seats as unavailable.
[0012] Furthermore, the specific process of sorting according to the heuristic algorithm is as follows: Input a list of attendees sorted using a greedy algorithm, and then sort the attendees according to a social relationship matrix, a work interaction frequency matrix, and a business relevance matrix. The evaluation function is used to calculate and output the seating allocation results based on the sorted list of attendees, social relationship matrix, work interaction frequency matrix, and business relevance matrix. Update the evaluation function value based on the current seating assignments of the attendees until all attendees have completed their seating assignments.
[0013] Furthermore, the specific formula for the evaluation function is as follows:
[0014] in, n Indicates the total number of attendees; w 1. w 2 and w 3 represents the weight of each factor, reflecting its importance in seating allocation; the social relationship score (i,j) represents the score of the closeness of the social relationship between participants i and j; the work interaction frequency score ( i , j () indicates the participants i and j Frequency of work interactions between them; Business relevance score ( i , j () indicates the participants i and j The importance score of the business relationship between them.
[0015] Secondly, the present invention also provides a seating allocation system based on a greedy and heuristic search strategy, comprising: The information acquisition module is used to acquire information about attendees and meeting rooms. The algorithm processing module is used to generate seat allocation results based on the obtained participant information and meeting room information, using greedy algorithms, heuristic algorithms, and logical algorithms. The result output module is used to output the seat allocation results, completing the seat allocation work based on greedy and heuristic search strategies.
[0016] Thirdly, the present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the seating allocation method based on the greedy and heuristic search strategy described above.
[0017] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the seating allocation method based on the greedy and heuristic search strategy described above.
[0018] Fifthly, the present invention also provides a computer program product, including computer instructions, characterized in that the computer instructions instruct a computing device to perform operations corresponding to the seating allocation method based on the greedy and heuristic search strategy described above.
[0019] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a seating allocation method based on a greedy and heuristic search strategy. By acquiring information about attendees and the meeting room, the method logically generates seating allocation results using both greedy and heuristic algorithms. The greedy algorithm selects the optimal or second-best seat at each step, quickly finding a suitable location for each attendee. By prioritizing the allocation of optimal seats and selecting second-best seats when necessary, this method fully utilizes meeting room seating resources and reduces seat waste. The heuristic algorithm uses heuristic information to guide the search process, avoiding blind searches and reducing computational load. This allows the method to quickly generate seating allocation results in practical applications.
[0020] Furthermore, advanced algorithms are used for automated processing in terms of efficient allocation, which not only greatly improves the speed and accuracy of seat allocation, but also reduces the tedious steps of manual operation, achieving unprecedented high efficiency. In terms of reasonable allocation, multiple factors such as user needs, spatial layout, and usage frequency are fully considered to ensure that the allocation of each seat can reach the optimal state, thereby providing users with a more humanized and personalized experience. Attached Figure Description
[0021] Figure 1 This is a flowchart of the seating allocation method based on greedy and heuristic search strategies in an embodiment of the present invention; Figure 2 This is a schematic diagram of the seating allocation framework based on greedy and heuristic search strategies in an embodiment of the present invention; Figure 3This is a schematic diagram illustrating the seating allocation principle based on greedy and heuristic search strategies in an embodiment of the present invention. Figure 4 This is a sequence diagram of meeting seating allocation in an embodiment of the present invention; Figure 5 This is a schematic diagram of the seating allocation system based on greedy and heuristic search strategies in an embodiment of the present invention. In the diagram: 1. Information acquisition module; 2. Algorithm processing module; 3. Result output module. Detailed Implementation
[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0023] The purpose of this invention is to provide a seating allocation method and related equipment based on a greedy and heuristic search strategy. Addressing the efficiency bottlenecks and inability to meet personalized needs in traditional methods when handling large-scale meetings, this invention integrates multiple advanced algorithms and intelligent logic to provide efficient and personalized seating allocation solutions for large conferences, thereby significantly improving meeting organization efficiency and participant satisfaction. This algorithm not only accurately matches the specific needs of participants but also ensures a reasonable seating layout to achieve a dignified yet harmonious meeting atmosphere. The algorithm's design fully considers the complexity of real-world application scenarios and, through continuous iteration and optimization, solves the technical problem of intelligent seating allocation in existing technologies.
[0024] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 See Figure 1 , Figure 2 as well as Figure 3 In one embodiment of the present invention, a seating allocation method based on a greedy and heuristic search strategy is provided, comprising: Step 1: Obtain information on attendees and meeting room details; Specifically, in the steps of obtaining participant information and meeting room information, the participant information includes the participant's name, identity information, job information, and department information; the meeting room information includes the size of the meeting room, seating layout, seat type, number of seats, and facilities in the meeting room.
[0025] Step 2: Based on the obtained information on attendees and meeting rooms, generate seating allocation results using a greedy algorithm, a heuristic algorithm, and a logical algorithm. Specifically, the process of generating seat allocation results logically based on the obtained attendee and meeting room information using greedy and heuristic algorithms is as follows: Read and parse participant information and meeting room information; Create a participant list and a meeting room seating matrix based on the read and parsed participant and meeting room information; Based on the priority order of attendees' information and the meeting room information, the attendees' list is sorted on the meeting room seating matrix using both greedy and heuristic algorithms.
[0026] The specific process of sorting using the greedy algorithm is as follows: S1, Set the list of attendees P ={ p 1, p 2,…, p n},in n This refers to the total number of attendees; setting the meeting room seating matrix. M ,in M [ i ][ j ] indicates the first i Line number j The seating status of the column; S2, for each participant p k ( k =1,2,…, n According to the objective function of the greedy algorithm S ( p k , M )= s Seating arrangements were made; among them S It is a selection function. p k These are the participants currently being considered. M It is a meeting room seating matrix. s It is the selected seat; S3, based on the participants p k The personnel information is used to search for available seats in the conference room seating matrix M. If the searched seat meets the seat status conditions, S4 is executed; otherwise, S5 is executed. S4. Assign the found optimal seats to the corresponding attendees. p kAnd update the state of the meeting room seating matrix M, marking the assigned seats as unavailable; S5. Find the nearest available seat to the optimal seat and assign it to the corresponding attendee. p k And update the state of the meeting room seating matrix M, marking the assigned seats as unavailable.
[0027] The specific process of sorting according to the heuristic algorithm is as follows: Input a list of attendees sorted using a greedy algorithm, and then sort the attendees according to a social relationship matrix, a work interaction frequency matrix, and a business relevance matrix. The evaluation function is used to calculate and output the seating allocation results based on the sorted list of attendees, social relationship matrix, work interaction frequency matrix, and business relevance matrix. Update the evaluation function value based on the current seating assignments of the attendees until all attendees have completed their seating assignments.
[0028] The specific formula for the evaluation function is as follows:
[0029] in, n Indicates the total number of attendees; w 1. w 2 and w 3 represents the weight of each factor, reflecting its importance in seating allocation; the social relationship score (i,j) represents the score of the closeness of the social relationship between participants i and j; the work interaction frequency score ( i , j () indicates the participants i and j Frequency of work interactions between them; Business relevance score ( i , j () indicates the participants i and j The importance score of the business relationship between them.
[0030] The social relationship matrix calculates a social relationship score between people sitting next to each other based on an evaluation function; the higher the score, the closer the relationship. The frequency of work interaction is calculated based on an evaluation function, with a score indicating the frequency of work interaction between people in adjacent seats. A higher score indicates more frequent interaction. Business relevance is calculated based on an evaluation function, which scores the business relevance between people in adjacent seats. A higher score indicates a closer business connection.
[0031] In this embodiment, when seat allocation is performed using greedy and heuristic search strategies, if there are seat overlaps or position conflicts, a greedy algorithm or a heuristic algorithm is used to reassign the seats of the relevant participants.
[0032] Step 3: Output the seat allocation results to complete the seat allocation based on the greedy and heuristic search strategy.
[0033] The method provided in this embodiment has the following characteristics: By combining attendees' historical data and behavioral patterns, the algorithm can intelligently analyze and predict their information, recommending the most suitable seating location for each attendee.
[0034] If unforeseen circumstances arise before or during a meeting that necessitate adjustments to the seating arrangement, the algorithm can respond quickly and optimize the seating allocation scheme in real time.
[0035] Considering the different needs of different types of meetings, the algorithm supports setting different weights based on factors such as importance and relationship closeness to meet diverse allocation needs.
[0036] To help users intuitively understand the seating distribution, the algorithm also provides rich visualizations, such as seating charts and heat maps, making it easier for managers to quickly grasp the venue layout.
[0037] The algorithm has good compatibility and can be applied to meeting rooms of various sizes, delivering excellent performance from small seminars to large international conferences.
[0038] In the seating allocation method based on greedy and heuristic search strategies provided in this embodiment, firstly, in terms of improving allocation efficiency, an advanced automated processing mechanism is adopted, which enables the originally tedious and complex seating allocation work to be completed quickly through the efficient operation of the algorithm, significantly reducing the time cost required for the operation.
[0039] Secondly, to ensure the rationality of the allocation, the individual characteristics, professional background, job level and other multi-dimensional information of the participants were fully considered. Combined with the specific layout and spatial structure of the meeting room, a scientific and reasonable seating arrangement was achieved to ensure that each participant can sit in a suitable position.
[0040] Furthermore, considering potential temporary changes or unforeseen events in practical applications, this algorithm exhibits excellent adaptability. It can flexibly adjust the original scheme to cope with various unforeseen situations, thereby maintaining the overall stability and reliability of seat allocation.
[0041] In this embodiment, the following preparations need to be made before starting algorithm selection and preparation: Define the data structure, which includes the data structure of attendees and seats, such as the attendees' priority, organization, position, and other information, as well as the seating layout and constraints of the meeting room.
[0042] Assign a priority to each participant, which can be based on factors such as their job level, the importance of their organization, and their role in the meeting.
[0043] Seating layout: Define the seating arrangement in the meeting room, including the location, number, and distribution of seats, taking into account the natural flow of the meeting and possible communication needs.
[0044] Secondly, based on the above preparations, the following three algorithms are integrated to implement seat allocation, with the greedy algorithm accounting for 30% of the weight, the heuristic algorithm accounting for 30% of the weight, and the logic-based algorithm accounting for 40% of the weight.
[0045] An efficient greedy algorithm is introduced to guide seat allocation. This algorithm comprehensively considers multiple key factors of attendees, including but not limited to personal priority, affiliated organization, and job title. Through this intelligent sorting mechanism, the system can ensure that attendees who play important roles or have high influence in the meeting receive the best seating positions.
[0046] Heuristic algorithms are used to further improve the quality of seating allocation. This algorithm not only assesses each participant's business network but also analyzes the meeting room's spatial layout and functional zoning. Based on this multi-dimensional information, the system will more accurately match participants' seats, thereby maximizing efficient interpersonal interaction and space utilization.
[0047] To ensure that the seating allocation process meets specific application scenarios and personalized needs, a logical algorithm has also been set up. This logic covers various situations, such as keeping adjacent seats to facilitate communication and collaboration, and reserving seats in specific areas for special purposes.
[0048] In summary, this embodiment provides a seating allocation method based on a greedy and heuristic search strategy. By acquiring information about attendees and the meeting room, the method logically generates seating allocation results using both greedy and heuristic algorithms. The greedy algorithm selects the optimal or second-best seat at each step, quickly finding a suitable location for each attendee. By prioritizing the allocation of optimal seats and selecting second-best seats when necessary, this method fully utilizes meeting room seating resources and reduces seat waste. The heuristic algorithm uses heuristic information to guide the search process, avoiding blind searches and reducing computational load. This allows the method to quickly generate seating allocation results in practical applications.
[0049] For efficient allocation, advanced algorithms are used for automated processing, which not only significantly improves the speed and accuracy of seat allocation but also reduces tedious manual operations, achieving unprecedented efficiency. In terms of rational allocation, multiple factors such as user needs, spatial layout, and usage frequency are fully considered to ensure that every seat is allocated optimally, thus providing users with a more humanized and personalized experience. Regarding flexible adaptability, this invention has a powerful contingency mechanism that can quickly respond to temporary changes or emergencies, ensuring the continuity and stability of seat allocation and maintaining efficient operation even in complex and ever-changing scenarios.
[0050] Example 2 according to Figure 4 As shown, this invention provides a seating allocation method based on a greedy and heuristic search strategy, with the following specific steps: Input the information of the attendees and the meeting room, including their names, positions, organizations, and priorities; A greedy algorithm is used to initially allocate seats based on attendee priority, affiliated unit, and job level. A heuristic algorithm is then applied to optimize the seat allocation based on factors such as business relevance, meeting room layout, and functional zoning. The computer automatically assigns seats to the current meeting's host. Logical algorithms are combined to handle special area allocations and adjacent seat assignments. The system optimizes seat sorting using three algorithms, calculates and generates seat allocation results based on weight ratios, and displays them to users, providing modification and adjustment functions.
[0051] Example 3 according to Figure 5 As shown, the present invention also provides a seating allocation system based on a greedy and heuristic search strategy, comprising: Information acquisition module 1 is used to acquire information about attendees and meeting rooms; Algorithm processing module 2 is used to generate seat allocation results based on the obtained participant information and meeting room information, using greedy algorithms and heuristic algorithms, and logical algorithms. Result output module 3 is used to output the seat allocation results, completing the seat allocation work based on greedy and heuristic search strategies.
[0052] Example 4 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a seating assignment program based on a greedy and heuristic search strategy.
[0053] When the processor executes the computer program, it implements the steps of the seating allocation method based on the greedy and heuristic search strategy described above, for example: Obtain information on attendees and meeting rooms; Based on the obtained information on attendees and meeting rooms, seat allocation results are generated using a logical algorithm based on greedy and heuristic algorithms. Output the seat allocation results to complete the seat allocation work based on greedy and heuristic search strategies.
[0054] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example: Information acquisition module 1 is used to acquire information about attendees and meeting rooms; Algorithm processing module 2 is used to generate seat allocation results based on the obtained participant information and meeting room information, using greedy algorithms and heuristic algorithms, and logical algorithms. Result output module 3 is used to output the seat allocation results, completing the seat allocation work based on greedy and heuristic search strategies.
[0055] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the mobile terminal.
[0056] For example, the computer program can be divided into an information acquisition module 1, an algorithm processing module 2, and a result output module 3; The specific functions of each module are as follows: Information acquisition module 1 is used to acquire information about attendees and meeting rooms; Algorithm processing module 2 is used to generate seat allocation results based on the obtained participant information and meeting room information, using greedy algorithms and heuristic algorithms, and logical algorithms. Result output module 3 is used to output the seat allocation results, completing the seat allocation work based on greedy and heuristic search strategies.
[0057] The mobile terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The mobile terminal may include, but is not limited to, a processor and memory.
[0058] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the mobile terminal, connecting various parts of the mobile terminal via various interfaces and lines.
[0059] The memory can be used to store the computer program and / or module. The processor implements various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.
[0060] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.
[0061] Example 5 The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the seating allocation method based on a greedy and heuristic search strategy.
[0062] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
[0063] Based on this understanding, all or part of the processes in the above method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described aggregated reinforcement learning resource scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.
[0064] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.
[0065] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.
[0066] Example 6 A computer program product includes computer instructions that instruct a computing device to perform operations corresponding to the seating allocation method based on a greedy and heuristic search strategy as described above.
[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A seating allocation method based on a greedy and heuristic search strategy, characterized in that, include: Obtain information on attendees and meeting rooms; Based on the obtained information on attendees and meeting rooms, seat allocation results are generated using a logical algorithm based on greedy and heuristic algorithms. Output the seat allocation results to complete the seat allocation work based on greedy and heuristic search strategies.
2. The seating allocation method based on a greedy and heuristic search strategy according to claim 1, characterized in that, In the steps of obtaining participant information and meeting room information, the participant information includes the participant's name, identity information, position information, and department information; the meeting room information includes the size of the meeting room, seating layout, seat type, number of seats, and facilities in the meeting room.
3. The seating allocation method based on a greedy and heuristic search strategy according to claim 1, characterized in that, The specific process of generating seat allocation results logically based on the obtained participant and meeting room information using greedy and heuristic algorithms is as follows: Read and parse participant information and meeting room information; Create a participant list and a meeting room seating matrix based on the read and parsed participant and meeting room information; Based on the priority order of attendees' information and the meeting room information, the attendees' list is sorted on the meeting room seating matrix using both greedy and heuristic algorithms.
4. The seating allocation method based on a greedy and heuristic search strategy according to claim 3, characterized in that, The specific process of sorting using the greedy algorithm is as follows: S1, Set the list of attendees P ={ p 1, p 2,…, p n },in n This refers to the total number of attendees; setting the meeting room seating matrix. M ,in M [ i ][ j ] indicates the first i Line number j The seating status of the column; S2, for each participant p k Based on the objective function of the greedy algorithm S ( p k , M )= s Seating arrangements were made; among them S It is a selection function. p k These are the participants currently being considered. M It is a meeting room seating matrix. s It is the selected seat; S3, based on the participants p k The personnel information is used to search for available seats in the conference room seating matrix M. If the searched seat meets the seat status conditions, S4 is executed; otherwise, S5 is executed. S4. Assign the found optimal seats to the corresponding attendees. p k And update the state of the meeting room seating matrix M, marking the assigned seats as unavailable; S5. Find the nearest available seat to the optimal seat and assign it to the corresponding attendee. p k And update the state of the meeting room seating matrix M, marking the assigned seats as unavailable.
5. The seating allocation method based on a greedy and heuristic search strategy according to claim 3, characterized in that, The specific process of sorting according to the heuristic algorithm is as follows: Input a list of attendees sorted using a greedy algorithm, and then sort the attendees according to a social relationship matrix, a work interaction frequency matrix, and a business relevance matrix. The evaluation function is used to calculate and output the seating allocation results based on the sorted list of attendees, social relationship matrix, work interaction frequency matrix, and business relevance matrix. Update the evaluation function value based on the current seating assignments of the attendees until all attendees have completed their seating assignments.
6. A seating allocation method based on a greedy and heuristic search strategy according to claim 5, characterized in that, The specific formula for the evaluation function is as follows: in, n Indicates the total number of attendees; w 1. w 2 and w 3 represents the weight of each factor, reflecting its importance in seating allocation; the social relationship score (i,j) represents the score of the closeness of the social relationship between participants i and j; the work interaction frequency score ( i , j () indicates the participants i and j Frequency of work interactions between them; Business relevance score ( i , j () indicates the participants i and j The importance score of the business relationship between them.
7. A seating allocation method based on a greedy and heuristic search strategy according to claim 3, characterized in that, In the seat allocation process based on greedy and heuristic search strategies, when there are seat overlaps or location conflicts, greedy or heuristic algorithms are used to reassign the seats of the relevant participants.
8. A seating allocation system based on a greedy and heuristic search strategy, characterized in that, include: The information acquisition module is used to acquire information about attendees and meeting rooms. The algorithm processing module is used to generate seat allocation results based on the obtained participant information and meeting room information, using greedy algorithms, heuristic algorithms, and logical algorithms. The result output module is used to output the seat allocation results, completing the seat allocation work based on greedy and heuristic search strategies.
9. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the seating allocation method based on a greedy and heuristic search strategy as described in any one of claims 1-7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the seating allocation method based on a greedy and heuristic search strategy as described in any one of claims 1-7.
11. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the operation corresponding to the seating allocation method based on greedy and heuristic search strategies as described in any one of claims 1-7.