Campus experimental equipment time-sharing reservation driven resource allocation optimization method and system

Abstract

The application provides a resource allocation optimization method and system driven by time-sharing reservation of campus experimental equipment, and relates to the technical field of resource allocation.The method comprises the following steps: obtaining the distribution of campus experimental equipment resources and performing equipment classification; receiving teaching experiment tasks in the campus, matching the tasks with M-type equipment, and then performing batch scheduling of teaching experiments; collecting sparse reservation requests to determine the marked equipment types of complementary adaptation abnormalities and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution; performing time adjustment of the sparse reservation requests of the marked equipment types based on complementary adaptation, and sending the adjustment results to the requesters for adjustment confirmation.The application solves the technical problem of low overall utilization of campus experimental equipment in the prior art due to the mutual isolation and lack of cooperation between rigid teaching task scheduling and flexible scientific research reservation demand, maximizes the utilization of fragmented resources in teaching scheduling, and improves the overall utilization and management efficiency of campus experimental equipment.

Description

Technical Field

[0001] This application relates to the field of resource allocation technology, specifically to a resource allocation optimization method and system driven by time-sharing reservation of campus experimental equipment. Background Technology

[0002] In the field of experimental equipment resource scheduling, teaching experiments and research experiments are typically treated as independent processes. Teaching experiments are usually scheduled centrally based on the semester's teaching plan, with classes as the unit and course time as the constraint, forming a relatively fixed equipment occupancy plan. Research experiments and other sporadic reservation requests are usually handled through a separate reservation system, using a first-come, first-served approach or manual coordination to arrange during equipment idle periods. Because teaching schedules and research reservations are logically isolated and lack effective information coordination, there is a significant structural contradiction in the actual utilization of equipment resources. Teaching task scheduling often focuses solely on teaching convenience, occupying a large amount of equipment time, but it may contain fragmented, untapped resources. Research reservations, when faced with time conflicts with teaching schedules, usually only receive simple rejection feedback, failing to identify and utilize equipment redundancy within teaching time periods. This disconnect between rigid tasks and flexible needs not only causes frequent mismatches and conflicts of equipment resources in the time dimension but also restricts the overall utilization rate and management efficiency of campus experimental equipment.

[0003] In summary, existing technologies suffer from technical problems due to the isolation and lack of coordination between rigid teaching task scheduling and flexible scientific research appointment needs, which leads to frequent time conflicts of equipment resources and further affects the overall utilization rate and management efficiency of campus experimental equipment. Summary of the Invention

[0004] The purpose of this application is to provide a resource allocation optimization method and system driven by time-sharing reservation for campus experimental equipment, in order to solve the technical problem in the prior art that the rigid teaching task scheduling and the flexible scientific research reservation demand are isolated from each other and lack coordination, resulting in frequent time conflicts of equipment resources, which further affects the overall utilization rate and management efficiency of campus experimental equipment.

[0005] To achieve the above objectives, this application provides a resource allocation optimization method and system driven by time-sharing reservation for campus experimental equipment.

[0006] Firstly, this application provides a resource allocation optimization method driven by time-sharing reservation for campus experimental equipment. This method is implemented through a resource allocation optimization system driven by time-sharing reservation for campus experimental equipment. The method includes: acquiring the distribution of campus experimental equipment resources and performing classification based on equipment type to generate M types of equipment; receiving teaching experiment tasks within the campus and matching them with the M types of equipment to schedule teaching experiments in batches, establishing first equipment time-sharing scheduling information; collecting sparse reservation requests within the campus other than teaching experiment tasks and matching them with the M types of equipment to perform complementary adaptation analysis of teaching experiments and scientific research experiments respectively, determining the marked equipment types with complementary adaptation anomalies and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution; based on the teaching experiment scheduling distribution and the sparse reservation request distribution, adjusting the reservation time of the marked equipment types based on complementary adaptation sparse reservation requests, and sending the adjustment results to the requester for confirmation.

[0007] Optionally, based on the semester teaching experiment task list from the teaching management system, the class size, planned weeks, standard duration of a single experiment, and equipment type requirements associated with each teaching experiment task are analyzed; according to the equipment type requirements, each type of equipment corresponding to each teaching experiment task is determined in the M-type equipment; the distribution location of each type of equipment is determined according to the equipment resource distribution; and combined with the class size, planned weeks, standard duration of a single experiment, and available time of the class, with the goal of minimizing the idle rate of laboratory equipment and the fragmentation of experiment time, and with the constraint of uniform time and space of the same class, batch scheduling is performed to generate the time-sharing scheduling information of the first equipment.

[0008] Optionally, the available time for a class is determined by obtaining the class schedule.

[0009] Optionally, based on the distribution of campus experimental equipment resources and the time-sharing scheduling information of the first equipment, the sparse resource distribution of the M-type equipment within the sparse reservation period is determined; the sparse reservation requests are parsed to perform type matching of experimental equipment, and a sparse request equipment demand distribution is established in combination with the reservation time; the complementarity and adaptability of the sparse request equipment demand distribution and the sparse resource distribution are analyzed, and equipment types with complementary adaptability anomalies are marked to generate the marked equipment types, and the teaching experiment scheduling distribution and sparse reservation request distribution corresponding to the marked equipment types are extracted.

[0010] Optionally, it can be determined whether the resources of each type of device in the sparse resource distribution at each time meet the demand distribution of the sparse requesting devices; if not, it can be determined that there is a complementary adaptation anomaly.

[0011] Optionally, historical experimental records of the M-type devices are collected; the device failure rate of the M-type devices is analyzed based on the historical experimental records; and the device failure rate is used to perform judgment and optimization for complementary adaptation anomalies.

[0012] Optionally, based on the sparse reservation request distribution, determine the set of timeout reservation requests that cause complementary adaptation anomalies for each reservation period, and the normal sparse reservation request distribution excluding the timeout reservation request set; compare the normal sparse reservation request distribution with the teaching experiment scheduling distribution to determine redundant sparse reservation periods and the number of redundancies; and perform interpolation matching on the timeout reservation request set based on complementary adaptation according to the number of redundancies to complete the sparse reservation request reservation time adjustment.

[0013] Optionally, obtain reservation confirmation information; perform experimental accuracy identification based on historical experimental records for each device in the M-type devices to determine the M-type experimental accuracy distribution; and perform device accuracy matching reservation based on the experimental accuracy requirements of sparse reservation requests according to the M-type experimental accuracy distribution.

[0014] Optionally, after the adjustment result is sent to the requester for confirmation, if the requester does not confirm within a preset time, the adjustment time slot will be automatically released for other users to make an appointment, and a reminder message will be sent to the requester.

[0015] Secondly, this application also provides a resource allocation optimization system driven by time-sharing reservation for campus experimental equipment, used to execute the resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in the first aspect. The resource allocation optimization system driven by time-sharing reservation for campus experimental equipment includes: an equipment classification module, used to acquire the distribution of campus experimental equipment resources and perform classification based on experimental equipment type to generate M types of equipment; a batch scheduling module, used to receive teaching experimental tasks within the campus, match them with the M types of equipment, and perform batch scheduling of teaching experiments to establish first equipment time-sharing scheduling information; a complementary adaptation analysis module, used to collect sparse reservation requests within the campus other than teaching experimental tasks, match them with the M types of equipment, and perform complementary adaptation analysis for teaching experiments and scientific research experiments respectively, determining the marked equipment types with complementary adaptation anomalies and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution; and a reservation dynamic adjustment module, used to adjust the reservation time of the marked equipment types based on complementary adaptation based on the teaching experiment scheduling distribution and the sparse reservation request distribution, and send the adjustment result to the requester for adjustment confirmation.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: By acquiring the distribution of campus experimental equipment resources and performing classification based on experimental equipment type, M types of equipment are generated; teaching experimental tasks within the campus are received and matched with the M types of equipment to schedule teaching experiments in batches, establishing the first equipment time-sharing scheduling information; sparse reservation requests within the campus other than teaching experimental tasks are collected and matched with the M types of equipment to perform complementary adaptation analysis of teaching experiments and scientific research experiments respectively, determining the marked equipment types with complementary adaptation anomalies and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution; based on the teaching experiment scheduling distribution and the sparse reservation request distribution, the reservation time of the marked equipment types is adjusted based on complementary adaptation sparse reservation requests, and the adjustment results are sent to the requester for adjustment confirmation. In other words, by classifying equipment and then specifically receiving teaching tasks for batch scheduling, a first-level equipment time-sharing scheduling information is established; sparse reservation requests are collected and subjected to complementary adaptation analysis with the first-level equipment time-sharing scheduling information to identify marked equipment types with adaptation anomalies due to insufficient resources, and to lock the relevant teaching schedule distribution and reservation request distribution; based on the teaching experiment schedule distribution and sparse reservation request distribution, reservation times for marked equipment types with conflicts are adjusted based on complementary adaptation, and the adjustment results are sent to users for confirmation. While prioritizing the stability of teaching order, the system maximizes the use of fragmented resources in the teaching schedule to absorb research reservations, thereby significantly improving the overall utilization rate of equipment and management efficiency.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the resource allocation optimization method driven by time-sharing reservation for campus experimental equipment in this application.

[0020] Figure 2 This is a schematic diagram of the resource allocation optimization system driven by time-sharing reservation of campus experimental equipment in this application.

[0021] Figure labeling: Equipment classification module 11, batch scheduling module 12, complementary adaptation analysis module 13, reservation dynamic adjustment module 14. Detailed Implementation

[0022] This application provides a resource allocation optimization method and system driven by time-sharing reservations for campus experimental equipment. It addresses the technical problem in existing technologies where rigid teaching task scheduling and flexible research reservation needs are isolated and lack coordination, leading to frequent time conflicts in equipment resources and further impacting the overall utilization and management efficiency of campus experimental equipment. By classifying equipment and then specifically receiving teaching tasks for batch scheduling, a first-level equipment time-sharing schedule is established. Sparse reservation requests are collected and compared with the first-level equipment time-sharing schedule information through complementary adaptation analysis. This identifies marked equipment types with adaptation anomalies due to insufficient resources and locks down the relevant teaching schedule distribution and reservation request distribution. Based on the teaching experiment schedule distribution and sparse reservation request distribution, reservation times for conflicting marked equipment types are adjusted based on complementary adaptation, and the adjustment results are sent to users for confirmation. While prioritizing the stability of teaching order, this method maximizes the use of fragmented resources in the teaching schedule to absorb research reservations, thereby significantly improving the overall utilization rate and management efficiency of campus experimental equipment.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides a resource allocation optimization method driven by time-sharing reservation for campus experimental equipment. The method is applied to a resource allocation optimization system driven by time-sharing reservation for campus experimental equipment. The specific steps of the method are as follows: Obtain the distribution of campus experimental equipment resources and perform classification based on experimental equipment type to generate M types of equipment.

[0025] Specifically, in conjunction with the Laboratory Management Office, various colleges, research institutes, and university-level public platforms will conduct on-site inventory and information verification of all experimental equipment within their respective areas of responsibility. Information collection must utilize standardized spreadsheets or mobile data collection applications to ensure consistent data formats. The core information items collected must include: unique equipment identifier, standard equipment name, model and specifications, serial number, specific storage location, equipment administrator, affiliated management unit, equipment status, equipment function description, and its core technical parameters.

[0026] Based on the main functions and application areas of the equipment, and in accordance with the equipment classification standards established by the school, each piece of equipment is categorized. A reasonable classification hierarchy is as follows: first, divide the equipment into primary categories according to disciplines or technical fields, and then divide each category into secondary subcategories according to technical principles and core functions. Based on the essential characteristics of the experimental equipment, such as its core functions, technical principles, and application areas, it is classified into different categories. For example, all equipment used for observing microstructures is classified into the microscopic imaging category, and all equipment used for separating and analyzing mixtures is classified into the chromatography-mass spectrometry category. The purpose of classification is to abstract thousands of specific pieces of equipment into a limited number of manageable equipment types, facilitating unified resource planning and scheduling. During the classification process, it is necessary to ensure that equipment of the same category has a high degree of similarity in experimental functions and can meet the same type of experimental needs. After completing the classification, the number, distribution location, and availability of equipment under each category are statistically analyzed, and a structured equipment resource distribution table is generated. This table uses equipment category as an index and records the total number of equipment in each category, the laboratories in which they are located, the specific number of equipment in each laboratory, and the real-time status of the equipment.

[0027] The classification results are denoted as Class M equipment, where M is the total number of equipment categories actually identified, and is a positive integer. By acquiring the distribution of campus experimental equipment resources and classifying them according to the type of experimental equipment, a unified set of equipment categories is formed, enabling the unified management and scheduling of experimental equipment scattered across different laboratories on campus.

[0028] The system receives teaching and experimental tasks from within the campus, matches them with the M-type equipment, schedules the teaching and experimental tasks in batches, and establishes time-sharing scheduling information for the first equipment.

[0029] Furthermore, this application also includes the following steps: based on the semester teaching experiment task list from the teaching management system, analyze the class size, planned weeks, standard duration of a single experiment, and equipment type requirements associated with each teaching experiment task; determine each type of equipment corresponding to each teaching experiment task in the M-type equipment according to the equipment type requirements; determine the distribution location of each type of equipment according to the equipment resource distribution; and, in combination with the class size, planned weeks, standard duration of a single experiment, and available time for the class, perform batch scheduling with the goal of minimizing laboratory equipment idle rate and experiment time fragmentation, and with the constraint of unified time and space for the same class, generate the first equipment time-sharing scheduling information.

[0030] Furthermore, this application also includes the following step: the available time for the class is determined by obtaining the class schedule.

[0031] Specifically, by interfaceing with the teaching management system, the system automatically retrieves the new semester's teaching experiment task list, which includes all required experiments, each corresponding to a specific class. The teaching management system is an information platform used by the school to manage student information, course schedules, grade records, and other academic affairs. It typically stores the curriculum plans for all majors across the school, the course schedules for each semester, and detailed class timetables. The semester teaching experiment task list is a summary list generated by the teaching management system at the beginning of each semester, containing all experimental teaching tasks to be completed within the semester. It clearly defines the core information for each experiment task, such as the corresponding class, course name, experiment project, and planned week. The system analyzes the semester teaching experiment task list item by item, extracting the associated parameters for each teaching experiment task, including class size, planned week, standard duration of a single experiment, and equipment type requirements. In short, it includes the number of participating students, the planned week range, the standard duration required to complete one experiment, and the type of equipment required for the experiment.

[0032] Based on the equipment type requirements, a matching search is performed within category M equipment to determine which category of equipment corresponds to each teaching experiment task. According to the distribution of campus experimental equipment resources, the specific locations of each category of equipment are identified, i.e., which laboratories they are located in and how many units are in each laboratory. Combining class size, planned weeks, standard duration of a single experiment, and available class time, the available time slots within a week for arranging experiments are analyzed, i.e., the class's available time.

[0033] Based on the location of each type of equipment, class size, planned weeks, standard duration of a single experiment, and available time for each class, scheduling begins in batches. The scheduling process follows two core principles: first, rigid constraints, meaning that experiments within the same class must be scheduled in the same laboratory during the same time slot to ensure all students participate simultaneously; second, optimization goals, meaning minimizing equipment idle time and fragmented experiment time. This means that scheduling will prioritize making equipment usage as continuous as possible, avoiding fragmented situations where equipment is used for an hour one day, half a day the next, and then another hour the day after, while maintaining a high occupancy rate of equipment during available periods. Minimizing equipment idle rate and experimental time fragmentation is the objective function, solved under the following hard constraints: Each device can only be used by one batch of experiments from one class at any given time; all batches of the same experimental task from a class must be assigned to the same laboratory room and be consecutive or closely adjacent in time. For example, if a class of 60 students needs to complete the experiment in two batches, with 30 students in each batch using 15 devices, then these two batches must be arranged in adjacent time slots in the same laboratory, 8:00-11:00 and 14:00-17:00; all batches must be arranged within the available time window of the corresponding class, and the duration of each batch must meet the standard duration; the assigned laboratory must have a sufficient number of similar devices.

[0034] All experimental tasks were iterated through, and attempts were made to allocate specific time and laboratory time to each task. Priority was given to scheduling tasks for the same class during their free time slots when laboratory equipment was sufficient. Efforts were also made to connect similar experimental tasks from different classes in terms of time, ensuring that the same batch of equipment could be used continuously. After repeated trials and optimizations, a detailed first-stage equipment time-sharing schedule covering the entire semester was generated, clearly identifying which class's experimental tasks occupied each day, each class, each laboratory, and each piece of equipment.

[0035] By analyzing the teaching experiment task list in the teaching management system and combining it with the distribution of equipment resources, class size, experiment duration, and class schedule, experiments are scheduled in batches. At the beginning of the semester, a reasonable plan for the use of teaching experiment equipment is formed to ensure that teaching experiment tasks are completed in a priority manner. At the same time, continuous scheduling reduces idle time of laboratory equipment and fragmented time.

[0036] Sparse reservation requests from within the campus, excluding teaching and experimental tasks, are collected and matched with the M-type devices. Complementary adaptation analysis is then performed on teaching and scientific research experiments to determine the types of marked devices with complementary adaptation anomalies, as well as the corresponding teaching experiment scheduling distribution and sparse reservation request distribution.

[0037] Furthermore, this application also includes the following steps: determining the sparse resource distribution of the M-type equipment within the sparse reservation period based on the campus experimental equipment resource distribution and the first equipment time-sharing scheduling information; parsing the sparse reservation requests to perform experimental equipment type matching, and establishing a sparse request equipment demand distribution in combination with the reservation time; analyzing the complementarity and adaptability between the sparse request equipment demand distribution and the sparse resource distribution, marking equipment types with complementary adaptability anomalies, generating the marked equipment types, and extracting the teaching experiment scheduling distribution and sparse reservation request distribution corresponding to the marked equipment types.

[0038] Furthermore, this application also includes the following steps: identifying whether the resources of each type of device at each time in the sparse resource distribution meet the demand distribution of the sparse requesting devices; if not, determining that there is a complementary adaptation anomaly.

[0039] Specifically, the system continuously collects equipment usage requests from relevant personnel, students, and other users. These requests, independent of pre-arranged teaching tasks, are referred to as sparse reservation requests. Each request includes information such as the type of equipment the applicant wishes to use, the desired experiment date and start time, and the expected duration. Sparse reservation requests are sporadic, non-batch, and non-scheduled equipment usage requests submitted by relevant personnel and student innovation project teams, outside of teaching experiments. These requests are typically characterized by high randomness, flexible single-use time, and diverse equipment types required.

[0040] The distribution of campus experimental equipment resources is retrieved, including the total number and location of each type of equipment. This is combined with the time-sharing scheduling information of the first equipment category, indicating which time slots are already occupied by teaching tasks. By subtracting the teaching-occupied time slots from the total number of equipment, the remaining number of each type of equipment available for reservation is calculated for each day, each time slot, and each set of equipment within a predetermined sparse reservation period (e.g., the following week). This sparse resource distribution, after deducting all time slots and equipment already occupied by teaching tasks from the first equipment time-sharing scheduling information, represents the remaining available time-resource gaps for each type of equipment within a specified sparse reservation period. It defines when, which type of equipment, and how many units are still available.

[0041] All collected sparse reservation requests are parsed and processed to determine the type of device required for each request, as well as the user's desired reservation date and time period. These demands are then mapped to specific time periods to establish the distribution of sparse request device demand, showing which time period and which type of device will be simultaneously contested by how many users in the coming week.

[0042] The distribution of sparse resources is compared with the distribution of sparse request device demand. For each future time period, for each device type, the number of idle devices within that time period is checked to see if it is greater than or equal to the number of reservation requests submitted by users. If the number of idle devices is sufficient, the supply and demand match is normal; if the number of idle devices is less than the number of requests (e.g., only 5 devices of a certain type are idle in a certain time period, but 8 reservation requests are received), then a complementary mismatch is determined to exist.

[0043] A summary analysis is performed on all device types identified as having complementary compatibility anomalies. Device types with complementary compatibility anomalies are marked. For each marked device, its complete teaching experiment schedule distribution and sparse reservation request distribution throughout the entire cycle are extracted. The teaching experiment schedule distribution related to the marked device type includes the specific time slots and quantities originally occupied by teaching tasks. The sparse reservation request distribution related to the marked device type that caused the anomaly includes the specific users whose demands exceeded available capacity and when they expected to use the equipment. For example, suppose imaging equipment suddenly malfunctions on Wednesday, requiring 3 machines to be shut down for maintenance, reducing the actual available equipment from 11 to 8. The sparse resource distribution shows 8 imaging devices actually available, and the sparse request device demand distribution shows 9 requests, which are not met, thus indicating a complementary compatibility anomaly. Imaging devices are marked as device types with complementary compatibility anomalies. On Wednesday, originally 8 machines were occupied by teaching, with a total of 9 requests, but only 8 machines were available. Therefore, at most 8 of these 9 requests can be satisfied, and 1 request cannot be scheduled according to the original time slot.

[0044] By collecting sparse reservation requests generated from scientific research or individual experiments and combining this with teaching experiment scheduling information to analyze the idle resources of various types of equipment in different time periods, a sparse resource distribution of equipment resources is established. By comparing scientific research reservation demands with equipment idle resources, resource conflicts between teaching experiments and scientific research reservations are accurately identified, and equipment types with complementary mismatch anomalies are marked.

[0045] Furthermore, this application also includes the following steps: collecting historical experimental records of the M-type devices; analyzing the device failure rate of the M-type devices based on the historical experimental records; and performing complementary adaptation anomaly judgment optimization based on the device failure rate.

[0046] Specifically, the system retrieves complete historical experimental records for all M-type equipment from the equipment management database over a given period. This includes not only the equipment's usage time, user, and experimental project, but also, and more specifically, the equipment's fault logs. For each fault log entry, the system records the specific time the fault occurred, its duration, and whether any ongoing experiments were affected.

[0047] Statistical analysis of failure rates is performed on each type of equipment. For all individuals within the same equipment category, the total number of failures for each device over the past 12 calendar months is counted. This count is then divided by the total planned usage counts for that equipment during the same period, i.e., the sum of all successful and failed usage appointments. This yields the average number of failures per unit operating time for that type of equipment, i.e., the failure rate. For example, if a scanning electron microscope was booked 150 times in the past year, and 3 of those failures resulted in unavailability, its failure rate would be 2%. The average failure rate for all equipment within the same category is then calculated, which is used for reliability estimation of newly commissioned or data-scarce individuals within that category.

[0048] When assessing complementarity and compatibility, the physical number of idle devices for each type during a specific time period is used. A failure rate is introduced to adjust this physical number of idle devices. Considering the probability of device failure during operation, the actual number of devices capable of stably completing the experiment should be less than the physical number. Specifically, the physical number of idle devices is multiplied by a reliability coefficient, calculated based on the failure rate, such as 1 minus the failure probability within a unit time period. After adjustment, the number of available devices is obtained. This number is then compared to the number of reservation requests during that time period. If the number of available devices is greater than or equal to the number of reservation requests, the supply and demand are considered to be in good condition, even if the physical number of devices is just about right. If the number of available devices is less than the number of reservation requests, a complementarity and compatibility anomaly is identified. This assessment method is more rigorous and realistic than simply looking at the physical number of devices because it pre-determines fault redundancy.

[0049] For example, suppose we are currently assessing resource availability for the time slot of 10:00-11:00 AM next Wednesday. Traditional assessment relies on the time-sharing scheduling information of the first device; currently, there is no teaching usage. All five electron microscopes are shown as available in the sparse resource distribution. If there are four sparse reservation requests, the resource is considered sufficient (5>4), with no anomalies. Before the assessment, we check the real-time status of these five theoretically available devices. We find that device S004 is a high-risk device with a failure rate of 4.0%. Including S004 in the reliable supply during scheduling poses a significant risk. Therefore, the expected number of reliably available devices is revised to four (excluding S004). At this point, the expected number of reliably available devices (4) equals the sparse reservation demand (4), and resource supply is at a critical state. When supply exactly equals demand, with no redundancy, it is considered a potential complementary mismatch anomaly and is marked. Because any minor unexpected event, such as another device needing temporary maintenance, could cause not all appointments to be fulfilled, an alert will be triggered to remind the administrator to pay attention, or in subsequent adjustments, an attempt will be made to find time slots with at least one redundant device for these four requests to enhance the reliability of the schedule.

[0050] By incorporating equipment failure rates to optimize the identification of complementary adaptation anomalies, the identification of resource conflicts is upgraded from a simple quantitative conflict dimension to a comprehensive quantity-quality conflict dimension. This proactively avoids the risk of concentrating a large number of appointments on high-failure-rate equipment, preventively reducing experimental interruptions, schedule delays, and user complaints caused by sudden equipment failures.

[0051] Based on the teaching experiment scheduling distribution and the sparse reservation request distribution, the reservation time of the marked device type is adjusted based on complementary adaptation, and the adjustment result is sent to the requester for adjustment confirmation.

[0052] Furthermore, this application also includes the following steps: determining the set of timeout reservation requests that cause complementary adaptation anomalies for each reservation period based on the sparse reservation request distribution, and the normal sparse reservation request distribution excluding the timeout reservation request set; comparing the normal sparse reservation request distribution with the teaching experiment scheduling distribution to determine the redundant sparse reservation periods and the number of redundancies; and performing interpolation matching on the timeout reservation request set based on complementary adaptation according to the number of redundancies to complete the sparse reservation request reservation time adjustment.

[0053] Specifically, based on the sparse reservation request distribution, each reservation request is analyzed in chronological order. For each specific reservation time period, the number of reservation requests within that period is examined. Combined with the number of idle devices during that time period, it is identified which requests were submitted after resources were just fully utilized. In other words, requests are sorted according to their submission time; those requests that were submitted earlier and had just exhausted all idle resources are considered acceptable, while subsequent requests are categorized as timed-out reservation requests. Simultaneously, the requests successfully accommodated within that time period, along with all requests from other resource-sufficient time periods, constitute the normal sparse reservation request distribution.

[0054] The distribution of normal sparse booking requests is overlaid and compared with the distribution of teaching and experimental scheduling to identify redundancy. For example, on a Wednesday morning, teaching tasks occupy 20 machines of a certain type for experiments, but there are a total of 30 machines of this type, so 10 machines are not occupied by teaching. Meanwhile, in the normal sparse booking request distribution, only 5 booking requests are scheduled for this type of machine during this time period. Therefore, on this Wednesday morning, teaching tasks and normal bookings combined only occupy 25 machines, leaving 5 machines idle. These 5 idle machines and the time period in which they occur are identified as redundant sparse booking time periods and the corresponding redundancy quantity.

[0055] The process iterates through each request in the set of timeout reservation requests, examining their original desired device type and experiment duration. For each request, it searches for an available time slot within the identified redundant and sparse reservation periods that meets its device type and duration requirements. If found, a new suggested reservation time is generated for the request, falling within the teaching task's duration but utilizing surplus equipment not fully occupied by the task. If a request cannot find a perfectly matching slot within the existing redundant periods (e.g., requiring 6 hours of continuous use but with only 4 hours of available time), the request is marked as currently unavailable and may trigger more advanced scheduling strategies, such as splitting the request or awaiting manual intervention. This process is repeated until all requests in the timeout reservation request set are processed or all possible slots are matched. After matching all timeout requests, an adjusted reservation scheme is obtained. Without altering the original teaching schedule, this scheme addresses conflicting requests that were originally unavailable by leveraging device redundancy within teaching periods.

[0056] The adjusted appointment time is sent directly to the submitter of the appointment request, i.e., the requester. The notification will include a clear prompt, requiring the requester to confirm within a preset time window. Simultaneously, a temporary hold status is set for the original desired time slot in the background. The notification should include details of the original appointment request, the system-suggested new appointment time, and a prompt for the user to confirm within the specified period. During the waiting period, user feedback is monitored. If the user clicks to confirm acceptance within the deadline, the adjustment officially takes effect, the equipment time-sharing scheduling information is updated, and equipment resources are locked for the user in the suggested new time slot. If the user refuses the adjustment, or fails to respond within the deadline, the temporary resources occupied by the adjustment suggestion are released, and the user is notified that the original request needs to be resubmitted due to incompatibility. By adjusting the time of research appointment requests tagged with equipment type based on complementary adaptation and sending the adjustment results to relevant personnel for confirmation, the conflict between research appointments and teaching experiments is effectively resolved, maximizing equipment utilization, reducing appointment conflicts and idle fragmentation, ensuring the smooth execution of research appointments, and improving the flexibility and reliability of the appointment system.

[0057] Furthermore, this application also includes the following steps: obtaining reservation confirmation information; performing experimental accuracy identification based on historical experimental records for each device in the M-type devices to determine the M-type experimental accuracy distribution; and performing device accuracy matching reservation based on the experimental accuracy requirements of the sparse reservation request according to the M-type experimental accuracy distribution.

[0058] Specifically, once a user confirms acceptance of the adjusted appointment time, the appointment is marked as pending specific equipment allocation and enters the refined equipment matching process. Historical experimental records for all equipment corresponding to the marked equipment type are retrieved, including experimental data for each device over the past year or longer. For each device, key indicators reflecting its accuracy are extracted. For example, for an analytical balance, the calibration deviation value before each use can be extracted; for a spectrometer, the standard deviation of wavelength scanning can be extracted; for a microscope, the measurement results of resolution test standards can be extracted. Through statistical analysis of this historical data, such as calculating the mean, standard deviation, or long-term drift, an accuracy score is generated for each device. For example, devices can be divided into three levels: high accuracy, medium accuracy, and normal accuracy, or a numerical accuracy index can be directly output. After completing the analysis of all devices, an experimental accuracy distribution table indexed by device type is generated, clearly listing the unique number of each device and its corresponding accuracy level. For example, for a spectrometer, the wavelength accuracy and photometric repeatability values ​​from its three most recent calibration reports are extracted, and their average value and fluctuation range are calculated as the current accuracy label for that device. Device A achieved an average resolution of 3.2nm and a standard deviation of 0.3nm in the last 20 resolution tests, demonstrating excellent long-term stability and was rated as a high-precision device. Device B achieved an average resolution of 4.8nm and a standard deviation of 0.5nm, with some tests approaching 5nm, and was rated as a relatively high-precision device.

[0059] The system reads the currently pending sparse reservation requests and extracts the experimental precision requirements that users may have specified when submitting their requests. Some requests may not have explicit precision requirements, in which case any available device is assigned by default; however, for those requests that explicitly require high precision or specify a specific precision threshold, matching is required based on the precision distribution. All pending reservation requests are traversed, and for each request, the required device type and precision requirements are determined. From the precision distribution of that type of device, all devices that meet the precision requirements are selected, i.e., the device precision index is not lower than the user's requirements. If multiple devices meet the conditions, the most suitable one is selected and assigned to the request based on the device's current status, such as whether it is under maintenance or its cumulative usage time.

[0060] By obtaining research appointment confirmation information and combining it with the equipment's historical experimental records for accuracy identification, an M-type experimental accuracy distribution can be generated. This enables the matching and arrangement of research appointments with experimental accuracy, ensuring that research experiments are not only reasonably arranged in terms of time, but also meet the experimental requirements in terms of equipment accuracy.

[0061] Furthermore, this application also includes the following steps: after sending the adjustment result to the requester for confirmation, if the requester does not confirm within a preset time, the adjustment time slot will be automatically released for other users to make reservations, and a reminder message will be sent to the requester.

[0062] Specifically, the adjusted appointment time, equipment allocation details, and related instructions will be sent to the research appointment applicant via platform messages, email, or other notification methods, clearly indicating the confirmation deadline. The suggested adjusted time slot will be temporarily locked for this applicant in the resource scheduling table to ensure that no other user can book the same time slot during this period, thus guaranteeing smooth use if the applicant confirms. A confirmation period will be created, such as 24 hours from the time of sending, and the applicant's feedback status will be continuously monitored in the background.

[0063] When the timer reaches the preset deadline, the confirmation status of the request is automatically checked. If the requester has clicked "confirm," the process proceeds normally to the next step: device accuracy matching. If the requester has not taken any action—neither confirming nor rejecting—an automatic release procedure is triggered. The program first unlocks the temporary lock on the adjusted time slot, restoring the device resource status from pending confirmation to available for booking, making it reappear in the public booking list for other users to browse and apply for. After the resource release is complete, a reminder message is generated and sent to the requester via a preset communication channel. The reminder typically includes basic information about the original request, the suggested adjusted time slot, the fact that the time slot has been released due to timeout failure to confirm, and instructions for the user to resubmit the application if needed. At this point, the adjustment process for this request ends, and the user needs to re-initiate a booking to continue using the device.

[0064] By automatically releasing unconfirmed reservation time slots and sending reminder messages, the problem of idle resources caused by unconfirmed scientific research reservation adjustments is solved. This ensures that experimental equipment time resources can be fully utilized, avoids long-term occupation by inactive reservations, and reminds relevant personnel to confirm or re-reserve as soon as possible, thereby improving system management efficiency and user experience, and achieving dynamic and efficient coordination between reservations and equipment resources.

[0065] In summary, the resource allocation optimization method for campus experimental equipment driven by time-sharing reservation provided in this application has the following technical effects: By acquiring the distribution of campus experimental equipment resources and performing classification based on experimental equipment type, M types of equipment are generated; teaching experimental tasks within the campus are received and matched with the M types of equipment to schedule teaching experiments in batches, establishing the first equipment time-sharing scheduling information; sparse reservation requests within the campus other than teaching experimental tasks are collected and matched with the M types of equipment to perform complementary adaptation analysis of teaching experiments and scientific research experiments respectively, determining the marked equipment types with complementary adaptation anomalies and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution; based on the teaching experiment scheduling distribution and the sparse reservation request distribution, the reservation time of the marked equipment types is adjusted based on complementary adaptation sparse reservation requests, and the adjustment results are sent to the requester for adjustment confirmation. In other words, by classifying equipment and then specifically receiving teaching tasks for batch scheduling, a first-level equipment time-sharing scheduling information is established; sparse reservation requests are collected and subjected to complementary adaptation analysis with the first-level equipment time-sharing scheduling information to identify marked equipment types with adaptation anomalies due to insufficient resources, and to lock the relevant teaching schedule distribution and reservation request distribution; based on the teaching experiment schedule distribution and sparse reservation request distribution, reservation times for marked equipment types with conflicts are adjusted based on complementary adaptation, and the adjustment results are sent to users for confirmation. While prioritizing the stability of teaching order, the system maximizes the use of fragmented resources in the teaching schedule to absorb research reservations, thereby significantly improving the overall utilization rate of equipment and management efficiency.

[0066] Example 2: Based on the same inventive concept as the resource allocation optimization method driven by time-sharing reservation of campus experimental equipment in Example 1, this application also provides a resource allocation optimization system driven by time-sharing reservation of campus experimental equipment. Please refer to the appendix. Figure 2 The campus experimental equipment time-sharing reservation-driven resource allocation optimization system includes: The equipment classification module 11 is used to obtain the distribution of campus experimental equipment resources and perform classification based on experimental equipment type to generate M types of equipment; the batch scheduling module 12 is used to receive teaching experimental tasks on campus, match them with the M types of equipment, and perform batch scheduling of teaching experiments to establish first equipment time-sharing scheduling information; the complementary adaptation analysis module 13 is used to collect sparse reservation requests on campus other than teaching experimental tasks, match them with the M types of equipment, and perform complementary adaptation analysis for teaching experiments and scientific research experiments respectively to determine the marked equipment types with complementary adaptation anomalies and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution; the reservation dynamic adjustment module 14 is used to adjust the reservation time of the marked equipment types based on complementary adaptation based on the teaching experiment scheduling distribution and the sparse reservation request distribution, and send the adjustment result to the requester for adjustment confirmation.

[0067] Furthermore, the batch scheduling module 12 in the campus experimental equipment time-sharing reservation-driven resource allocation optimization system is also used for: based on the semester teaching experimental task list from the teaching management system, parsing the class size, planned weeks, standard duration of a single experiment, and equipment type requirements associated with each teaching experimental task; determining each type of equipment corresponding to each teaching experimental task in the M-type equipment according to the equipment type requirements; determining the distribution location of each type of equipment according to the equipment resource distribution; and, in combination with the class size, planned weeks, standard duration of a single experiment, and available time for the class, performing batch scheduling with the goal of minimizing laboratory equipment idle rate and experimental time fragmentation, and with the constraint of unified time and space for the same class, generating the first equipment time-sharing scheduling information.

[0068] Furthermore, the batch scheduling module 12 in the resource allocation optimization system driven by time-sharing reservation of campus experimental equipment is also used for: determining the available time of the class by obtaining the class timetable.

[0069] Furthermore, the complementary adaptation analysis module 13 in the campus experimental equipment time-sharing reservation-driven resource allocation optimization system is also used to: determine the sparse resource distribution of the M-type equipment within the sparse reservation period based on the campus experimental equipment resource distribution and the first equipment time-sharing scheduling information; parse the sparse reservation requests to perform experimental equipment type matching, and establish a sparse request equipment demand distribution in combination with the reservation time; analyze the complementary adaptability between the sparse request equipment demand distribution and the sparse resource distribution, mark equipment types with complementary adaptability anomalies, generate the marked equipment types, and extract the teaching experiment scheduling distribution and sparse reservation request distribution corresponding to the marked equipment types.

[0070] Furthermore, the complementary adaptation analysis module 13 in the resource allocation optimization system driven by time-sharing reservation of campus experimental equipment is also used to: identify whether the resources of each type of equipment in the sparse resource distribution at each time meet the demand distribution of the sparse requesting equipment; if not, determine that there is a complementary adaptation anomaly.

[0071] Furthermore, the complementary adaptation analysis module 13 in the campus experimental equipment time-sharing reservation-driven resource allocation optimization system is also used for: collecting historical experimental records of the M-type equipment; analyzing the equipment failure rate of the M-type equipment based on the historical experimental records; and performing complementary adaptation anomaly judgment optimization based on the equipment failure rate.

[0072] Furthermore, the reservation dynamic adjustment module 14 in the resource allocation optimization system driven by time-sharing reservation of campus experimental equipment is also used to: determine the set of overdue reservation requests that cause complementary adaptation anomalies for each reservation period based on the sparse reservation request distribution, and the normal sparse reservation request distribution other than the overdue reservation request set; compare the normal sparse reservation request distribution with the teaching experiment scheduling distribution to determine the redundant sparse reservation periods and the number of redundancies; and perform interpolation matching on the overdue reservation request set based on complementary adaptation according to the number of redundancies to complete the reservation time adjustment of sparse reservation requests.

[0073] Furthermore, the reservation dynamic adjustment module 14 in the resource allocation optimization system driven by time-sharing reservation of campus experimental equipment is also used for: obtaining reservation confirmation information; performing experimental accuracy identification based on historical experimental records for each device in the M-type devices to determine the M-type experimental accuracy distribution; and performing device accuracy matching reservation based on the experimental accuracy requirements of sparse reservation requests according to the M-type experimental accuracy distribution.

[0074] Furthermore, the reservation dynamic adjustment module 14 in the campus experimental equipment time-sharing reservation-driven resource allocation optimization system is also used to: after sending the adjustment result to the requester for adjustment confirmation, if the requester does not confirm within a preset time, automatically release the adjusted time slot for other users to make reservations, and send a reminder message to the requester.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The resource allocation optimization method and specific examples of the time-sharing reservation-driven campus experimental equipment in the aforementioned embodiment 1 are also applicable to the resource allocation optimization system of the time-sharing reservation-driven campus experimental equipment in this embodiment. Through the foregoing detailed description of the resource allocation optimization method of the time-sharing reservation-driven campus experimental equipment, those skilled in the art can clearly understand the resource allocation optimization system of the time-sharing reservation-driven campus experimental equipment in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A resource allocation optimization method driven by time-sharing reservation for campus experimental equipment, characterized in that, include: Obtain the distribution of campus experimental equipment resources and perform classification based on experimental equipment type to generate M types of equipment; Receive teaching experiment tasks within the campus, match them with the M-type equipment, schedule the teaching experiments in batches, and establish the first equipment time-sharing scheduling information; Collect sparse reservation requests within the campus other than teaching and experimental tasks, and match them with the M-type devices. Then perform complementary adaptation analysis on teaching experiments and scientific research experiments respectively to determine the types of marked devices with complementary adaptation anomalies and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution. Based on the teaching experiment scheduling distribution and the sparse reservation request distribution, the reservation time of the marked device type is adjusted based on complementary adaptation, and the adjustment result is sent to the requester for adjustment confirmation.

2. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 1, characterized in that, The system receives teaching experiment tasks from within the campus, matches them with the aforementioned M-type equipment, schedules the teaching experiments in batches, and establishes time-sharing scheduling information for the first equipment, including: Based on the semester teaching experiment task list from the teaching management system, the number of students in the class, the planned number of weeks, the standard duration of a single experiment, and the equipment type requirements associated with each teaching experiment task are analyzed. According to the equipment type requirements, determine the corresponding equipment for each teaching experiment task in the M category of equipment; Based on the distribution of equipment resources, the distribution location of each type of equipment is determined. Combining the number of students in the class, the planned number of weeks, the standard duration of a single experiment, and the available time of the class, with the goal of minimizing the idle rate of laboratory equipment and the fragmentation of experiment time, and with the constraint of uniform time and space of the same class, the equipment is scheduled in batches to generate the time-sharing scheduling information of the first equipment.

3. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 2, characterized in that, The available time for a class is determined by obtaining the class schedule.

4. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 1, characterized in that, Collect sparse reservation requests within the campus, excluding teaching and experimental tasks, and match them with the aforementioned M types of equipment. Perform complementary adaptation analysis on teaching and research experiments separately to determine the types of equipment with complementary adaptation anomalies and the corresponding distribution of teaching experiment scheduling and sparse reservation requests, including: Based on the campus experimental equipment resource distribution and the first equipment time-sharing scheduling information, determine the sparse resource distribution of the M-type equipment within the sparse reservation period; The sparse reservation requests are analyzed to match the types of experimental equipment, and the distribution of equipment demand for sparse requests is established by combining the reservation time. The complementary compatibility between the sparse request device demand distribution and the sparse resource distribution is analyzed. Device types with complementary compatibility anomalies are marked, and the marked device types are generated. The teaching experiment scheduling distribution and sparse reservation request distribution corresponding to the marked device types are extracted.

5. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 4, characterized in that, Analyzing the complementary compatibility between the sparse request device demand distribution and the sparse resource distribution includes: Identify whether the resources of each type of device in the sparse resource distribution meet the demand distribution of the sparse requesting devices at each time point; If not, it is determined that there is a complementary adaptation anomaly.

6. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 5, characterized in that, Determining the existence of complementary adaptation anomalies also includes: Collect historical experimental records of the aforementioned M-type devices; Analyze the equipment failure rate of the M-type equipment based on the historical experimental records; The complementary adaptation anomaly is determined and optimized based on the equipment failure rate.

7. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 5, characterized in that, Based on the teaching experiment scheduling distribution and the sparse reservation request distribution, the reservation time for the marked device type is adjusted based on complementary adaptation, including: Based on the sparse reservation request distribution, determine the set of timeout reservation requests that cause complementary adaptation anomalies for each reservation period, and the normal sparse reservation request distribution excluding the set of timeout reservation requests. By comparing the normal sparse reservation request distribution with the teaching experiment scheduling distribution, the redundant sparse reservation time periods and the number of redundancies are determined. Based on the redundancy quantity, the set of timeout reservation requests is matched with complementary adaptation to complete the adjustment of the reservation time for sparse reservation requests.

8. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 1, characterized in that, After sending the adjustment results to the requester for confirmation, it also includes: Get appointment confirmation information; Perform experimental accuracy identification based on historical experimental records for each device in the M-class devices to determine the experimental accuracy distribution of the M-class devices; Based on the M-type experimental accuracy distribution, equipment accuracy matching and reservation are performed according to the experimental accuracy requirements of sparse reservation requests.

9. The resource allocation optimization method driven by time-sharing reservation for campus experimental equipment as described in claim 1, characterized in that, After the adjustment results are sent to the requester for confirmation, if the requester does not confirm within the preset time, the adjustment time slot will be automatically released for other users to make an appointment, and a reminder message will be sent to the requester.

10. A resource allocation optimization system driven by time-sharing reservation for campus experimental equipment, characterized in that, The steps for implementing the resource allocation optimization method driven by time-sharing reservation of campus experimental equipment according to any one of claims 1 to 9, wherein the resource allocation optimization system driven by time-sharing reservation of campus experimental equipment comprises: The equipment classification module is used to obtain the distribution of campus experimental equipment resources and perform classification based on the type of experimental equipment to generate M types of equipment; The batch scheduling module is used to receive teaching and experimental tasks on campus, match them with the M-type equipment, and perform batch scheduling of teaching and experimental tasks to establish the first equipment time-sharing scheduling information. The complementary adaptation analysis module is used to collect sparse reservation requests on campus other than teaching and experimental tasks, and then perform complementary adaptation analysis on teaching and scientific research experiments after matching them with the M types of equipment to determine the types of marked equipment with complementary adaptation anomalies and the corresponding teaching experiment scheduling distribution and sparse reservation request distribution. The reservation dynamic adjustment module is used to adjust the reservation time of the marked device type based on the teaching experiment scheduling distribution and the sparse reservation request distribution, and send the adjustment result to the requester for adjustment confirmation.

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