A smart campus management method and system

Abstract

The application provides a kind of wisdom campus management method and system, belong to wisdom campus technical field, method includes: through acquisition module, cumulative boot duration and single continuous running duration are obtained from laboratory equipment, and project switching frequency is obtained from registration terminal, time period is divided according to equipment start-stop time node, and equipment running cycle list is obtained;For the equipment number belonging to the abnormal high value cluster, obtain the historical experiment time period record from the registration terminal, rearrange the record using the time period reorganization method, and determine the optimized experiment time period arrangement table;Update the dispatch table of laboratory management system through the experiment time period arrangement table, obtain the updated dispatch table content, evaluate the improvement of effective experiment time, and obtain the final evaluation result of the all-day available duration of the equipment. Significantly improve the all-day available duration of the equipment, reduce unnecessary loss.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a smart campus management method and system. Background Technology

[0002] In the construction of smart campuses, the monitoring of laboratory equipment operation is directly related to the efficient utilization of teaching resources and the overall management level of the school. Whether the equipment is used reasonably and whether experimental teaching is fully carried out have become one of the core indicators for evaluating the quality of a smart campus. Traditional monitoring mainly focuses on the cumulative uptime of the equipment, assuming that the longer the equipment is up, the higher the utilization rate. This method can reflect the busy and idle status of the equipment in most scenarios, but in actual experimental teaching, there is often a phenomenon where the equipment seems to be running at full capacity, but the actual time that students can conduct effective experiments is seriously insufficient. Most current management systems only record the total time from the time the equipment is turned on to the time it is turned off, or simply count the number of times it is used each day, but ignore the hidden time loss caused by frequent switching of experimental projects. When teachers or students change different experimental projects multiple times in a short period of time on the same equipment, each switch requires a fixed process such as preheating, parameter adjustment, cooling or cleaning. These processes themselves do not produce effective experimental results, but they must occupy the equipment's running time. As a result, the same uptime may result in the loss of effective experimental time due to multiple switching of projects. The equipment appears to be highly utilized, but the actual hands-on time allocated to students is greatly reduced. The core challenge of this phenomenon lies in the fact that the actual utilization efficiency of equipment no longer depends solely on the operating time, but is directly constrained by the number of project switching sessions and the proportion of preheating and cooling time caused by each switching. The more frequent the switching, the higher the proportion of ineffective preheating and cooling time, and the greater the gap between the total operating time and the effective experimental time, creating an abnormal contradiction where longer operation time results in lower utilization. Therefore, accurately identifying and quantifying the ineffective preheating and cooling time caused by each project switching during monitoring, and accordingly extracting the truly usable effective time from the total operating time, has become a key issue in the reasonable assessment and time-slot optimization of equipment utilization in current smart campus laboratories. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a smart campus management method, comprising:

[0004] The cumulative uptime, single continuous running time and number of project switching of laboratory equipment are obtained by the data acquisition module. The time period is divided according to the start and stop time nodes to obtain the equipment operation cycle list.

[0005] The number of preheating and cooling repetitions is identified based on the equipment operation cycle list. Cluster analysis is used to determine the distribution level of the number of preheating and cooling repetitions. Based on the distribution level, high switching cycles are determined, and the total proportion of preheating and cooling time periods within the high switching cycles is extracted.

[0006] Identify the decrease in the total proportion of the preheating and cooling period as the duration of a single continuous run, extract the period compression amount corresponding to the decrease, and obtain the period allocation standard based on the deviation of the period compression amount from the preset threshold.

[0007] The equipment operation cycle list is segmented using the time period allocation standard to obtain the operation segments and sub-cycle sets of a single experimental task. The distribution level of the preheating and cooling ratio in the sub-cycle is evaluated, high-level sub-cycles are identified, the effective experimental time of the high-level sub-cycles is extracted, and the usage cycle with insufficient effective experimental time is determined.

[0008] The total available time loss is obtained by accumulating the preheating and cooling periods according to the usage cycle. The loss is then clustered based on the distribution level of the preheating and cooling ratio to identify loss clusters, evaluate the distribution of abnormally high values, and obtain the equipment number to which the abnormally high value cluster belongs.

[0009] For the device number, historical experimental time period records are obtained, and rearranged using a time period reorganization method to determine an optimized experimental time period arrangement table.

[0010] The management system scheduling table is updated by updating the experimental time period arrangement table, the effective experimental time increase is evaluated, and the evaluation result of the equipment's full-day usability is obtained.

[0011] Furthermore, the cumulative uptime, single continuous operation time, and number of project switches of the laboratory equipment are obtained through the data acquisition module. Time periods are then divided according to start-up and shutdown time nodes to obtain a list of equipment operation cycles, including:

[0012] The acquisition module reads the operating status data recorded by the built-in sensors of the laboratory equipment, obtains the cumulative power-on time value from the start to the stop of the equipment, records the single continuous running time value for each uninterrupted operation, and extracts the number of project switching from the registration terminal database.

[0013] The device start-up and shutdown time nodes are identified based on the timestamp information in the cumulative power-on time and single continuous operation time. The operation record is segmented according to the start-up and shutdown time nodes, and the number of item switching is counted in each segment to obtain a device operation cycle list containing time information and switching information.

[0014] Furthermore, based on the equipment operation cycle list, the number of preheating and cooling repetitions is identified. Cluster analysis is used to determine the distribution level of the number of preheating and cooling repetitions. Based on the distribution level, high switching cycles are determined, and the total proportion of preheating and cooling time periods within the high switching cycles is extracted, including:

[0015] Based on the project switching time and equipment operating status changes recorded in the equipment operation cycle list, identify the preheating period and cooling period after each project switching, count the number of occurrences of the preheating period and cooling period in each operation cycle, and obtain a preheating and cooling repetition dataset.

[0016] The K-means clustering algorithm was used to cluster the preheating and cooling repetition dataset. The distribution levels were divided according to the cluster center values, and the cycles that belonged to the highest value category were marked as high switching cycles.

[0017] For the high switching cycle, the start and end times of the preheating and cooling periods are extracted from the equipment operation log, the duration of each period is calculated and accumulated to obtain the total proportion of the preheating and cooling periods within the high switching cycle.

[0018] Furthermore, the method identifies the decreasing percentage of the total preheating and cooling period as the duration of a single continuous run, extracts the period compression corresponding to the decreasing percentage, and obtains a period allocation standard based on the deviation of the period compression from a preset threshold, including:

[0019] Identify the correspondence between the total proportion of preheating and cooling periods within the high switching cycle and the duration of a single continuous run. Sort the high switching cycles from shortest to longest according to the duration of a single continuous run. Extract the runtime of each cycle and the corresponding preheating and cooling proportion value to construct a set of data pairs of duration and proportion.

[0020] The data set of duration and proportion is fitted using a linear regression method, and the slope of the fitted line is used as the compression coefficient. The compression amount of each period is calculated by multiplying the compression coefficient by the actual running time.

[0021] The difference between the compression amount of the specified time period and the preset compression threshold is obtained. The percentage of the absolute value of the difference relative to the preset threshold is calculated as the degree of deviation. The adjustment range and direction are determined based on the degree of deviation and the sign of the difference.

[0022] Based on the adjustment range and direction, the duration parameters in the original time period division rules are corrected, and the proportion of effective experimental time periods and preheating and cooling time periods to be allocated to each operating cycle is recalculated according to the corrected parameters to obtain the adjusted time period allocation standard.

[0023] Furthermore, the equipment operation cycle list is segmented using the time period allocation standard to obtain the operation segments and sub-cycle sets of a single experimental task. The distribution level of the preheating and cooling ratio in the sub-cycles is evaluated, high-level sub-cycles are identified, the effective experimental time of the high-level sub-cycles is extracted, and the usage cycle with insufficient effective experimental time is determined, including:

[0024] The equipment operation cycle list is segmented using the time period allocation standard. The start and end times of each experimental task are identified according to the standard duration of a single experiment. The continuous operation cycle is divided into independent operation segments to obtain the sub-cycle set of each experimental task.

[0025] The preheating and cooling ratio of each sub-cycle in the sub-cycle set is evaluated, the ratio of the cumulative preheating and cooling time to the total sub-cycle time is calculated, and the ratio values ​​of all sub-cycles are divided into three levels: low, medium and high according to the quantile. Sub-cycles classified into high-level sub-cycles are marked as high-ratio sub-cycles.

[0026] For the high-proportion sub-cycle, the total duration of the sub-cycle is subtracted from the cumulative preheating and cooling time to obtain the effective experimental time. If the effective experimental time is lower than the preset minimum experimental time threshold, it is marked as a device usage cycle with insufficient effective experimental time.

[0027] Furthermore, based on the accumulated preheating and cooling periods according to the usage cycle, the total available time loss for the entire day is obtained. Combined with the distribution level of the preheating and cooling ratio, cluster analysis is performed on the loss amount to identify loss clusters, evaluate the distribution of abnormally high values, and obtain the equipment numbers to which the abnormally high value clusters belong, including:

[0028] Based on the equipment usage cycle with insufficient effective experimental time, the duration of the preheating and cooling periods in each cycle is extracted, and the duration loss value of each cycle is accumulated. The loss dataset is then compiled, and the total of the loss dataset is calculated to obtain the total available time loss for the whole day.

[0029] Using the distribution level of the preheating and cooling ratio in the sub-cycle as a feature, the loss dataset is subjected to DBSCAN density clustering processing, and loss clusters are divided according to the density distribution of data points.

[0030] The numerical distribution of each data point within the loss cluster is evaluated. If the loss of a certain data point exceeds a preset loss threshold, it is determined to be an abnormally high value. The device number corresponding to the abnormally high value is extracted from the device operation record.

[0031] Furthermore, historical experimental time period records are obtained for the device number, and rearranged using a time period reorganization method to determine an optimized experimental time period arrangement table, including:

[0032] For the device number to which the abnormally high value cluster belongs, query the historical experimental period records from the registration terminal database, extract the experimental project name, start time, end time and number of switching between projects for each record, and determine the similarity of project types based on keyword matching in the experimental project name. Similar projects are classified into the same category to obtain the classified period record set.

[0033] The classified time period record set is processed by time period recombination method. The time period records in the same category are arranged in chronological order, the time interval between adjacent records is calculated, and if the interval is less than a preset merging threshold, they are merged into continuous time periods to obtain the merged time period sequence of each category.

[0034] Based on the time period sequence, the experimental order is rearranged according to the principle of reducing the number of project switching. Experiments requiring the same preheating conditions are arranged in consecutive time periods. The interval time between different types of experiments is set according to the equipment cooling requirements. The project name, start time, end time and equipment operating parameters of each time period are recorded to obtain the optimized experimental time period arrangement table.

[0035] Furthermore, by updating the management system's scheduling table through the aforementioned experimental time period arrangement table, the effective experimental time increase is evaluated, and the evaluation result of the equipment's total available time throughout the day is obtained, including:

[0036] The laboratory management system's scheduling table is updated by updating the experimental time period schedule table. The project name, start and end time, and equipment parameters for each time period in the schedule table are written into the scheduling table database to replace the original records, and the updated scheduling table content is obtained.

[0037] Extract the effective experimental time period data before and after the update from the updated scheduling table, calculate the difference between the sum of the effective time periods after the update and the sum of the effective time periods before the update as the improvement amount, and obtain the final evaluation result of the equipment's available time throughout the day.

[0038] Secondly, the present invention also provides a smart campus management system, the system comprising:

[0039] The data acquisition and cycle division module is used to obtain the cumulative uptime, single continuous running time and project switching times of laboratory equipment, and divide the time period according to the start and stop time nodes to obtain the equipment operation cycle list.

[0040] The high switching cycle identification module is used to identify the number of preheating and cooling repetitions based on the equipment operation cycle list, use cluster analysis to analyze the distribution level of the number of preheating and cooling repetitions, determine the high switching cycle based on the distribution level, and extract the total proportion of the preheating and cooling period within the high switching cycle.

[0041] The time period allocation standard adjustment module is used to identify the decrease in the total proportion of the preheating and cooling time period with the duration of a single continuous run, extract the time period compression amount corresponding to the decrease, and obtain the time period allocation standard based on the deviation of the time period compression amount from the preset threshold.

[0042] The operation cycle segmentation module is used to segment the equipment operation cycle list according to the time period allocation standard, obtain the operation segment and sub-cycle set of a single experimental task, evaluate the distribution level of the preheating and cooling ratio in the sub-cycle, identify high-level sub-cycles, extract the effective experimental time of the high-level sub-cycles, and determine the usage cycle with insufficient effective experimental time.

[0043] The available time loss analysis module is used to accumulate the preheating and cooling periods according to the usage cycle to obtain the total available time loss for the whole day, and to perform cluster analysis on the loss amount in combination with the distribution level of the preheating and cooling ratio to determine the loss clusters, evaluate the distribution of abnormally high values, and obtain the equipment number to which the abnormally high value clusters belong.

[0044] The experimental time period optimization module is used to obtain historical experimental time period records for the device number, rearrange them using a time period reorganization method, and determine the optimized experimental time period arrangement table.

[0045] The scheduling update and evaluation module is used to update the management system scheduling table through the experimental period arrangement table, evaluate the effective experimental time increase, and obtain the evaluation result of the equipment's full-day available time.

[0046] This invention provides a smart campus management system, mainly comprising:

[0047] The data acquisition and cycle division module is used to obtain the cumulative uptime and single continuous running time of laboratory equipment through the acquisition module, and to obtain the number of project switching from the registration terminal. The time period is divided according to the equipment start-up and shutdown time nodes to obtain the equipment operation cycle list.

[0048] The high switching cycle identification module is used to identify the number of preheating and cooling repetitions triggered by the switching frequency of the project according to the equipment operation cycle list. It uses cluster analysis to evaluate the distribution level of the repetition frequency, marks the cycles that belong to the high frequency category as high switching cycles, and extracts the total proportion of preheating and cooling time periods within high switching cycles.

[0049] The time period allocation standard adjustment module is used to identify the decrease in the total proportion of preheating and cooling time periods within high switching cycles as the duration of a single continuous run increases, extract the time period compression amount corresponding to the decrease, evaluate the significance of the adjustment based on the deviation of the time period compression amount from the preset threshold, and obtain the adjusted time period allocation standard.

[0050] The operation cycle segmentation module is used to segment the equipment operation cycle list according to the time period allocation standard, obtain the operation segment corresponding to a single experimental task, obtain the sub-cycle set for each experimental task, evaluate the distribution level of the preheating and cooling ratio in the sub-cycle, identify and classify it into a high-level sub-cycle, extract the effective experimental time in the high-level sub-cycle, and determine the equipment usage cycle with insufficient effective experimental time.

[0051] The available time loss analysis module is used to accumulate the preheating and cooling periods based on the equipment usage cycle with insufficient effective experimental time to obtain the total available time loss for the whole day. It performs cluster analysis on the loss amount by combining the distribution level of the preheating and cooling proportion in the sub-cycle, determines the loss cluster based on the grouping results of the loss amount obtained by clustering, evaluates the distribution of abnormal high values ​​in the loss cluster, and obtains the equipment number to which the abnormal high value cluster belongs.

[0052] The experimental time period optimization module is used to obtain historical experimental time period records from the registration terminal for the device number to which the abnormally high value cluster belongs, rearrange the records using a time period reorganization method, and determine the optimized experimental time period arrangement table.

[0053] The scheduling update and evaluation module is used to update the scheduling table of the laboratory management system through the experimental time period arrangement table, obtain the updated scheduling table content, evaluate the increase in effective experimental time, and obtain the final evaluation result of the equipment's available time throughout the day.

[0054] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0055] This invention discloses a smart campus management method, proposing a complete solution to the problems of repeated preheating and cooling, insufficient effective experimental time, and loss of available time throughout the day caused by frequent project switching in laboratory equipment operation. This invention collects equipment operation data and project switching records, divides operating cycles and identifies high-switching cycles, analyzes the proportion of preheating and cooling periods and its decreasing trend with operating time, and then formulates adjusted time allocation standards, optimizes operating segment segmentation and sub-cycle evaluation, accurately identifies cycles with insufficient effective experimental time and abnormally high-value loss equipment, and finally generates an optimized experimental scheduling table through time period reorganization, updating the scheduling management mechanism. The core innovation of this invention lies in significantly improving the available time of equipment throughout the day, reducing unnecessary losses, achieving efficient utilization of laboratory resources, and improving the scientific and economical nature of experimental management through data-driven cycle analysis and time period optimization. Attached Figure Description

[0056] Figure 1 This is a flowchart of a smart campus management method according to the present invention.

[0057] Figure 2 This is a schematic diagram of a smart campus management method according to the present invention.

[0058] Figure 3 This is a schematic diagram of the structure of a smart campus management system according to the present invention. Detailed Implementation

[0059] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 are within the scope of protection of the present invention.

[0060] like Figures 1-3 This embodiment of a smart campus management method and system may specifically include:

[0061] S101. The cumulative power-on time and single continuous operation time are obtained from the laboratory equipment through the acquisition module, and the number of project switching is obtained from the registration terminal. The equipment operation cycle list is obtained by dividing the time period according to the equipment start-up and shutdown time nodes.

[0062] The system acquires operational status data recorded by built-in sensors of the laboratory equipment via a data acquisition module. This data includes the cumulative operating time from startup to shutdown, the duration of each continuous run (uninterrupted operation), and the number of item switches entered by students or teachers on the equipment's interface from the registration terminal database. Based on the timestamps in the cumulative operating time and the duration of each continuous run, the system identifies the equipment's start and stop times. If the time interval between two consecutive startups exceeds a preset threshold, it is considered an independent operating cycle. The operating records are segmented according to these start and stop times, and the number of item switches is counted within each segment. This determines the start and end times, duration, and switching frequency of each operating cycle, resulting in a list of equipment operating cycles containing both time and switching information.

[0063] In one embodiment, by acquiring the operating status data of the laboratory equipment's built-in sensors and the project switching records in the registration terminal database, the acquisition module establishes a connection with the laboratory equipment controller via a serial communication interface. It reads the operating status data recorded by the built-in sensors at fixed time intervals. This operating status data includes the equipment's start-up time, stop time, and status identifiers during operation. When the equipment starts, the acquisition module records the start-up timestamp and continuously monitors the operating status until the equipment stops, at which point it records the stop timestamp. The difference between the two timestamps represents the duration of a single continuous run. Next, the system extracts experimental project information from the registration terminal database, which uses a relational architecture to store data entered by students or teachers on the equipment's operating interface. Whenever an experimenter changes an experimental project, the system automatically adds a new switching record to the database, including the switching time, the names of the preceding and following projects, and the operator's identifier. The number of project switching records for the same equipment within a specific time period is counted to obtain the number of project switching times. Then, the system identifies the equipment's start-up and stop time nodes based on the timestamp information in the cumulative power-on time and the duration of a single continuous run. When the time interval between two adjacent start-ups exceeds a preset threshold (set to 30 minutes), it is determined to be an independent operating cycle. The operation records are segmented according to the start and stop time nodes. The number of item switching is counted in each segment, and finally a list of equipment operation cycles containing the start and end time, duration and switching frequency of each operation cycle is generated.

[0064] S102. Identify the number of preheating and cooling repetitions triggered by the project switching frequency based on the equipment operation cycle list, use cluster analysis to evaluate the distribution level of the repetition frequency, mark the cycles belonging to the high frequency category as high switching cycles, and extract the total proportion of preheating and cooling periods within high switching cycles.

[0065] Based on the project switching times and equipment operating status changes recorded in the equipment operation cycle list, the preheating period from startup to stable operation and the cooling period from operation to complete shutdown are identified after each project switching. The occurrence frequency of the preheating and cooling periods within each operation cycle is counted to obtain a dataset of preheating and cooling repetition frequencies for each operation cycle. The K-means clustering algorithm is used to cluster the dataset of preheating and cooling repetition frequencies. The distribution level of repetition frequencies is divided according to the value of the cluster centers. If the repetition frequency of a certain operation cycle belongs to the category of the highest-valued cluster center, then that cycle is marked as a high-switching cycle. For the operation records of the high-switching cycles, the start and end times of each preheating and cooling period are extracted from the equipment operation log. The duration of each period is calculated and accumulated to obtain the total duration. The total accumulated duration is divided by the total operation duration of the high-switching cycle to determine the total proportion of preheating and cooling periods within the high-switching cycle.

[0066] In one implementation, the preheating period refers to the time from when the experimental equipment is started up until it reaches a stable operating temperature or operating parameters.

[0067] For example, after a physics lab spectrometer is turned on, the light source needs about 15 minutes to warm up and reach a stable light intensity output. During this period, although the equipment is running, accurate spectral measurements cannot be performed. The cooling period refers to the time required for the equipment to return to ambient temperature or its initial state after the experiment. When students change experiments, these warm-up and cooling processes repeat, creating a window of time where the equipment is turned on but cannot be used for experiments.

[0068] Specifically, based on the item switching times recorded in the equipment operation cycle list, the system identifies the changes in equipment status before and after each switch. By reading equipment sensor data, the process of the equipment temperature rising from room temperature to operating temperature is the preheating period, and the process of the temperature dropping from operating temperature to room temperature is the cooling period. For each operation cycle, all preheating and cooling processes that occur within it are statistically analyzed to form a repetition count dataset.

[0069] It should be noted that the specific process of the K-means clustering algorithm in processing the preheating and cooling repetition dataset is as follows: the input is the repetition dataset of all running cycles, and the output is the divided high, medium, and low-level categories. First, the system uses the repetition count of each running cycle as a one-dimensional feature input, setting the number of clusters K to 3. The algorithm randomly initializes three cluster centers and then performs iterative calculations: in each iteration, the distance from each data point to the three cluster centers is calculated, and the data point is assigned to the nearest cluster center; the mean of all data points in each cluster is recalculated as the new cluster center. The above process is repeated until the cluster centers no longer change significantly or the maximum number of iterations is reached. After clustering, the three cluster centers are sorted according to their numerical values, and the category corresponding to the cluster center with the highest numerical value is defined as the high-frequency category. If the repetition count of a certain running cycle is assigned to the high-frequency category, then that cycle is marked as a high-switching cycle. This marking method can adaptively identify abnormal cycles with frequent switching, without relying on a fixed threshold setting.

[0070] Preferably, when calculating the total proportion of preheating and cooling periods within high switching cycles, the system accurately extracts the start and end times of each preheating and cooling period from the equipment operation log. The duration of each period is obtained by subtracting the timestamps, and the durations of all preheating and cooling periods are summed to obtain the total duration of invalid periods. Dividing this total duration by the total runtime of the high switching cycle yields the proportion of preheating and cooling periods within that cycle. This proportion directly reflects the degree of time utilization efficiency loss of the equipment under frequent switching conditions.

[0071] S103. Identify the decrease in the total proportion of preheating and cooling periods within a high switching cycle as the duration of a single continuous run increases, extract the period compression amount corresponding to the decrease, evaluate the significance of the adjustment based on the deviation of the period compression amount from the preset threshold, and obtain the adjusted period allocation standard.

[0072] The correlation between the total proportion of preheating and cooling periods within high switching cycles and the duration of a single continuous run is identified. The high switching cycle data are sorted from shortest to longest continuous run duration, and the runtime of each cycle and its corresponding preheating and cooling proportion are extracted to construct a set of data pairs of duration and proportion. A linear regression method is used to fit the data pairs, and the slope of the fitted line is used as the compression coefficient. The compression amount of each cycle is calculated by multiplying the compression coefficient by the actual runtime. The difference between the compression amount and a preset compression threshold is obtained, and the percentage of the absolute value of the difference relative to the preset threshold is used as the deviation degree. If the deviation degree exceeds a preset significance level, adjustment is required. The adjustment magnitude and direction are determined based on the deviation degree and the sign of the difference. The duration parameters in the original time-sharing rules are corrected based on the adjustment magnitude and direction. The proportion of effective experimental time periods and preheating and cooling periods to be allocated to each running cycle is recalculated according to the corrected parameters to obtain the adjusted time-sharing allocation standard.

[0073] In one implementation, when identifying the correspondence between the total proportion of preheating / cooling periods within high switching cycles and the duration of a single continuous run, the system first extracts all data records marked as high switching cycles from the equipment operation log. Each record contains the total run time of the cycle, the duration of a single continuous run, and the cumulative duration of the preheating / cooling period. By dividing the cumulative preheating / cooling duration by the total run time, the proportion of the preheating / cooling period for each high switching cycle is obtained. These records are then sorted in ascending order of the duration of a single continuous run, forming a data sequence with increasing duration.

[0074] Specifically, the calculation of the deceleration rate involves segmenting the sorted data. The system divides the duration of a single continuous run into multiple intervals, each spanning 30 minutes. Within each interval, the average preheating / cooling percentage for all high switching cycles is calculated. The difference between the average percentages of two adjacent intervals, divided by the interval span of 30 minutes, yields the rate of change of the percentage for that segment. Since the preheating / cooling percentage decreases with increasing continuous run duration, this rate is negative, and its absolute value is the deceleration rate. The deceleration rates of each segment are summed to obtain the overall decrease, reflecting the overall decline in the preheating / cooling percentage as the run duration increases.

[0075] It should be noted that the calculation of the time period compression adopts a regression analysis method. The system uses the single continuous running time as the independent variable x and the total proportion of preheating and cooling time as the dependent variable y. A linear regression is performed on all high switching cycle data using the least squares method to obtain the linear equation y = ax + b. Here, the slope 'a' represents the rate of change of the preheating and cooling proportion with running time, i.e., the compression coefficient, whose dimension is proportion value / time unit. The physical meaning of this coefficient is: the decrease in the proportion of preheating and cooling time for each unit increase in running time. By multiplying the compression coefficient by the actual running time, the theoretical reduction in the proportion of preheating and cooling for a specific running time can be calculated; this reduction is the time period compression. For example, when the compression coefficient is -0.002 (negative values ​​indicate decrease), the preheating and cooling proportion theoretically decreases by 0.2 percentage points for every 1 minute increase in running time.

[0076] Preferably, the degree of deviation is quantified using a relative deviation method. The preset compression threshold is derived from historical operating data of the laboratory equipment and is typically set as the expected value of the theoretical compression.

[0077] For example, the adjustment magnitude and direction are determined based on the numerical value and sign of the deviation. When the time period compression is greater than a preset threshold, the difference is positive, indicating that the device has greater optimization potential, and the adjustment direction is to increase the allocation ratio of effective experimental time periods.

[0078] Understandably, the revision process of the time slot allocation standard involves adjusting the original division rules. In the original rules, the ratio of effective experimental time slots to preheating / cooling time slots was set based on empirical values. The system recalculates this ratio according to the adjustment magnitude and direction. When the adjustment direction is to increase effective time slots, the system increases the allocation weight of effective time slots according to the adjustment magnitude, correspondingly reduces the weight of preheating / cooling time slots, and then performs normalization to obtain a new allocation ratio. According to the new ratio, the system reallocates the time slots for each operating cycle, determines the effective experimental time and allowable preheating / cooling time for each cycle, and generates the adjusted time slot allocation standard. This standard provides a quantitative basis for time slot allocation in laboratory management, enabling refined management of equipment operating efficiency. Furthermore, the adjusted time slot allocation standard also includes optimization suggestions for project switching timing. When the single continuous running time of a certain piece of equipment is short, frequent switching leads to an excessively high proportion of preheating / cooling; the system suggests extending the single running time or merging similar experimental projects. This approach reduces the number of switching operations and improves the effective utilization rate of equipment. The adjusted standards provide a quantifiable basis for time allocation in laboratory management, enabling refined management of equipment operating efficiency.

[0079] S104. The equipment operation cycle list is segmented using the time period allocation standard to obtain the operation segment corresponding to a single experimental task. The sub-cycle set for each experimental task is obtained, the distribution level of the preheating and cooling ratio in the sub-cycle is evaluated, and the sub-cycles are identified and classified into high-level sub-cycles. The effective experimental time in the high-level sub-cycles is extracted, and the equipment usage cycle with insufficient effective experimental time is determined.

[0080] The equipment operation cycle list is segmented using a time-segmentation standard. Based on the standard duration of a single experiment as specified in the time-segmentation standard, the start and end times of each experimental task in the operation cycle list are identified. Continuous operation cycles are divided into independent operation segments according to the identified time points. The sub-cycle set of each experimental task is obtained through the correspondence between the experimental task number and the operation segment. The preheating and cooling ratio of each sub-cycle in the sub-cycle set is evaluated. The start and end times of the preheating and cooling periods within each sub-cycle are extracted from the equipment operation log. The ratio of the cumulative preheating and cooling time to the total sub-cycle time is calculated to obtain the ratio value. All sub-cycles are sorted from smallest to largest by their ratio values ​​and then divided into three levels (low, medium, and high) based on their percentile. Sub-cycles with a ratio value exceeding 75% of the percentile are classified as high-level. For high-level sub-cycles, the cumulative preheating and cooling time is subtracted from the total sub-cycle time to obtain the effective experimental time within that sub-cycle. If the effective experimental time is lower than a preset minimum experimental time threshold, the corresponding equipment usage cycle is marked as an equipment usage cycle with insufficient effective experimental time.

[0081] In one implementation, the time allocation standard includes a standard duration parameter for a single experiment, which is preset according to the basic requirements of different types of experimental projects.

[0082] For example, the standard duration for a chemical titration experiment is 45 minutes, and the standard duration for a spectral analysis experiment is 60 minutes. The system automatically segments the equipment operation cycle list based on these standard durations. When multiple experimental tasks are detected within an operation cycle, the system determines the start and end times of each task by identifying characteristic points of equipment status changes.

[0083] Specifically, the identification of operational segments relies on abrupt changes in operating parameters recorded by the equipment's sensors. When parameters such as equipment power, temperature, or speed change significantly within a short period, the system determines this as the end of an experimental task and the beginning of the next. Operational segments obtained in this way are matched with experimental task numbers to form a set of sub-cycles corresponding to each experimental task. Each sub-cycle contains complete time-span information for that experimental task, from preparation and execution to completion.

[0084] It should be noted that the assessment of the preheating / cooling ratio uses a statistical ranking method. The system extracts the specific start and end times of the preheating and cooling periods within each sub-cycle from the equipment operation logs, and calculates the cumulative preheating / cooling duration using timestamps. This cumulative duration is divided by the total sub-cycle duration to obtain the preheating / cooling ratio. After collecting the ratio values ​​for all sub-cycles, the system sorts them from smallest to largest: sub-cycles in the top 25% are classified as low-level, those between 25% and 75% as medium-level, and those exceeding 75% as high-level. This statistical distribution-based ranking method can adaptively identify sub-cycles with abnormally high preheating / cooling ratios.

[0085] Preferably, the effective experimental time is calculated using a simple subtraction operation. For sub-cycles classified as higher-level, the system subtracts the cumulative duration of the preheating and cooling periods from the total duration of that sub-cycle to obtain the actual effective time available for experimental operation. The minimum experimental time threshold is set according to the basic requirements of the experimental project; for example, basic chemistry experiments require at least 20 minutes of effective operation time. When the effective experimental time of a sub-cycle is lower than this threshold, it indicates that although the equipment is running during that period, the actual time students can perform experimental operations is severely insufficient. The system marks this as an equipment usage cycle with insufficient effective experimental time, providing a basis for subsequent period optimization.

[0086] S105. Based on the equipment usage cycle with insufficient effective experimental time, the preheating and cooling period is added to obtain the total available time loss for the whole day. Combined with the distribution level of the preheating and cooling ratio in the sub-cycle, the loss is clustered. Based on the loss grouping results obtained from the clustering, the loss clusters are determined. The distribution of abnormally high values ​​in the loss clusters is evaluated to obtain the equipment number to which the abnormally high value clusters belong.

[0087] Based on the equipment usage cycles marked as having insufficient effective experimental time, the duration of the preheating and cooling periods within each cycle is extracted, and the time loss value for each cycle is accumulated. The loss values ​​of all cycles with insufficient time are summarized to form a loss dataset. The sum of all values ​​in the loss dataset is calculated to obtain the total available time loss for the entire day. Using the distribution level of the preheating and cooling ratios within sub-cycles as features, the loss dataset is subjected to DBSCAN density clustering. The neighborhood radius is set to half the standard deviation of the loss value, and the minimum number of points is 3. Loss amounts are grouped according to the density distribution of data points, and data points with similar loss characteristics are grouped into the same loss cluster. The numerical distribution of each data point in the loss cluster is evaluated, and the mean and standard deviation of the loss amount within each cluster are calculated. If the loss amount of a data point exceeds a preset loss threshold, the data point is determined to be an abnormally high value. The equipment identifier corresponding to the abnormally high value is extracted from the equipment operation record to obtain the equipment number to which the abnormally high value belongs.

[0088] In one implementation, the loss dataset is generated based on equipment usage cycles identified earlier as having insufficient effective experimental time. The system extracts detailed time records for the preheating and cooling periods from each marked cycle and calculates the duration of each period using timestamps.

[0089] For example, if a cycle contains 3 preheating processes and 3 cooling processes, with each preheating process taking 10 minutes and each cooling process taking 8 minutes, then the time loss value for that cycle is 54 minutes. Collect the loss values ​​for all cycles with insufficient time to form a loss dataset containing multiple data points.

[0090] Specifically, the DBSCAN density clustering algorithm's parameter settings are based on the distribution characteristics of the loss data. The system first calculates the standard deviation σ of the loss dataset as a measure of data dispersion. The neighborhood radius ε is set to σ / 2, which determines the range within which neighboring points are searched. Choosing half the standard deviation adapts to the data dispersion, avoiding overly dense or sparse clustering. The minimum number of points is set to 3, indicating that at least 3 data points are needed to form a dense region. The algorithm scans the neighborhood of each data point, identifying core points, boundary points, and noise points, grouping density-connected core points and their reachable points into the same cluster. Simultaneously, the system uses the preheating / cooling ratio of the sub-cycle as an auxiliary feature in clustering, ensuring that data points with similar loss characteristics and ratios are grouped into the same loss cluster.

[0091] It should be noted that the identification of abnormally high values ​​employs outlier detection methods from statistics. The mean μ and standard deviation σ of the data points within each loss cluster are calculated separately. The system sets an outlier threshold of μ + 2σ; data points exceeding this threshold are considered statistically significant outliers. According to the normal distribution theory, approximately 95% of the data should fall within the range of μ ± 2σ; therefore, data points exceeding μ + 2σ represent extreme cases and exhibit significant anomalous characteristics. This method adaptively identifies outliers within each cluster, rather than using a globally uniform threshold.

[0092] Preferably, equipment numbers are extracted by establishing a mapping relationship between loss values ​​and equipment records. Each loss data point retains its source information, including equipment identifier, operating date, and cycle number. When a data point is identified as an abnormally high value, the system traces its source information, queries the equipment operation record database, and obtains the corresponding equipment number, model, and laboratory location. By aggregating all equipment numbers corresponding to abnormally high values, managers can accurately locate equipment with serious time utilization efficiency problems, providing a basis for targeted optimization and adjustments.

[0093] S106. For the device number to which the abnormally high value cluster belongs, obtain the historical experimental time period records from the registration terminal, rearrange the records using the time period reorganization method, and determine the optimized experimental time period arrangement table.

[0094] For each device number associated with an abnormally high cluster, historical experimental time period records corresponding to that device number are retrieved from the registration terminal database. The experimental project name, start time, end time, and number of switches between projects are extracted from each record. Keyword matching in the experimental project name is used to determine project type similarity, grouping similar projects into the same category to obtain a categorized time period record set. The categorized time period record set is then processed using a time period reorganization method. Time period records within the same category are arranged chronologically, and the time interval between adjacent records is calculated. If the interval is less than a preset merging threshold, adjacent records are merged into consecutive time periods, resulting in merged time period sequences for each category. Based on these time period sequences, the experimental order is rearranged according to the principle of reducing the number of project switches. Experiments requiring the same preheating conditions are placed in consecutive time periods. Intervals between different categories of experiments are set according to equipment cooling requirements. The project name, start time, end time, and equipment operating parameters for each time period are recorded to determine the optimized experimental time period arrangement table.

[0095] In one implementation, queries for historical experimental period records are indexed based on device IDs. The registration terminal database uses a relational database architecture, with each record containing a unique device identifier, experimental project name, operator information, start timestamp, and end timestamp. The system uses the device ID as the primary key to retrieve all experimental records for that device within the past month, forming the raw dataset to be optimized.

[0096] Specifically, the determination of project type similarity relies on a keyword matching mechanism. The system pre-establishes a dictionary of experimental project categories, classifying common chemical laboratory projects into categories such as solution preparation, titration analysis, spectroscopic determination, and chromatographic separation. Each category corresponds to a set of characteristic keywords; for example, the titration analysis category includes keywords such as "titration," "standardization," and "acid-base." By extracting keywords from the project names and matching them with the dictionary, the system automatically determines the category to which the project belongs, grouping experimental records of the same category together.

[0097] It should be noted that the merging threshold setting during the time-segment reorganization process takes into account the actual operating characteristics of the equipment. When the interval between two experimental time segments of the same category is small, the system determines that they can be merged into a continuous time segment because the equipment temperature and operating parameters do not change much during this time interval, and reheating is not required. The merged continuous time segments reduce the number of repetitions of preheating and cooling, thereby improving the effective utilization rate of the equipment. The rearrangement of the experimental sequence follows the principle of grouping and continuity. The system groups similar experimental items together in continuous time segments, reducing the switching between different types of experiments, so that the equipment maintains the same operating state for a longer period of time and avoids frequent parameter adjustments.

[0098] In one possible implementation, the interval between different types of experiments is dynamically set based on equipment cooling requirements. The system automatically calculates the required interval based on the temperature difference between preceding and subsequent experiments and reserves corresponding buffer periods in the scheduling table. The optimized experiment scheduling table is stored in a structured format, with each record containing information such as the time period number, project name, start time, end time, equipment temperature setpoint, and rotation speed parameters, providing laboratory managers with a clear equipment usage plan. This detailed equipment usage plan provides laboratory managers with a clear equipment usage schedule, thereby improving equipment operating efficiency.

[0099] S107. Update the scheduling table of the laboratory management system through the experimental time period arrangement table, obtain the updated scheduling table content, evaluate the increase in effective experimental time, and obtain the final evaluation result of the equipment's available time throughout the day.

[0100] The laboratory management system's scheduling table is updated by updating the experimental time slot schedule. The project name, start and end time, and equipment parameters for each time slot in the schedule are read and written into the scheduling table database to replace the original records, resulting in the updated scheduling table content. The effective experimental time slot data before and after the update are extracted from the scheduling table content. The difference between the total effective time slots after the update and the total effective time slots before the update is calculated as the improvement amount. This improvement amount is divided by the total daily operating time of the equipment to obtain the improvement ratio, thus yielding the final evaluation result of the equipment's total daily usable time.

[0101] In one implementation, the laboratory management system's scheduling table is stored in a relational database, containing fields such as equipment number, time period number, project name, start time, end time, and operator. Before updating, the system backs up the original scheduling records for subsequent comparative analysis. The scheduling table is updated line by line using data from the scheduling table, with each record uniquely identified by its time period number to ensure the accuracy of data replacement.

[0102] Specifically, the extraction of effective experimental periods is based on the preheating and cooling markers in the scheduling table, which distinguish between preheating, cooling, and effective experimental periods. The system filters out the periods marked as effective experiments from the scheduling table and sums up the duration of these periods to obtain the total effective periods. After the update, the total effective periods increase, and the improvement is divided by the total daily operating time of 480 minutes to obtain the improvement ratio.

[0103] It should be noted that the final evaluation results are presented in report form, including multiple indicators such as equipment utilization rate, effective time percentage, and preheating / cooling period percentage. These quantitative indicators allow laboratory managers to intuitively understand the optimization effects and provide data support for further improvements in equipment scheduling.

[0104] This invention provides a smart campus management system, the system comprising:

[0105] The data acquisition and cycle division module is used to obtain the cumulative uptime, single continuous running time and project switching times of laboratory equipment, and divide the time period according to the start and stop time nodes to obtain the equipment operation cycle list.

[0106] The high switching cycle identification module is used to identify the number of preheating and cooling repetitions based on the equipment operation cycle list, use cluster analysis to determine the distribution level of the number of preheating and cooling repetitions, determine the high switching cycle based on the distribution level, and extract the total proportion of the preheating and cooling period within the high switching cycle.

[0107] The time period allocation standard adjustment module is used to identify the decrease in the total proportion of the preheating and cooling time period with the duration of a single continuous run, extract the time period compression amount corresponding to the decrease, and obtain the time period allocation standard based on the deviation of the time period compression amount from the preset threshold.

[0108] The operation cycle segmentation module is used to segment the equipment operation cycle list according to the time period allocation standard, obtain the operation segment and sub-cycle set of a single experimental task, evaluate the distribution level of the preheating and cooling ratio in the sub-cycle, identify high-level sub-cycles, extract the effective experimental time of the high-level sub-cycles, and determine the usage cycle with insufficient effective experimental time.

[0109] The available time loss analysis module is used to accumulate the preheating and cooling periods according to the usage cycle to obtain the total available time loss for the whole day, and to perform cluster analysis on the loss amount in combination with the distribution level of the preheating and cooling ratio to determine the loss clusters, evaluate the distribution of abnormally high values, and obtain the equipment number to which the abnormally high value clusters belong.

[0110] The experimental time period optimization module is used to obtain historical experimental time period records for the device number, rearrange them using a time period reorganization method, and determine the optimized experimental time period arrangement table.

[0111] The scheduling update and evaluation module is used to update the management system scheduling table through the experimental time period arrangement table, evaluate the effective experimental time increase, and obtain the evaluation result of the equipment's full-day available time.

[0112] The above description is merely a specific implementation of this specification. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the scope of protection of this specification is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this specification, and these modifications or substitutions should all be covered within the scope of protection of this specification.

Claims

1. A smart campus management method, characterized in that, include: The cumulative uptime, single continuous running time and number of project switching of laboratory equipment are obtained by the data acquisition module. The time period is divided according to the start and stop time nodes to obtain the equipment operation cycle list. The number of preheating and cooling repetitions is identified based on the equipment operation cycle list. Cluster analysis is used to determine the distribution level of the number of preheating and cooling repetitions. Based on the distribution level, high switching cycles are determined, and the total proportion of preheating and cooling time periods within the high switching cycles is extracted. Identify the decrease in the total proportion of the preheating and cooling period as the duration of a single continuous run, extract the period compression amount corresponding to the decrease, and obtain the period allocation standard based on the deviation of the period compression amount from the preset threshold. The equipment operation cycle list is segmented using the time period allocation standard to obtain the operation segments and sub-cycle sets of a single experimental task. The distribution level of the preheating and cooling ratio in the sub-cycle is evaluated, high-level sub-cycles are identified, the effective experimental time of the high-level sub-cycles is extracted, and the usage cycle with insufficient effective experimental time is determined. The total available time loss is obtained by accumulating the preheating and cooling periods according to the usage cycle. The loss is then clustered based on the distribution level of the preheating and cooling ratio to identify loss clusters, evaluate the distribution of abnormally high values, and obtain the equipment number to which the abnormally high value cluster belongs. For the device number, historical experimental time period records are obtained, and rearranged using a time period reorganization method to determine an optimized experimental time period arrangement table. The management system scheduling table is updated by updating the experimental time period arrangement table, the effective experimental time increase is evaluated, and the evaluation result of the equipment's full-day usability is obtained.

2. The smart campus management method according to claim 1, characterized in that, The cumulative uptime, single continuous uptime, and number of project switches of the laboratory equipment are acquired through the data acquisition module. Time periods are then divided according to start-up and shutdown time nodes to obtain a list of equipment operation cycles, including: The acquisition module reads the operating status data recorded by the built-in sensors of the laboratory equipment, obtains the cumulative power-on time value from the start to the stop of the equipment, records the single continuous running time value for each uninterrupted operation, and extracts the number of project switching from the registration terminal database. The device start-up and shutdown time nodes are identified based on the timestamp information in the cumulative power-on time and single continuous operation time. The operation record is segmented according to the start-up and shutdown time nodes, and the number of item switching is counted in each segment to obtain a device operation cycle list containing time information and switching information.

3. The smart campus management method according to claim 1, characterized in that, The number of preheating and cooling repetitions is identified based on the equipment operation cycle list. Cluster analysis is used to determine the distribution level of the number of preheating and cooling repetitions. High switching cycles are determined based on the distribution level, and the total proportion of preheating and cooling time periods within the high switching cycles is extracted, including: Based on the project switching time and equipment operating status changes recorded in the equipment operation cycle list, identify the preheating period and cooling period after each project switching, count the number of occurrences of the preheating period and cooling period in each operation cycle, and obtain a preheating and cooling repetition dataset. The K-means clustering algorithm was used to cluster the preheating and cooling repetition dataset. The distribution levels were divided according to the cluster center values, and the cycles that belonged to the highest value category were marked as high switching cycles. For the high switching cycle, the start and end times of the preheating and cooling periods are extracted from the equipment operation log, the duration of each period is calculated and accumulated to obtain the total proportion of the preheating and cooling periods within the high switching cycle.

4. The smart campus management method according to claim 1, characterized in that, Identify the decreasing percentage of the total preheating and cooling period as the duration of a single continuous run, extract the period compression corresponding to the decreasing percentage, and obtain a period allocation standard based on the deviation of the period compression from a preset threshold, including: Identify the correspondence between the total proportion of preheating and cooling periods within the high switching cycle and the duration of a single continuous run. Sort the high switching cycles from shortest to longest according to the duration of a single continuous run. Extract the runtime of each cycle and the corresponding preheating and cooling proportion value to construct a set of data pairs of duration and proportion. The data set of duration and proportion is fitted using a linear regression method, and the slope of the fitted line is used as the compression coefficient. The compression amount of each period is calculated by multiplying the compression coefficient by the actual running time. The difference between the compression amount of the specified time period and the preset compression threshold is obtained. The percentage of the absolute value of the difference relative to the preset threshold is calculated as the degree of deviation. The adjustment range and direction are determined based on the degree of deviation and the sign of the difference. Based on the adjustment range and direction, the duration parameters in the original time period division rules are corrected, and the proportion of effective experimental time periods and preheating and cooling time periods to be allocated to each operating cycle is recalculated according to the corrected parameters to obtain the adjusted time period allocation standard.

5. The smart campus management method according to claim 1, characterized in that, The equipment operation cycle list is segmented using the time period allocation standard to obtain the operation segments and sub-cycle sets of a single experimental task. The distribution level of the preheating and cooling ratio in the sub-cycles is evaluated, high-level sub-cycles are identified, the effective experimental time of the high-level sub-cycles is extracted, and the usage cycle with insufficient effective experimental time is determined, including: The equipment operation cycle list is segmented using the time period allocation standard. The start and end times of each experimental task are identified according to the standard duration of a single experiment. The continuous operation cycle is divided into independent operation segments to obtain the sub-cycle set of each experimental task. The preheating and cooling ratio of each sub-cycle in the sub-cycle set is evaluated, the ratio of the cumulative preheating and cooling time to the total sub-cycle time is calculated, and the ratio values ​​of all sub-cycles are divided into three levels: low, medium and high according to the quantile. Sub-cycles classified into high-level sub-cycles are marked as high-ratio sub-cycles. For the high-proportion sub-cycle, the total duration of the sub-cycle is subtracted from the cumulative preheating and cooling time to obtain the effective experimental time. If the effective experimental time is lower than the preset minimum experimental time threshold, it is marked as a device usage cycle with insufficient effective experimental time.

6. The smart campus management method according to claim 1, characterized in that, The total available time loss is obtained by accumulating the preheating and cooling periods based on the usage cycle. Cluster analysis is then performed on the loss based on the distribution level of the preheating and cooling percentage to identify loss clusters. The distribution of abnormally high values ​​is evaluated, and the device numbers belonging to these abnormally high value clusters are obtained, including: Based on the equipment usage cycle with insufficient effective experimental time, the duration of the preheating and cooling periods in each cycle is extracted, and the duration loss value of each cycle is accumulated. The loss dataset is then compiled, and the total of the loss dataset is calculated to obtain the total available time loss for the whole day. Using the distribution level of the preheating and cooling ratio in the sub-cycle as a feature, the loss dataset is subjected to DBSCAN density clustering processing, and loss clusters are divided according to the density distribution of data points. The numerical distribution of each data point within the loss cluster is evaluated. If the loss of a certain data point exceeds a preset loss threshold, it is determined to be an abnormally high value. The device number corresponding to the abnormally high value is extracted from the device operation record.

7. The smart campus management method according to claim 1, characterized in that, Historical experimental time period records were obtained for the device number, and rearranged using a time period reorganization method to determine an optimized experimental time period arrangement table, including: For the device number to which the abnormally high value cluster belongs, query the historical experimental period records from the registration terminal database, extract the experimental project name, start time, end time and number of switching between projects for each record, and determine the similarity of project types based on keyword matching in the experimental project name. Similar projects are classified into the same category to obtain the classified period record set. The classified time period record set is processed by time period recombination method. The time period records in the same category are arranged in chronological order, the time interval between adjacent records is calculated, and if the interval is less than a preset merging threshold, they are merged into continuous time periods to obtain the merged time period sequence of each category. Based on the time period sequence, the experimental order is rearranged according to the principle of reducing the number of project switching. Experiments requiring the same preheating conditions are arranged in consecutive time periods. The interval time between different types of experiments is set according to the equipment cooling requirements. The project name, start time, end time and equipment operating parameters of each time period are recorded to obtain the optimized experimental time period arrangement table.

8. The smart campus management method according to claim 1, characterized in that, The management system scheduling table is updated by updating the experimental time period scheduling table to evaluate the effective experimental time increase and obtain the evaluation result of the equipment's total available time throughout the day, including: The laboratory management system's scheduling table is updated by updating the experimental time period schedule table. The project name, start and end time, and equipment parameters for each time period in the schedule table are written into the scheduling table database to replace the original records, and the updated scheduling table content is obtained. Extract the effective experimental time period data before and after the update from the updated scheduling table, calculate the difference between the sum of the effective time periods after the update and the sum of the effective time periods before the update as the improvement amount, and obtain the final evaluation result of the equipment's available time throughout the day.

9. A smart campus management system, characterized in that, The system includes: The data acquisition and cycle division module is used to obtain the cumulative uptime, single continuous running time and project switching times of laboratory equipment, and divide the time period according to the start and stop time nodes to obtain the equipment operation cycle list. The high switching cycle identification module is used to identify the number of preheating and cooling repetitions based on the equipment operation cycle list, use cluster analysis to analyze the distribution level of the number of preheating and cooling repetitions, determine the high switching cycle based on the distribution level, and extract the total proportion of the preheating and cooling period within the high switching cycle. The time period allocation standard adjustment module is used to identify the decrease in the total proportion of the preheating and cooling time period with the duration of a single continuous run, extract the time period compression amount corresponding to the decrease, and obtain the time period allocation standard based on the deviation of the time period compression amount from the preset threshold. The operation cycle segmentation module is used to segment the equipment operation cycle list according to the time period allocation standard, obtain the operation segment and sub-cycle set of a single experimental task, evaluate the distribution level of the preheating and cooling ratio in the sub-cycle, identify high-level sub-cycles, extract the effective experimental time of the high-level sub-cycles, and determine the usage cycle with insufficient effective experimental time. The available time loss analysis module is used to accumulate the preheating and cooling periods according to the usage cycle to obtain the total available time loss for the whole day, and to perform cluster analysis on the loss amount in combination with the distribution level of the preheating and cooling ratio to determine the loss clusters, evaluate the distribution of abnormally high values, and obtain the equipment number to which the abnormally high value clusters belong. The experimental time period optimization module is used to obtain historical experimental time period records for the device number, rearrange them using a time period reorganization method, and determine the optimized experimental time period arrangement table. The scheduling update and evaluation module is used to update the management system scheduling table through the experimental period arrangement table, evaluate the effective experimental time increase, and obtain the evaluation result of the equipment's full-day available time.

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