Laboratory resource optimal distribution system based on cloud computing and big data analysis

Abstract

The invention relates to the technical field of resource optimization, in particular to a laboratory resource optimal distribution system based on cloud computing and big data analysis, which comprises a resource state monitoring module, a resource demand analysis module, a resource dynamic adjustment module, a resource efficiency analysis module and a resource optimization execution module. According to the method, through time interval grouping and interval difference calculation, in combination with periodic reagent consumption trend analysis, high-demand and low-demand intervals of laboratory resources are accurately positioned, the accuracy and pertinence of resource allocation are optimized, meanwhile, excess resources are recycled and stored, and meanwhile, insufficient parts are supplemented; the conditions of excessive consumption and unused resources are concluded, the recognition and response capabilities of differences between resource allocation and actual demands are improved, resource waste or insufficient allocation caused by static allocation is reduced, and high adaptability of resource utilization is realized by optimizing the equipment starting sequence and the reagent allocation rhythm. Powerful support is provided for the continuity and the high efficiency of the experiment process.

Description

Technical Field

[0001] The present invention relates to the technical field of resource optimization, and particularly to a laboratory resource optimization allocation system based on cloud computing and big data analysis. Background Art

[0002] The technical field of resource optimization mainly focuses on how to use and manage limited resources more effectively to improve efficiency and performance and reduce costs. Resources can be physical, such as machines, manpower, time, and space; or digital, such as computing power, storage space, and network bandwidth. The applications of the technology are extensive, covering from supply chain management to data center resource management, and then to the optimization of smart grids and public services. The core technologies include data analysis, cloud computing, artificial intelligence, etc., which can achieve data-driven decision support, optimize resource allocation and utilization efficiency.

[0003] Among them, the laboratory resource optimization allocation system based on cloud computing and big data analysis is a system designed specifically for the laboratory environment, aiming to improve the utilization efficiency of resources and the effectiveness of laboratory operations. By integrating cloud computing and big data analysis technologies, it can analyze the usage situation of laboratory resources in real time and perform intelligent scheduling and allocation accordingly. Its main uses include, but are not limited to, automatically managing the schedules of laboratory equipment, reagents, and personnel to ensure that resources are utilized optimally, reducing waste, and supporting more research activities with the same or fewer resources, improving the overall laboratory operation efficiency.

[0004] The existing technologies mostly perform resource allocation based on fixed rules, failing to effectively integrate the dynamic demand changes of laboratory equipment and reagents, and it is difficult to quickly respond to complex resource demand scenarios. There is a lack of accurate judgment of the peak and trough intervals of equipment usage, and resource allocation is prone to redundancy or insufficiency. The static allocation method fails to flexibly respond to demand fluctuations, resulting in a decrease in the utilization efficiency of laboratory equipment resources, and at the same time, the lag in reagent inventory management increases the risk of waste. The existing technologies fail to monitor the consumption and allocation status of resources in real time, and it is difficult to effectively identify the deviation between resource allocation and actual demand. There is a lack of a dynamic optimization mechanism in resource management, resulting in limited capabilities of the laboratory in coping with demand fluctuations and efficiently managing resources. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a laboratory resource optimization allocation system based on cloud computing and big data analysis.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: The laboratory resource optimization allocation system based on cloud computing and big data analysis includes:

[0007] Based on the operating status information of laboratory equipment and the reagent inventory information, the resource status monitoring module collects the equipment operation time and usage frequency, classifies and summarizes the occupancy rate of laboratory equipment and the inventory consumption, statistically analyzes the reagent consumption by batch, and generates resource usage change information;

[0008] Based on the resource usage change information, the resource demand analysis module groups the laboratory equipment operation records by time period, calculates the interval difference of the laboratory equipment operation time, combines the calculation results with the periodic changes of the reagent inventory consumption, and screens the high-demand intervals and low-demand intervals through threshold comparison to generate the experimental resource demand trend analysis result;

[0009] Based on the experimental resource demand trend analysis result, the resource dynamic adjustment module compares the upper and lower limits of the current laboratory equipment allocation and reagent allocation, matches them with the high-demand intervals and low-demand intervals, recovers and reserves the excess part, supplements the insufficient part, and generates an experimental resource allocation plan;

[0010] The resource efficiency analysis module monitors the actual operation time and usage frequency of laboratory equipment, calculates the time difference between the laboratory equipment operation time and the allocated time in the experimental resource allocation plan respectively, collects the non-operation time periods of laboratory equipment and classifies and records them, synchronously counts the reagent consumption batches and usage amounts in combination with the recorded content, performs a difference operation on the actual reagent usage amount and the allocated amount in the experimental resource allocation plan, summarizes the excess consumption batches and non-consumed batches, and comprehensively generates resource utilization adjustment information;

[0011] Based on the resource utilization adjustment information, the resource optimization execution module re-adjusts the startup sequence of laboratory equipment, adjusts the reagent replenishment frequency, transfers the laboratory equipment that exceeds the operation completion time to standby, adjusts the reagents to the rated demand range, and performs global dynamic optimization allocation of laboratory equipment and reagents to generate the experimental resource optimization configuration result.

[0012] As a further solution of the present invention, the steps for obtaining the resource usage change information are as follows:

[0013] Collect the operating status information of laboratory equipment and the reagent inventory information, record the equipment model parameters, equipment operation time parameters and reagent inventory parameters, count the cumulative operation duration of the equipment and the inventory consumption records, and generate the initial set of equipment operation time and inventory changes;

[0014] Based on the initial set of equipment operation time and inventory changes, calculate the total operation time and total reagent consumption of each equipment, classify and mark them according to the operation time length and reagent consumption level, analyze the operation characteristics and inventory change trends of various types of equipment, and generate the equipment operation grouping and inventory change set;

[0015] Based on the classification markers and inventory consumption trend data in the device operation grouping and inventory change set, analyze the change in reagent consumption by batch, analyze the batch consumption fluctuation, and generate resource usage change information.

[0016] As a further solution of the present invention, the calculation and acquisition steps of the interval difference are as follows:

[0017] Call the laboratory equipment operation record data in the resource usage change information, divide the equipment operation records into multiple time period groups according to a fixed time period, collect the equipment operation time parameters within each time period group and perform preliminary summarization to generate a set of laboratory equipment operation record time period groupings;

[0018] Based on the set of laboratory equipment operation record time period groupings, count the total equipment operation time of each time period group, and at the same time analyze the time series of the time period groupings, calculate the average operation time and standard deviation of each time period, and generate a set of statistical characteristics of laboratory equipment operation time;

[0019] Based on the total amount, average value and standard deviation parameters in the set of statistical characteristics of laboratory equipment operation time, compare the difference between the total operation time and the average value of each time period, and use the formula:

[0020]

[0021] Calculate the operation time difference value Δ between time period i and time period j ij , and generate laboratory equipment operation time interval difference information, where T i is the total operation time of time period i, T j is the total operation time of time period j, and n is the number of equipment operation records between time periods.

[0022] As a further solution of the present invention, the acquisition steps of the experimental resource demand trend analysis result are as follows:

[0023] Combine the laboratory equipment operation time interval difference information and reagent inventory consumption data, collect the total reagent consumption of each interval according to the time series, analyze the periodic change value, correlate the reagent consumption amount with the operation time difference of the corresponding time period, and generate a set of interval reagent consumption characteristics;

[0024] Based on the set of interval reagent consumption characteristics, count the total reagent consumption of each interval and the corresponding time length, and use the formula:

[0025]

[0026] Calculate the reagent consumption rate R of interval i i, which reflects the usage of reagents per unit time and generates a set of reagent consumption rates for the time interval. Among them, C i is the total amount of reagent consumed in interval i, and t i is the time length of interval i;

[0027] According to the set of reagent consumption rates for the time interval, compare with a preset threshold and divide into high-demand intervals and low-demand intervals. Record the total reagent consumption, consumption rate, and operating time difference in the high-demand and low-demand intervals, compare and analyze the data characteristics within the group, reveal the dynamic change trend of laboratory resource usage, and obtain the experimental resource demand trend analysis result.

[0028] As a further solution of the present invention, the steps for obtaining the experimental resource allocation plan are as follows:

[0029] According to the experimental resource demand trend analysis result, count the equipment usage parameters and reagent consumption parameters in the high-demand and low-demand intervals, combine with the record of the available number of laboratory equipment and reagent inventory, determine the upper and lower limit ranges of interval resource allocation, and obtain the basic data of the upper and lower limits of laboratory resource allocation;

[0030] Based on the basic data of the upper and lower limits of laboratory resource allocation, through the excess and deficiency parts of interval resource allocation, use the formula:

[0031]

[0032] Calculate the resource allocation deviation ratio E i for interval i to obtain the deviation data of the upper and lower limits of resource allocation. Among them, A i is the actual resource usage amount in the interval, L i is the lower limit value of resource allocation, and U i is the upper limit value of resource allocation;

[0033] According to the deviation data of the upper and lower limits of resource allocation, mark the excess equipment and reagents as recycled reserves, mark the deficiency part as additional supply, plan the redistribution of the recycled reserve resources, and determine the source allocation of additional supply for the deficiency part to generate an experimental resource allocation plan.

[0034] As a further solution of the present invention, the steps for collecting, classifying, and recording the non-operating time periods of laboratory equipment are as follows:

[0035] Call the equipment allocation time parameters in the experimental resource allocation plan, collect the start time and end time of the allocation of each equipment, match with the operating time in the actual operating record of the laboratory equipment, calculate the operating duration of the equipment by time period, and generate an equipment operating time matching record;

[0036] Based on the matching records of the device operation time, calculate the time difference between the actual operation time of each device and the allocated time in the experimental resource allocation plan item by item, count the difference intervals during the operation time, mark the part with a positive time difference as the non-operation time period of the device, sort out the non-operation time periods and group them by device to generate a set of non-operation time periods of laboratory devices;

[0037] Call the set of non-operation time periods of laboratory devices, combine the device numbers and time period parameters, classify and count the non-operation time periods according to the device categories and operation frequencies to generate a classification record of non-operation time periods of laboratory devices.

[0038] As a further solution of the present invention, the steps for obtaining the resource utilization adjustment information are as follows:

[0039] According to the classification record of non-operation time periods of laboratory devices, collect the recorded time ranges related to reagent use, match the reagent consumption batches and actual usage data in the corresponding time periods one by one, sort and classify the reagent use batches and the device operation time periods in chronological order, and establish a preliminary correspondence relationship between the reagent consumption batches and the device time periods;

[0040] Based on the preliminary correspondence relationship between the reagent consumption batches and the device time periods, calculate the deviation between the actual reagent usage and the allocated amount in the experimental resource allocation plan, mark the batches with usage exceeding the allocated amount as over-consumption batches, mark the batches with usage lower than the allocated amount as non-consumed batches, and sort out and form a reagent use deviation record;

[0041] According to the reagent use deviation record, compare the time periods with concentrated distribution of over batches with the operation efficiency of laboratory devices, identify the time intervals with unreasonable resource allocation, re-plan the usage priorities of non-consumed batches of reagents according to time periods, and combine the device operation frequency and time period characteristics to generate resource utilization adjustment information.

[0042] As a further solution of the present invention, the steps for obtaining the optimized allocation result of experimental resources are as follows:

[0043] Based on the resource utilization adjustment information, re-adjust the startup order of laboratory devices, compare the operation completion time of the devices with the experimental demand time periods one by one, classify the device states exceeding the operation completion time, and transfer them to the standby state to generate a device state adjustment result;

[0044] According to the device state adjustment result, corresponding to the time periods of the device operation state and the reagent allocation requirements, adjust the replenishment frequency of the reagent in turn, adjust the reagent demand in each time period to the rated range, and generate a reagent allocation adjustment result by updating the matching relationship record of the device and the reagent;

[0045] Combining the reagent allocation adjustment result and the equipment status adjustment result, verify the dynamic matching result of each equipment and reagent combination, re-adjust and allocate the equipment and reagent combinations that do not meet the optimization standard, and perform global dynamic optimization configuration of laboratory equipment and reagents to obtain the experimental resource optimization configuration result.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0047] In the present invention, through time period grouping and interval difference calculation, combined with the analysis of periodic reagent consumption trends, the high-demand and low-demand intervals of laboratory resources are accurately located, the accuracy and pertinence of resource allocation are optimized, at the same time, excess resources are recycled and reserved, and the insufficient part is supplemented at the same time. The status of excess consumption and unused resources is summarized, the ability to identify and respond to the difference between resource allocation and actual demand is improved, the resource waste or insufficient allocation caused by static allocation is reduced, and the high adaptability of resource utilization is achieved by optimizing the equipment startup sequence and reagent allocation rhythm, providing strong support for the continuity and efficiency of the experimental process. Brief Description of the Drawings

[0048] Figure 1 is the system flow chart of the present invention;

[0049] Figure 2 is the flow chart for obtaining the resource usage change information of the present invention;

[0050] Figure 3 is the flow chart for calculating and obtaining the interval difference of the present invention;

[0051] Figure 4 is the flow chart for obtaining the analysis result of the experimental resource demand trend of the present invention;

[0052] Figure 5 is the flow chart for obtaining the experimental resource allocation plan of the present invention;

[0053] Figure 6 is the flow chart for collecting and classifying the records of the non-operating time periods of the laboratory equipment of the present invention;

[0054] Figure 7 is the flow chart for obtaining the resource utilization adjustment information of the present invention;

[0055] Figure 8 is the flow chart for obtaining the experimental resource optimization configuration result of the present invention. Detailed Embodiments

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0058] Please refer to Figure 1 , the laboratory resource optimization allocation system based on cloud computing and big data analysis includes:

[0059] The resource status monitoring module collects the equipment operation time and usage frequency based on the laboratory equipment operation status information and reagent inventory information, classifies and summarizes the occupancy rate of laboratory equipment and the inventory consumption, statistically analyzes the reagent consumption by batch, and generates resource usage change information.

[0060] The resource demand analysis module groups the laboratory equipment operation records by time period based on the resource usage change information, calculates the interval difference of the laboratory equipment operation time, combines the calculation results with the periodic change of the reagent inventory consumption, and screens the high-demand interval and low-demand interval through threshold comparison to generate the experimental resource demand trend analysis result.

[0061] The resource dynamic adjustment module compares the upper and lower limits of the current laboratory equipment allocation and reagent allocation based on the experimental resource demand trend analysis result, matches them with the high-demand interval and low-demand interval, recovers and reserves the excess part, and supplements the insufficient part to generate the experimental resource allocation plan.

[0062] The resource efficiency analysis module monitors the actual operation time and usage frequency of laboratory equipment according to the experimental resource allocation plan, calculates the time difference between the laboratory equipment operation time and the allocated time in the experimental resource allocation plan respectively, collects the non-operation time period of laboratory equipment and classifies and records it, synchronously counts the reagent consumption batches and usage amounts in combination with the recorded content, performs a difference operation on the actual reagent usage amount and the allocated amount in the experimental resource allocation plan, summarizes the excess consumption batches and non-consumption batches, and comprehensively generates resource utilization adjustment information.

[0063] Based on the resource utilization adjustment information, the resource optimization execution module readjusts the startup sequence of laboratory equipment, adjusts the reagent replenishment frequency, transfers the laboratory equipment that exceeds the operation completion time to standby, adjusts the reagents to the rated demand range, conducts global dynamic optimization allocation of laboratory equipment and reagents, and generates the experimental resource optimization configuration result.

[0064] The resource usage change information includes the equipment occupancy rate statistical value, the reagent inventory consumption rate statistical value, and the reagent batch consumption statistical value; the experimental resource demand trend analysis result includes the equipment operation time interval difference statistical value, the reagent periodic consumption interval statistical value, and the demand high-low interval classification value; the experimental resource allocation plan includes the equipment allocation recovery reserve quantity, the reagent additional supply quantity, and the resource allocation matching value; the resource utilization adjustment information includes the equipment operation time deviation value, the reagent over-consumption batch statistical value, and the reagent non-consumption batch statistical value; the experimental resource optimization configuration result includes the equipment startup sequence adjustment plan, the reagent replenishment frequency adjustment value, and the resource global allocation optimization value.

[0065] Please refer to Figure 2 , and the steps for obtaining the resource usage change information are as follows:

[0066] Collect the operation status information of laboratory equipment and the reagent inventory information, record the equipment model parameters, equipment operation time parameters, and reagent inventory quantity parameters, count the cumulative operation duration of the equipment and the inventory consumption records, and generate the initial set of equipment operation time and inventory changes.

[0067] Based on the data in the operation status information of laboratory equipment and the reagent inventory information, first extract the equipment model parameters, screen all equipment records to ensure equipment model matching, compare the operation time parameters of the qualified equipment one by one, calculate the specific duration of each equipment in each operation, and use the cumulative calculation method to summarize the operation time of all batches to form the total operation time record of the equipment. While obtaining the equipment operation time, extract the reagent inventory quantity parameters and the corresponding reagent consumption records of the equipment, match the reagent inventory quantity and the consumption quantity each time according to the equipment and batches to ensure the consistency of inventory changes and consumption quantities. For the missing or abnormal records in the data, conduct comparison and inspection, and fill in the data through interpolation or adjacent record extrapolation methods to ensure the integrity and consistency of the results, and finally form the initial set of equipment operation time and inventory changes.

[0068] Based on the initial set of equipment operation time and inventory changes, calculate the total operation time and total reagent consumption of each equipment, classify and label them according to the operation time length and reagent consumption level, analyze the operation characteristics and inventory change trends of various types of equipment, and generate the equipment operation grouping and inventory change set.

[0069] Based on the initial set of equipment operation time and inventory changes, the cumulative operation time value of each piece of equipment and the reagent inventory change records are analyzed independently. First, summarize the operation time data of each piece of equipment, calculate the total operation time, and at the same time group the records according to the equipment model, and count the operation characteristics of each group of equipment, including the minimum value, maximum value, and distribution characteristics of the operation time; then, calculate the reagent consumption records corresponding to each piece of equipment, count the total reagent consumption, and sort them from high to low according to the consumption. Further, according to the range of operation time and reagent consumption, the equipment is divided into high-consumption groups, medium-consumption groups, and low-consumption groups, and the equipment in each group is marked separately. Finally, integrate the grouping information and the inventory consumption change trend, record the operation characteristics of the equipment and the inventory usage characteristics, and generate the equipment operation grouping and inventory change set.

[0070] Based on the classification marks and inventory consumption trend data in the equipment operation grouping and inventory change set, analyze the change of reagent consumption by batch, analyze the batch consumption fluctuation, and generate the resource usage change information;

[0071] Call the classification marks and inventory consumption trend data in the equipment operation grouping and inventory change set. First, rearrange the equipment operation records and reagent consumption data according to the batch to ensure the time continuity of the data between batches. Analyze each item of reagent consumption data for each batch. By comparing between batches, calculate the change rate of consumption, and use the difference method to analyze the volatility of the consumption data. According to the fluctuation results, classify and file the equipment with large reagent consumption fluctuations, extract the rate of inventory reduction, and use the successive accumulation method to analyze the inventory change trend of the equipment operation grouping. Finally, combine the operation status of the equipment grouping and the dynamic change information of reagent consumption, and count the resource usage of the equipment to generate the resource usage change information.

[0072] Please refer to Figure 3 , the calculation steps for obtaining the interval difference are as follows:

[0073] Call the laboratory equipment operation record data in the resource usage change information, divide the equipment operation records into multiple time period groups according to a fixed time period, collect the equipment operation time parameters within each time period group and conduct a preliminary summary to generate the laboratory equipment operation record time period grouping set;

[0074] Call the operation record data of laboratory equipment in the resource usage change information, divide the operation records into time periods according to a fixed time cycle, extract the equipment operation time records for each time period, organize the operation times by time grouping, perform cumulative calculation on multiple operation records within the same time period, generate the total equipment operation time data for that time period, and further check for outliers or missing records. For outliers, correct them using the median method of adjacent upper and lower data, and for missing records, complete them through interpolation. Finally, organize and obtain the time period grouping set of laboratory equipment operation records.

[0075] Based on the time period grouping set of laboratory equipment operation records, count the total equipment operation time for each time period group. At the same time, analyze the time series of the time period grouping, calculate the average operation time and standard deviation for each time period, and generate the statistical feature set of laboratory equipment operation time.

[0076] Based on the time period grouping set of laboratory equipment operation records, count the total equipment operation time for each time period, calculate the total operation time for each time period. At the same time, analyze the operation records within the time period according to the time series, calculate the average operation time for each time period, and statistically calculate the standard deviation to represent the fluctuation characteristics of the operation time. Conduct a correlation check on the statistical data of each time period to ensure the integrity and consistency of the data. Recheck and correct the data records that do not meet the conditions. Finally, extract the total operation time, average value, and standard deviation for each time period to generate the statistical feature set of laboratory equipment operation time.

[0077] Based on the total amount, average value, and standard deviation parameters in the statistical feature set of laboratory equipment operation time, compare the difference between the total operation time and the average value of each time period, using the formula:

[0078]

[0079] Calculate the operation time difference value Δ between time period i and time period j ij , generate the operation time interval difference information of laboratory equipment, where, T i is the total operation time of time period i, T j is the total operation time of time period j, and n is the number of equipment operation records between time periods;

[0080] T i : The total operation time of time period i, sourced from the statistical feature set of laboratory equipment operation records, collected as T i = 150 hours;

[0081] T j : The total operation time of time period j, sourced from the same set, collected as T j = 100 hours;

[0082] n: The number of device operation records between time periods, obtained from the grouped statistics of time period operation records, and the statistic is n = 10.

[0083] Calculate the absolute value of the running time difference between time period i and time period j: |T i -T j | = |150 - 100| = 50;

[0084] Calculate the square of the running time difference: |T i -T j | 2 = 50 2 = 2500;

[0085] Calculate the average value of the square of the running time difference:

[0086] Take the square root to obtain the final difference value:

[0087] The result shows that the running time difference between time period i and time period j is 15.81 hours. This value represents the average difference degree of the device running time between the two time periods. The larger the value, the more significant the difference in the device running conditions between time period i and time period j; it can be further used for characteristic marking between time periods to help identify the time intervals with significant changes in the running time distribution characteristics.

[0088] Please refer to Figure 4 , and the steps to obtain the analysis result of the experimental resource demand trend are as follows:

[0089] Combining the information on the differences in the running time intervals of laboratory equipment and the reagent inventory consumption data, collect the total reagent consumption in each interval according to the time series, analyze the periodic change values, correlate the reagent consumption with the running time difference in the corresponding time period, and generate a set of reagent consumption characteristics for each interval;

[0090] Call the interval difference information of the running time of laboratory equipment and the reagent inventory consumption data. According to the time grouping rules, divide the laboratory operation records into several interval groups in chronological order, extract the running time difference value and reagent inventory change records for each interval, and itemize and organize the corresponding timestamps and consumption amounts for each record. For the reagent inventory consumption data, accumulate multiple records within each time interval to form the total reagent consumption for each interval, and ensure that each timestamp in the record can accurately correspond to the relevant interval. For the periodic change value of the inventory, use the successive difference method to calculate the change value of the reagent inventory each time, that is, subtract the inventory quantity at the previous time point from the inventory quantity at the subsequent time point, and organize the change values at all time points into a periodic change data set. During the processing, mark and correct the missing or abnormal data, and use the values at the surrounding time points for linear interpolation repair to ensure integrity. After the above organization and calculation, generate an interval reagent consumption characteristic set including the interval running time difference value, total reagent consumption, and periodic change.

[0091] Based on the interval reagent consumption characteristic set, count the total reagent consumption and the corresponding time length for each interval, and use the formula:

[0092]

[0093] Calculate the reagent consumption rate R of interval i i , which reflects the usage of reagents per unit time, and generate a reagent consumption rate set for the time interval. Among them, C i is the total reagent consumption of interval i, and t i is the time length of interval i;

[0094] C i : The total reagent consumption of interval i, which is calculated by accumulating the reagent consumption records. For example, if interval i contains three consumption records of 12L, 15L, and 18L, then C i = 12 + 15 + 18 = 45L;

[0095] t i : The time length of interval i, which is directly called through the time grouping setting. For example, the interval time length is t i = 10h.

[0096] Substitute C i and T i into the formula:

[0097]

[0098] The results show that the reagent consumption rate in interval i is 4.5 L / h, which represents the average consumption level of the reagent in this interval. A higher value indicates that the reagent consumption is more intensive in this interval, corresponding to experimental activities with high demand; a lower value indicates that the reagent consumption is sparser, corresponding to relatively gentle experimental activities.

[0099] Based on the set of reagent consumption rates for time intervals, compare with the preset threshold and divide into high-demand intervals and low-demand intervals. Record the total reagent consumption, consumption rate, and operation time differences in high-demand intervals and low-demand intervals, compare and analyze the data characteristics within the groups, reveal the dynamic change trend of laboratory resource usage, and obtain the analysis results of experimental resource demand trends;

[0100] Call the set of reagent consumption rates for time intervals. First, sort the reagent consumption rates and operation time differences for each interval, classify according to the resource demand threshold, mark the intervals higher than the set threshold as high-demand intervals, and mark the intervals lower than the set threshold as low-demand intervals. Extract the characteristic data for high-demand intervals and low-demand intervals, including the total reagent consumption, reagent consumption rate, and operation time differences, and analyze and compare these data item by item. During the extraction and analysis process, conduct time series analysis on the reagent consumption trend in high-demand intervals to identify the peak periods and durations of reagent consumption, and at the same time summarize the operation characteristics of low-demand intervals and analyze the stability or volatility of their reagent consumption. Combine the above analysis results to generate a data table comparing the operation and reagent consumption characteristics of all classified intervals, clarify the overall demand change trend of laboratory resources based on these data, and organize and form the analysis results of experimental resource demand trends.

[0101] Please refer to Figure 5 , the steps to obtain the experimental resource allocation plan are as follows:

[0102] According to the analysis results of experimental resource demand trends, count the equipment usage parameters and reagent consumption parameters in high-demand intervals and low-demand intervals, and combine the records of the available quantity of current laboratory equipment and reagent inventory to determine the upper and lower limit ranges of interval resource allocation, and obtain the basic data of laboratory resource allocation upper and lower limits;

[0103] Based on the characteristic data of the high-demand interval and low-demand interval in the experimental resource demand trend analysis results, first, screen and sort out the equipment usage records and reagent consumption records in each interval. Summarize the equipment usage in each interval according to the actual operating time range, and at the same time, perform cumulative calculations on the reagent consumption data to generate the total equipment usage and total reagent consumption in each interval. Combining the current resource status of the laboratory, count the available quantity of equipment, and calculate the overall available capacity of laboratory resources, including the upper and lower limit values of equipment allocation. For the reagent inventory, count the current inventory of each reagent category, use the inventory safety threshold as the lower limit of allocation, and use the maximum available quantity calculated by combining the current inventory and demand as the upper limit of allocation, and sort out and generate the upper and lower limit values of reagent allocation. Match the upper and lower limit range data of equipment allocation and reagent allocation with the actual usage in each interval respectively. After ensuring the integrity of the interval data, finally form the upper and lower limit basic data of laboratory resource allocation for subsequent analysis and calculation.

[0104] Based on the upper and lower limit basic data of laboratory resource allocation, through the excess and deficiency parts of interval resource allocation, use the formula:

[0105]

[0106] Calculate the resource allocation deviation ratio E of interval i i , to obtain the upper and lower limit deviation data of resource allocation, where, A i is the actual resource usage of the interval, L i is the lower limit value of resource allocation, U i is the upper limit value of resource allocation;

[0107] A i : The actual resource usage of interval i, obtained by accumulating the equipment usage data and reagent consumption data recorded in the laboratory. For example, the equipment usage in a certain interval is A i = 120 times, and the reagent consumption is A i = 300 liters;

[0108] L i : The lower limit value of resource allocation for interval i, set by combining experimental resource demand trend analysis with resource safety threshold. For example, the lower limit value is L i = 100;

[0109] U i : The upper limit value of resource allocation for interval i, set according to laboratory resource capacity and maximum usage demand. For example, the upper limit value is U i = 200.

[0110] Substitute specific values:

[0111]

[0112] The result shows that the resource allocation deviation ratio in interval i is 2, which reflects that the actual resource usage within the interval is greater than the upper limit of the allocation range and is in an over-allocation state. E i A value close to 0 indicates that the resource allocation is close to the lower limit and additional supply is needed; E i A value close to 1 indicates that the resource allocation is close to the upper limit and resource recovery can be considered; it provides a quantitative basis for subsequent resource allocation strategies for decision-making on resource recovery or addition.

[0113] Based on the upper and lower limit deviation data of resource allocation, mark the equipment and reagents in the excess part as recovery reserves, mark the insufficient part as additional supply, plan the redistribution of the recovered reserve resources, and determine the source allocation of additional supply for the insufficient part to generate an experimental resource allocation plan;

[0114] According to the calculation results of the upper and lower limit deviations of laboratory resource allocation, first divide the resource allocation situation of each interval into an excess interval and a shortage interval based on the calculated deviation ratio. For the excess interval, extract the equipment and reagent resource data, and mark the redundant equipment and reagents as resources to be recycled according to the statistics of equipment types, reagent types and their remaining quantities. Rearrange these resources to be recycled by interval, and generate a resource recovery reserve plan based on equipment types, reagent types and resource usage priorities. For the shortage interval, extract the shortage amounts of its equipment and reagents, calculate the actual demand of each interval according to the equipment usage situation and reagent consumption trend, combine the available resources in the recovery reserve plan, perform resource allocation matching, give priority to allocating the available resources to the high-demand intervals, and formulate an external supplementary supply plan for the part where the demand exceeds the available amount, calculate the additional resource demand and clarify the procurement types and quantities, and finally organize and form an experimental resource allocation plan.

[0115] Please refer to Figure 6 , and the steps for collecting and classifying the records of the non-operating time periods of laboratory equipment are as follows:

[0116] Call the equipment allocation time parameters in the experimental resource allocation plan, collect the start time and end time of the allocation of each piece of equipment, match them with the running time in the actual running records of laboratory equipment, calculate the running duration of the equipment by time period, and generate an equipment running time matching record;

[0117] Call the device allocation time parameters in the experimental resource allocation plan, extract the start time and end time in the allocation records item by item, match them with the actual running time of the devices with the same number in the actual running records of laboratory devices, and verify the allocation time and running time respectively to ensure that the time axes of the two are aligned. For running records across time periods, split them according to the allocation time range, and accurately allocate the running time to the corresponding allocation time periods to prevent overlap or omission of time period data. By comparing each running time period with the allocation time period one by one, record the allocation time and actual running time within each time period respectively, providing clear basic data for subsequent time difference calculation. After finishing the collation, save the allocation time and actual running time records of each device as independent entries to ensure the integrity and accuracy of the device time data.

[0118] Based on the device running time matching records, calculate the time difference between the actual running time of each device and the allocation time in the experimental resource allocation plan item by item, count the difference intervals during the running time, mark the part with a positive time difference as the device non-running time period, sort out the non-running time periods and group them by device to generate a set of non-running time periods of laboratory devices;

[0119] Based on the sorted allocation time and actual running time records, calculate the running time difference of each device in each allocation time period according to the device number. Subtract the running time from the allocation time to obtain the duration of the non-running time, and extract the specific start and end time points of the non-running time period. For records with a positive time difference, mark them as the device non-running time period. At the same time, check whether the time difference is continuous, and merge adjacent non-running time periods into a single interval to ensure that there are no duplicates in the non-running time periods in the records. After sorting out each non-running time period of each device item by item, classify them according to the device number, correspond the non-running time period with the allocation number of the device one by one, and sort the time sequence relationship of the non-running time periods to generate a complete set of non-running time periods for subsequent classification and analysis.

[0120] Call the set of non-running time periods of laboratory devices, combine the device number and time period parameters, classify and count the non-running time periods according to the device category and running frequency to generate a classification record of non-running time periods of laboratory devices;

[0121] Call the set of unoperated time periods generated by the device, classify the unoperated time period data by device category and usage frequency. First, count the total amount of unoperated time periods, classify the devices with high-frequency unoperation as the key objects of concern. At the same time, analyze the distribution of unoperated time periods to determine whether the unoperated time is concentrated in certain specific time intervals. For devices with less unoperated time, count their corresponding allocation records and actual operation records to identify whether there are abnormalities in their resource utilization efficiency. Combine the usage frequency to classify the devices into two categories: high-usage devices and low-usage devices. Organize the classification results of unoperated time periods to form a classification statistical record of unoperated time periods, and mark the high-frequency unoperated devices and their time periods as optimization objects, providing a clear basis for subsequent optimal allocation of device resources.

[0122] Please refer to Figure 7 , the steps for obtaining resource utilization adjustment information are as follows:

[0123] According to the classification record of unoperated time periods of laboratory equipment, collect the recorded time range related to reagent usage, match the reagent consumption batches and actual usage data within the corresponding time periods one by one, sort the reagent usage batches and the operation time periods of the equipment in chronological order, and establish a preliminary correspondence between the reagent consumption batches and the equipment time periods.

[0124] Call the classification record of unoperated time periods of laboratory equipment, retrieve the operation time periods of equipment related to reagent consumption item by item, and match the equipment numbers within each time period with the batch numbers in the reagent usage records one by one. When specifically executing, first extract the allocation time range and actual operation time periods of each equipment number, and compare with the time range marked in the reagent consumption record to determine the reagent usage batches and actual consumption amounts that completely overlap with the equipment operation time. For partially overlapping time periods, trim them according to the start and end times to ensure the integrity of the corresponding relationship of reagent consumption amounts in the record. After completing the preliminary matching, sort the records of reagent batches and usage amounts in chronological order to form an association table between time periods and reagent batches. During the sorting process, for the situations of overlapping time periods or multiple devices corresponding to the same batch, record the specific conflict time points and equipment numbers respectively to provide clear basic data support for subsequent analysis.

[0125] Based on the preliminary correspondence between the reagent consumption batches and the equipment time periods, calculate the deviation between the actual reagent usage amount and the allocated amount in the experimental resource allocation plan, mark the batches with usage amounts exceeding the allocated amount as over-consumed batches, mark the batches with usage amounts lower than the allocated amount as unconsumed batches, and organize them to form a reagent usage deviation record.

[0126] Based on the matching records of reagent consumption batches and equipment operation time periods, sequentially extract the actual usage of each batch of reagents and the corresponding allocated amounts in the experimental resource allocation plan, and calculate and compare the data of the two. During the specific operation, extract the actual consumption of each batch from the reagent usage records, and at the same time call the allocated amount data of the corresponding batch in the allocation plan, calculate the difference, and mark each difference corresponding to its affiliated equipment number and time period. During the marking process, classify the records with consumption higher than the allocated amount as over-consumption batches, and record the specific values and time distributions of their over-consumed parts; classify the records with consumption lower than the allocated amount as unconsumed batches, and record the specific quantities and allocated time ranges of the unused parts. At the same time, for continuously occurring over-consumption batches or unconsumed batches, classify them by equipment number into concentrated distribution time periods for subsequent in-depth analysis of bottleneck links in resource usage.

[0127] According to the reagent usage deviation records, compare the concentrated distribution time periods of over-consumption batches with the operating efficiency of laboratory equipment, identify the time intervals with unreasonable resource allocation, re-plan the usage priorities of unconsumed batches of reagents by time period, and combine the equipment operation frequency and time period characteristics to generate resource utilization adjustment information;

[0128] Call the classified records of reagent usage differences, and compare and analyze the time period characteristics of over-consumption batches and unconsumed batches with the equipment operation frequency item by item, and statistically analyze the comprehensive characteristics of equipment usage efficiency and reagent usage efficiency within each time period. During the specific implementation, extract the time period data concentratedly distributed in over-consumption batches, analyze the matching situation between the total equipment usage time and the actual reagent usage amount within this time period, and analyze the time synchronization between high-frequency equipment operation and reagent over-usage. For unconsumed batches, extract the corresponding equipment non-operation time periods and reagent allocation records, and analyze whether the unconsumed parts are concentrated in time intervals with low equipment usage frequency. After sorting, summarize the main reasons for over-consumption batches as insufficient resource allocation in high-demand intervals, and summarize the main reasons for unconsumed batches as redundant resource allocation in low-usage equipment or low-demand time periods, and finally generate resource utilization adjustment information to provide data basis and classification results for subsequent optimization suggestions.

[0129] Please refer to Figure 8 , the steps to obtain the optimized allocation results of experimental resources are as follows:

[0130] Based on the resource utilization adjustment information, re-adjust the startup sequence of laboratory equipment, compare the equipment operation completion time with the experimental demand time periods one by one, classify the equipment status beyond the operation completion time, and transfer it to the standby state to generate equipment status adjustment results;

[0131] Based on the resource utilization adjustment information, analyze the startup sequence of laboratory equipment one by one. First, extract the startup period and allocation time of each device according to the historical operation records of the laboratory equipment, and sort out the equipment operation time periods. Compare the actual operation periods of the equipment with the allocated periods item by item to identify the differences between the startup periods and the planned allocation periods. Then, for the identified differences, gradually adjust the startup sequence of the laboratory equipment according to the preset time priority and operation urgency rules. During the adjustment process, for equipment with overlapping operation periods, give priority to starting the equipment with a shorter operation time or a higher priority experimental task. After completing the adjustment of all equipment startup sequences, generate a re-ordered result of the laboratory equipment startup sequence.

[0132] According to the equipment status adjustment result, adjust the replenishment frequency of the reagent in turn corresponding to the time periods of the equipment operation status and reagent allocation requirements, adjust the reagent demand in each time period to the rated range, and generate the reagent allocation adjustment result by updating the matching relationship record of the equipment and the reagent;

[0133] According to the adjusted startup sequence of the laboratory equipment, analyze and process the reagent demand and replenishment frequency one by one. First, extract the reagent demand corresponding to each device, and calculate the actual reagent replenishment amount of each device item by item by associating the equipment operation period with the reagent replenishment record. Then, compare the reagent replenishment record of each device with the reagent allocation amount to identify the situations where the reagent replenishment frequency does not match the demand, and adjust the reagent demand of the devices with too high or too low frequency one by one to ensure that the reagent replenishment time is consistent with the equipment operation period. For the insufficient parts during the reagent replenishment frequency adjustment process, add additional replenishment batches, and finally form an adjusted reagent replenishment frequency plan.

[0134] Combining the reagent allocation adjustment result and the equipment status adjustment result, verify the dynamic matching result of each equipment and reagent combination, re-adjust and allocate the equipment and reagent combinations that do not meet the optimization standard, and perform global dynamic optimization configuration of the laboratory equipment and reagents to obtain the experimental resource optimization configuration result;

[0135] Combining the adjusted startup sequence of the laboratory equipment and the reagent replenishment frequency plan, first extract the operation completion time of the laboratory equipment, check the operation status of the equipment, screen out the equipment whose operation time exceeds the planned period, transfer the over-running equipment to the standby state one by one, and at the same time count the actual replenishment amount and usage amount of the reagent demand, extract the reagent batches with excessive replenishment, classify the excess reagents from the current batch to subsequent experiments, calculate the difference for the reagent batches with insufficient replenishment and add additional replenishment, complete the global adjustment of the reagent, and finally generate an optimized experimental resource allocation plan after completing the re-allocation of the equipment and the reagent.

[0136] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A laboratory resource optimization allocation system based on cloud computing and big data analysis, characterized in that: The system comprises: The resource status monitoring module collects equipment operation time and usage frequency based on laboratory equipment operation status information and reagent inventory information, classifies and summarizes laboratory equipment occupancy rate and inventory consumption, performs statistical analysis on reagent consumption by batch, and generates resource usage change information; The resource demand analysis module groups the laboratory equipment operation records into time periods based on the resource usage change information, calculates the interval differences of the laboratory equipment operation time, and screens the high demand interval and the low demand interval through threshold comparison according to the calculation results combined with the periodic changes of the reagent inventory consumption, and generates the experimental resource demand trend analysis results; The resource dynamic adjustment module compares the upper and lower limits of the current laboratory equipment allocation and reagent allocation based on the experimental resource demand trend analysis results, matches them with the high demand interval and the low demand interval, and generates an experimental resource allocation plan; The resource efficiency analysis module monitors the actual operation time and usage frequency of laboratory equipment, calculates the time difference between the operation time of laboratory equipment and the allocated time in the experimental resource allocation plan, collects the time period when the laboratory equipment is not in operation and classifies and records it, and synchronously counts the reagent consumption batches and usage in combination with the record content, performs difference calculation on the actual reagent usage and the allocated amount in the experimental resource allocation plan, summarizes the excess consumption batches and unconsumed batches, and comprehensively generates resource utilization adjustment information; The resource optimization execution module readjusts the startup sequence of laboratory equipment, adjusts the reagent replenishment frequency, performs global dynamic optimization allocation of laboratory equipment and reagents based on the resource utilization adjustment information, and generates an experimental resource optimization configuration result.

2. The laboratory resource optimization allocation system based on cloud computing and big data analysis according to claim 1 is characterized in that: The steps for obtaining the resource usage change information are as follows: Collect laboratory equipment operation status information and reagent inventory information, record equipment model parameters, equipment operation time parameters and reagent inventory parameters, count the cumulative operation time of equipment and inventory consumption records, and generate an initial set of equipment operation time and inventory changes; Based on the initial set of equipment operation time and inventory changes, the total operation time and total reagent consumption of each equipment are calculated, and classified and marked according to the length of operation time and reagent consumption level, and the operation characteristics and inventory change trends of each category of equipment are analyzed to generate equipment operation grouping and inventory change set; Based on the equipment operation grouping and classification tags and inventory consumption trend data in the inventory change set, the changes in reagent consumption are analyzed by batch, the batch consumption fluctuations are analyzed, and resource usage change information is generated.

3. The laboratory resource optimization allocation system based on cloud computing and big data analysis according to claim 2 is characterized in that: The steps for calculating and obtaining the interval difference are as follows: Calling the laboratory equipment operation record data in the resource usage change information, dividing the equipment operation record into multiple time period groups according to a fixed time period, collecting and preliminarily summarizing the equipment operation time parameters in each time period group, and generating a laboratory equipment operation record time period grouping set; Based on the laboratory equipment operation record period grouping set, the total equipment operation time of each period group is counted, and at the same time, the time series of the period grouping is analyzed to calculate the average and standard deviation of the operation time of each period, and generate a laboratory equipment operation time statistical feature set; Based on the total amount, average value and standard deviation parameters in the laboratory equipment running time statistical feature set, the difference between the total amount of running time in each period and the average value is compared, and the formula is used: Calculate the running time difference Δ between period i and period j ij , generate the laboratory equipment operation time interval difference information, where T i is the total running time of period i, T j is the total running time of period j, and n is the number of records of equipment operation during the period.

4. The laboratory resource optimization allocation system based on cloud computing and big data analysis according to claim 3 is characterized in that: The steps for obtaining the experimental resource demand trend analysis results are as follows: Combined with the laboratory equipment operation time interval difference information and reagent inventory consumption data, the total reagent consumption of each interval is collected according to the time series, the periodic change value is analyzed, the reagent consumption is associated with the operation time difference of the corresponding time period, and the interval reagent consumption characteristic set is generated; Based on the interval reagent consumption characteristic set, the total reagent consumption and the corresponding time length of each interval are counted, using the formula: Calculate the reagent consumption rate R in interval i i , reflects the usage of reagents per unit time, and generates a set of reagent consumption rates in the time interval, where C i is the total reagent consumption in interval i, t i is the time length of interval i; According to the reagent consumption rate set in the time interval, it is compared with the preset threshold and divided into high-demand intervals and low-demand intervals. The total reagent consumption, consumption rate and running time difference between the high-demand intervals and the low-demand intervals are recorded, and the data characteristics within the grouping are compared and analyzed to reveal the dynamic change trend of laboratory resource utilization and obtain the experimental resource demand trend analysis results.

5. The laboratory resource optimization allocation system based on cloud computing and big data analysis according to claim 4 is characterized in that: The steps for obtaining the experimental resource allocation plan are: According to the experimental resource demand trend analysis results, the equipment usage parameters and reagent consumption parameters in the high demand interval and the low demand interval are counted, and the upper and lower limits of interval resource allocation are determined in combination with the current available quantity of laboratory equipment and reagent inventory records, so as to obtain the basic data of the upper and lower limits of laboratory resource allocation; Based on the upper and lower limit basic data of laboratory resource allocation, the excess and insufficient parts of interval resource allocation are calculated using the formula: Calculate the resource allocation deviation ratio E of interval i i , get the upper and lower limit deviation data of resource allocation, where A i is the actual resource usage of the interval, L i Assign a lower limit to the resource, U i Assigning upper limits to resources; According to the upper and lower limit deviation data of resource allocation, the excess equipment and reagents are marked as recovery reserves, and the insufficient part is marked as additional supply. The redistribution plan of the recovery reserve resources is carried out, and the source allocation of additional supply is determined for the insufficient part to generate an experimental resource allocation plan.

6. The laboratory resource optimization allocation system based on cloud computing and big data analysis according to claim 5 is characterized in that: The steps for collecting and classifying the records of the time period when the laboratory equipment is not in operation are as follows: Call the equipment allocation time parameters in the experimental resource allocation plan, collect the allocation start time and end time of each device, match them with the operating time in the actual operation record of the laboratory equipment, calculate the operating time of the equipment according to the time period, and generate the equipment operation time matching record; Based on the equipment running time matching record, the time difference between the actual running time of each equipment and the allocated time in the experimental resource allocation plan is calculated item by item, the difference interval in the running time is counted, and the part with a positive time difference is marked as the equipment non-operating time period, and the non-operating time period is sorted and grouped by equipment to generate a set of laboratory equipment non-operating time periods; The laboratory equipment non-operating time period set is called, and the non-operating time periods are classified and counted according to the equipment category and the operating frequency in combination with the equipment number and the time period parameter, so as to generate a classification record of the laboratory equipment non-operating time period.

7. The laboratory resource optimization allocation system based on cloud computing and big data analysis according to claim 6 is characterized in that: The steps for obtaining the resource utilization adjustment information are as follows: According to the classification records of the non-operating time period of the laboratory equipment, the recording time range related to the use of reagents is collected, and the reagent consumption batches and actual usage data in the corresponding time period are matched one by one, and the reagent use batches and the operating time period of the equipment are sorted and classified in chronological order to establish a preliminary correspondence between the reagent consumption batches and the equipment time period; Based on the preliminary correspondence between the reagent consumption batches and the equipment time periods, the deviation between the actual reagent usage and the allocated amount in the experimental resource allocation plan is calculated, batches with usage exceeding the allocated amount are marked as excess consumption batches, and batches with usage below the allocated amount are marked as unconsumed batches, and the reagent usage deviation records are compiled; According to the reagent usage deviation record, the time period when the excess batches are concentrated is compared with the operating efficiency of the laboratory equipment to identify the time interval when resource allocation is unreasonable, and the usage priority of unconsumed batches of reagents is re-planned according to the time period. Combined with the equipment operation frequency and time period characteristics, resource utilization adjustment information is generated.

8. The laboratory resource optimization allocation system based on cloud computing and big data analysis according to claim 7 is characterized in that: The steps for obtaining the experimental resource optimization configuration result are: Based on the resource utilization adjustment information, the startup sequence of the laboratory equipment is readjusted, the operation completion time of the equipment is compared with the experimental demand period one by one, the equipment status that exceeds the operation completion time is classified, and the equipment status is transferred to the standby state, and the equipment status adjustment result is generated; According to the equipment status adjustment result, corresponding to the equipment operation status and the time period of the reagent allocation demand, the reagent replenishment frequency is adjusted in turn, the reagent demand in each time period is adjusted to within the rated range, and the reagent allocation adjustment result is generated by updating the matching relationship record between the equipment and the reagent; Combining the reagent allocation adjustment result with the equipment status adjustment result, the dynamic matching result of each equipment and reagent combination is verified, and the equipment and reagent combinations that do not meet the optimization standard are readjusted and allocated to perform global dynamic optimization configuration of laboratory equipment and reagents to obtain the experimental resource optimization configuration result.

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