A system and method for intelligent management based on laboratory processes

Abstract

The present invention discloses a system and method for intelligent management based on laboratory processes, relating to the technical field of intelligent management. The system of the present invention includes: an experimental task classification and sorting module, a historical data analysis and task duration prediction module, an overlapping period analysis and intelligent scheduling module, and a real-time monitoring and task progress adjustment module; the experimental task classification and sorting module is responsible for obtaining an initial experimental task list and performing task classification and sorting; the historical data analysis and task duration prediction module uses historical data to predict the total duration of experimental tasks and provides accurate task duration predictions; the overlapping period analysis and intelligent scheduling module processes the time overlap between tasks and intelligently adjusts the task execution order and equipment usage time; the real-time monitoring and task progress adjustment module tracks the task execution situation in real time, analyzes the deviation between the actual and predicted values, and makes necessary progress adjustments.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management, and particularly to a system and method for intelligent management based on laboratory processes. Background Art

[0002] In modern scientific research and industrial experiments, laboratories are the core places for carrying out various important activities such as scientific research, technical verification, product development, etc. Laboratory management involves the scheduling and management of resources such as laboratory equipment, experimental processes, and experimental data. Traditional laboratory management models usually rely on manual arrangements. Especially in the resource scheduling of equipment, there are often many deficiencies. First of all, the use and scheduling of laboratory equipment usually rely on manual management; due to the large variety of experimental tasks and significant differences in their respective requirements, it is difficult to achieve real-time and precise adjustment of the use time periods of equipment and experimental arrangements; in addition, when multiple experimental tasks need to use the same piece of equipment, the use time periods of the equipment may overlap, resulting in delays in the experimental progress.

[0003] In this context, many laboratories and research institutions have gradually started to adopt equipment reservation systems for laboratory equipment management, usually using a fixed reservation method based on time periods. Experimental personnel can view the available time of the equipment and make reservations. However, existing equipment reservation systems often lack a flexible scheduling mechanism and cannot dynamically adjust the available time periods of the equipment according to the actual execution time of the experimental tasks. When a certain experimental task cannot be completed as planned due to overtime or other reasons, the system cannot automatically adjust the reservation time of subsequent tasks, resulting in a shortening of the available time for subsequent reserved experimental tasks, thus affecting the overall progress of the laboratory. Summary of the Invention

[0004] The purpose of the present invention is to provide a system and method for intelligent management based on laboratory processes to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for intelligent management based on laboratory processes includes the following steps:

[0007] Step S100. Obtain an initial experimental task list from the laboratory management platform. By analyzing the initial experimental task list, classify the experimental tasks in the initial experimental task list and divide them into several experimental task types; for each experimental task type, generate an initial experimental task sorting list in combination with the initial experimental task list;

[0008] Step S200. For each type of experiment, based on the corresponding initial experimental task sorted list, obtain the corresponding historical experimental data, associate the historical experimental data with the experimental tasks in the initial experimental task sorted list; analyze the association results to obtain the predicted total duration of the experimental tasks in the initial experimental task sorted list.

[0009] Step S300. Based on the predicted total duration of the experimental tasks in the initial experimental task sorted list, identify the target experimental task pairs; analyze the target experimental task pairs to obtain the overlapping time periods of the target experimental task pairs, and intelligently allocate the usage order of the same experimental equipment for the target experimental task pairs during the overlapping time periods, and conduct corresponding analysis and adjustment on the reservation time periods of subsequent experimental tasks.

[0010] Step S400. Conduct experimental tasks in sequence according to the order of the initial experimental task sorted list. For each completed experimental task, obtain the corresponding actual total duration, and compare the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two; and based on the deviation between the actual total duration and the predicted total duration of the currently completed experimental tasks, conduct corresponding analysis and adjustment on the uncompleted experimental tasks in the current initial experimental task sorted list.

[0011] Furthermore, step S100 includes:

[0012] S101. Obtain the initial experimental task list from the laboratory management platform, and the initial experimental task list records the specific description information of the experimental tasks; extract the experimental task numbers from the initial experimental task list, and each experimental task number is unique; for each experimental task, extract the corresponding required experimental equipment name from the initial experimental task list, and convert the required experimental equipment name into a unified vector format to form the required experimental equipment vector X_ID, and X_ID = [x1, x2,..., xn], where X_ID represents the required experimental equipment vector of the experimental task ID, x1 represents the first-dimensional feature value of the required experimental equipment vector, x2 represents the second-dimensional feature value of the required experimental equipment vector, and so on, xn represents the nth-dimensional feature value of the required experimental equipment vector, where n represents the types of experimental equipment existing in the laboratory.

[0013] S102. Aggregate the required experimental equipment vectors of all experimental tasks in the initial experimental task list, and calculate the similarity between the required experimental equipment vector of each experimental task and other required experimental equipment vectors in turn, and the corresponding calculation formula is:

[0014] S(X_a, X_b) = |X_a ∩ X_b| / n;

[0015] Among them, S(X_a, X_b) represents the similarity between the required experimental equipment vectors corresponding to experimental task a and experimental task b, and |X_a ∩ X_b| represents the number of equal eigenvalues of the same-dimensional features in the required experimental equipment vectors corresponding to experimental task a and experimental task b; those with a similarity not equal to 0 are grouped into one category, and all experimental tasks in the initial experimental task list are traversed, so as to divide the experimental tasks in the initial experimental task list into several experimental task types.

[0016] S103. For each experimental task type, obtain the specific description information of the corresponding experimental task from the initial experimental task list, obtain the reservation period T0 of the corresponding experimental task according to the specific description information of the experimental task, and generate the initial experimental task sorting list of the corresponding experimental task type according to the chronological order of the start timestamps of the reservation period T0 of the experimental task.

[0017] Further, step S200 includes:

[0018] S201. For each experimental type, based on the corresponding initial experimental task sorting list, obtain the historical experimental data of each experimental task in the initial experimental task sorting list from the laboratory management platform; for each experimental task, associate the experimental task number with the corresponding historical experimental data, so as to obtain the required experimental equipment sequence F, and F = {f1, f2,.., fm}, where f1 represents the name of the first experimental equipment used in the experimental task, f2 represents the name of the second experimental equipment used in the experimental task, and so on, fm represents the name of the mth experimental equipment used in the experimental task, and m represents the number of experimental equipment used in the experimental task.

[0019] S202. According to the required experimental equipment sequence F and combined with the historical experimental data of the corresponding experimental task, calculate the average usage duration μt and standard deviation σt of each experimental equipment in the required experimental equipment sequence F in the historical experimental data, so as to obtain the predicted usage duration tf of the corresponding experimental equipment, and tf = μt + α·σt, where α represents the adjustment coefficient, which can be adjusted according to the actual situation of the task (for example, based on the usage frequency of the equipment or the complexity of the experiment); summarize the usage durations tf of all experimental equipment in the required experimental equipment sequence F, so as to obtain the predicted total duration sum_tf of the corresponding experimental task.

[0020] Further, step S300 includes:

[0021] S301. Based on the initial experimental task sorted list, extract the predicted total duration of all experimental tasks in the initial experimental task sorted list, extract the start timestamp of the reservation period for each experimental task in the initial experimental task sorted list, and denote it as t0; calculate the predicted time period T1 for each experimental task in the initial experimental task sorted list, and T1 = [t0, t0 + sum_tf]; according to the order of the initial experimental task sorted list, and in combination with the predicted total duration of all experimental tasks, with reference to the calculation method of the predicted time period T1 of the first experimental task, calculate the predicted time periods Ti for other experimental tasks in the initial experimental task sorted list in turn, where i represents the order number of other experimental tasks;

[0022] S302. Summarize the predicted time periods of all experimental tasks in the initial experimental task sorted list, and sort the predicted time periods of all experimental tasks according to the order of the initial experimental task sorted list; judge in turn whether there is an overlapping relationship between the predicted time periods of two adjacent experimental tasks in the initial experimental task sorted list, extract the experimental task numbers of all overlapping relationships, denoted as the target experimental task pairs, and expressed as (IDj, IDj + 1), where j represents the order number of the experimental task in the initial experimental task sorted list; summarize all target experimental task pairs to form the target experimental task set E, and E = {e1, e2,..., ev}, where e1 represents the first target experimental task pair, e2 represents the second target experimental task pair, and so on, ev represents the vth target experimental task pair;

[0023] S303. For each target experimental task pair in the target experimental task set E, calculate the overlapping period Tc of the predicted time periods of the target experimental task pair; based on the experimental task numbers of the target experimental task pair, obtain the corresponding required experimental equipment sequences and the predicted usage durations of each experimental equipment in the required experimental equipment sequences respectively; according to the overlapping period Tc, find the required experimental equipment of the target experimental task pair during the overlapping period Tc; if the required experimental equipment corresponding to two adjacent experimental tasks in the target experimental task pair is different, do not perform any processing; if the required experimental equipment corresponding to two adjacent experimental tasks in the target experimental task pair is the same, then compare the timestamps of using this experimental equipment for two adjacent experimental tasks in the target experimental task pair, determine the order of using the experimental equipment according to the sequence relationship of the timestamps, and calculate the predicted timestamp t1 for the completion of the target experimental task pair according to the order of using the experimental equipment. If the predicted timestamp t1 is greater than the initial timestamp t0 of the reservation period of the subsequent experimental task in the initial experimental task sorted list, then sequentially extend the reservation periods of the subsequent experimental tasks by the time span of t1 - t0, and output the adjusted reservation periods to the relevant personnel for confirmation and adjustment by the relevant personnel; otherwise, do not perform any adjustment.

[0024] Further, step S400 includes:

[0025] Perform the experimental tasks in sequence according to the order of the initial experimental task sorted list. For each completed experimental task, obtain the corresponding actual total duration ts, compare the actual total duration of the corresponding experimental task with the predicted total duration, so as to obtain the deviation △t = ts - sum_tf between the two; if △t is greater than 0, obtain the sequence W of uncompleted experimental tasks in the current initial experimental task sorted list, and for each experimental task in the sequence W of uncompleted experimental tasks, perform analysis and adjustment with reference to the analysis process in step S300; if △t is less than or equal to 0, do not perform any processing.

[0026] A system for intelligent management based on laboratory processes, including: an experimental task classification and sorting module, a historical data analysis and task duration prediction module, an overlapping period analysis and intelligent scheduling module, and a real-time monitoring and task progress adjustment module;

[0027] The experimental task classification and sorting module obtains the initial experimental task list from the laboratory management platform, classifies the experimental tasks in the initial experimental task list by analyzing the initial experimental task list, and divides them into several experimental task types; for each experimental task type, combine the initial experimental task list to generate an initial experimental task sorted list;

[0028] The historical data analysis and task duration prediction module obtains the corresponding historical experimental data for each experimental type based on the corresponding initial experimental task sorted list, and associates the historical experimental data with the experimental tasks in the initial experimental task sorted list; analyze the association results to obtain the predicted total duration of the experimental tasks in the initial experimental task sorted list;

[0029] The overlapping period analysis and intelligent scheduling module identifies the target experimental task pairs based on the predicted total duration of the experimental tasks in the initial experimental task sorted list; analyze the target experimental task pairs to obtain the overlapping periods of the target experimental task pairs, and intelligently allocate the usage order of the same experimental equipment for the target experimental task pairs during the overlapping periods, and perform corresponding analysis and adjustment on the reservation periods of subsequent experimental tasks;

[0030] The real-time monitoring and task progress adjustment module performs the experimental tasks in sequence according to the order of the initial experimental task sorted list. For each completed experimental task, obtain the corresponding actual total duration, and compare the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two; and perform corresponding analysis and adjustment on the uncompleted experimental tasks in the current initial experimental task sorted list according to the deviation between the actual total duration and the predicted total duration of the currently completed experimental tasks.

[0031] Further, the experimental task classification and sorting module includes a data acquisition unit, a task classification unit, and a sorted list generation;

[0032] The data acquisition unit obtains an initial experimental task list from the laboratory management platform, and the initial experimental task list records the specific description information of the experimental tasks; the task classification unit classifies the experimental tasks by analyzing the experimental equipment information required for the experimental tasks and using a similarity calculation method; for each type of experimental task, the sorted list generation sorts the experimental tasks corresponding to each type of experimental task based on the reservation period of the experimental tasks, so as to generate an initial experimental task sorted list.

[0033] Further, the historical data analysis and task duration prediction module includes a historical data correlation analysis unit and a task duration prediction unit;

[0034] The historical data correlation analysis unit obtains historical experimental data from the laboratory management platform and correlates it with the experimental tasks in the initial experimental task sorted list; by analyzing the historical data, the required experimental equipment sequence for each experimental task is obtained; the task duration prediction unit calculates the predicted usage duration of the corresponding experimental equipment according to the required experimental equipment sequence and in combination with the historical experimental data of the corresponding experimental task; the usage durations of all the experimental equipment in the required experimental equipment sequence are summarized to obtain the predicted total duration of the corresponding experimental task.

[0035] Further, the overlapping period analysis and intelligent scheduling module includes an overlapping period analysis unit and an intelligent scheduling unit;

[0036] The overlapping period analysis unit analyzes whether there is a time overlap between the experimental tasks according to the predicted duration and reservation period of the experimental tasks, identifies and extracts the target experimental task pairs; for the target experimental task pairs, the intelligent scheduling unit schedules based on the usage order of the same equipment and predicts the task completion time of the overlapping period; if there is a delay, the intelligent scheduling unit intelligently adjusts the reservation period of the subsequent tasks and outputs adjustment information for relevant personnel to confirm and modify.

[0037] The progress monitoring unit performs the experimental tasks in sequence according to the order of the initial experimental task sorted list. For each completed experimental task, the corresponding actual total duration is obtained; the task adjustment unit compares the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two; and according to the deviation between the actual total duration and the predicted total duration of the currently completed experimental tasks, corresponding analysis and adjustment are performed on the uncompleted experimental tasks in the current initial experimental task sorted list.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: Through intelligent analysis based on historical experimental data and predicted total duration, the present invention realizes dynamic adjustment of experimental tasks. Different from the traditional fixed-time reservation system, the system can automatically adjust the usage period of equipment according to the actual progress of experimental tasks, thus avoiding problems such as shortened time for subsequent tasks or equipment conflicts caused by delays in experimental tasks, and ensuring more accurate overall progress of the laboratory. The intelligent scheduling mechanism proposed by the present invention can automatically adjust the usage arrangement of equipment by analyzing the overlapping periods of experimental tasks and the usage order of experimental equipment. Especially when multiple experimental tasks need to share the same equipment, it can intelligently allocate the usage order according to the actual situation, reduce manual intervention, and improve the utilization efficiency of equipment. Traditional laboratory equipment scheduling relies on manual management, which is prone to omissions and low efficiency. The present invention reduces the workload of manual scheduling and processing through an intelligent management process, improves management efficiency, and effectively reduces the risk of equipment conflicts and experimental task delays caused by human errors. Through the analysis and prediction of historical experimental data, the present invention can accurately calculate the total duration of each experimental task and make corresponding adjustments to subsequent tasks based on the actual completion situation, minimizing the adverse effects caused by the uncertainty of experimental time, thereby improving the accuracy and completion rate of experimental tasks. Through the intelligent scheduling and dynamic adjustment of experimental equipment, the present invention makes full use of the idle time of each equipment, reduces equipment idle and waiting time, and improves the utilization rate of equipment. At the same time, it reasonably adjusts the reservation period of experimental tasks, avoiding resource waste caused by task conflicts and improper scheduling. By combining the deviation between the prediction and the actual completion time, the present invention can timely detect and correct the deviation in the experimental progress, ensuring the timely completion of experimental tasks. The system can automatically adjust the arrangement of subsequent tasks according to the actual progress during the experiment, making the laboratory progress smoother and more controllable. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0040] Figure 1 is a schematic diagram of the modules of a system for intelligent management based on laboratory processes according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Please refer to Figure 1 , the present invention provides a technical solution:

[0043] A system for intelligent management based on laboratory processes, comprising: an experimental task classification and sorting module, a historical data analysis and task duration prediction module, an overlapping period analysis and intelligent scheduling module, and a real-time monitoring and task progress adjustment module;

[0044] The experimental task classification and sorting module obtains an initial experimental task list from the laboratory management platform. By analyzing the initial experimental task list, it classifies the experimental tasks in the initial experimental task list and divides them into several experimental task types; for each experimental task type, it generates an initial experimental task sorting list in combination with the initial experimental task list;

[0045] The historical data analysis and task duration prediction module, for each experimental type, based on the corresponding initial experimental task sorting list, obtains the corresponding historical experimental data, and associates the historical experimental data with the experimental tasks in the initial experimental task sorting list; analyzes the association result to obtain the predicted total duration of the experimental tasks in the initial experimental task sorting list;

[0046] The overlapping period analysis and intelligent scheduling module, based on the predicted total duration of the experimental tasks in the initial experimental task sorting list, identifies target experimental task pairs; analyzes the target experimental task pairs to obtain the overlapping periods of the target experimental task pairs, and intelligently allocates the usage order of the same experimental equipment for the target experimental task pairs during the overlapping periods, and conducts corresponding analysis and adjustment on the reservation periods of subsequent experimental tasks;

[0047] The real-time monitoring and task progress adjustment module conducts experimental tasks in sequence according to the order of the initial experimental task sorting list. For each completed experimental task, it obtains the corresponding actual total duration, and compares the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two; and based on the deviation between the actual total duration and the predicted total duration of the currently completed experimental tasks, it conducts corresponding analysis and adjustment on the uncompleted experimental tasks in the current initial experimental task sorting list.

[0048] The experimental task classification and sorting module includes a data collection unit, a task classification unit, and a sorting list generation;

[0049] The data acquisition unit obtains the initial experimental task list from the laboratory management platform, and the initial experimental task list records the specific description information of the experimental tasks; the task classification unit classifies the experimental tasks by analyzing the experimental equipment information required for the experimental tasks and using a similarity calculation method; for each type of experimental task, the sorting list generates a sorted list of the experimental tasks corresponding to each type of experimental task based on the reservation period of the experimental tasks, so as to generate an initial sorted list of experimental tasks.

[0050] The historical data analysis and task duration prediction module includes a historical data correlation analysis unit and a task duration prediction unit;

[0051] The historical data correlation analysis unit obtains historical experimental data from the laboratory management platform and correlates it with the experimental tasks in the initial sorted list of experimental tasks; by analyzing the historical data, the required experimental equipment sequence for each experimental task is obtained; the task duration prediction unit calculates the predicted usage duration of the corresponding experimental equipment according to the required experimental equipment sequence and in combination with the historical experimental data of the corresponding experimental task; the usage durations of all the experimental equipment in the required experimental equipment sequence are summarized to obtain the predicted total duration of the corresponding experimental task.

[0052] The overlapping period analysis and intelligent scheduling module includes an overlapping period analysis unit and an intelligent scheduling unit;

[0053] The overlapping period analysis unit analyzes whether there is a time overlap between the experimental tasks according to the predicted duration and reservation period of the experimental tasks, identifies and extracts the target experimental task pairs; for the target experimental task pairs, the intelligent scheduling unit schedules based on the usage order of the same equipment and predicts the task completion time of the overlapping period; if there is a delay, the reservation period of the subsequent tasks is intelligently adjusted, and the adjustment information is output for relevant personnel to confirm and modify.

[0054] The real-time monitoring and task progress adjustment module includes a progress monitoring unit and a task adjustment unit;

[0055] The progress monitoring unit performs the experimental tasks in sequence according to the order of the initial sorted list of experimental tasks. For each completed experimental task, the corresponding actual total duration is obtained; the task adjustment unit compares the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two; and based on the deviation between the actual total duration and the predicted total duration of the currently completed experimental tasks, the uncompleted experimental tasks in the current initial sorted list of experimental tasks are analyzed and adjusted accordingly.

[0056] A method for intelligent management based on laboratory processes includes the following steps:

[0057] Step S100. Obtain the initial experimental task list from the laboratory management platform. By analyzing the initial experimental task list, classify the experimental tasks in the initial experimental task list and divide them into several experimental task types. For each experimental task type, generate an initial experimental task sorting list in combination with the initial experimental task list.

[0058] Step S200. For each experimental type, based on the corresponding initial experimental task sorting list, obtain the corresponding historical experimental data, and associate the historical experimental data with the experimental tasks in the initial experimental task sorting list. Analyze the association results to obtain the predicted total duration of the experimental tasks in the initial experimental task sorting list.

[0059] Step S300. Based on the predicted total duration of the experimental tasks in the initial experimental task sorting list, identify the target experimental task pairs. Analyze the target experimental task pairs to obtain the overlapping time periods of the target experimental task pairs, and intelligently allocate the usage order of the same experimental equipment for the target experimental task pairs during the overlapping time periods, and conduct corresponding analysis and adjustment on the reservation time periods of subsequent experimental tasks.

[0060] Step S400. Conduct experimental tasks in sequence according to the order of the initial experimental task sorting list. For each completed experimental task, obtain the corresponding actual total duration, and compare the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two. And based on the deviation between the actual total duration and the predicted total duration of the currently completed experimental tasks, conduct corresponding analysis and adjustment on the uncompleted experimental tasks in the current initial experimental task sorting list.

[0061] Step S100 includes:

[0062] S101. Obtain the initial experimental task list from the laboratory management platform. The initial experimental task list records the specific description information of the experimental tasks. Extract the experimental task numbers from the initial experimental task list, and each experimental task number is unique. For each experimental task, extract the corresponding required experimental equipment name from the initial experimental task list and convert the required experimental equipment name into a unified vector format to form the required experimental equipment vector X_ID, and X_ID = [x1, x2,..., xn], where X_ID represents the required experimental equipment vector of the experimental task ID, x1 represents the first-dimensional feature value of the required experimental equipment vector, x2 represents the second-dimensional feature value of the required experimental equipment vector, and so on, xn represents the nth-dimensional feature value of the required experimental equipment vector, where n represents the types of experimental equipment existing in the laboratory.

[0063] In this embodiment, converting the names of the required experimental equipment into a unified vector format usually involves natural language processing (NLP) techniques, especially the text embedding method. The goal is to convert the text information of the equipment names into numerical vectors so that these vectors can be compared, classified, or further processed in subsequent analysis and processing.

[0064] Suppose we use the bag-of-words model to convert the names of the required experimental equipment into a unified vector format. Specifically: First, extract all the words in the equipment names and construct a vocabulary that contains all the unique words; for each equipment name, convert it into a vector, and the dimension of the vector is equal to the size of the vocabulary. Each element in the vector represents whether a word appears in the equipment name (1 means it appears, 0 means it does not appear).

[0065] Suppose the names of the only equipment in the laboratory are "A" and "B", then the dimension of the required experimental equipment vector is 2, and the corresponding vocabulary is: ["A", "B"]. For the experimental task with the required experimental equipment being "A", the corresponding vector format is: [1, 0]; for the experimental task with the required experimental equipment being "B", the corresponding vector format is: [0, 1].

[0066] S102. Aggregate the required experimental equipment vectors of all experimental tasks in the initial experimental task list. For the required experimental equipment vector of each experimental task, calculate the similarity with other required experimental equipment vectors in turn, and the corresponding calculation formula is:

[0067] S(X_a,X_b)=|X_a∩X_b| / n;

[0068] Where S(X_a,X_b) represents the similarity between the required experimental equipment vectors corresponding to experimental task a and experimental task b, and |X_a∩X_b| represents the number of equal eigenvalues of the same dimension features in the required experimental equipment vectors corresponding to experimental task a and experimental task b; classify those with similarity not equal to 0 into one category, and traverse all experimental tasks in the initial experimental task list, so as to divide the experimental tasks in the initial experimental task list into several experimental task types;

[0069] S103. For each type of experimental task, obtain the specific description information of the corresponding experimental task from the initial experimental task list, obtain the reservation period T0 of the corresponding experimental task according to the specific description information of the experimental task, and generate an initial experimental task sorting list of the corresponding experimental task type according to the chronological order of the start timestamps of the reservation periods T0 of the experimental tasks.

[0070] Step S200 includes:

[0071] S201. For each type of experiment, based on the corresponding sorted list of initial experimental tasks, obtain the historical experimental data of each experimental task in the sorted list of initial experimental tasks from the laboratory management platform; for each experimental task, associate the experimental task number with the corresponding historical experimental data, so as to obtain the required experimental equipment sequence F, and F = {f1, f2,.., fm}, where f1 represents the name of the first experimental equipment used in the experimental task, f2 represents the name of the second experimental equipment used in the experimental task, and so on, fm represents the name of the mth experimental equipment used in the experimental task, and m represents the number of experimental equipment used in the experimental task;

[0072] S202. According to the required experimental equipment sequence F and combined with the historical experimental data of the corresponding experimental task, calculate the average usage duration μt and standard deviation σt of each experimental equipment in the required experimental equipment sequence F in the historical experimental data, so as to obtain the predicted usage duration tf of the corresponding experimental equipment, and tf = μt + α·σt, where α represents the adjustment coefficient and can be adjusted according to the actual situation of the task (for example, based on the usage frequency of the equipment or the complexity of the experiment); summarize the usage durations tf of all experimental equipment in the required experimental equipment sequence F, so as to obtain the predicted total duration sum_tf of the corresponding experimental task.

[0073] Step S300 includes:

[0074] S301. Based on the sorted list of initial experimental tasks, extract the predicted total duration of all experimental tasks in the sorted list of initial experimental tasks, extract the start timestamp of the reservation period of each experimental task in the sorted list of initial experimental tasks, and represent it as t0; calculate the predicted time period T1 of each experimental task in the sorted list of initial experimental tasks, and T1 = [t0, t0 + sum_tf]; according to the order of the sorted list of initial experimental tasks and combined with the predicted total duration of all experimental tasks, referring to the calculation method of the predicted time period T1 of the first experimental task, calculate the predicted time periods Ti of other experimental tasks in the sorted list of initial experimental tasks in turn, where i represents the serial number of other experimental tasks;

[0075] S302. Aggregate the predicted time periods of all experimental tasks in the initial experimental task sorted list, and sort the predicted time periods of all experimental tasks in the order of the initial experimental task sorted list; sequentially determine whether there is an overlapping relationship between the predicted time periods of two adjacent experimental tasks in the initial experimental task sorted list, extract the experimental task numbers of all overlapping relationships, denoted as target experimental task pairs, and expressed as (IDj, IDj+1), where j represents the sequence number of the experimental task in the initial experimental task sorted list; aggregate all target experimental task pairs to form a target experimental task set E, and E = {e1, e2,..., ev}, where e1 represents the first target experimental task pair, e2 represents the second target experimental task pair, and so on, ev represents the vth target experimental task pair;

[0076] S303. For each target experimental task pair in the target experimental task set E, calculate the overlapping period Tc of the predicted time periods of the target experimental task pair; based on the experimental task numbers of the target experimental task pair, respectively obtain the corresponding required experimental equipment sequences and the predicted usage durations of each experimental equipment in the required experimental equipment sequences; according to the overlapping period Tc, find the required experimental equipment of the target experimental task pair during the overlapping period Tc; if the required experimental equipment corresponding to two adjacent experimental tasks in the target experimental task pair is different, no processing is performed; if the required experimental equipment corresponding to two adjacent experimental tasks in the target experimental task pair is the same, then compare the timestamps of the two adjacent experimental tasks in the target experimental task pair when using this experimental equipment, determine the order of using the experimental equipment according to the chronological relationship of the timestamps, and calculate the predicted timestamp t1 for the completion of the target experimental task pair according to the order of using the experimental equipment. If the predicted timestamp t1 is greater than the initial timestamp t0 of the reservation period of the subsequent experimental task in the initial experimental task sorted list, then sequentially extend the reservation periods of the subsequent experimental tasks by a time span of t1 - t0, and output the adjusted reservation periods to the relevant personnel for confirmation and adjustment by the relevant personnel; otherwise, no adjustment is made.

[0077] In this embodiment, assume that there are three experimental tasks of a certain experimental type, and the corresponding initial experimental task sorted list is Experiment 1, Experiment 2, and Experiment 3, and the corresponding reservation periods are respectively:

[0078] [8:00, 9:00], [9:00, 10:00], [10:00, 12:00]; and the required experimental equipment sequences of the three experimental tasks are respectively:

[0079] F1 = {f3, f1, f4}, F2 = {f4, f2, f1, f5}, F3 = {f3, f1, f4};

[0080] Assume that the predicted time periods T1 of the three experimental tasks are respectively:

[0081] [8:00, 9:30], [9:00, 10:00], [10:00, 12:30];

[0082] It is obtained by comparison that Experiment 1 and Experiment 2 are target experiment pairs, and the corresponding overlapping period Tc is: [9:00, 9:30]. Assume that the experimental equipment corresponding to the overlapping period Tc is f4. Assume that the predicted timestamp of Experiment 1 using f4 is earlier than that of Experiment 2. Then, for the experimental equipment f4, the experimental task of Experiment 1 is carried out first, and then the experimental task of Experiment 2 is carried out. Assume that the timestamp when the target experiment pair is completed is 10:20. Since 10:20 is greater than the initial timestamp 10:00 of the reservation period of Experiment 3, the reservation period of Experiment 3 needs to be postponed by 20 minutes, that is, the reservation period of Experiment 3 is modified to [10:20, 12:20], and the modified reservation period of Experiment 3 is output to the relevant personnel for final determination by the relevant personnel.

[0083] Step S400 includes:

[0084] The experimental tasks are carried out in sequence according to the order of the initial experimental task schedule list. After each experimental task is completed, the corresponding actual total duration ts is obtained, and the actual total duration of the corresponding experimental task is compared with the predicted total duration, so as to obtain the deviation △t = ts - sum_tf between the two. If △t is greater than 0, the unfinished experimental task sequence W in the current initial experimental task schedule list is obtained, and for each experimental task in the unfinished experimental task sequence W, analysis and adjustment are carried out with reference to the analysis process in Step S300. If △t is less than or equal to 0, no processing is carried out.

[0085] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0086] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method based on intelligent management of laboratory processes, characterized in that: The method comprises the following steps: Step S100. Obtain an initial experimental task list from the laboratory management platform, and construct a required experimental equipment vector by analyzing the initial experimental task list; classify the experimental tasks in the initial experimental task list according to the required experimental equipment vector, and divide them into several experimental task types; for each experimental task type, combine the initial experimental task list, and generate an initial experimental task sorting list; Step S200. For each experiment type, based on the corresponding initial experiment task ranking list, obtain the corresponding historical experiment data, associate the historical experiment data with the experiment tasks in the initial experiment task ranking list; analyze the association results to obtain the predicted total duration of the experiment tasks in the initial experiment task ranking list; Step S300. Based on the predicted total duration of the experimental tasks in the initial experimental task ranking list, identify the target experimental task pair; the target experimental task pair refers to two adjacent experimental tasks in the initial experimental task ranking list with overlapping predicted time periods; analyze the target experimental task pair to obtain the overlapping time period of the target experimental task pair, and intelligently allocate the usage order of the same experimental equipment of the target experimental task pair in the overlapping time period, and analyze and adjust the reservation time period of subsequent experimental tasks accordingly; Step S400. Perform the experimental tasks in the order of the initial experimental task sorting list. After completing each experimental task, obtain the corresponding actual total duration, and compare the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two; and according to the deviation between the actual total duration of the currently completed experimental task and the predicted total duration, perform corresponding analysis and adjustments on the unfinished experimental tasks in the current initial experimental task sorting list.

2. A method based on intelligent management of laboratory processes according to claim 1, characterized in that: The step S100 includes: S101. Obtain an initial experimental task list from the laboratory management platform, wherein the initial experimental task list records specific description information of the experimental tasks; extract the experimental task number from the initial experimental task list, and each experimental task number is unique; for each experimental task, extract the corresponding required experimental equipment name from the initial experimental task list, and convert the required experimental equipment name into a unified vector format, thereby forming a required experimental equipment vector X_ID, and X_ID=[x1,x2,...,xn], wherein X_ID represents the required experimental equipment vector of the experimental task ID, x1 represents the first dimension eigenvalue of the required experimental equipment vector, x2 represents the second dimension eigenvalue of the required experimental equipment vector, and so on, xn represents the nth dimension eigenvalue of the required experimental equipment vector, wherein n represents the type of experimental equipment present in the laboratory; S102. Summarize the experimental equipment vectors required for all experimental tasks in the initial experimental task list, and calculate the similarity between the experimental equipment vector required for each experimental task and other required experimental equipment vectors in turn, and the corresponding calculation formula is: S(X_a,X_b)=|X_a∩X_b| / n; Where S(X_a,X_b) represents the similarity between the required experimental equipment vectors corresponding to experimental task a and experimental task b, and |X_a∩X_b| represents the number of the same dimension feature values ​​in the required experimental equipment vectors corresponding to experimental task a and experimental task b; those with similarities not equal to 0 are classified into one category, and all experimental tasks in the initial experimental task list are traversed, so as to divide the experimental tasks in the initial experimental task list into several experimental task types; S103. For each type of experimental task, obtain the specific description information of the corresponding experimental task from the initial experimental task list, obtain the appointment time period T0 of the corresponding experimental task according to the specific description information of the experimental task, and generate the initial experimental task sorting list of the corresponding experimental task type according to the sequence of the starting timestamps of the appointment time period T0 of the experimental task.

3. A method based on intelligent management of laboratory processes according to claim 2, characterized in that: The step S200 includes: S201. For each experiment type, based on the corresponding initial experiment task sorting list, obtain the historical experiment data of each experiment task in the initial experiment task sorting list from the laboratory management platform; for each experiment task, associate the experiment task number with the corresponding historical experiment data to obtain the required experiment equipment sequence F, and F={f1,f2,..,fm}, where f1 represents the name of the first experiment equipment used in the experiment task, f2 represents the name of the second experiment equipment used in the experiment task, and so on, fm represents the name of the mth experiment equipment used in the experiment task, and m represents the number of experiment equipment used in the experiment task; S202. According to the required experimental equipment sequence F and in combination with the historical experimental data of the corresponding experimental task, calculate the average usage time μt and standard deviation σt of each experimental equipment in the required experimental equipment sequence F in the historical experimental data, so as to obtain the predicted usage time tf of the corresponding experimental equipment, and tf=μt+α·σt, where α represents the adjustment coefficient; summarize the usage time tf of all experimental equipment in the required experimental equipment sequence F, so as to obtain the predicted total duration sum_tf of the corresponding experimental task.

4. A method based on intelligent management of laboratory processes according to claim 3, characterized in that: The step S300 includes: S301. Based on the initial experimental task sorting list, extract the predicted total duration of all experimental tasks in the initial experimental task sorting list, extract the starting timestamp of the appointment time period of each experimental task in the initial experimental task sorting list, and express it as t0; calculate the predicted time period T1 of each experimental task in the initial experimental task sorting list, and T1=[t0,t0+sum_tf]; according to the order of the initial experimental task sorting list, combined with the predicted total duration of all experimental tasks, refer to the calculation method of the predicted time period T1 of the first experimental task, and calculate the predicted time periods Ti of other experimental tasks in the initial experimental task sorting list in sequence, where i represents the sequence number of other experimental tasks; S302. Summarize the predicted time periods of all experimental tasks in the initial experimental task sorting list, and sort the predicted time periods of all experimental tasks according to the order of the initial experimental task sorting list; determine in turn whether there is an overlapping relationship between the predicted time periods of two adjacent experimental tasks in the initial experimental task sorting list, extract the experimental task numbers of all overlapping relationships, record them as target experimental task pairs, and express them as (IDj, IDj+1), where j represents the sequence number of the experimental task in the initial experimental task sorting list; summarize all target experimental task pairs to form a target experimental task set E, and E={e1,e2,...,ev}, where e1 represents the first target experimental task pair, e2 represents the second target experimental task pair, and so on, ev represents the vth target experimental task pair; S303. For each target experimental task pair in the target experimental task set E, calculate the overlap period Tc of the predicted time period of the target experimental task pair; based on the experimental task number of the target experimental task pair, respectively obtain the corresponding required experimental equipment sequence and the predicted usage time of each experimental equipment in the required experimental equipment sequence; according to the overlap period Tc, find the required experimental equipment of the target experimental task pair in the overlap period Tc; if the required experimental equipment corresponding to the two adjacent experimental tasks in the target experimental task pair are different, no processing is performed .... If the equipment is the same, compare the timestamps of the two adjacent experimental tasks in the target experimental task pair when using the experimental equipment, determine the order of using the experimental equipment according to the sequence of timestamps, and calculate the predicted timestamp t1 of the completion of the target experimental task pair according to the order of using the experimental equipment. If the predicted timestamp t1 is greater than the initial timestamp t0 of the appointment time period of the next experimental task in the initial experimental task sorting list, then the appointment time period of the subsequent experimental tasks will be postponed by the time span of t1-t0 in sequence, and the adjusted appointment time period will be output to the relevant personnel for confirmation and adjustment; otherwise, no adjustment will be made.

5. A method based on intelligent management of laboratory processes according to claim 4, characterized in that: The step S400 includes: Perform the experimental tasks in sequence according to the order of the initial experimental task sorting list. After each experimental task is completed, obtain the corresponding actual total duration ts, compare the actual total duration of the corresponding experimental task with the predicted total duration, and obtain the deviation △t=ts-sum_tf between the two; if △t is greater than 0, obtain the unfinished experimental task sequence W in the current initial experimental task sorting list, and analyze and adjust each experimental task in the unfinished experimental task sequence W with reference to the analysis process in step S300; if △t is less than or equal to 0, do not perform any processing.

6. A system based on intelligent management of laboratory processes, applied to a method based on intelligent management of laboratory processes according to any one of claims 1 to 5, characterized in that: The system includes: an experimental task classification and sorting module, a historical data analysis and task duration prediction module, an overlapping period analysis and intelligent scheduling module, and a real-time monitoring and task progress adjustment module; The experimental task classification and sorting module obtains an initial experimental task list from the laboratory management platform, classifies the experimental tasks in the initial experimental task list by analyzing the initial experimental task list, and divides the experimental tasks into several experimental task types; for each experimental task type, combined with the initial experimental task list, generates an initial experimental task sorting list; The historical data analysis and task duration prediction module obtains corresponding historical experimental data for each experiment type based on the corresponding initial experimental task ranking list, associates the historical experimental data with the experimental tasks in the initial experimental task ranking list; analyzes the association results to obtain the predicted total duration of the experimental tasks in the initial experimental task ranking list; The overlapping time period analysis and intelligent scheduling module identifies the target experimental task pairs based on the predicted total duration of the experimental tasks in the initial experimental task sorting list; analyzes the target experimental task pairs to obtain the overlapping time periods of the target experimental task pairs, and intelligently allocates the usage order of the same experimental equipment of the target experimental task pairs in the overlapping time periods, and analyzes and adjusts the reservation time periods of subsequent experimental tasks accordingly; The real-time monitoring and task progress adjustment module performs experimental tasks in the order of the initial experimental task sorting list. After each experimental task is completed, the corresponding actual total duration is obtained, and the actual total duration of the corresponding experimental task is compared with the predicted total duration to obtain the deviation between the two; and according to the deviation between the actual total duration of the currently completed experimental task and the predicted total duration, the unfinished experimental tasks in the current initial experimental task sorting list are analyzed and adjusted accordingly.

7. A system based on intelligent management of laboratory processes according to claim 6, characterized in that: The experimental task classification and sorting module includes a data acquisition unit, a task classification unit, and a sorting list generation; The data acquisition unit obtains an initial experimental task list from the laboratory management platform, and the initial experimental task list records specific description information of the experimental tasks; the task classification unit classifies the experimental tasks by analyzing the experimental equipment information required for the experimental tasks and using a similarity calculation method; the sorting list is generated for each experimental task type, and based on the appointment time period of the experimental tasks, the experimental tasks corresponding to each experimental task type are sorted, thereby generating an initial experimental task sorting list.

8. The system based on intelligent management of laboratory processes according to claim 6, characterized in that: The historical data analysis and task duration prediction module includes a historical data association analysis unit and a task duration prediction unit; The historical data association analysis unit obtains historical experimental data from the laboratory management platform and associates it with the experimental tasks in the initial experimental task sorting list; by analyzing the historical data, the required experimental equipment sequence for each experimental task is obtained; the task duration prediction unit calculates the predicted usage time of the corresponding experimental equipment based on the required experimental equipment sequence and in combination with the historical experimental data of the corresponding experimental task; and summarizes the usage time of all experimental equipment in the required experimental equipment sequence to obtain the predicted total duration of the corresponding experimental task.

9. The system based on intelligent management of laboratory processes according to claim 6, characterized in that: The overlapping period analysis and intelligent scheduling module includes an overlapping period analysis unit and an intelligent scheduling unit; The overlapping time period analysis unit analyzes whether there is time overlap between experimental tasks according to the predicted duration of the experimental tasks and the scheduled time period, and identifies and extracts the target experimental task pairs; the intelligent scheduling unit schedules the target experimental task pairs based on the usage order of the same equipment, and predicts the task completion time of the overlapping time period; If there is a delay, the appointment time period for subsequent tasks will be intelligently adjusted, and the adjustment information will be output for relevant personnel to confirm and modify.

10. A system based on intelligent management of laboratory processes according to claim 6, characterized in that: The real-time monitoring and task progress adjustment module includes a progress monitoring unit and a task adjustment unit; The progress monitoring unit performs the experimental tasks in the order of the initial experimental task ranking list, and obtains the corresponding actual total duration after completing each experimental task; the task adjustment unit compares the actual total duration of the corresponding experimental task with the predicted total duration to obtain the deviation between the two; and according to the deviation between the actual total duration of the currently completed experimental task and the predicted total duration, the unfinished experimental tasks in the current initial experimental task ranking list are analyzed and adjusted accordingly.

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