A laboratory automated data processing and management method, system, device, and medium

Abstract

This invention relates to the field of task management technology and discloses a method, system, equipment, and medium for automated data processing and management in laboratories. The method includes: acquiring laboratory equipment and resource information; establishing a simulation model of the laboratory equipment; collecting task instructions and breaking them down into atomic tasks; arranging the execution order of the atomic tasks based on a comprehensive analysis of the inherent sequence constraints of the task instructions, maximum resource utilization, minimum scheduling times, and optimal equipment working time; and executing the tasks using this execution order. This not only effectively avoids equipment idleness and malfunctions and reduces resource waste, but also improves the automation and accuracy of experimental operations. Furthermore, by analyzing equipment working time and consumable expiration dates, it is possible to reduce energy consumption and operating costs while ensuring smooth experimentation, further enhancing the level of automated laboratory management.

Description

Technical Field

[0001] This invention relates to the field of task management technology, specifically to a laboratory automated data processing and management method, system, equipment, and medium. Background Technology

[0002] With the continuous development of laboratory automation technology, intelligent equipment, automated pipetting systems, and experimental workflow management platforms are becoming increasingly widespread. Laboratories that traditionally relied on manual operation are rapidly evolving towards automation and intelligence. Technologies such as digital twins, intelligent scheduling, and dynamic resource optimization are widely used in manufacturing, logistics, and smart healthcare. Some cutting-edge laboratories are also beginning to explore the use of equipment simulation models for monitoring and managing experimental equipment and resources. Existing automation systems can initially achieve the automatic execution of basic operating instructions and some experimental resource management, thus initially improving experimental efficiency and reducing human error rates. However, current technologies mainly focus on equipment-level automation and lack integrated system designs that enable fine-grained scheduling, resource consumption optimization, and dynamic risk perception at the task level.

[0003] Existing laboratory automation systems generally suffer from the following shortcomings when dealing with complex experimental processes: First, they lack the ability to dynamically model the global resource and equipment status, failing to reflect crucial information such as equipment failures, idle status, and consumable resource expiration dates in real time. Second, existing scheduling systems mostly employ static sequential execution strategies, failing to flexibly adjust task execution order based on the inherent order constraints of task instructions, resulting in low equipment utilization efficiency, rigid resource scheduling, and an inability to achieve dynamic optimization based on the overall system status. Third, in terms of resource management, current systems commonly adopt simple "first-come, first-served" or "quantity-priority matching" strategies, failing to comprehensively consider resource failure risks, residual utility, and the timeliness of experimental consumables, leading to significant resource waste and hindering the maximization of resource utilization. Furthermore, in terms of scheduling optimization, traditional methods primarily use a single objective function (such as minimizing total time) as the criterion, lacking the ability to couple and model multiple factors such as equipment movement distance, experimental equipment usage time, and consumable failure risks. Therefore, existing technologies cannot achieve comprehensive management effects such as full-process digital twin modeling of laboratory automation processes, atomic decomposition of task instructions, dynamic priority scheduling, and maximum resource utilization control. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing laboratory automation task management methods suffer from low resource management and utilization rates, high losses, and unreasonable task scheduling.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a laboratory automated data processing and management method, comprising:

[0007] Obtain information on laboratory equipment and resources.

[0008] Establish a simulation model of the laboratory equipment, abstract all experimental equipment into a graph model composed of nodes and edges, update the equipment status in real time, and define the transfer cost and resource weight between nodes.

[0009] Collect task instructions, break them down into atomic tasks, and analyze the sequence and resource requirements of the tasks.

[0010] Based on the inherent sequence constraints of task instructions, maximum resource utilization, and the comprehensive analysis results of minimum scheduling times and optimal equipment working time, the execution order of the atomic tasks is arranged.

[0011] The tasks are executed by utilizing the execution order of the atomic tasks.

[0012] As a preferred embodiment of the laboratory automated data processing and management method described in this invention, the equipment information includes, but is not limited to, the fault status, operating power, and usage status of the automated pipetting equipment and experimental equipment.

[0013] The resource information includes the type, specifications, expiration date, and remaining quantity of experimental consumables.

[0014] As a preferred embodiment of the laboratory automated data processing and management method described in this invention, the equipment simulation model includes, firstly, abstracting all automated pipetting devices and experimental equipment in the laboratory into a diagram. The set of nodes in Sum of edges Each node To represent a specific experimental unit, the node state is represented by a binary indicator variable:

[0015]

[0016] Node status Updated in real time over time, for any two nodes Define the inter-node transition cost matrix The elements are:

[0017]

[0018] in, They are nodes Three-dimensional coordinate vectors in experimental space, Represents Euclidean distance. To represent the cost of node movement, a dynamic effective graph is introduced. ,in:

[0019]

[0020] in, This indicates the available device connections at the current time. A valid connection is only allowed if both nodes are idle and fault-free at the current time.

[0021] Describe the state dependencies and transition priorities between nodes, and define the node weight function. :

[0022]

[0023] in, For nodes Current resource availability rate This is the modulation sensitivity coefficient. For resource threshold, Mapped to Interval.

[0024] In a preferred embodiment of the laboratory automation data processing and management method described in this invention, the task instructions include a set of laboratory automation operation tasks, denoted as... Each task instruction Contains a set of ordered operation steps .

[0025] For any operation step Further decomposed into a set of atomic operation instructions Each atomic operation instruction Defined as a single, smallest indivisible unit of experimental operation, defining a task decomposition mapping. Specific task instructions The corresponding set of all atomic tasks express:

[0026]

[0027] That is, each task instruction Through operation steps level Fully expand to the corresponding set of atomic tasks .

[0028] In a preferred embodiment of the laboratory automated data processing and management method described in this invention, the inherent sequence constraints include primary constraints and secondary constraints, simultaneously controlling the execution order of atomic tasks, wherein:

[0029] For the operation steps within the task instructions, a first-level sequential constraint directed acyclic graph model is constructed, defined as follows: , where the set of nodes Indicates task instructions The following are the operational steps, and the edge collection satisfy:

[0030]

[0031] For task instructions Any two different operation steps and If a sequential dependency exists, i.e., steps Must be in the steps If completed before starting, it will be in a directed acyclic graph. Establish a line from point to Directed edge .

[0032] First-level constraints are defined as first-level step partial order relations. Antisymmetry, transitive closure:

[0033]

[0034] For atomic tasks within a single operation step, construct a second-level sequentially constrained directed acyclic subgraph. , where the set of nodes Indicate steps Atomic tasks and edge sets in satisfy:

[0035]

[0036] If atomic task Must If completed before starting, a directed edge will be created.

[0037] Second-level constraints form local partial order relations at the atomic task level. .

[0038] Define constraint combination mapping function :

[0039]

[0040] Each task instruction By combining the first-level step sequence diagram Sequence diagram of internal atomic tasks in each step The entire structure is mapped to a complete and executable task chain structure.

[0041] Define the adjacency matrix of global constraints. :

[0042]

[0043] Among them, nodes It is an atomic task node, and all direct and indirect execution order relationships are derived through the matrix closure.

[0044] As a preferred embodiment of the laboratory automated data processing and management method of the present invention, the maximum utilization of resources includes matching the types, specifications and quantities of required experimental consumables according to the task instructions.

[0045] When selecting experimental consumables, among the matched experimental consumables, priority should be given to those that are within their validity period and have the closest expiration date.

[0046] As a preferred embodiment of the laboratory automated data processing and management method of the present invention, the minimum number of scheduling steps includes using the minimum moving distance of the automated pipetting device as the target when sorting the atomic tasks.

[0047] The optimal working time of the device includes taking the minimum working time of the experimental device as the target when sorting the atomic tasks.

[0048] The comprehensive analysis process of the minimum number of scheduling attempts and the optimal working time of the equipment is as follows:

[0049] Arbitrarily sort all the atomic tasks to obtain all the sequences, forming a sequence set H.

[0050] Each sequence in H is simplified. If n consecutive atomic tasks are the same, then the n consecutive atomic tasks are merged into m atomic tasks.

[0051]

[0052] Where m represents the number of atomic tasks after merging n consecutive atomic tasks. h is the index, representing the h-th atomic task among the n consecutive atomic tasks. Z represents the amount of experimental equipment occupied by the h-th atomic task. Z represents the occupancy of experimental equipment when saturation occurs, matching n consecutive atomic tasks to the atomic tasks that can be executed.

[0053] After merging each sequence, a new sequence set H+ is obtained. Each sequence in H+ is then analyzed:

[0054] By analyzing the execution interval of the atomic tasks for each experimental consumable in each sequence of H+, the failure rate of the experimental consumable in the sequence is calculated. If a failure exists, the sequence is excluded; if no failure exists, the sequence is retained. This results in a set H++ of sequences without any experimental consumable failures.

[0055] In each sequence of H++, the total distance traveled by the automated pipetting device and the total working time of the experimental equipment are evaluated. The sequence corresponding to the minimum value is selected as the output result, represented as follows:

[0056]

[0057] in, and Representing atomic tasks and atomic tasks The residual utility value of the corresponding consumable unit Indicates experimental equipment The probability of the first resource failing in the task execution chain. The utility modulation sensitivity coefficient, This is the modulation coefficient for the equipment load index. This represents the resource failure penalty coefficient.

[0058] A laboratory automated data processing and management system employing the method described in this invention, wherein:

[0059] The data acquisition module obtains information about the laboratory's equipment and resources.

[0060] The modeling module is used to create simulation models of the laboratory equipment.

[0061] The task publishing module collects task instructions and breaks them down into atomic tasks.

[0062] The analysis module arranges the execution order of the atomic tasks based on the inherent sequence constraints of the task instructions, the maximum resource utilization, and the result of the conflict between the minimum number of scheduling attempts and the optimal working time of the equipment.

[0063] The execution module performs task execution by utilizing the execution order of the atomic tasks.

[0064] A computer device includes a memory and a processor. The memory stores a computer program, wherein the processor executes the computer program to implement the steps of the method described in any one of the present invention.

[0065] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the present invention.

[0066] The beneficial effects of this invention are as follows: The laboratory automation data processing and management method provided by this invention, by introducing equipment simulation models and intelligent scheduling algorithms, can monitor the status and resource information of laboratory equipment in real time, optimize the task execution sequence, and improve the utilization efficiency of equipment and resources. Specifically, through precise equipment scheduling and resource matching, it not only effectively avoids equipment idleness and failure, reducing resource waste, but also improves the automation and accuracy of experimental operations. Furthermore, by analyzing equipment working time and consumable expiration dates, this invention can reduce energy consumption and operating costs while ensuring the smooth progress of experiments, further enhancing the level of laboratory automation management and providing strong technical support for the efficient operation and innovative research of laboratories. Attached Figure Description

[0067] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 The first embodiment of the present invention provides an overall flowchart of a laboratory automated data processing and management method. Detailed Implementation

[0069] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0070] Example 1, referring to Figure 1 As an embodiment of the present invention, a laboratory automated data processing and management method is provided, comprising:

[0071] S1: Obtain information on laboratory equipment and resources.

[0072] Furthermore, equipment information includes, but is not limited to, the fault status, operating power, and usage status of automated pipetting equipment and experimental equipment (robotic arms, heating / cooling devices, centrifuges, incubators, etc.). This information originates from the equipment control system or industrial communication protocols (such as OPCUA, Modbus, etc.). Resource information includes the type, specifications, expiration date, and remaining quantity of experimental consumables, sourced from the laboratory inventory management module or RFID identification system.

[0073] S2: Establish a simulation model of the laboratory equipment, abstract all experimental equipment into a graph model composed of nodes and edges, update the equipment status in real time, and define the transfer cost and resource weight between nodes.

[0074] The equipment simulation model includes, firstly, abstracting all automated pipetting devices and experimental equipment in the laboratory into a diagram. The set of nodes in Sum of edges Each node To represent a specific experimental unit, the node state is represented by a binary indicator variable:

[0075]

[0076] Node status Updated in real time over time, for any two nodes Define the inter-node transition cost matrix The elements are:

[0077]

[0078] in, They are nodes Three-dimensional coordinate vectors in experimental space, Represents Euclidean distance. To represent the cost of node movement, a dynamic effective graph is introduced. ,in:

[0079]

[0080] in, This indicates the available device connections at the current time, meaning that a valid connection is only allowed if both nodes are idle and fault-free at the current time.

[0081] Describe the state dependencies and transition priorities between nodes, and define the node weight function. :

[0082]

[0083] in, For nodes Current resource availability rate This is the modulation sensitivity coefficient. For resource threshold, Mapped to Interval.

[0084] The task instructions include a set of laboratory automation operation tasks, denoted as... Each task instruction Contains a set of ordered operation steps .

[0085] For any operation step Further decomposed into a set of atomic operation instructions Each atomic operation instruction Defined as a single, smallest indivisible unit of experimental operation, defining a task decomposition mapping. Specific task instructions The corresponding set of all atomic tasks express:

[0086]

[0087] That is, each task instruction Through operation steps level Fully expand to the corresponding set of atomic tasks .

[0088] By establishing simulation models of laboratory equipment, precise modeling and real-time monitoring of laboratory equipment and operating positions can be achieved, thereby optimizing the collaboration and task execution efficiency of various devices within the laboratory. Specifically, by modeling automated pipetting devices and experimental equipment as nodes, calculating the movement distance between nodes, and quantifying the state of each node, the real-time status of each device and operating position can be clearly understood.

[0089] S3: Collect task instructions, break down the task instructions into atomic tasks, and analyze the sequence and resource requirements between tasks.

[0090] The task instructions include a set of laboratory automation operation tasks, denoted as... Each task instruction Contains a set of ordered operation steps .

[0091] For any operation step Further decomposed into a set of atomic operation instructions Each atomic operation instruction Defined as a single, smallest indivisible unit of experimental operation, defining a task decomposition mapping. Specific task instructions The corresponding set of all atomic tasks express:

[0092]

[0093] That is, each task instruction Through operation steps level Fully expand to the corresponding set of atomic tasks .

[0094] By breaking down tasks into atomic tasks, the system can flexibly adjust the execution order, parallelism, and device allocation of tasks according to actual needs, avoiding problems such as excessive complexity or excessive resource consumption of individual tasks.

[0095] In one embodiment of the present invention, in a biomedical experiment, the task instruction is to "transfer the liquid sample from test tube A to test tube B, heat it to 40°C and maintain it for 10 minutes, and then perform centrifugation".

[0096] Based on the task instructions, they can be broken down into multiple operation steps, and each operation step can be further broken down into multiple operation instructions, ultimately transforming into atomic tasks.

[0097] Task instructions: Transfer the liquid sample from test tube A to test tube B, heat to 40°C and maintain for 10 minutes, then centrifuge.

[0098] Disassembly steps:

[0099] Operation Step 1: Pipetting operation.

[0100] Operation Instruction 1: Start the automatic pipetting device and remove the liquid from test tube A.

[0101] Operation Instruction 2: Transfer the extracted liquid to test tube B.

[0102] Operation Step 2: Heating Operation.

[0103] Operation Instruction 1: Start the heating equipment and heat test tube B to 40°C.

[0104] Operation Instruction 2: Keep test tube B at 40°C for 10 minutes.

[0105] Operation step 3: Centrifugation.

[0106] Operation Instruction 1: Place test tube B into the centrifuge.

[0107] Operation command 2: Start the centrifuge and set the appropriate speed and time for centrifugation.

[0108] Atomic Task:

[0109] Atomic Task 1: Start the automated pipetting equipment and remove the liquid from test tube A.

[0110] Atomic Task 2: Transfer the extracted liquid to test tube B.

[0111] Atomic Task 3: Start the heating equipment and heat test tube B to 40°C.

[0112] Atomic Task 4: Keep test tube B at 40°C for 10 minutes.

[0113] Atomic Task 5: Place test tube B into the centrifuge.

[0114] Atomic Task 6: Start the centrifuge and set the appropriate speed and time for centrifugation.

[0115] S4: Arrange the execution order of the atomic tasks based on the inherent sequence constraints of the task instructions, the maximum resource utilization, the minimum number of scheduling attempts, and the optimal working time of the equipment.

[0116] The inherent order constraints include both primary and secondary constraints that simultaneously constrain the order of atomic tasks. Both levels of constraints are the most basic constraints for the later statement "arbitrarily sort all atomic tasks to obtain all sequences, forming a sequence set H".

[0117] The inherent order constraints include primary and secondary constraints, which simultaneously control the execution order of atomic tasks, wherein:

[0118] For the operation steps within the task instructions, a first-level sequential constraint directed acyclic graph model is constructed, defined as follows: , where the set of nodes Indicates task instructions The following are the operational steps, and the edge collection satisfy:

[0119]

[0120] For task instructions Any two different operation steps and If a sequential dependency exists, i.e., steps Must be in the steps If completed before starting, it will be in a directed acyclic graph. Establish a line from point to Directed edge .

[0121] First-level constraints are defined as first-level step partial order relations. Antisymmetry, transitive closure:

[0122]

[0123] For atomic tasks within a single operation step, construct a second-level sequentially constrained directed acyclic subgraph. , where the set of nodes Indicate steps Atomic tasks and edge sets in satisfy:

[0124]

[0125] If atomic task Must If completed before starting, a directed edge will be created.

[0126] Second-level constraints form local partial order relations at the atomic task level. .

[0127] Define constraint combination mapping function :

[0128]

[0129] Each task instruction By combining the first-level step sequence diagram Sequence diagram of internal atomic tasks in each step The entire structure is mapped to a complete and executable task chain structure.

[0130] Define the adjacency matrix of global constraints. :

[0131]

[0132] Among them, nodes It is an atomic task node, and all direct and indirect execution order relationships are derived through the matrix closure.

[0133] It should be noted that primary and secondary constraints ensure that tasks are executed in the logical order of experimental operations. For example, certain experimental operations must be completed before starting (e.g., sample pipetting must precede heating). This constraint helps prevent operational errors and ensures the scientific rigor and effectiveness of the experiment. Ensuring the logical order of atomic tasks helps avoid multiple operations occupying the same equipment or resources simultaneously. For example, pipetting and heating operations cannot be performed on the same equipment at the same time, avoiding equipment or task conflicts and improving the smoothness and efficiency of the experiment. Constraining the task order helps the system ensure that each atomic task is executed at the appropriate time during scheduling. By clearly defining which tasks must be completed before others, the system can avoid incorrect scheduling and ensure that experimental tasks are carried out smoothly when equipment and resources are available.

[0134] Furthermore, maximizing resource utilization includes matching the types, specifications, and quantities of required experimental consumables according to task instructions. When selecting experimental consumables, priority is given to those within their expiration period and closest to their expiration date from the matched consumables.

[0135] Minimum scheduling counts include minimizing the travel distance of the automated pipetting equipment when sorting atomic tasks. Optimal equipment operating time includes minimizing the operating time of the experimental equipment when sorting atomic tasks.

[0136] Minimum scheduling counts include using the minimum travel distance of the automated pipetting device as the target when sorting atomic tasks.

[0137] The optimal operating time of the equipment includes setting the minimum operating time of the experimental equipment as the target when sorting atomic tasks.

[0138] The comprehensive analysis process of minimum scheduling times and optimal equipment operating time is as follows:

[0139] Arbitrarily sort all the atomic tasks to obtain all the sequences, forming a sequence set H.

[0140] Each sequence in H is simplified. If n consecutive atomic tasks are the same, then the n consecutive atomic tasks are merged into m atomic tasks.

[0141]

[0142] Where m represents the number of atomic tasks after merging n consecutive atomic tasks. h is the index, representing the h-th atomic task among the n consecutive atomic tasks. Z represents the amount of experimental equipment occupied by the h-th atomic task. Z represents the occupancy of experimental equipment when saturation occurs, matching n consecutive atomic tasks to the atomic tasks that can be executed.

[0143] After merging each sequence, a new sequence set H+ is obtained. Each sequence in H+ is then analyzed:

[0144] By analyzing the execution interval of the atomic tasks for each experimental consumable in each sequence of H+, the failure rate of the experimental consumable in the sequence is calculated. If a failure exists, the sequence is excluded; if no failure exists, the sequence is retained. This results in a set H++ of sequences without any experimental consumable failures.

[0145] In each sequence of H++, the total distance traveled by the automated pipetting device and the total working time of the experimental equipment are evaluated. The sequence corresponding to the minimum value is selected as the output result, represented as follows:

[0146]

[0147] in, and Representing atomic tasks and atomic tasks The residual utility value of the corresponding consumable unit Indicates experimental equipment The probability of the first resource failing in the task execution chain. The utility modulation sensitivity coefficient, This is the modulation coefficient for the equipment load index. This represents the resource failure penalty coefficient.

[0148] It should be noted that minimizing the travel distance of automated pipetting equipment can reduce equipment operating time and energy consumption, improve the efficiency of laboratory equipment, and reduce unnecessary physical movement and equipment idle time. This helps improve the smoothness of the overall experimental process and save on equipment usage costs. By minimizing the working time of experimental equipment, excessive consumption of equipment in certain tasks is avoided, improving equipment utilization efficiency. Shorter working times also avoid the risk of failure caused by prolonged equipment operation, while helping to improve the lifespan and stability of the equipment. Merging atomic tasks reduces the number of consecutive executions of the same task, avoiding repetitive equipment operations and improving task scheduling efficiency. This not only reduces redundant operations but also makes the task sequence more compact and coordinated, thereby optimizing the overall scheduling strategy. Analyzing the execution interval of experimental consumables in the task sequence ensures that resources such as reagents and consumables used in the experiment do not become invalid due to expiration or insufficient inventory. By eliminating invalid task sequences, the validity and reliability of the resources required for task execution are guaranteed, thereby avoiding experimental failures or retrying due to resource issues.

[0149] Failure rate typically refers to the ability of experimental consumables (such as reagents and supplies) to remain valid within a task sequence due to excessively long time intervals between tasks. For example, reagent A has a validity period of 5 minutes. If the interval between actions performed on reagent A in a task sequence exceeds 5 minutes, reagent A will become invalid. For instance, suppose reagent A is needed in the first task of a sequence, and that task's execution time is T1. The second task uses the reagent at time T2. If T2 - T1 > 5 minutes, then reagent A is invalid and cannot be used.

[0150] To prevent reagent and consumable failures, a "failure monitoring mechanism" can be added to the task scheduling process to ensure that relevant resources (such as reagents) do not fail due to excessive waiting time when certain tasks are being executed. The specific design is as follows:

[0151] Each task specifies the required consumables and their effective duration:

[0152] Add the required consumable information for each atomic task, including the type, specifications, expiration date, and remaining quantity of the consumable. For example, reagent A has an effective duration of 5 minutes, and reagent B has an effective duration of 30 minutes.

[0153] Task interval monitoring:

[0154] When scheduling tasks, the system calculates the time interval between every two tasks to ensure that all consumables used are used within their valid duration. For example, if Task 1 uses reagent A and Task 2 uses the same reagent, the time interval between Task 1 and Task 2 must not exceed the valid duration of reagent A (5 minutes).

[0155] Failure assessment and elimination of invalid sequences:

[0156] The system calculates time intervals within the task sequence to assess whether consumables in each task will expire. If some consumables cannot be used within their expiration period in a task sequence, the system marks the sequence as invalid and readjusts the task order.

[0157] For example, if reagent A for Task 1 expires in the task sequence, the task sequence will be excluded, and the system will select a new valid task sequence.

[0158] Dynamically adjust the task order:

[0159] If some tasks in a task sequence cause consumables to become invalid, the system will prioritize rescheduling these tasks to be executed within a shorter time interval. Alternatively, the system can select alternative experimental consumables (if alternatives are available), or adjust the task order to ensure the consumables remain effective.

[0160] Comprehensive evaluation and optimization:

[0161] Design a comprehensive evaluation function that considers the effective duration of consumables used in each task and the time interval between task executions, selecting task sequences that minimize consumable failures. The optimized task sequences should ensure efficient resource utilization while preventing consumable failures.

[0162] Arrange all atomic tasks in sequence and calculate the time interval between each task.

[0163] Check if each consumable can be used within its validity period. If a consumable expires, adjust the task sequence.

[0164] Based on the optimization algorithm, the optimal task sequence is selected so that all consumables can be used within their validity period, and the execution order of tasks satisfies the constraints of equipment and resources.

[0165] By incorporating a failure monitoring mechanism, consumable failures can be effectively avoided. Optimizing the task sequence requires not only considering the optimal configuration of equipment and time, but also ensuring that resources (such as reagents and consumables) are used appropriately within their expiration dates. This approach can improve the stability and efficiency of the experimental process and avoid waste of experimental resources.

[0166] Total distance traveled by the automated pipetting device: F represents the number of scheduling attempts. This represents the distance traveled by the automated pipetting device in the f-th iteration. f represents the index of the number of times the device is scheduled.

[0167] Total working time of experimental equipment: B represents the number of devices. This represents the operating time of the b-th device. 'b' represents the device index.

[0168] S5: Perform task execution by utilizing the execution order of atomic tasks.

[0169] On the other hand, this embodiment also provides a laboratory automated data processing and management system, which includes:

[0170] The data acquisition module obtains information about the laboratory's equipment and resources.

[0171] The modeling module is used to create simulation models of the laboratory equipment.

[0172] The task publishing module collects task instructions and breaks them down into atomic tasks.

[0173] The analysis module arranges the execution order of atomic tasks based on the inherent sequence constraints of task instructions, maximum resource utilization, and the result of the conflict between minimum scheduling times and optimal device working time.

[0174] The execution module executes tasks by utilizing the execution order of atomic tasks.

[0175] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0176] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0177] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0178] It should be understood that various parts of the present invention can be implemented using a combination of hardware, software, and firmware. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using a combination of any of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0179] Example 2 is an embodiment of the present invention, which provides a laboratory automated data processing and management method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0180] This experiment selected four tasks: Task A, Task B, Task C, and Task D. Each task requires completing multiple operational steps, some of which are identical. For example, Task A and Task C both include the step of "adding liquid using an automated pipetting device," and Task B and Task D both include the step of "heating in an incubator."

[0181] In traditional scheduling methods, the system executes all operation steps of each task separately.

[0182] Each task involves multiple operational steps, and tasks are scheduled using both traditional scheduling methods and the method of this invention. The experiment records data such as the execution time of each task, equipment usage time, and the benefits after merging the steps.

[0183] By comparing the execution times of the conventional method and the method of this invention, it can be seen that in tasks A and C, the execution time is optimized because the same operation step of "adding liquid using an automated pipetting device" is combined. For example, in the conventional method, tasks A and C each require 10 minutes to add liquid, while in the method of this invention, the liquid adding step is performed only once, reducing wasted time. In task D, the heating times of the conventional method and the method of this invention are 20 minutes and 15 minutes, respectively, which also demonstrates that the merging of tasks reduces the reuse time of the equipment.

[0184] In traditional scheduling methods, the "automatic pipetting equipment liquid addition" steps of Task A and Task C are executed separately. However, in the scheduling of this invention, these operation steps are executed together, making equipment movement and use more efficient and reducing unnecessary operations. The method of this invention can greatly reduce equipment idle time and repetitive work, improve equipment utilization, and avoid repetitive operations.

[0185] By combining identical operational steps, the travel distance of automated pipetting equipment and incubators is reduced. For example, after combining the liquid addition steps of the automated pipetting equipment in Task A and Task C, the equipment only needs to be moved once, thus saving repeated travel time and distance.

[0186] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A laboratory automated data processing and management method, characterized in that, include: Obtain information on laboratory equipment and resources; Establish a simulation model of laboratory equipment, abstract all automated pipetting equipment and experimental equipment into a graph model composed of nodes and edges, update the equipment status in real time, and define the transfer cost and resource weight between nodes; Collect task instructions, break them down into atomic tasks, and analyze the sequence and resource requirements of the tasks. Based on the inherent sequence constraints of task instructions, maximum resource utilization, and the comprehensive analysis results of minimum scheduling times and optimal equipment working time, the execution order of the atomic tasks is arranged. The tasks are executed according to the execution order of the atomic tasks; The equipment information includes the fault status, operating power, and usage status of automated pipetting equipment and experimental equipment; The resource information includes the type, specifications, expiration date, and remaining quantity of experimental consumables; The equipment simulation model includes, firstly, abstracting all automated pipetting devices and experimental equipment in the laboratory into a diagram. The set of nodes in Sum of edges Each node To represent a specific experimental unit, the node state is represented by a binary indicator variable: ， Node status Updated in real time over time, for any two nodes Define the inter-node transition cost matrix The elements are: ， in, They are nodes Three-dimensional coordinate vectors in experimental space, Represents Euclidean distance. To represent the cost of node movement, a dynamic effective graph is introduced. ,in: ， in, This indicates the available device connections at the current time, meaning that a valid connection is only allowed if both nodes are idle and fault-free at the current time. Describe the state dependencies and transition priorities between nodes, and define the node weight function. : ， in, For nodes Current resource availability rate This is the modulation sensitivity coefficient. For resource threshold, Mapped to interval; The task instructions include a set of laboratory automation operation tasks, denoted as... Each task instruction Contains a set of ordered operation steps ; For any operation step Further decomposed into a set of atomic operation instructions Each atomic operation instruction Defined as a single, smallest indivisible unit of experimental operation, defining a task decomposition mapping. Specific task instructions The corresponding set of all atomic tasks express: ， That is, each task instruction Through operation steps level Fully expand to the corresponding set of atomic tasks ; The inherent order constraints include primary constraints and secondary constraints, which simultaneously control the execution order of atomic tasks, wherein: For the operation steps within the task instructions, a first-level sequential constraint directed acyclic graph model is constructed, defined as follows: , where the set of nodes Indicates task instructions The following are the operational steps, and the edge collection satisfy: ， For task instructions Any two different operation steps and When there is a sequential dependency, i.e., steps Must be in the steps If completed before starting, it will be in a directed acyclic graph. Establish a line from point to Directed edge ; First-level constraints are defined as first-level step partial order relations. Antisymmetry, transitive closure: ， For atomic tasks within a single operation step, construct a second-level sequentially constrained directed acyclic subgraph. , where the set of nodes Indicate steps Atomic tasks and edge sets in satisfy: ， When atomic task Must If completed before starting, then directed edges are created; Second-level constraints form local partial order relations at the atomic task level. ; Define constraint combination mapping function : ， Each task instruction By combining the first-level step sequence diagram Sequence diagram of internal atomic tasks in each step The entire structure is mapped to a complete and executable task chain structure. Define the adjacency matrix of global constraints. : ， Among them, nodes It is an atomic task node, and all direct and indirect execution order relationships are derived through the matrix closure.

2. The laboratory automated data processing and management method as described in claim 1, characterized in that: The maximum utilization rate of resources includes matching the types, specifications, and quantities of required experimental consumables according to the task instructions; When selecting experimental consumables, choose the consumables that are within their validity period and have the closest expiration date among the matched consumables.

3. The laboratory automated data processing and management method as described in claim 2, characterized in that: The minimum number of scheduling attempts includes using the minimum travel distance of the automated pipetting device as the target when sorting the atomic tasks. The optimal working time of the device includes taking the minimum working time of the experimental device as the target when sorting the atomic tasks; The comprehensive analysis process of the minimum number of scheduling attempts and the optimal working time of the equipment is as follows: Arbitrarily sort all the atomic tasks to obtain all the sequences, forming a sequence set H; Each sequence in H is simplified. If N consecutive adjacent atomic tasks are the same, the N adjacent atomic tasks are merged into Q atomic tasks. ， Where Q represents the number of atomic tasks after merging N consecutively adjacent atomic tasks; h is the index, representing the h-th atomic task among the N consecutively adjacent atomic tasks. Z represents the amount of experimental equipment occupied by the h-th atomic task; Z represents the amount of experimental equipment occupied when the N consecutive adjacent atomic tasks are matched to be able to execute the atomic tasks. After merging each sequence, a new sequence set H+ is obtained; each sequence in H+ is then analyzed: By analyzing the execution interval of the atomic tasks for each experimental consumable in each sequence of H+, the failure degree of the experimental consumable in the sequence is calculated. If failure exists, the sequence is excluded; if no failure exists, the sequence is retained; thus, the set of sequences H++ without experimental consumable failure is obtained. In each sequence of H++, the total distance traveled by the automated pipetting device and the total working time of the experimental equipment are evaluated. The sequence corresponding to the minimum value is selected as the output result, represented as follows: ， in, and Representing atomic task nodes and atomic task nodes The residual utility value of the corresponding consumable unit Indicates experimental equipment The probability of the first resource failing in the task execution chain. The utility modulation sensitivity coefficient, This is the modulation coefficient for the equipment load index. This represents the resource failure penalty coefficient.

4. A laboratory automated data processing and management system, employing the laboratory automated data processing and management method as described in any one of claims 1 to 3, characterized in that, include: The data acquisition module obtains information about the laboratory's equipment and resources. The modeling module is used to create simulation models of laboratory equipment. The task publishing module collects task instructions and breaks them down into atomic tasks. The analysis module arranges the execution order of the atomic tasks based on the inherent sequence constraints of the task instructions, the maximum resource utilization, and the result of the conflict between the minimum number of scheduling attempts and the optimal working time of the equipment. The execution module performs task execution by utilizing the execution order of the atomic tasks.

5. A computer device, comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the laboratory automated data processing and management method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the laboratory automated data processing and management method as described in any one of claims 1-3.

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