Laboratory automatic data processing and management method, system, equipment and medium

Abstract

The invention relates to the technical field of task management, and discloses a laboratory automatic data processing and management method, system, equipment and medium, and the method comprises the steps: obtaining the equipment information and resource information of a laboratory; and establishing an equipment simulation model of the laboratory. A task instruction is collected, and the task instruction is disassembled into atomic tasks. And arranging the execution sequence of the atomic tasks according to the inherent sequence constraint of the task instruction, the maximum resource utilization rate, the minimum scheduling frequency and the comprehensive analysis result of the optimal working time of the equipment. And performing task execution by using the execution sequence of the atomic tasks. Idle and faults of equipment are effectively avoided, resource waste is reduced, and automation and accuracy of experimental operation are improved. Besides, by analyzing the working time of the equipment and the validity period of the consumables, the energy consumption and the operation cost can be reduced while the smooth proceeding of the experiment is ensured, and the automatic management level of the laboratory is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of task management, and specifically to a method, system, device and medium for laboratory automation data processing and management. Background Art

[0002] With the continuous development of laboratory automation technology, intelligent devices, automatic pipetting systems, and experimental process management platforms have gradually become popular. Traditional laboratories relying on manual operations are accelerating their evolution towards automation and intelligence. Technologies such as digital twin, intelligent scheduling, and resource dynamic optimization have been widely applied in the manufacturing, logistics, and smart healthcare fields, and some leading laboratories have also started to explore the use of equipment simulation models for experimental equipment monitoring and resource management. Existing automation systems can initially achieve the automatic execution of basic operation instructions and partial experimental resource management, initially improving experimental efficiency and reducing the human error rate. However, the current technology mainly focuses on equipment-level automation and lacks an integrated system design for fine-grained scheduling, resource consumption optimization, and dynamic perception of failure risks at the task level.

[0003] When facing complex experimental processes, existing laboratory automation systems generally have the following deficiencies: First, they lack the ability to dynamically model the global resource state and equipment state and cannot reflect important information such as equipment failures, idle states, and the expiration dates of consumable resources in real time. Second, most existing scheduling systems adopt static sequential execution strategies and fail to flexibly adjust the task execution order according to the inherent sequential constraints of task instructions, resulting in low equipment utilization efficiency, rigid resource scheduling, and the inability to achieve dynamic optimization based on the overall system state. Third, in terms of resource management, currently, simple "first come, first served" or "quantity-first matching" strategies are generally adopted, and the failure risks, remaining utility, and timeliness of experimental consumables are not comprehensively considered, resulting in serious resource waste and difficulty in achieving the maximum utilization of resources. In addition, in terms of scheduling optimization, traditional methods mainly use a single objective function (such as minimizing the total time) as the criterion and lack the ability to couple and model multiple factors such as the moving distance of equipment, the usage duration of experimental equipment, and the failure risk of consumables. Therefore, the existing technology cannot achieve the comprehensive management effects of full-process digital twin modeling, atomic decomposition of task instructions, dynamic priority scheduling, and control of the maximum resource utilization rate in the laboratory automation process. Summary of the Invention

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

[0005] Therefore, the technical problem solved by the present invention is that existing laboratory automation task management methods have problems such as low resource management and utilization rate, high losses, and unreasonable task arrangements.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A method for laboratory automation data processing and management, comprising:

[0007] Obtain the equipment information and resource information of the laboratory.

[0008] Establish an equipment simulation model for the laboratory, 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, disassemble the task instructions into atomic tasks, and analyze the sequence and resource requirements between tasks.

[0010] Arrange the execution order of the atomic tasks according to the comprehensive analysis results of the inherent order constraints of the task instructions, the maximum resource utilization rate, the minimum scheduling times, and the optimal working time of the equipment.

[0011] Execute tasks using the execution order of the atomic tasks.

[0012] As a preferred solution of the method for laboratory automation data processing and management according to the present invention, wherein: the equipment information includes, but is not limited to, the failure status, working power, and usage status of automatic pipetting equipment and experimental equipment.

[0013] The resource information includes the types, specifications, expiration dates, and remaining quantities of experimental consumables.

[0014] As a preferred solution of the method for laboratory automation data processing and management according to the present invention, wherein: the equipment simulation model includes, first, abstracting all automatic pipetting equipment and experimental equipment in the laboratory into a set of nodes and a set of edges in the graph, each node represents a specific experimental unit, and the node status is represented by a binary indicator variable:

[0015]

[0016] Node status is updated in real time over time. For any two nodes , define the transfer cost matrix between nodes, and the elements are:

[0017]

[0018] wherein, are the three-dimensional coordinate vectors of nodes in the experimental space respectively, and represents the Euclidean distance. Indicates the node movement cost and introduces a dynamic effective graph , where:

[0019]

[0020] Among them, Indicates the available connections between devices at the current time point, that is, only when two nodes are both idle and fault-free at the current time, are valid connections allowed to be formed.

[0021] Describes the state dependence relationship and transfer priority between nodes, and defines the node weight function :

[0022]

[0023] Among them, Is the current resource remaining rate of node , Is the modulation sensitivity coefficient, Is the resource threshold, Is mapped to Interval.

[0024] As a preferred solution of the laboratory automation data processing and management method described in the present invention, where: the task instructions include a set of laboratory automation operation tasks, denoted as , where each task instruction Contains a set of ordered operation step sets .

[0025] For any operation step , it is further disassembled into a set of atomic operation instructions , where each atomic operation instruction Is defined as a single, smallest indivisible experimental operation unit, and defines the task decomposition mapping , where the specific task instruction The corresponding set of all atomic tasks Indicates:

[0026]

[0027] That is, each task instruction Is fully expanded to the corresponding set of atomic tasks through the operation step hierarchy . .

[0028] As a preferred solution of the laboratory automation data processing and management method described in the present invention, where: the inherent order constraints include primary constraints and secondary constraints, and simultaneously control the execution order of atomic tasks, where:

[0029] Construct a first-level sequential constraint directed acyclic graph model for the operation steps in the task instruction, defined as , where the node set represents the operation steps under the task instruction , and the edge set satisfies:

[0030]

[0031] For any two different operation steps in the task instruction and , if there is a sequential dependency relationship, that is, step must be completed before step starts, then a directed edge is established in the directed acyclic graph from to in the directed edge .

[0032] The first-level constraint is defined as the antisymmetric, transitive closure on the first-level step partial order relationship :

[0033]

[0034] For the atomic tasks within a single operation step, construct a second-level sequential constraint directed acyclic subgraph , where the node set represents the atomic tasks in step , and the edge set satisfies:

[0035]

[0036] If the atomic task must be completed before starts, then a directed edge is established.

[0037] The second-level constraint forms a local partial order relationship at the atomic task level.

[0038] Define the constraint combination mapping function :

[0039]

[0040] Each task instruction is mapped as a set of complete executable task chain structures by combining the first-level step sequence diagram with the atomic task sequence diagrams within each step ;

[0041] Adjacency matrix defining the overall constraint relationship :

[0042]

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

[0044] As a preferred solution of the laboratory automation data processing and management method described in the present invention, wherein: the maximum resource utilization rate includes, according to the task instruction, matching the types, specifications and quantities of required experimental consumables.

[0045] When selecting experimental consumables, among the matched experimental consumables, preferentially select the experimental consumables that are within the validity period and closest to the end of the validity period.

[0046] As a preferred solution of the laboratory automation data processing and management method described in the present invention, wherein: the minimum scheduling times include, when sorting the atomic tasks, taking the minimum value of the moving distance of the automatic pipetting device as the target.

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

[0048] The comprehensive analysis process of the minimum scheduling times and the optimal working time of the device is specifically as follows:

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

[0050] Simplify each sequence in H. If n consecutive adjacent atomic tasks are the same task, then merge the adjacent n atomic tasks into m atomic tasks.

[0051]

[0052] Among them, m represents the number of atomic tasks after merging n consecutive adjacent atomic tasks. h is an index, representing the hth atomic task among n consecutive adjacent atomic tasks. represents the occupancy of the hth atomic task on the experimental device. Z represents the occupancy when the experimental device capable of executing the atomic task is saturated for n consecutive adjacent atomic tasks.

[0053] After completing the merging of each sequence, a new sequence set H+ is obtained. Analyze each sequence in H+:

[0054] By analyzing the execution intervals of the atomic tasks of each experimental consumable in each sequence of H+, the failure degree of the experimental consumable in the sequence is calculated. If there is a failure, the sequence is excluded. If there is no failure, the sequence is retained. A set of sequences H++ without failure of experimental consumables is obtained.

[0055] In each sequence of H++, the total moving distance of the automatic pipetting device and the total working duration of the experimental device are evaluated, and the sequence corresponding to the minimum value is selected as the output result, which is expressed as:

[0056]

[0057] Among them, and respectively represent the remaining utility values of the consumable units corresponding to the atomic tasks and the atomic task The risk probability of the first resource failure in the task chain executed on the experimental device is represented by represents the experimental device The risk probability of the first resource failure in the task chain executed on it, is the utility modulation sensitivity coefficient, is the device load index modulation coefficient, is the resource failure penalty coefficient.

[0058] A laboratory automation data processing and management system adopting the method as described in the present invention, wherein:

[0059] The acquisition module acquires the device information and resource information of the laboratory.

[0060] The modeling module establishes a device simulation model of the laboratory.

[0061] The task publishing module acquires task instructions and disassembles the task instructions into atomic tasks.

[0062] The analysis module arranges the execution order of the atomic tasks according to the inherent order constraint of the task instructions, the maximum resource utilization rate, and the confrontation results of the minimum scheduling times and the optimal working time of the device.

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

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

[0065] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of the method described in any one of the present invention are implemented.

[0066] Advantages of the present invention: The laboratory automation data processing and management method provided by the present invention can monitor the status of laboratory equipment and resource information in real time, optimize the task execution order, and improve the utilization efficiency of equipment and resources by introducing equipment simulation models and intelligent scheduling algorithms. Specifically, through precise equipment scheduling and resource matching, it not only effectively avoids equipment idleness and failures, reduces resource waste, but also improves the automation and accuracy of experimental operations. In addition, by analyzing the working hours of equipment and the expiration dates of consumables, the present invention can reduce energy consumption and operating costs while ensuring the smooth progress of experiments, further improving the level of laboratory automation management, and providing strong technical support for the efficient operation and innovative research of the laboratory. Description of the Drawings

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0068] Figure 1 It is the overall flowchart of a laboratory automation data processing and management method provided for the first embodiment of the present invention. Detailed Embodiments

[0069] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0070] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a laboratory automation data processing and management method, including:

[0071] S1: Obtain the equipment information and resource information of the laboratory.

[0072] Furthermore, the equipment information includes, but is not limited to, the failure status, working power, and usage status of automatic pipetting equipment and experimental equipment (such as robotic arms, heating / cooling devices, centrifuges, incubators, etc.). It is sourced from the equipment control system or industrial communication protocols (such as OPCUA, Modbus, etc.). The resource information includes information such as the types, specifications, expiration dates, and remaining quantities of experimental consumables, sourced from the laboratory inventory management module or RFID identification system.

[0073] S2: Establish an equipment simulation model for the laboratory, 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, first, abstracting all automatic pipetting equipment and experimental equipment in the laboratory into a set of nodes and a set of edges in the graph. Each node represents a specific experimental unit, and the node status is represented by a binary indicator variable:

[0075]

[0076] Node status is updated in real time over time. For any two nodes , define the transfer cost matrix between nodes , and the elements are:

[0077]

[0078] where are the three-dimensional coordinate vectors of nodes in the experimental space respectively, represents the Euclidean distance, represents the node movement cost, and introduce a dynamic effective graph , where:

[0079]

[0080] where represents the available connections between devices at the current time point, that is, only when both nodes are idle and fault-free at the current time, are valid connections allowed to be formed;

[0081] describes the state dependence relationship and transfer priority between nodes, and define the node weight function :

[0082]

[0083] where is the current resource remaining rate of node , is the modulation sensitivity coefficient, is the resource threshold, is mapped to the interval.

[0084] The task instructions include the set of laboratory automation operation tasks, denoted as , where each task instruction Contains a set of ordered operation step sets .

[0085] For any operation step , it is further decomposed into a set of atomic operation instructions , where each atomic operation instruction is defined as a single, minimally indivisible experimental operation unit, defining a task decomposition mapping , where the set of all atomic tasks corresponding to the specific task instruction is represented as:

[0086]

[0087] That is, each task instruction is fully expanded to the corresponding set of atomic tasks through the operation step hierarchy . .

[0088] By establishing a device simulation model for the laboratory, accurate modeling and real-time monitoring of laboratory devices and operation locations are achieved to optimize the collaboration and task execution efficiency of various devices in the laboratory. Specifically, by modeling the automatic pipetting device and experimental devices as nodes, calculating the moving distances between nodes, and quantifying the status of each node, the real-time status of each device and operation location can be clearly understood.

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

[0090] The task instructions include the set of laboratory automation operation tasks, denoted as , where each task instruction contains a set of ordered operation step sets .

[0091] For any operation step , it is further decomposed into a set of atomic operation instructions , where each atomic operation instruction is defined as a single, minimally indivisible experimental operation unit, defining a task decomposition mapping , where the set of all atomic tasks corresponding to the specific task instruction is represented as:

[0092]

[0093] That is, each task instruction is fully expanded to the corresponding set of atomic tasks through the operation step hierarchy ​​Fully expand to the corresponding set of atomic tasks 。

[0094] After decomposing the task into atomic tasks, the system can flexibly adjust the execution order, parallelism, and device allocation of the tasks according to actual needs, avoiding the problems of excessive complexity of a single task or excessive resource occupation.

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

[0096] According to the task instruction, it can be decomposed into multiple operation steps, and each operation step can be further decomposed into multiple operation instructions, and finally transformed into atomic tasks.

[0097] Task instruction: Pipette a liquid sample from test tube A to test tube B, heat it to 40°C and keep it for 10 minutes, and then perform a centrifugation operation.

[0098] Decomposition steps:

[0099] Operation step 1: Pipetting operation.

[0100] Operation instruction 1: Start the automatic pipetting device and take out the liquid in test tube A.

[0101] Operation instruction 2: Transfer the taken-out liquid to test tube B.

[0102] Operation step 2: Heating operation.

[0103] Operation instruction 1: Start the heating device 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 operation.

[0106] Operation instruction 1: Put test tube B into the centrifuge.

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

[0108] Atomic tasks:

[0109] Atomic task 1: Start the automatic pipetting device and take out the liquid in test tube A.

[0110] Atomic task 2: Transfer the taken-out liquid to test tube B.

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

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

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

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

[0115] S4: Arrange the execution order of the atomic tasks according to the comprehensive analysis results of the inherent order constraints of the task instructions, the maximum utilization rate of resources, the minimum number of scheduling times, and the optimal working time of the equipment.

[0116] The inherent order constraints include that the primary constraint and the secondary constraint simultaneously constrain the order of the atomic tasks. The constraints at both levels are the most basic constraint conditions for "arbitrarily sorting all the atomic tasks to obtain all sequences and forming a sequence set H" in the following text.

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

[0118] For the operation steps in the task instructions, construct a primary order constraint directed acyclic graph model, defined as , where the node set represents the operation steps under the task instruction , and the edge set satisfies:

[0119]

[0120] For any two different operation steps in the task instruction and , if there is a sequential dependency relationship, that is, step must be completed before step starts, then in the directed acyclic graph a directed edge pointing from to is established.

[0121] The primary constraint is defined as the antisymmetric, transitive closure on the primary step partial order relationship :

[0122]

[0123] For the atomic tasks within a single operation step, construct a secondary order constraint directed acyclic subgraph , where the node set represents the atomic tasks in step , and the edge set Satisfy:

[0124]

[0125] If the atomic task Must be completed before Starts, then create a directed edge.

[0126] The secondary constraints form a local partial order relation of the atomic task hierarchy .

[0127] Define the constraint combination mapping function :

[0128]

[0129] Each task instruction By combining the first-level step sequence diagram And the atomic task sequence diagram within each step , is globally mapped to a set of complete executable task chain structures;

[0130] Define the adjacency matrix of the overall constraint relationship :

[0131]

[0132] Among them, the node Is an atomic task node, and all direct and indirect execution sequence relationships are deduced through matrix closure.

[0133] It should be noted that through the first-level constraints and the second-level constraints, it is ensured that the tasks are executed in the logical order in the experimental operation. For example, some experimental operations must start after the previous operation is completed (such as: the pipetting operation of the sample must precede the heating operation). Such constraints help prevent the confusion of operation steps and ensure the scientificity and effectiveness of the experimental process. Ensuring the reasonable order of atomic tasks helps avoid the situation where multiple operations occupy the same equipment or resource at the same time. For example, the pipetting and heating operations cannot be performed on the same equipment at the same time, avoiding equipment conflicts or task conflicts and improving the smoothness and efficiency of the experiment. Constraining the task order helps the system ensure that each atomic task can be executed at an appropriate time during scheduling. By clarifying which tasks must be completed before other tasks, the system can avoid incorrect scheduling and ensure the smooth progress of experimental tasks when the equipment and resources are available.

[0134] Furthermore, the maximum resource utilization rate includes, according to the task instructions, matching the types, specifications and quantities of the required experimental consumables. When selecting experimental consumables, among the matched experimental consumables, preferentially select the experimental consumables that are within the validity period and closest to the end of the validity period.

[0135] The minimum scheduling times include, when sorting atomic tasks, taking the minimum value of the moving distance of the automatic pipetting device as the goal. The optimal working time of the device includes, when sorting atomic tasks, taking the minimum value of the working duration of the experimental device as the goal.

[0136] The minimum scheduling times include, when sorting atomic tasks, taking the minimum value of the moving distance of the automatic pipetting device as the goal.

[0137] The optimal working time of the device includes, when sorting atomic tasks, taking the minimum value of the working duration of the experimental device as the goal.

[0138] The comprehensive analysis process of the minimum scheduling times and the optimal working time of the device is specifically as follows:

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

[0140] Simplify each sequence in H. If n consecutive adjacent atomic tasks are the same task, then merge the adjacent n atomic tasks into m atomic tasks.

[0141]

[0142] Among them, m represents the number of atomic tasks after merging n consecutive adjacent atomic tasks. h is an index, indicating the h-th atomic task among n consecutive adjacent atomic tasks. represents the occupancy of the h-th atomic task on the experimental device. Z represents the occupancy when the experimental device capable of executing atomic tasks is saturated for n consecutive adjacent atomic tasks.

[0143] After completing the merging of each sequence, a new sequence set H+ is obtained. Analyze each sequence in H+:

[0144] By analyzing the execution intervals of the atomic tasks of each experimental consumable in each sequence in H+, calculate the failure degree of the experimental consumable in the sequence. If there is a failure, then exclude the sequence. If there is no failure, then retain the sequence. Obtain a sequence set H++ without failure of experimental consumables.

[0145] In each sequence in H++, evaluate the total moving distance of the automatic pipetting device and the total working duration of the experimental device, and select the sequence corresponding to the minimum value as the output result, expressed as:

[0146]

[0147] Among them, and respectively represent the atomic tasks and atomic tasks The remaining utility value of the corresponding consumable unit Indicates the experimental equipment The risk probability of the first resource failure in the task chain executed on it, is the utility modulation sensitivity coefficient, is the equipment load index modulation coefficient, is the resource failure penalty coefficient.

[0148] It should be noted that by minimizing the moving distance of the automatic pipetting equipment, the operation time and energy consumption of the equipment can be reduced, the efficiency of laboratory equipment can be improved, unnecessary physical movement and equipment idle time can be reduced. This helps to improve the smoothness of the overall experimental process and save the equipment usage cost. By minimizing the working duration of the experimental equipment, overconsumption of the equipment in certain tasks can be avoided, and the usage efficiency of the equipment can be improved. A shorter working time can also avoid the risk of equipment failure caused by long-term operation, and at the same time help to improve the life cycle and stability of the equipment. Merging atomic tasks and reducing the number of consecutive executions of the same task can avoid repeated operations of the equipment and improve the efficiency of task scheduling. This can not only reduce redundant operations, but also make the task sequence more compact and coordinated, thereby optimizing the overall scheduling strategy. Analyzing the execution intervals of experimental consumables in the task sequence can ensure that resources such as reagents and consumables used in the experiment do not fail due to exceeding the expiration date or insufficient inventory. By excluding the failed task sequences, the effectiveness and reliability of the resources required during task execution can be guaranteed, thereby avoiding experimental failures or retries caused by resource problems.

[0149] The degree of failure usually refers to in the task execution sequence, we need to ensure that when arranging the task sequence, experimental consumables (such as reagents, consumables, etc.) do not fail due to too long an execution time interval. For a simple example: The effective duration of reagent A is 5 minutes. If the interval between the front and back operations when reagent A is used in the task sequence exceeds 5 minutes, reagent A will fail. For example, assume that reagent A needs to be used in the first task in the task sequence, and the execution time of this task is T1, and the time to use this reagent in the second task is T2. If T2 - T1 > 5 minutes, then reagent A fails and cannot be used anymore.

[0150] To avoid the failure problems of reagents and consumables, a "failure monitoring mechanism" can be added during the task sorting process to ensure that during the execution of certain tasks, relevant resources (such as reagents) do not fail due to waiting for too long. The specific design is as follows:

[0151] Indicate the required consumables and their effective durations in each task:

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

[0153] Task interval monitoring:

[0154] When sorting tasks, the system calculates the time interval between every two tasks to ensure that all consumables used are utilized within their effective durations. 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 effective duration of reagent A (5 minutes).

[0155] Failure degree assessment and elimination of invalid sequences:

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

[0157] For example, if the reagent A for task 1 expires after the due time in the task sequence, this task sequence will be excluded, and the system will reselect a valid task sequence.

[0158] Dynamic adjustment of task order:

[0159] If some tasks in a certain task sequence will cause the consumables to expire, the system will preferentially adjust these tasks to be executed within a shorter time interval. Alternatively, the system can select alternative experimental consumables (if available) or adjust the task order to ensure the effectiveness of the consumables.

[0160] Comprehensive evaluation and optimization:

[0161] Design a comprehensive evaluation function that takes into account the effective durations of the consumables used in each task and the time intervals between task executions, and selects those task sequences that can avoid consumable expiration to the greatest extent. The optimized task sequence should ensure the efficient utilization of resources as much as possible while ensuring that the consumables do not expire.

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

[0163] Check whether each consumable can be used within its expiration date. If a certain consumable expires after the due time, adjust the task sequence.

[0164] According to the optimization algorithm, select the optimal task sequence so that all consumables can be used within their expiration dates, and the execution order of the tasks satisfies the constraints of the equipment and resources.

[0165] By adding a failure monitoring mechanism, the failure problem of consumables can be effectively avoided. The optimization of the task sequence should not only consider the optimal allocation of equipment and time, but also ensure the reasonable use of resources (such as reagents and consumables) within their validity periods. This approach can improve the stability and efficiency of the experimental process and avoid the waste of experimental resources.

[0166] Total moving distance of the automatic pipetting device: . F represents the number of scheduling times. Represents the moving distance of the automatic pipetting device at the f-th time. f represents the index of the scheduling times.

[0167] Total working duration of the experimental equipment: . B represents the number of devices. Represents the working duration of the b-th device. b represents the index of the device.

[0168] S5: Use the execution order of atomic tasks to execute tasks.

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

[0170] A collection module to obtain the device information and resource information of the laboratory.

[0171] A modeling module to establish a device simulation model of the laboratory.

[0172] A task publishing module to collect task instructions and disassemble the task instructions into atomic tasks.

[0173] An analysis module to arrange the execution order of atomic tasks according to the inherent order constraints of task instructions, the maximum utilization rate of resources, and the confrontation results of the minimum number of scheduling times and the optimal working time of devices.

[0174] An execution module to execute tasks using the execution order of atomic tasks.

[0175] If the above functions are implemented in the form of software function 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 the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0176] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0177] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways if necessary, and then storing it in a computer memory.

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

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

[0180] In this experiment, four tasks are selected, namely Task A, Task B, Task C, and Task D. Each task requires multiple operation steps, and some of these operation steps are the same. For example, both Task A and Task C include the step of "adding liquid with an automatic pipetting device", and both Task B and Task D include the step of "heating in an incubator".

[0181] In the traditional scheduling method, the system will execute all the operation steps of each task separately.

[0182] The execution of each task includes multiple operation steps. The tasks are scheduled according to the traditional scheduling method and the method of the present invention respectively. The experiment records data such as the execution time of each task, the usage duration of the equipment, and the benefits after step merging.

[0183] By comparing the execution times of the traditional method and the method of the present invention, it can be obtained that in Tasks A and C, due to the merging of the same operation step of "adding liquid with an automatic pipetting device", their execution times are optimized. For example, in the traditional method, Tasks A and C respectively require 10 minutes for liquid addition, while in the method of the present invention, the liquid addition steps of the two only need to be executed once, reducing time waste. In Task D, the heating times of the traditional method and the method of the present invention are 20 minutes and 15 minutes respectively, which also reflects that the merging between tasks reduces the repeated use time of the equipment.

[0184] In the traditional scheduling method, the "adding liquid with an automatic pipetting device" steps of Tasks A and C are executed separately, while in the scheduling of the present invention, these operation steps are merged for execution, making the movement and use of the equipment more efficient and reducing unnecessary operations. The method of the present invention can greatly reduce the idle time and repetitive work of the equipment, improve the utilization rate of the equipment, and avoid repeated operations at the same time.

[0185] By combining the same operation steps, the moving distances of the automatic pipetting device and the incubator are reduced. For example, after combining the liquid addition steps of the automatic pipetting device in Task A and Task C, the device only needs to move once, thus saving the repetitive moving 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 not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for laboratory automation data processing and management, characterized in that, It includes: Obtain the equipment information and resource information of the laboratory; Establish an equipment simulation model for the laboratory, 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; Collect task instructions, disassemble the task instructions into atomic tasks, and analyze the sequence and resource requirements between tasks; Arrange the execution order of the atomic tasks according to the comprehensive analysis results of the inherent order constraints, maximum resource utilization rate, minimum scheduling times, and optimal working time of the equipment of the task instructions; Use the execution order of the atomic tasks to execute the tasks.

2. The laboratory automation data processing and management method according to claim 1, characterized in that: The equipment information includes the failure status, working power, and usage status of automatic pipetting equipment and experimental equipment; The resource information includes the types, specifications, expiration dates, and remaining quantities of experimental consumables.

3. A laboratory automation data processing and management method according to claim 2, characterized in that: The device simulation model includes, first, abstracting all the automatic pipetting devices and experimental devices in the laboratory as the node set in the figure and the edge set Each node represents a specific experimental unit, and the node state is represented by a binary indicator variable: , Node status is updated in real time over time. For any two nodes , a defines the transfer cost matrix between nodes , and the elements are as follows: , where are the three-dimensional coordinate vectors of the nodes in the experimental space, represents the Euclidean distance, represents the node movement cost, and a dynamic effective graph is introduced , where: , where represents the connections between devices available at the current time point, that is, only when both nodes are idle and fault-free at the current time, an effective edge connection is allowed to be formed; Describe the state dependency relationship and transfer priority between nodes, and define the node weight function : , where is the node The current resource remaining rate, is the modulation sensitivity coefficient, is the resource threshold, is mapped to interval.

4. A laboratory automation data processing and management method according to claim 3, characterized in that: The task instructions include a set of laboratory automation operation tasks, denoted as , where each task instruction contains a set of ordered operation step sets ; For any operation step , it is further decomposed into a set of atomic operation instructions , where each atomic operation instruction is defined as a single, minimally indivisible experimental operation unit, and a task decomposition mapping is defined, where the entire set of atomic tasks corresponding to the specific task instruction is expressed as: , that is, each task instruction is fully expanded through the operation step hierarchy to the corresponding atomic task set .

5. The laboratory automation data processing and management method according to claim 4, wherein: The inherent order constraints include primary constraints and secondary constraints, which simultaneously control the execution order of atomic tasks, where: Construct a first-level sequential constraint directed acyclic graph model for the operation steps in the task instruction, defined as , where the node set represents the operation steps under the task instruction , and the edge set satisfies: , for the task instruction among any two different operation steps and , if there is a sequential dependency relationship, that is, step must be completed before step starts, then in the directed acyclic graph , establish a directed edge pointing from to ; the first-level constraint is defined as the antisymmetric, transitive closure on the first-level step partial order : For the atomic tasks within a single operation step, a second-level sequential constraint directed acyclic subgraph is constructed , where the node set represents the atomic tasks in step , and the edge set satisfies: , if the atomic task must be completed before starting, then a directed edge is established; The second-level constraints form a local partial order relation of the atomic task hierarchy ; Define the constraint combination mapping function : , each task instruction By combining the first-level step sequence diagram with the internal atomic task sequence diagram of each step , it is overall mapped into a set of complete executable task chain structures; Adjacency matrix defining the overall constraint relationship : , where the node is an atomic task node, and all direct and indirect execution order relationships are derived through matrix closure.

6. The laboratory automation data processing and management method according to claim 5, characterized in that: The maximum resource utilization rate includes matching the types, specifications, and quantities of required experimental consumables according to the task instructions; When selecting experimental consumables, among the matched experimental consumables, preferentially select the experimental consumables that are within the expiration date and closest to the end of the expiration date.

7. A laboratory automation data processing and management method according to claim 6, characterized in that: The minimum scheduling times include taking the minimum value of the moving distance of the automatic pipetting equipment as the goal when sorting the atomic tasks; The optimal working time of the equipment includes taking the minimum value of the working duration of the experimental equipment as the goal when sorting the atomic tasks; The comprehensive analysis process of the minimum scheduling times and the optimal working time of the equipment is specifically: Arbitrarily sort all atomic tasks to obtain all sequences, forming a sequence set H; Simplify each sequence in H. If n consecutive adjacent atomic tasks are the same task, then merge the adjacent n atomic tasks into m atomic tasks; , where m represents the number of atomic tasks after merging n continuously adjacent atomic tasks; h is an index representing the h-th atomic task among n continuously adjacent atomic tasks; represents the occupancy of the h-th atomic task on the experimental equipment; Z represents the occupancy of the experimental equipment that can execute atomic tasks when saturated for n continuously adjacent atomic tasks; After each sequence in H is merged, a new sequence set H+ is obtained; analyze each sequence in H+: By analyzing the execution interval of the atomic tasks of each experimental consumable in each sequence in H+, calculate the failure degree of the experimental consumable in the sequence. If there is a failure, exclude the sequence; if there is no failure, retain the sequence; obtain a sequence set H++ without failure of experimental consumables; In each sequence in H++, evaluate the total moving distance of the automatic pipetting equipment and the total working duration of the experimental equipment, and select the sequence corresponding to the minimum value as the output result, expressed as: , where and represent the remaining utility values of the consumable units corresponding to the atomic tasks and atomic task respectively represents the experimental equipment is the risk probability of the first resource failure in the task chain executed on it, is the utility modulation sensitivity coefficient, is the equipment load index modulation coefficient, is the resource failure penalty coefficient.

8. A laboratory automation data processing and management system, which applies a laboratory automation data processing and management method as described in any one of claims 1 to 7, characterized in that, It includes: A collection module that obtains the equipment information and resource information of the laboratory; A modeling module that establishes an equipment simulation model for the laboratory; A task publishing module that collects task instructions and disassembles the task instructions into atomic tasks; An analysis module that arranges the execution order of the atomic tasks according to the confrontation results of the inherent order constraints, maximum resource utilization rate, minimum scheduling times, and optimal working time of the equipment of the task instructions; An execution module that uses the execution order of the atomic tasks to execute the tasks.

9. 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, the steps of the laboratory automation data processing and management method according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the laboratory automation data processing and management method according to any one of claims 1-7 are implemented.

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