Multitask parallel experiment system and method for laboratory

Abstract

The invention relates to the technical field of laboratory control, in particular to a multitask parallel experiment system and method for a laboratory, and the system comprises a task management module which is used for receiving and splitting a plurality of experiment plans submitted by a user, and obtaining a plurality of experiment tasks corresponding to the experiment plans; the task constraint module is used for performing condition constraint on the experiment task according to the constraint rule to obtain a constraint experiment task with a constraint condition; the material detection module is used for acquiring material container resource information of the experiment platform, performing material container resource detection on the constraint experiment task according to the material container resource information, and generating a resource satisfaction signal; and the multi-task scheduling module is used for constructing a multi-thread scheduling strategy based on the thread pool management mechanism and the resource satisfaction signal, and scheduling the execution module to execute a plurality of experiment plans according to the multi-thread scheduling strategy. The utilization rate of experimental resources can be optimized, so that the execution efficiency of experimental tasks and the stability of experimental operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of laboratory control, and in particular to a multi-task parallel experiment system and method for a laboratory. BACKGROUND

[0002] In the field of chemical experiments, the execution of laboratory experiment tasks usually relies on manual operation or single-task automated equipment. In a multi-task experiment environment, the experimental steps, resource requirements, and experimental equipment occupation of different experiment tasks differ greatly, and manual scheduling cannot ensure the optimal execution order of experiment tasks, which can easily lead to low efficiency of experiment task execution, resource waste, and uneven utilization of experimental equipment.

[0003] However, the automatic chemical experiment platforms currently available on the market are usually customized designed according to specific experiment types, have limited applicable experiment types, and can only execute steps of a single experiment, which leads to a long time required for completing an experiment, and thus there is room for improvement. SUMMARY

[0004] The present application provides a multi-task parallel experiment system and method for a laboratory, which can optimize the utilization of experimental resources and thus improve the execution efficiency of experiment tasks and the stability of experiment operation.

[0005] In a first aspect, the present application provides a multi-task parallel experiment system for a laboratory, comprising: a task management module configured to receive a plurality of experiment plans submitted by a user, split the experiment plans, and obtain a plurality of experiment tasks corresponding to the experiment plans; a task constraint module configured to conditionally constrain the experiment tasks according to constraint rules and obtain constraint experiment tasks with constraint conditions; a material detection module configured to obtain material container resource information of an experiment platform, perform material container resource detection on the constraint experiment tasks according to the material container resource information, and generate a resource satisfaction signal; a multi-task scheduling module configured to construct a multi-thread scheduling strategy based on a thread pool management mechanism and the resource satisfaction signal, and execute a plurality of the experiment plans with a scheduling execution module according to the multi-thread scheduling strategy.

[0006] By adopting the technical scheme, the experiment process is refined by the task management module, so as to improve the flexibility of experiment task scheduling, the execution of the experiment task is ensured to comply with the experiment rules by the task constraint module, and problems such as incorrect experiment execution sequence or unmatched reagents are avoided, so as to improve the success rate and repeatability of the experiment, the experiment task is ensured to have enough experimental raw materials before execution by the material detection module, and the experiment task is prevented from being interrupted due to material shortage, so as to improve the stability of the experiment operation, the experiment equipment resources are reasonably utilized by the multi-task scheduling module, and the parallel execution efficiency of the experiment task is improved, so as to improve the execution efficiency of the experiment task and the stability of the experiment operation.

[0007] In some embodiments in combination with the first aspect, the several experiment plans submitted by the user include: an experiment name, an experiment execution device, an experiment raw material, an experiment step, and an experiment reaction time.

[0008] By adopting the technical scheme, the experiment plan has complete information, which can facilitate the subsequent task management module to split and schedule the experiment task, so as to improve the accuracy and execution efficiency of the experiment task.

[0009] In some embodiments in combination with the first aspect, the task constraint module includes: A rule construction submodule is configured to establish a constraint logic framework for the logical relationship between the experiment raw materials and the experiment steps in different experiment plans, add a constraint condition according to the numerical value of the experiment raw material corresponding to each experiment step, and add each constraint condition to the constraint logic framework to generate a constraint rule. A constraint task submodule is configured to constrain each experiment task by using the constraint rule to obtain several constraint steps, mark each constraint step in the corresponding experiment plan, and obtain the constraint experiment task with the constraint condition.

[0010] By adopting the technical scheme, the execution of the experiment task is ensured to comply with the experiment dependency relationship by the rule construction submodule, so as to avoid experiment failure caused by incorrect experiment step execution, and the logical sequence of the experiment task execution can be determined by the constraint task submodule, so as to improve the reliability of the experiment operation and avoid experiment task execution failure caused by experiment conditions not meeting the requirements.

[0011] In some embodiments in combination with the first aspect, the material detection module includes: A material collection submodule is configured to collect material container resource information of the experiment platform. A material comparison submodule is configured to compare the experiment raw material of the constraint experiment task according to the material container resource information to obtain a comparison result. a detection analysis submodule configured to perform material container resource detection according to the comparison result, and generate a resource satisfaction signal if the material container resource satisfies the execution condition of the current constraint experiment task.

[0012] By using the above technical solutions, the inventory of reagents, equipment and experimental consumables in the laboratory is obtained in real time by the material collection submodule, the material management capability of the experiment task is improved, and it is determined whether the reagent and equipment resources required by the experiment task are sufficient by the material comparison submodule, so as to reduce the waiting time of the experiment task caused by insufficient resources, and the dynamic monitoring of the experimental resources is realized by the detection analysis submodule, so as to ensure that the experiment task can be executed smoothly, thereby improving the operation stability of the experiment system.

[0013] In combination with some embodiments of the first aspect, in some embodiments, the task constraint module is further configured to, if the number of experiment plans exceeds a preset number, perform conditional constraint on the experiment plans according to the resource satisfaction signal, and update a plurality of experiment plans in combination with the corresponding constraint experiment tasks.

[0014] By using the above technical solutions, when the number of experiment plans exceeds a preset threshold, conditional constraint is performed based on the resource satisfaction signal, so as to ensure that the execution priority of the experiment task meets the resource availability, thereby avoiding execution delay of the experiment task caused by resource shortage or equipment occupation, and the experiment plans are updated in combination with the constraint experiment tasks, so as to optimize the experiment scheduling, ensure that the experiment task is executed in an optimal order, improve the utilization rate of experimental resources, and reduce the experiment waiting time.

[0015] In combination with some embodiments of the first aspect, in some embodiments, the multi-task scheduling module comprises: a thread scheduling submodule configured to perform thread pool management on the experiment plans based on the resource satisfaction signal and a thread pool management mechanism, and obtain a multi-thread scheduling strategy; a task scheduling submodule configured to perform task allocation on the constraint experiment tasks according to the multi-thread scheduling strategy, issue a control instruction and a cooperative work instruction to execute an experiment operation of the experiment plan, and obtain an experiment result.

[0016] By using the above technical solutions, the experiment task is efficiently executed within the range of available resources by the thread scheduling submodule, so as to prevent the experiment task from being blocked due to insufficient computing resources or equipment resources, thereby improving the experiment throughput, improving the automation degree of the laboratory by the task scheduling submodule, reducing manual intervention, and improving the accuracy of the experiment operation.

[0017] In some embodiments of the first aspect, in some embodiments, during the experimental operation, if the experimental step with the constraint condition in the constraint experimental task is reached, a material lock mechanism is triggered, and the material lock mechanism is used to constrain multiple preparations of the same material, and it is marked that the material has been locked.

[0018] By using the above technical solution, by triggering the material lock mechanism, it can be ensured that the use of the same material in different experimental tasks will not cause resource conflicts, and it can prevent the experimental execution process from being abnormal due to repeated calling of the material in the experimental process, thereby improving the stability of the execution of the experimental task.

[0019] In some embodiments of the first aspect, in some embodiments, the material detection module is further configured to, if the material container resource does not meet the execution condition of the current constraint experimental task, detect the material of another experimental plan; and advance the execution order of the experimental plan that meets the execution condition.

[0020] By using the above technical solution, when the material container resource does not meet the execution condition of the experimental task, the material of other experimental plans is detected, which can prioritize the execution of the experimental task with available resources in the case of limited experimental resources, optimize experimental scheduling, reduce waste of experimental resources, improve the operation efficiency of the experimental system, and improve the parallel execution capability of the experimental system.

[0021] In some embodiments of the first aspect, in some embodiments, the experimental control module is further configured to receive the control instruction and the collaborative work instruction sent by the multi-task scheduling module, control the mechanical arm to perform corresponding experimental operations according to the control instruction, and coordinate different experimental operation tables to perform collaborative experimental operations according to the collaborative work instruction.

[0022] By using the above technical solution, the experimental control module improves the accuracy of the experimental operation, ensures that the experimental task is executed according to the preset process, and coordinates the experimental tasks of different experimental operation tables according to the collaborative work instruction, which can ensure that multiple experimental tasks can be performed collaboratively, thereby improving the task execution capability of the experimental system and improving the overall efficiency of the experiment.

[0023] In a second aspect, the present application provides a multi-task parallel experiment method of a laboratory, which is applied to the multi-task parallel experiment system of the laboratory as described in the first aspect, and includes the following steps: S1, receiving a plurality of experimental plans submitted by a user, and splitting the experimental plans to obtain a plurality of experimental tasks corresponding to the experimental plans; S2, performing condition constraint on the experimental tasks according to constraint rules to obtain constraint experimental tasks with constraint conditions; S3, acquire material container resource information of the experimental platform, perform material container resource detection on the constraint experiment task according to the material container resource information, and generate a resource satisfaction signal; S4, based on the thread pool management mechanism and the resource satisfaction signal, construct a multi-thread scheduling strategy, and execute a plurality of experiment plans by a scheduling execution module according to the multi-thread scheduling strategy.

[0024] By adopting the above technical solution, the experiment plan submitted by the user is received by the task management module and split into experiment tasks, which can refine the experiment process, thereby improving the flexibility of experiment task scheduling. The experiment task is conditionally constrained by the task constraint module, which can ensure that the execution of the experiment task conforms to the experiment rules, avoiding problems such as incorrect experiment execution order or reagent mismatch, thereby improving the success rate and repeatability of the experiment. The material detection module acquires the material container resource information of the experimental platform and performs resource detection, which can ensure that there is enough experimental raw material before the experiment task is executed, avoiding experiment task interruption due to material shortage, thereby improving the stability of the experiment operation. The multi-task scheduling module constructs a multi-thread scheduling strategy based on the thread pool management mechanism and the resource satisfaction signal, which can reasonably utilize experimental equipment resources and improve the parallel execution efficiency of experiment tasks, achieving the purpose of improving the execution efficiency of experiment tasks and the stability of experiment operation.

[0025] In summary, the present application has at least one of the following beneficial technical effects: 1. The experiment plan submitted by the user is received by the task management module and split into experiment tasks, which can refine the experiment process, thereby improving the flexibility of experiment task scheduling. The experiment task is conditionally constrained by the task constraint module, which can ensure that the execution of the experiment task conforms to the experiment rules, avoiding problems such as incorrect experiment execution order or reagent mismatch, thereby improving the success rate and repeatability of the experiment. The material detection module acquires the material container resource information of the experimental platform and performs resource detection, which can ensure that there is enough experimental raw material before the experiment task is executed, avoiding experiment task interruption due to material shortage, thereby improving the stability of the experiment operation. The multi-task scheduling module constructs a multi-thread scheduling strategy based on the thread pool management mechanism and the resource satisfaction signal, which can reasonably utilize experimental equipment resources and improve the parallel execution efficiency of experiment tasks, achieving the purpose of improving the execution efficiency of experiment tasks and the stability of experiment operation. 2. The experiment control module improves the accuracy of the experiment operation, ensuring that the experiment task is executed according to the preset process. By coordinating the experiment tasks of different experiment operation tables according to the cooperative work instruction, multiple experiment tasks can be cooperatively performed, thereby improving the task execution capability of the experiment system and the overall efficiency of the experiment. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a structural schematic diagram of a multi-task parallel experiment system of a laboratory in Embodiment One of the present application; Figure 2 is a working process schematic diagram of a multi-task parallel experiment method of a laboratory in Embodiment Two of the present application; Figure 3 is another working process schematic diagram of a multi-task parallel experiment method of a laboratory in the present application; Figure 4 is another structural schematic diagram of a multi-task parallel experiment system of a laboratory in the present application. DETAILED DESCRIPTION

[0027] The following embodiments will help those skilled in the art to further understand the role of the present application, but do not limit the present application in any form. It should be noted that, for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made. These all belong to the protection scope of the present application.

[0028] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to thoroughly understand the embodiments of the present application, but not for limitation. It should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits and methods are omitted to avoid unnecessary details that hinder the description of the present application.

[0029] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, whole, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0030] The present application is further described in detail below in conjunction with the accompanying drawings.

[0031] Embodiment One The multi-task parallel experiment system of a laboratory provided by the embodiments of the present application, as shown in Figure 1 includes a task management module, a task constraint module, a material detection module and a multi-task scheduling module. The task management module is used to receive a plurality of experiment plans submitted by a user, split the experiment plans, and obtain a plurality of experiment tasks corresponding to the experiment plans.

[0032] Among them, the plurality of experiment plans submitted by the user includes: experiment name, experiment execution device, experiment raw material, experiment step and experiment reaction time, etc.

[0033] A task constraint module is configured to conditionally constrain the experimental tasks according to constraint rules to obtain constraint experimental tasks with constraint conditions.

[0034] Specifically, the experimental tasks in the experimental plan are not completely independent, and have certain logical relationships, such as sequence, experimental dependence, and environmental requirements. For example, in a chemical experiment, the mixing of reagent A must be performed after the dissolution of reagent B is completed, otherwise the experiment may fail. Moreover, the experimental delivery does not concern the step operation of material preparation. Therefore, the first constraint information needs to be established to ensure that the experiment can be performed according to the sequence and prevent the experiment from failing due to incorrect operation.

[0035] A material detection module is configured to obtain material container resource information of the experimental platform, perform material container resource detection on the constraint experimental tasks according to the material container resource information, and generate a resource satisfaction signal.

[0036] Specifically, the experimental tasks usually need to use specific reagents, samples, or experimental equipment, such as solvents and catalysts for chemical experiments, and cell culture medium and buffer solution for biological experiments. Before the experimental tasks are performed, the material detection module first checks the material inventory of the experimental platform to ensure that the required experimental resources are sufficient and available, and prevent the experiment from being interrupted due to lack of reagents or equipment.

[0037] A multi-task scheduling module is configured to construct a multi-thread scheduling strategy based on a thread pool management mechanism and the resource satisfaction signal, and perform the experimental plan by a scheduling execution module according to the multi-thread scheduling strategy.

[0038] Specifically, the multi-task scheduling module first allocates computing resources and execution threads for the experimental tasks based on the thread pool management mechanism, reasonably allocates threads based on the priority, execution sequence, and resource availability of the experimental tasks, and then coordinates the execution sequence of the experimental tasks according to the constructed multi-thread scheduling strategy to ensure that the experimental tasks are performed according to the optimal path.

[0039] In a specific embodiment, the task constraint module comprises: A rule construction submodule is configured to establish a constraint logic framework for the logical relationship between experimental materials and experimental steps in different experimental plans, add numerical values according to the experimental materials corresponding to each experimental step to establish corresponding constraint conditions, and add each constraint condition to the constraint logic framework to generate constraint rules.

[0040] Specifically, after the experimental plan is parsed, the rule construction submodule first analyzes the execution relationship of the experimental steps, extracts the order between the experimental tasks, extracts the dependency relationship of the experimental steps, and establishes the logical constraint framework between the experimental tasks. For example, in an organic synthesis experiment, "reaction solvent addition" must be performed before "catalyst addition", otherwise it may cause the experiment to fail.

[0041] And because in the experimental process, the operation of adding materials and performing reactions exists multiple times and randomly, and there may be a case of adding materials after performing reactions, and the step operation of preparing materials is not concerned when the experiment is issued, for each experimental material involved in each experimental step, the experimental parameter constraint is defined, the material adding and reaction are taken as constraint conditions, all constraint conditions are stored in the constraint logic framework, and the constraint rule is generated for reference in subsequent experimental task scheduling and execution.

[0042] The constraint task submodule is used to constrain each experimental task by using the constraint rule to obtain a plurality of constraint steps, and each constraint step is marked in the corresponding experimental plan to obtain a constraint experimental task with a constraint condition.

[0043] Specifically, the constraint task submodule reads the constraint rule and matches each experimental task according to the constraint rule, i.e., marks the links involving material adding and reaction in the experimental task sequence, thereby obtaining a constraint experimental task with a constraint condition.

[0044] In a specific embodiment, the material detection module comprises: The material collection submodule is used to collect the material container resource information of the experimental platform.

[0045] Specifically, by reading the database, the material resource information on the experimental platform can be obtained in real time, including the storage information of experimental materials such as reagents, samples, and solvents. For example, the user regularly updates the inventory quantity of all materials in the laboratory by using the inventory management system. Therefore, the experimental system can realize material collection by querying the database information in real time.

[0046] The material comparison submodule is used to compare the material container resource quantity with the experimental material of the constraint experimental task to obtain a comparison result.

[0047] Specifically, by obtaining the experimental material information required by the experimental task and matching it with the actual inventory data obtained from the database, it is determined whether the material required by the experimental task is sufficient. For example, in an organic synthesis experiment, if 100 mL of ethanol is required for a certain experimental task, and there are currently 200 mL of inventory, the comparison result is "satisfy condition", otherwise the comparison result is "resource insufficient", and the missing material information is recorded.

[0048] The detection analysis submodule is configured to detect the material container resources according to the comparison result, and generate a resource satisfaction signal if the material container resources satisfy the execution condition of the current constraint experiment task.

[0049] Specifically, after matching the experiment requirements with the inventory resources, the executability of the experiment task is further determined according to the comparison result. If all the experimental materials meet the requirements, a resource satisfaction signal is generated, allowing the experiment task to enter the task scheduling queue. If the experimental materials are insufficient, a resource deficiency signal is generated, and the experiment task will be postponed or adjusted in sequence.

[0050] In a specific embodiment, the task constraint module is further configured to, if the number of experiment plans exceeds a preset number, perform conditional constraint on the experiment plans according to the resource satisfaction signal, and update the experiment plans in combination with the corresponding constraint experiment task.

[0051] It can be seen that, since the experimental resources of the laboratory are limited, when the number of experiment plans input by the user exceeds the preset number, it is determined whether the experimental resources can support the parallel execution of all experiment tasks according to the resource satisfaction signal, and then the supply of the experimental resources such as reagents, experimental equipment, and burettes is analyzed. According to the constraint information of the constraint experiment task and the occupation of the experimental resources, constraint conditions are added to the experiment plans. If the material resources are sufficient, all the experiment plans are executed in the original order. If the material resources are insufficient, the experiment plans are optimized and adjusted according to the priority of the experiment task, the resource occupation, and the task dependency relationship.

[0052] Exemplarily, the experiment plans or experiment tasks that meet the number of material resources, have less occupation of experimental resources, and are issued first are prioritized, thereby improving the execution efficiency of the experiment system and enabling the experiment to proceed smoothly under optimal conditions.

[0053] In a specific embodiment, the multi-task scheduling module comprises: The thread scheduling submodule is configured to perform thread pool management on the experiment plans based on the resource satisfaction signal and a thread pool management mechanism, to obtain a multi-thread scheduling strategy.

[0054] Specifically, by acquiring the resource satisfaction signal, it is ensured that the experimental task will enter the scheduling queue only when the required materials, reagents, equipment and other resources for the experiment meet the requirements, and based on the thread pool management mechanism, the thread allocation of the experimental task is dynamically adjusted according to the number of experimental tasks, the availability of experimental equipment and the execution priority of the experimental task, so as to obtain a multi-thread scheduling strategy. And by controlling the number of concurrent experimental tasks, it is ensured that the execution of the experimental task will not exceed the maximum carrying capacity of the experimental equipment, and the experimental task will be performed under reasonable calculation and resource constraints. Under the premise that the experimental resources are available, the experimental task is efficiently allocated through the thread pool management mechanism, and the parallel execution of the experimental task is ensured.

[0055] The task scheduling submodule is configured to allocate the constraint experimental task according to the multi-thread scheduling strategy, issue control instructions and cooperative work instructions to execute the experimental operation of the experimental plan, and obtain the experimental result.

[0056] Specifically, when the experimental task enters the execution phase, the experimental task is allocated to the corresponding experimental equipment according to the multi-thread scheduling strategy, and control instructions are issued to drive the automated experimental equipment to perform operations, such as controlling the mechanical arm to pick up containers, etc.

[0057] Further, in the experimental process involving multiple experimental plans, by sending cooperative work instructions, it is ensured that different experimental tasks are executed synchronously or in a logical order to prevent resource conflicts or experimental failures, so that after the experiment is completed, the task scheduling submodule records the experimental data and feeds back the task execution status for subsequent data analysis and experimental optimization.

[0058] In a specific embodiment, during the experimental operation, if the experimental step with a constraint condition in the constraint experimental task is reached, the material lock mechanism is triggered. The material lock mechanism is used to constrain the preparation of the same material multiple times and marks that the material has been locked.

[0059] Specifically, during the experimental operation, when the experimental task is executed to the experimental step with a constraint condition, the system detects whether the experimental step involves shared experimental materials. If the experimental materials are repeatedly used in multiple experimental tasks, the material lock mechanism is triggered to prevent experimental conflicts or experimental failures due to resource competition. After the material lock mechanism is triggered, it marks that the experimental material has been occupied, limits other experimental tasks from simultaneously calling the same material, and ensures that the same batch of experimental raw materials will not cause proportioning error or resource shortage due to the calling of concurrent experimental tasks.

[0060] Further, after the experimental task is completed, the system detects the use state of the current material, and if the experimental task no longer needs the material, the material locking mechanism releases the material locking state, allowing the subsequent experimental task to continue to use the material, thereby improving the rational scheduling of experimental resources and ensuring the stability of the experimental process and the accuracy of the data.

[0061] In a specific embodiment, the material detection module is further configured to detect the material of another experimental plan if the material container resource does not meet the execution condition of the current constrained experimental task, and to advance the execution order of the experimental plan that meets the execution condition.

[0062] Specifically, when it is detected that the material container resource of the current experimental task does not meet the execution condition, the module detects the material of other experimental plans in the experimental plan queue, queries whether there is an experimental plan that has sufficient material resources and is not limited by other constraint conditions, and if there is an experimental task that meets the execution condition, the execution order of the experimental plan is adjusted, and the executable experimental plan is executed in advance to reduce the experimental waiting time and improve the utilization rate of the experimental equipment. At the same time, after adjusting the order, the module continues to monitor the experimental tasks affected by the shortage of material resources, and re-executes the material detection after the resources are supplemented or the experimental order is optimized, to ensure that all experimental tasks are successfully completed under the optimal resource allocation strategy.

[0063] In a specific embodiment, the experimental control module is configured to receive the control instruction and the cooperative work instruction sent by the multi-task scheduling module, control the robot arm to perform corresponding experimental operations according to the control instruction, and coordinate different experimental operation tables to perform cooperative experimental operations according to the cooperative work instruction.

[0064] Specifically, by receiving the control instruction sent by the multi-task scheduling module and analyzing the specific operation requirements of the experimental task, the experimental operations such as reagent suction, sample transfer, stirring and mixing, temperature control reaction, etc. are performed by controlling the robot arm to ensure accurate execution of the experimental task. At the same time, in the experimental process involving multiple experimental operation tables, the experimental tasks of different experimental operation tables are synchronized by receiving and processing the cooperative work instruction, avoiding experimental resource conflicts, improving experimental efficiency and utilization of experimental equipment, and ensuring the coherence of the entire experimental process and the stability of the automated execution.

[0065] On the basis of Embodiment One, the multi-task parallel experimental system of the laboratory can further be provided with an alarm processing module and a display module.

[0066] As a key component ensuring stable system operation and experimental safety, the alarm processing module receives real-time feedback from the material detection module and multiple experimental control modules regarding various abnormal information such as material shortages, action deviations, and abnormal process parameters. It then activates a preset alarm mechanism, notifying operators through sound, light, and information push notifications. Based on the type and severity of the abnormality, it invokes corresponding strategies to coordinate with relevant modules and equipment for troubleshooting and repair. After the abnormality is resolved, it automatically performs an alarm recovery operation, resetting the system status to ensure the smooth progress of the experiment.

[0067] The display module is a key component for system-user interaction, featuring multiple functional pages. The experimental platform status information page utilizes real-time data acquisition and dynamic updating technology to display the usage status of resources such as material containers, including material inventory and module operating status, allowing operators to easily monitor resource status in real time. The operation and maintenance management page primarily stores and retrieves experimental logs and alarm logs. Operators can query by time, type, and other dimensions to trace the experimental process, locate abnormal events, and assist in system troubleshooting and maintenance. Furthermore, the page stores system configuration information, allowing operators to configure network communication parameters and device connections, ensuring stable communication between the display module and other modules, and providing a convenient way for system customization and resource optimization.

[0068] Example 2 Embodiment 2 of this application provides a multi-task parallel experimental method for the laboratory described in Embodiment 1, such as... Figure 2 As shown, it includes the following steps: S1. Receive several experimental plans submitted by users, break down the experimental plans, and obtain several experimental tasks corresponding to the experimental plans; S2. Apply constraints to the experimental task according to the constraint rules to obtain a constrained experimental task with constraints. S3. Obtain the material container resource information of the experimental platform, perform material container resource detection on the constrained experimental task based on the material container resource information, and generate a resource satisfaction signal; S4. Based on the thread pool management mechanism and resource satisfaction signal, construct a multi-thread scheduling strategy, and execute several experimental plans by the scheduling execution module according to the multi-thread scheduling strategy.

[0069] The steps and methods involved in the above embodiment two correspond to those in embodiment one. For specific implementation details, please refer to the relevant description section of embodiment one.

[0070] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A laboratory multi-task parallel experimental system, characterized in that, include: The task management module is used to receive several experimental plans submitted by users, split the experimental plans, and obtain several experimental tasks corresponding to the experimental plans. The task constraint module is used to impose condition constraints on the experimental task according to the constraint rules, so as to obtain a constrained experimental task with constraints. The material detection module is used to acquire material container resource information of the experimental platform, perform material container resource detection on the constrained experimental task based on the material container resource information, and generate a resource satisfaction signal. The multi-task scheduling module is used to construct a multi-thread scheduling strategy based on the thread pool management mechanism and the resource satisfaction signal, and to execute several experimental plans by the scheduling execution module according to the multi-thread scheduling strategy.

2. The laboratory multi-task parallel experimental system according to claim 1, characterized in that, The experimental plans submitted by the user include: experimental name, experimental equipment, experimental materials, experimental steps, and experimental reaction time.

3. The laboratory multi-task parallel experimental system according to claim 2, characterized in that, The task constraint module includes: The rule construction submodule is used to establish a constraint logic framework for the logical relationship between the experimental materials and the experimental steps in different experimental plans. It adds values ​​to the experimental materials corresponding to each experimental step to establish corresponding constraint conditions, and adds each constraint condition to the constraint logic framework to generate constraint rules. The constraint task submodule is used to constrain each of the experimental tasks using the constraint rules to obtain several constraint steps, and to mark each constraint step in the corresponding experimental plan to obtain the constrained experimental task with constraints.

4. A laboratory multi-task parallel experimental system according to claim 1, characterized in that, The material detection module includes: The material acquisition submodule is used to collect material container resource information of the experimental platform; The material comparison submodule is used to compare the material container resource information with the experimental raw materials of the constraint experiment task to obtain the comparison result. The detection and analysis submodule is used to detect material container resources based on the comparison results. If the material container resources meet the execution conditions of the current constraint experiment task, then the resource satisfaction signal is generated.

5. A laboratory multi-task parallel experimental system according to claim 1, characterized in that, The task constraint module is further configured to, if the number of experimental plans exceeds a preset number, impose conditional constraints on the experimental plans based on the resource satisfaction signal, and update several experimental plans in conjunction with the corresponding constrained experimental tasks.

6. A laboratory multi-task parallel experimental system according to claim 5, characterized in that, The multi-task scheduling module includes: The thread scheduling submodule is used to perform thread pool management on the experimental plan based on the resource satisfaction signal and thread pool management mechanism to obtain a multi-thread scheduling strategy. The task scheduling submodule is used to allocate tasks to the constrained experiment tasks according to the multi-threaded scheduling strategy, issue control instructions and collaborative work instructions to execute the experimental operations of the experimental plan, and obtain experimental results.

7. A laboratory multi-task parallel experimental system according to claim 3, characterized in that, During the experimental operation, if the experimental step with constraints is reached in the constrained experimental task, the material locking mechanism is triggered. The material locking mechanism is used to constrain the multiple preparations of the same material and marks that the material has been locked.

8. A laboratory multi-task parallel experimental system according to claim 4, characterized in that, The material detection module is further configured to perform material detection on another experimental plan if the material container resources do not meet the execution conditions of the current constraint experiment task; and to advance the execution order of the experimental plan that meets the execution conditions.

9. A laboratory multi-task parallel experimental system according to claim 6, characterized in that, Also includes: The experimental control module is used to receive control commands and collaborative work commands sent by the multi-task scheduling module, control the robotic arm to perform corresponding experimental operations according to the control commands, and coordinate different experimental operating platforms to perform collaborative experimental operations according to the collaborative work commands.

10. A laboratory multi-task parallel experimental method, applied to a laboratory multi-task parallel experimental system as described in any one of claims 1-9, characterized in that, Includes the following steps: S1. Receive several experimental plans submitted by the user, and break down the experimental plans to obtain several experimental tasks corresponding to the experimental plans; S2. Apply conditional constraints to the experimental task according to the constraint rules to obtain a constrained experimental task with constraints. S3. Obtain the material container resource information of the experimental platform, perform material container resource detection on the constrained experimental task based on the material container resource information, and generate a resource satisfaction signal; S4. Based on the thread pool management mechanism and the resource satisfaction signal, a multi-thread scheduling strategy is constructed, and several experimental plans are executed by the scheduling execution module according to the multi-thread scheduling strategy.

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