Automatic scheduling processing method and apparatus for experiment plan, device and readable medium

Abstract

The present disclosure relates to an automatic scheduling processing method and apparatus for an experiment plan, an electronic device and a computer readable medium. The method comprises: acquiring experiment information corresponding to at least one experiment plan to be processed, wherein the experiment information comprises a plurality of reaction tasks; on the basis of the experiment information, determining a constraint condition for an experiment plan corresponding to the experiment information; determining an optimization target of the at least one experiment plan; on the basis of the optimization target and the constraint condition, optimizing an execution sequence of the plurality of reaction tasks corresponding to the at least one experiment plan to generate an experiment scheduling strategy; and automatically calling resources in an intelligent laboratory on the basis of the experiment scheduling strategy, to execute the at least one experiment plan to generate an experiment result. According to the automatic scheduling processing method and apparatus for an experiment plan, the electronic device and the computer readable medium in the present disclosure, flexible automatic scheduling for chemical experiments based on human-machine collaboration can be implemented, thereby reducing the operation cost of an automated laboratory, improving the working efficiency, and guaranteeing accurate execution of the experiment plan.

Description

Method, device, equipment and readable medium for automatic scheduling of experimental plans Technical Field

[0001] The present disclosure relates to the field of automatic processing of chemical experiments, and in particular to a method, device, electronic device and computer-readable medium for automatically scheduling and processing experimental plans. Background Art

[0002] Automation technology is widely used in the production of bulk chemicals such as plastics and petroleum products, as well as fertilizers, significantly improving production efficiency. However, experience gained in chemical digitization and automation is difficult to directly transfer to chemical laboratories. This is because an automated production line in chemical production is used to produce only one or a few products, and the tasks of each link are relatively fixed. However, chemical laboratories typically undertake a variety of experiments. Different experimental processes vary. Even within the same operational step, different methods often differ depending on the substance or reaction. For example, during the addition process, some highly reactive substances require dropwise addition, while others cannot interact with oxygen and require gas displacement during the addition process. Different operations also require different equipment. Due to this diversity, a single automated device cannot handle all reactions, and a combination of multiple automated devices and manual experiments is often required. Technical issues

[0003] Existing laboratory scheduling methods primarily focus on automated laboratory instruments and do not address the scheduling of human tasks, making it impossible to manage the entire laboratory. These existing methods, lacking the ability to schedule automated equipment that requires human-machine collaboration, lead to increased laboratory costs. Many automation solutions require a large number of manipulators and transport vehicles equipped with manipulators to transfer and manipulate materials. However, these manipulators are expensive, contributing to the high cost of automated laboratories. Technical Solutions

[0004] According to one aspect of the present disclosure, a method for automatic scheduling of experimental plans is proposed, the method comprising: obtaining experimental information corresponding to at least one experimental plan to be processed, the experimental information including a plurality of reaction tasks; determining constraints for the corresponding experimental plan based on the experimental information; determining an optimization target for the at least one experimental plan; optimizing the execution order of the plurality of reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraints to generate an experimental scheduling strategy; and automatically mobilizing resources in an intelligent laboratory based on the experimental scheduling strategy to execute the at least one experimental plan and generate experimental results.

[0005] In an exemplary embodiment of the present disclosure, obtaining experimental information corresponding to at least one experimental plan to be processed includes: obtaining an experimental flowchart corresponding to the at least one experimental plan to be processed; obtaining multiple reaction tasks corresponding to the at least one experimental flowchart; obtaining task information corresponding to the multiple reaction tasks; and generating at least one experimental information through the experimental flowchart, reaction tasks, and task information corresponding to the at least one experimental plan.

[0006] In an exemplary embodiment of the present disclosure, constraints are determined for the experimental plan corresponding to the experimental information, including: obtaining a set of execution subjects of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraints; obtaining reaction times of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraints; obtaining a reaction order of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraints.

[0007] In an exemplary embodiment of the present disclosure, a set of execution subjects of multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and constraint conditions are set, including: obtaining the material properties and reaction properties of the reaction task from the experimental information; determining the execution subject of the reaction task based on the material properties and reaction properties; generating the execution subject set corresponding to the experimental plan based on the execution subjects of multiple reaction tasks; and setting execution constraints for each execution subject in the execution subject set.

[0008] In an exemplary embodiment of the present disclosure, the reaction times of multiple reaction tasks corresponding to the experimental plan are obtained from the experimental information and constraints are set, including: setting a fixed reaction time and a fixed time constraint for the reaction task according to the execution subject of the reaction task; setting a prescribed reaction time and a flexible time constraint for the reaction task according to the execution subject of the reaction task.

[0009] In an exemplary embodiment of the present disclosure, the reaction sequence of multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and constraints are set, including: extracting an experimental flowchart from the experimental information; obtaining the reaction sequence of the multiple reaction tasks according to the experimental flowchart; and setting sequential time constraints for the multiple reaction tasks according to the reaction sequence.

[0010] In an exemplary embodiment of the present disclosure, determining the optimization objective of the at least one experimental plan includes: determining the end time constraint of the at least one experimental plan; setting an experimental weight for each experimental plan in the at least one experimental plan; and determining that the optimization objective of the at least one experimental plan is to minimize the completion time.

[0011] In an exemplary embodiment of the present disclosure, the execution order of multiple reaction tasks corresponding to the at least one experimental plan is optimized based on the optimization objective and the constraint conditions to generate an experimental scheduling strategy, including: optimizing the execution order of multiple reaction tasks corresponding to the at least one experimental plan based on the optimization objective and the constraint conditions through a mixed integer linear programming algorithm; and / or optimizing the execution order of multiple reaction tasks corresponding to the at least one experimental plan based on the optimization objective and the constraint conditions through a constraint programming algorithm.

[0012] In an exemplary embodiment of the present disclosure, the execution entity of the experimental plan includes an intelligent material transfer vehicle; based on the optimization target and the constraint conditions, the execution order of multiple reaction tasks corresponding to the at least one experimental plan is optimized to generate an experimental scheduling strategy, including: obtaining map information of the intelligent laboratory; based on the optimization target and the constraint conditions, the execution order of multiple reaction tasks corresponding to the at least one experimental plan is optimized to generate an initial scheduling strategy; based on the initial scheduling strategy and the map information, the experimental scheduling strategy is generated in a step-by-step manner in combination with the trajectory information of the intelligent material transfer vehicle.

[0013] In an exemplary embodiment of the present disclosure, the experimental scheduling strategy is generated based on the initial scheduling strategy and the map information in a step-by-step manner combined with the trajectory information of the intelligent material transfer vehicle, including: extracting the execution order of multiple reaction tasks from the initial scheduling strategy; extracting multiple areas from the map information; adding the path information of the intelligent material transfer vehicle to each of the multiple areas one by one; optimizing the path information of the intelligent material transfer vehicle in each area to generate an optimized path; updating the initial scheduling strategy according to the optimized path to generate the experimental scheduling strategy.

[0014] According to one aspect of the present disclosure, an automatic scheduling processing device for experimental plans is proposed, which includes: an information module for obtaining experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information includes multiple reaction tasks; a constraint module for determining constraint conditions for the corresponding experimental plan based on the experimental information; a target module for determining the optimization target of the at least one experimental plan; a scheduling module for optimizing the execution order of the multiple reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraint conditions to generate an experimental scheduling strategy; and an execution module for automatically retrieving resources in the intelligent laboratory based on the experimental scheduling strategy to execute the at least one experimental plan and generate experimental results.

[0015] According to one aspect of the present disclosure, an electronic device is proposed, which includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0016] According to one aspect of the present disclosure, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described above is implemented.

[0017] According to the automatic scheduling processing method, device, electronic device and computer-readable medium of the experimental plan disclosed in the present invention, experimental information corresponding to at least one experimental plan to be processed is obtained, and the experimental information includes multiple reaction tasks; constraint conditions are determined for the corresponding experimental plan based on the experimental information; the optimization target of the at least one experimental plan is determined; the execution order of the multiple reaction tasks corresponding to the at least one experimental plan is optimized based on the optimization target and the constraint conditions, and an experimental scheduling strategy is generated; based on the experimental scheduling strategy, resources in the intelligent laboratory are automatically called up to execute the at least one experimental plan to generate experimental results, so that chemical experiments can be flexibly and automatically scheduled in a human-machine integrated manner, the operating costs of the automated laboratory can be reduced, work efficiency can be improved, and the accurate execution of the experimental plan can be ensured.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure. Beneficial effects

[0019] The present invention discloses an automatic scheduling processing method, device, electronic device and computer-readable medium for experimental plans, which can perform flexible automated scheduling of chemical experiments in a human-machine integrated manner, reduce the operating costs of automated laboratories, improve work efficiency, and ensure the accurate execution of experimental plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Fig. 1 is a schematic diagram of a system for automatically scheduling and processing an experiment plan according to an exemplary embodiment.

[0021] FIG2 is a flowchart showing a method for automatically scheduling an experiment plan according to an exemplary embodiment.

[0022] FIG3 is a flowchart showing a method for automatically scheduling an experiment plan according to another exemplary embodiment.

[0023] FIG4 is a flowchart showing a method for automatically scheduling an experiment plan according to another exemplary embodiment.

[0024] FIG5 is a schematic diagram showing a method for automatically scheduling an experiment plan according to another exemplary embodiment.

[0025] FIG6 is a schematic diagram showing a method for automatically scheduling an experiment plan according to another exemplary embodiment.

[0026] Fig. 7 is a block diagram showing an apparatus for automatically scheduling an experiment plan according to an exemplary embodiment.

[0027] Fig. 8 is a block diagram of an electronic device according to an exemplary embodiment.

[0028] Fig. 9 is a block diagram showing a computer-readable medium according to an exemplary embodiment. Best Mode for Carrying Out the Invention

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the drawings represent like or similar parts, and thus repetitive description thereof will be omitted.

[0030] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0033] It should be understood that although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below can be referred to as the second component without departing from the teachings of the present disclosure. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more of them.

[0034] Those skilled in the art will understand that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present disclosure, and therefore cannot be used to limit the scope of protection of the present disclosure.

[0035] Through research into existing technologies, the applicant in this case discovered that, in chemical laboratory scenarios, automated equipment struggles to handle the uncertainties inherent in chemical operations. Furthermore, in actual production, some automated equipment is too expensive, resulting in lower efficiency gains than employing humans. Therefore, scheduling laboratory equipment alone is insufficient in human-machine integration scenarios.

[0036] Scheduling only chemical equipment can reduce execution efficiency. This is because machine scheduling doesn't take human tasks into account, and task execution times and status are often uncertain. This is especially true for human tasks, leading to situations where humans are waiting for machines, or vice versa (for example, a task's start time has arrived, but the preceding human-run task hasn't yet completed). Scheduling only chemical equipment can also cause task interruptions: for example, a machine-run task that requires human initiation must begin while the human is already performing other tasks.

[0037] In view of the technical defects in the existing technology, this application proposes an automatic scheduling processing method that can meet the uncertainty and diversity requirements in chemical experiment scenarios, combine manual operation and machine equipment to perform experiment scheduling, meet the needs of actual operations, and greatly improve experimental efficiency.

[0038] Fig. 1 is a schematic diagram of a system for automatically scheduling and processing an experiment plan according to an exemplary embodiment.

[0039] As shown in Figure 1, a chemical experiment scenario may include intelligent containers, weighing equipment, reactors, post-processing reactors, and automatic analyzers. It may also include auxiliary equipment such as material transfer vehicles and robotic arms.

[0040] Among them, the smart container is used to load the reaction substrate;

[0041] Weighing equipment is used to obtain a preset amount of reaction substrate. The weighing equipment can also include automatic weighing equipment, micro-automatic weighing equipment, manual weighing equipment, etc.

[0042] The reactor is used to carry the reaction substrate, perform the experimental reaction, and generate the reaction result. More specifically, it can include an automatic reactor, a traditional reaction vessel, a stirrer, etc.

[0043] The automatic analyzer is used to analyze the intermediate reactants and generate analysis results.

[0044] The post-processing module is used to perform subsequent operations or treatments on the intermediate products or reaction results of the chemical reaction. It is worth mentioning that the subsequent reaction processing can also continue in the reactor.

[0045] Material transfer vehicle, used to automatically deliver materials to corresponding devices according to different process steps;

[0046] It may also include a robotic arm for grabbing reaction substrates or transferring materials to corresponding devices.

[0047] In the specific application scenario described in Figure 1, when the user submits one or more experimental plans, the method in this application can automatically estimate the reaction time in one or more experimental plans, and then sort the operation sequence and time of the equipment or execution subjects (machines or people) in the smart laboratory, and generate operation instructions so that the execution subjects can perform chemical reaction operations according to the instructions.

[0048] After starting one or more experimental plans, the system controls the smart cabinet to take out the raw material bottles required for the experiment, and the smart cabinet's robotic arm places them on a material-retrieving platform.

[0049] Afterwards, the material can be transported to the batching area by a material transfer vehicle;

[0050] The weighing operation is performed by automatic weighing equipment or humans depending on the properties of the substance.

[0051] The material transfer vehicle then transports the materials to the reaction area and returns the raw material bottles to the smart container for storage.

[0052] The appropriate reactor is selected according to the type of reaction, and the feeding operation is completed by a robotic arm.

[0053] After the reaction is completed, the robotic arm samples and automatically pre-processes the mixed solution after the reaction and performs automatic analysis.

[0054] Deliver materials to subsequent execution entities according to the operating steps and experimental scheduling strategies in the flowchart corresponding to the experimental plan;

[0055] Finally, the material transfer vehicle delivers the products into the smart container for warehousing.

[0056] Fig. 2 is a flow chart showing a method for automatically scheduling an experiment plan according to an exemplary embodiment. The method 20 for automatically scheduling an experiment plan includes at least steps S202 to S208.

[0057] As shown in FIG2 , in S202, experimental information corresponding to at least one experimental plan to be processed is obtained, wherein the experimental information includes multiple reaction tasks. For example, the following steps may be performed: obtaining an experimental flowchart corresponding to the at least one experimental plan to be processed; obtaining multiple reaction tasks corresponding to the at least one experimental flowchart; obtaining task information corresponding to the multiple reaction tasks; and generating at least one experimental information item using the experimental flowchart, reaction tasks, and task information corresponding to the at least one experimental plan.

[0058] In this application, reactions can include combination reactions, decomposition reactions, displacement reactions, metathesis reactions, and other categories. An experimental plan can include one or more reaction tasks. The reaction task information can indicate the reaction conditions, environmental information, reaction time, and other content required for the reaction.

[0059] It is worth mentioning that in this application, the experimental flow chart corresponds to the chemical experiment flow chart. It refers to a directed graph constructed by the tasks and task sequences in the chemical experiment, with each node representing a chemical experiment task.

[0060] (For example, the process of collecting materials, weighing, and reacting in Figure 1.) A simple arrow indicates that the previous task is performed first, and the next task is executed after it is completed. Parallel tasks controlled by a parallel task switch: This means that the processes between the start and end of the parallel task are carried out simultaneously.

[0061] In S204, constraints are determined for the corresponding experimental plan based on the experimental information. For example, the set of execution entities for the multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and the constraints are set; the reaction time of the multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and the constraints are set; and the reaction order of the multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and the constraints are set.

[0062] The details of “determining constraint conditions for the corresponding experimental plan based on the experimental information” will be described in the embodiment corresponding to FIG. 3 .

[0063] In S206, an optimization objective of the at least one experimental plan is determined. This may include determining an end time constraint for the at least one experimental plan, setting an experimental weight for each experimental plan in the at least one experimental plan, and determining that the optimization objective of the at least one experimental plan is to minimize completion time.

[0064] The details of “determining the optimization target of the at least one experimental plan” will be described in the embodiment corresponding to FIG. 3 .

[0065] In S208, the execution order of the multiple reaction tasks corresponding to the at least one experimental plan is optimized based on the optimization objective and the constraints to generate an experimental scheduling strategy. For example, the execution order of the multiple reaction tasks corresponding to the at least one experimental plan can be optimized based on the optimization objective and the constraints using a mixed integer linear programming algorithm; or, for example, the execution order of the multiple reaction tasks corresponding to the at least one experimental plan can be optimized based on the optimization objective and the constraints using a constraint programming algorithm.

[0066] More specifically, the optimization algorithms described in this application may include MILP, CP-SAT, and available solvers include but are not limited to commercial solvers such as Gurobi, CPLEX, XPress, and open source solvers such as SCIP and CBC.

[0067] In S210 , resources in the smart laboratory are automatically retrieved based on the experiment scheduling strategy to execute the at least one experiment plan and generate an experiment result.

[0068] According to the automatic scheduling processing method of experimental plans disclosed in the present invention, experimental information corresponding to at least one experimental plan to be processed is obtained, and the experimental information includes multiple reaction tasks; constraint conditions are determined for the corresponding experimental plan based on the experimental information; the optimization target of the at least one experimental plan is determined; the execution order of the multiple reaction tasks corresponding to the at least one experimental plan is optimized based on the optimization target and the constraint conditions, and an experimental scheduling strategy is generated; based on the experimental scheduling strategy, resources in the intelligent laboratory are automatically called up to execute the at least one experimental plan to generate experimental results. This method can perform flexible and automated scheduling of chemical experiments in a human-machine integrated manner, reduce the operating costs of automated laboratories, improve work efficiency, and ensure the accurate execution of experimental plans.

[0069] It should be clearly understood that the present disclosure describes how to form and use specific examples, but the principles of the present disclosure are not limited to any details of these examples. On the contrary, based on the teachings of the contents disclosed in this disclosure, these principles can be applied to many other embodiments.

[0070] Figure 3 is a flow chart of an automatic scheduling method for an experimental plan according to another exemplary embodiment. The process 30 shown in Figure 3 is a detailed description of S204 "determining constraint conditions for the corresponding experimental plan based on experimental information" in the process shown in Figure 2.

[0071] As shown in FIG3 , in S302 , constraints are determined for the corresponding experimental plan based on the experimental information. For example, assuming that there are m experimental flow charts that need to be arranged: fl1, fl2, ..., fl m ;

[0072] Process fl i There are several tasks on the flowchart:

[0073] In S304, a set of execution entities for multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and constraints are set. For example, the material properties and reaction properties of the reaction tasks are obtained from the experimental information; the execution entities of the reaction tasks are determined based on the material properties and reaction properties; the set of execution entities corresponding to the experimental plan is generated based on the execution entities of the multiple reaction tasks; and execution constraints are set for each execution entity in the set of execution entities.

[0074] In this application, reactions within the same experimental plan can be performed by different automated equipment or by humans. For example, the same reaction can be performed using a microwave reactor, a room-temperature reactor, a flow reactor, and other equipment. The choice of equipment type is generally based on the properties of the reaction or the substance. For example, a microwave reactor is selected for a non-hazardous high-temperature reaction (reaction properties); an automatic weighing device is used for a liquid with good fluidity (substance properties).

[0075] In the flowchart, select available equipment or operators for each task.

[0076] For task t ij , with a user-configurable rule engine, defines which automation equipment can perform tasks based on the properties of the material and the nature of the reaction, and sets the l ij Operators, define variables Among them ijc Indicates whether the cth operator performs task t ij .

[0077] There is an equation

[0078] In step S306, the reaction times of the multiple reaction tasks corresponding to the experimental plan are obtained from the experimental information and constraints are set. A fixed reaction time and a fixed time constraint can be set for the reaction task based on the execution subject of the reaction task; a prescribed reaction time and a flexible time constraint can be set for the reaction task based on the execution subject of the reaction task.

[0079] For task t ij , let its starting time be sij , the time to end the experiment is e ij , the duration of each task is d ij =e ij -s ij (2)

[0080] Predeterminable fixed-duration tasks (such as machine tasks)

[0081] Let d ij =c ij , where c ij is a constant. (3)

[0082] Flexible tasks, such as tasks operated by humans. The execution time is in c ij and The task between indicates that the execution time of the task must be greater than a constant c ij and is less than a variable If there are no subsequent tasks scheduled, the execution time of the task can be extended (for example, if the reaction reaches 3 a.m., it does not have to be terminated immediately, and stirring can be continued until the next day for processing).

[0083] make

[0084] In S308, a reaction sequence of multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and constraints are set. For example, an experimental flowchart can be extracted from the experimental information; the reaction sequence of the multiple reaction tasks is obtained based on the experimental flowchart; and sequence time constraints are set for the multiple reaction tasks based on the reaction sequence.

[0085] For two adjacent tasks on the same flowchart, ij , t ik ;

[0086] t ij In t ik Execute e immediately after completion ik =s ij (5)

[0087] t ij and t ik Start at the same time ik =s ij (6)

[0088] t ij In t ik Start within a certain time range after the start:

[0089] s ik +l lb ≤s ij <sik +l ub , where l lb and l ub is a constant. (7)

[0090] t ij In t ik After the end of a certain range of time starts:

[0091] e ik +l lb ≤s ij <e ik +l ub , where l lb and l ub is a constant. (8)

[0092] On the same operation object c, tasks do not overlap:

[0093] make s ij ≥e i′j′ -M1×y iji′j′ -M2×(2-o ijc -o i′j′c ) (9) s i′j′ ≥e ij -M1×(1-y iji′j′ )-M2×(2-o ijc -o i′j′c ) (10)

[0094] Where M1 and M2 are very large constants, which can be 10 in practice. 6 .

[0095] Optimization goal: The last task of each experiment can be set to The corresponding end time is Add constraints to the end time of each experiment: obj=minmize∑ i z i (12)

[0096] where w i is the weight of the experiment, z i The optimization goal is to minimize the completion time of all experiments. The algorithm will induce the experiment with higher weight to be completed as soon as possible.

[0097] Figure 4 is a flowchart illustrating a method for automatically scheduling an experimental plan according to another exemplary embodiment. Process 40 shown in Figure 4 provides a detailed description of "optimizing the execution order of multiple reaction tasks corresponding to at least one experimental plan based on the optimization objective and the constraints to generate an experimental scheduling strategy" when the experimental plan's execution entity includes an intelligent material transfer vehicle.

[0098] When optimizing the scheduling of intelligent material transfer vehicles, the above optimization model can be incorporated into the intelligent material transfer vehicle factor. Since the travel time of the intelligent material transfer vehicle between different work areas is uncertain, the travel of the intelligent material transfer vehicle must consider two factors simultaneously:

[0099] 1. The shortest path for intelligent material transfer vehicles

[0100] 2. Arrangement of tasks.

[0101] As shown in FIG4 , in S402 , the map information of the smart laboratory is obtained. The map information of the laboratory may be shown in FIG5 .

[0102] Suppose the laboratory has working areas: w1, w2, ..., w m , intelligent material transfer vehicle in the work area i , w j The walking time between them is d ij .

[0103] Suppose there are six workspaces a, b, c, d, e, and f. Among them, workspaces a, b, and c are closer, and workspaces d, e, and f are closer. However, the distance between them is far.

[0104] As shown in Figure 6, if only scheduling is considered and the order of tasks is a, e, b, f, c, g, then the intelligent material transfer vehicle will have to travel a long distance, wasting considerable time. If only the intelligent material transfer vehicle's travel time is considered as the shortest, tasks in the workspace may be idle due to personnel or equipment waiting for the intelligent material transfer vehicle, delaying task completion.

[0105] In S404, the execution order of the plurality of reaction tasks corresponding to the at least one experimental plan is optimized based on the optimization objective and the constraints to generate an initial scheduling strategy. The execution order of the reaction tasks can be optimized to generate the initial scheduling strategy using the method described in FIG2 .

[0106] In S406 , the experimental scheduling strategy is generated based on the initial scheduling strategy and the map information in a step-by-step manner in combination with the trajectory information of the intelligent material transfer vehicle.

[0107] In one embodiment, for example, the execution order of multiple reaction tasks can be extracted from the initial scheduling strategy; multiple areas can be extracted from the map information; the path information of the intelligent material transfer vehicle can be added to each of the multiple areas one by one; the path information of the intelligent material transfer vehicle can be optimized in each area to generate an optimized path; and the initial scheduling strategy can be updated according to the optimized path to generate the experimental scheduling strategy.

[0108] For any two tasks t that may be transported by the intelligent material transfer vehicle ij ,t i′j′ , due to t ij ,t i′j′ , may be arranged in different work areas, and can be ordered: s ij ≥e i′j′ +d ww′ -M3×y iji′j′ -M4×(2-a ijc -a i′j′c ) (13) s i′j′ ≥e ij +d ww′ -M3×(1-y iji′j′ ) -M4×(2-o ijc -o i′j′c ) (14)

[0109] Among them, M3 and M4 are very large constants, which can be 10 in practice. 6 .

[0110] While Types 9 and 10 are similar in form, they do not necessarily limit the number of tasks within a given workspace. However, tasks in any two different workspaces can be scheduled for execution sequentially, allowing them to be transported by intelligent material transfer vehicles. Therefore, Types 13 and 14 introduce significantly greater complexity than Types 9 and 10.

[0111] Directly solving the problem after introducing the intelligent material transfer vehicle will significantly increase the algorithm's solution time. By the time the algorithm finds a solution based on the current state of the laboratory, the actual situation in the laboratory may have changed significantly, making the algorithm's solution unsuitable for actual laboratory scheduling.

[0112] In this application, this complex problem is solved in two steps.

[0113] Without considering the problem of intelligent material transfer vehicles, solve the laboratory scheduling algorithm.

[0114] Repeat the following process:

[0115] The tasks corresponding to the solutions are arranged in the order of start and end time: The corresponding workspace is

[0116] Add constraints to the model in 1 in the order of regions

[0117] Try to find new solutions.

[0118] If there is no feasible solution and no satisfactory solution has been obtained before, replace Equation 15 with

[0119] where d * is a variable, and Add optimization objectives. * = 0 must be a solution that satisfies the constraints. This problem must have an optimal solution. Find the corresponding The task is to i and its subsequent tasks to eliminate problems (that is, the intelligent material transfer vehicle does not transport the materials of the experiment in this problem, but transports them in the next round. In actual operation, this task can also be completed by a human operator).

[0120] If there is a feasible solution, set the latest completion time obj corresponding to the feasible solution p (12), then add constraint obj to the optimization objective p >minmize∑ i Z i (17)

[0121] Try to solve again. If a feasible solution is obtained, repeat the operation (a). If no feasible solution is obtained, then the current solution is the optimal solution.

[0122] The use of this algorithm greatly reduces the complexity of the solution. Compared with formulas 13 and 14, formula 15 avoids the use of the double big M method, and the number of equations is reduced from O(n 2 ) is reduced to O(n), where n is the number of tasks.

[0123] The optimization method in this application innovatively proposes starting by considering only laboratory tasks, ignoring material transportation, and gradually adding material transportation constraints to the model. Initially, since there are no material transportation constraints, the distance between workspaces is assumed to be zero. Once the distances between some workspaces are added, the process of solving Equation 17 gradually transforms a problem that only considers scheduling, not routing, into one that considers both scheduling and routing, driving the model to find a better path to iterate the current solution. This continues until the remaining solution, even if other paths are considered time-saving, do not lead to a better solution.

[0124] Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments can be implemented as a computer program executed by a CPU. When executed by the CPU, the computer program performs the functions defined in the above method provided by the present disclosure. The program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk.

[0125] Furthermore, it should be noted that the aforementioned figures are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the aforementioned figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0126] The following are embodiments of the apparatus disclosed herein, which can be used to implement the method embodiments disclosed herein. For details not disclosed in the apparatus embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0127] FIG7 is a block diagram of an apparatus for automatically scheduling an experimental plan according to an exemplary embodiment. As shown in FIG7 , the apparatus 70 for automatically scheduling an experimental plan includes: an information module 702 , a constraint module 704 , a target module 706 , a scheduling module 708 , and an execution module 710 .

[0128] Information module 702 is used to obtain experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information includes multiple reaction tasks; information module 702 is also used to obtain an experimental flow chart corresponding to the at least one experimental plan to be processed; obtain multiple reaction tasks corresponding to the at least one experimental flow chart; obtain task information corresponding to multiple reaction tasks; and generate at least one experimental information through the experimental flow chart, reaction tasks, and task information corresponding to the at least one experimental plan.

[0129] The constraint module 704 is used to determine the constraint conditions for the corresponding experimental plan based on the experimental information; the constraint module 704 is also used to obtain the execution subject set of multiple reaction tasks corresponding to the experimental plan from the experimental information and set the constraint conditions; obtain the reaction time of multiple reaction tasks corresponding to the experimental plan from the experimental information and set the constraint conditions; obtain the reaction order of multiple reaction tasks corresponding to the experimental plan from the experimental information and set the constraint conditions.

[0130] The target module 706 is used to determine the optimization target of the at least one experimental plan; the target module 706 is also used to determine the end time constraint of the at least one experimental plan; set the experimental weight for each experimental plan in the at least one experimental plan; and determine that the optimization target of the at least one experimental plan is to minimize the completion time.

[0131] The scheduling module 708 is used to optimize the execution order of multiple reaction tasks corresponding to the at least one experimental plan based on the optimization objectives and the constraints, and generate an experimental scheduling strategy; the scheduling module 708 is also used to optimize the execution order of multiple reaction tasks corresponding to the at least one experimental plan based on the optimization objectives and the constraints through a mixed integer linear programming algorithm; and / or optimize the execution order of multiple reaction tasks corresponding to the at least one experimental plan based on the optimization objectives and the constraints through a constraint programming algorithm.

[0132] The execution module 710 is configured to automatically call resources in the smart laboratory based on the experiment scheduling strategy to execute the at least one experiment plan and generate an experiment result.

[0133] According to the automatic scheduling processing device for experimental plans disclosed in the present invention, experimental information corresponding to at least one experimental plan to be processed is obtained, wherein the experimental information includes multiple reaction tasks; constraint conditions are determined for the corresponding experimental plan based on the experimental information; the optimization target of the at least one experimental plan is determined; the execution order of the multiple reaction tasks corresponding to the at least one experimental plan is optimized based on the optimization target and the constraint conditions, and an experimental scheduling strategy is generated; based on the experimental scheduling strategy, resources in the intelligent laboratory are automatically called up to execute the at least one experimental plan to generate experimental results, thereby enabling flexible and automated scheduling of chemical experiments in a human-machine integrated manner, reducing the operating costs of automated laboratories, improving work efficiency, and ensuring the accurate execution of experimental plans.

[0134] Fig. 8 is a block diagram of an electronic device according to an exemplary embodiment.

[0135] The electronic device 800 according to this embodiment of the present disclosure is described below with reference to Figure 8. The electronic device 800 shown in Figure 8 is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0136] As shown in FIG8 , electronic device 800 is implemented as a general-purpose computing device. Components of electronic device 800 may include, but are not limited to, at least one processing unit 810, at least one storage unit 820, a bus 830 connecting various system components (including storage unit 820 and processing unit 810), a display unit 840, and the like.

[0137] The storage unit stores program code, which can be executed by the processing unit 810 to enable the processing unit 810 to perform the steps described in this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 810 can perform the steps shown in Figures 2, 3, and 4.

[0138] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0139] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0140] Bus 830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0141] The electronic device 800 can also communicate with one or more external devices 800' (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), devices that allow a user to interact with the electronic device 800, and / or any device that allows the electronic device 800 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). This communication can occur via an input / output (I / O) interface 850. Furthermore, the electronic device 800 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 860. The network adapter 860 can communicate with other modules of the electronic device 800 via the bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 800, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0142] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, as shown in FIG9 , the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.

[0143] The software product can be any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0144] The computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. The data signal propagated may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or component. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0145] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0146] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by a device, the computer-readable medium realizes the following functions: obtaining experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information includes multiple reaction tasks; determining constraints for the corresponding experimental plan based on the experimental information; determining the optimization target of the at least one experimental plan; optimizing the execution order of the multiple reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraints to generate an experimental scheduling strategy; and automatically mobilizing resources in the smart laboratory based on the experimental scheduling strategy to execute the at least one experimental plan and generate experimental results.

[0147] Those skilled in the art will appreciate that the modules described above can be distributed in the device according to the description of the embodiment, or can be modified accordingly to be used in one or more devices that are different from the embodiment. The modules of the above embodiment can be combined into one module or further divided into multiple submodules.

[0148] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0149] The exemplary embodiments of the present disclosure are specifically shown and described above. It should be understood that the present disclosure is not limited to the detailed structures, settings or implementation methods described herein; on the contrary, the present disclosure is intended to cover various modifications and equivalent settings included within the scope of the appended claims.

Claims

1. A method for automatically scheduling an experimental plan, characterized in that: include: Acquire experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information includes a plurality of reaction tasks; Determine constraints for the corresponding experimental plan based on the experimental information; determining an optimization goal for the at least one experimental plan; Optimizing the execution order of the multiple reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraint condition to generate an experimental scheduling strategy; Based on the experiment scheduling strategy, resources in the smart laboratory are automatically called to execute the at least one experiment plan to generate an experiment result.

2. The method according to claim 1, characterized in that Acquire experimental information corresponding to at least one experimental plan to be processed, including: Obtaining an experimental flow chart corresponding to the at least one experimental plan to be processed; Obtaining multiple reaction tasks corresponding to at least one experimental flow chart; Get task information corresponding to multiple reaction tasks; At least one experimental information is generated through the experimental flow chart, reaction task, and task information corresponding to the at least one experimental plan.

3. The method according to claim 1, characterized in that Determine the constraints for the corresponding experimental plan based on the experimental information, including: Obtaining a set of execution entities of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraint conditions; Obtaining the reaction times of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraint conditions; The reaction order of multiple reaction tasks corresponding to the experimental plan is obtained from the experimental information and constraint conditions are set.

4. The method according to claim 3, characterized in that Obtaining a set of execution entities of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraint conditions, including: Obtaining material properties and reaction properties of the reaction task from the experimental information; Determine the execution subject of the reaction task according to the material properties and reaction properties; Generate the execution subject set corresponding to the experimental plan according to the execution subjects of multiple reaction tasks; An execution constraint condition is set for each execution subject in the execution subject set.

5. The method according to claim 3, characterized in that Obtaining the reaction times of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraint conditions, including: Setting a fixed reaction time and a fixed time constraint condition for the reaction task according to an execution subject of the reaction task; A prescribed reaction time is set for the reaction task according to an execution subject of the reaction task and a flexible time constraint condition is set.

6. The method according to claim 3, characterized in that Obtaining the reaction order of multiple reaction tasks corresponding to the experimental plan from the experimental information and setting constraint conditions, including: Extracting an experimental flow chart from the experimental information; Acquire the reaction order of the plurality of reaction tasks according to the experimental flow chart; Sequential time constraints are set for the plurality of reaction tasks according to the reaction sequence.

7. The method according to claim 1, characterized in that Determining an optimization goal of the at least one experimental plan includes: Determining an end time constraint of the at least one experimental plan; Setting an experimental weight for each experimental plan in the at least one experimental plan; The optimization goal of the at least one experimental plan is determined to be minimization of completion time.

8. The method according to claim 1, characterized in that The method further comprises optimizing the execution order of the plurality of reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraint condition to generate an experimental scheduling strategy, including: Optimizing the execution order of the multiple reaction tasks corresponding to the at least one experimental plan based on the optimization objective and the constraint conditions by a mixed integer linear programming algorithm; and / or The execution order of the multiple reaction tasks corresponding to the at least one experimental plan is optimized based on the optimization target and the constraint conditions through a constraint programming algorithm.

9. The method according to claim 1, characterized in that The execution entities of the experimental plan include intelligent material transfer vehicles; The method further comprises optimizing the execution order of the plurality of reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraint condition to generate an experimental scheduling strategy, including: Get the map information of the smart laboratory; Optimizing the execution order of the multiple reaction tasks corresponding to the at least one experimental plan based on the optimization goal and the constraint condition to generate an initial scheduling strategy; The experimental scheduling strategy is generated based on the initial scheduling strategy and the map information in a step-by-step manner combined with the trajectory information of the intelligent material transfer vehicle.

10. The method according to claim 9, characterized in that The experimental scheduling strategy is generated based on the initial scheduling strategy and the map information in a step-by-step manner in combination with the trajectory information of the intelligent material transfer vehicle, including: Extracting the execution order of multiple reaction tasks from the initial scheduling strategy; Extracting a plurality of regions from the map information; Adding path information of the intelligent material transfer vehicle to each of the plurality of areas one by one; Optimizing the path information of the intelligent material transfer vehicle in each area to generate an optimized path; The initial scheduling strategy is updated according to the optimization path to generate the experimental scheduling strategy.

11. An automatic scheduling processing device for experimental plans, characterized in that: include: An information module, used to obtain experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information includes multiple reaction tasks; A constraint module, used to determine constraint conditions for the corresponding experimental plan according to the experimental information; A target module, used to determine the optimization target of the at least one experimental plan; A scheduling module, used to optimize the execution order of multiple reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraint conditions, and generate an experimental scheduling strategy; The execution module is used to automatically call resources in the smart laboratory based on the experiment scheduling strategy to execute the at least one experiment plan to generate an experiment result.

12. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 10.

13. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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