CN116882691A - Automatic scheduling processing method, device and equipment for experiment plan and readable medium - Google Patents
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Abstract
The present disclosure relates to an automatic scheduling processing method, apparatus, electronic device, and computer-readable medium for experiment plans. The method comprises the following steps: acquiring experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information comprises a plurality of reaction tasks; determining constraint conditions for the corresponding experiment plans according to the experiment information; determining an optimization objective of the at least one experimental plan; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition to generate an experiment scheduling strategy; and automatically invoking resources in an intelligent laboratory based on the experiment scheduling policy to execute the at least one experiment plan to generate an experiment result. The automatic scheduling processing method, the device, the electronic equipment and the computer readable medium of the experiment plan can perform man-machine combined flexible automatic scheduling on chemical experiments, reduce the operation cost of an automatic laboratory, improve the working efficiency and ensure the accurate execution of the experiment plan.
Description
Technical Field
The present disclosure relates to the field of automated processing of chemical experiments, and in particular, to an automated scheduling method, apparatus, electronic device, and computer readable medium for experimental planning.
Background
The automation technology is widely applied to the production of petroleum products such as plastics and the like and large chemical products such as chemical fertilizers and the like, and the production efficiency is greatly improved. However, the experience of chemical digitization and automation is difficult to migrate directly into the laboratory context of chemical classes, because: an automatic assembly line for chemical production is only used for producing one or more commodities, and the tasks of each link are relatively fixed. Chemical experiments generally accept various types of experiments. The flow of different experiments is also different, and even the same operation links, the operation methods are often different due to different substances or different reactions. For example, in the charging step, some substances with more severe reactions need to be added dropwise, some substances cannot contact oxygen, and gas needs to be replaced in the charging process. Different operations may also use different devices. Because of such diversity, one automation device cannot solve all the problems of the reaction, and often multiple automation devices and manual experiments are required to be combined.
The existing laboratory scheduling method in the market is mainly aimed at an automatic instrument in a laboratory, and does not relate to the arrangement of personnel tasks, so that no method is available for arranging the whole experimental task. The prior art method results in increased laboratory costs due to the lack of scheduling of human-machine collaborative automation equipment. Many automated solutions require a large number of manipulators and transport carts with manipulators for material transfer and handling. However, the price of the robot is not low, which results in high costs for the automated laboratory.
Accordingly, there is a need for a new method, apparatus, electronic device, and computer readable medium for automated scheduling of experimental plans to solve the above-mentioned problems.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the disclosure provides an automatic scheduling processing method, an apparatus, an electronic device, and a computer readable medium for an experiment plan, which can perform man-machine combined flexible automatic scheduling on chemical experiments, reduce the operation cost of an automatic laboratory, improve the working efficiency, and ensure the accurate execution of the experiment plan.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to an aspect of the disclosure, an automatic scheduling processing method of an experiment plan is provided, the method comprising: acquiring experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information comprises a plurality of reaction tasks; determining constraint conditions for the corresponding experiment plans according to the experiment information; determining an optimization objective of the at least one experimental plan; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition to generate an experiment scheduling strategy; and automatically invoking resources in an intelligent laboratory based on the experiment scheduling policy to execute the at least one experiment plan to generate an experiment result.
In an exemplary embodiment of the present disclosure, obtaining experimental information corresponding to at least one experimental plan to be processed includes: acquiring an experimental flow chart corresponding to the at least one experimental plan to be processed; acquiring a plurality of reaction tasks corresponding to at least one experimental flow chart; task information corresponding to a plurality of reaction tasks is obtained; and generating at least one piece of experimental information through the experimental flow chart, the reaction task and the task information corresponding to the at least one experimental plan.
In one exemplary embodiment of the present disclosure, determining constraints for its corresponding experimental plan based on experimental information includes: acquiring an execution main body set of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions; obtaining reaction time of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions; and obtaining the reaction sequence of a plurality of reaction tasks corresponding to the experiment plan from the experiment information and setting constraint conditions.
In an exemplary embodiment of the present disclosure, obtaining, from the experiment information, a set of execution subjects of a plurality of reaction tasks corresponding to an experiment plan and setting constraint conditions, includes: acquiring material properties and reaction properties of a reaction task from the experimental information; determining an execution main body of the reaction task according to the material property and the reaction property; generating the execution subject set corresponding to the experiment plan according to the execution subjects of the plurality of reaction tasks; setting execution constraints for each execution body in the set of execution bodies.
In an exemplary embodiment of the present disclosure, obtaining reaction times of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions includes: setting fixed reaction time and fixed time constraint conditions for the reaction task according to an execution main body of the reaction task; and setting a specified reaction time and an elastic time constraint condition for the reaction task according to the execution main body of the reaction task.
In an exemplary embodiment of the present disclosure, obtaining a reaction sequence of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions includes: extracting an experimental flow chart from the experimental information; obtaining the reaction sequence of the plurality of reaction tasks according to the experimental flow chart; and setting sequence time constraint conditions for the plurality of reaction tasks according to the reaction sequence.
In one exemplary embodiment of the present disclosure, determining the optimization objective 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 of the at least one experimental plan; an optimization objective of the at least one experimental plan is determined to minimize the completion time.
In an exemplary embodiment of the present disclosure, optimizing an execution order of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization objective and the constraint condition, generating an experiment scheduling policy includes: optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraint condition through a mixed integer linear programming algorithm; and/or optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition through a constraint planning algorithm.
In one exemplary embodiment of the disclosure, the subject of execution of the experiment plan includes an intelligent material transfer cart; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition, and generating an experiment scheduling strategy, wherein the method comprises the following steps: acquiring map information of an intelligent laboratory; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition, and generating an initial scheduling strategy; and generating the experimental scheduling strategy by combining the track information of the intelligent material transfer vehicle in a gradual increasing mode based on the initial scheduling strategy and the map information.
In an exemplary embodiment of the present disclosure, generating the experimental scheduling policy based on the initial scheduling policy and the map information in a stepwise increasing manner in combination with track information of an intelligent material transfer vehicle includes: extracting the execution sequence of a plurality of reaction tasks from the initial scheduling strategy; extracting a plurality of areas 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; and updating the initial scheduling strategy according to the optimized path to generate the experimental scheduling strategy.
According to an aspect of the present disclosure, an apparatus for automatically scheduling an experimental plan is provided, the apparatus comprising: the information module is used for acquiring experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information comprises a plurality of reaction tasks; the constraint module is used for determining constraint conditions for the corresponding experiment plans according to the experiment information; a goal module for determining an optimization goal of the at least one experimental plan; the scheduling module is used for optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition, and generating an experiment scheduling strategy; and the execution module is used for automatically calling resources in the intelligent laboratory based on the experiment scheduling strategy to execute the at least one experiment plan to generate an experiment result.
According to an aspect of the present disclosure, there is provided an electronic device including: one or more processors; a storage means 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 are caused to implement the methods as described above.
According to an aspect of the present disclosure, a computer-readable medium is presented, on which a computer program is stored, which program, when being executed by a processor, implements a method as described above.
According to the automatic scheduling processing method, the device, the electronic equipment and the computer readable medium of the experimental plan, experimental information corresponding to at least one experimental plan to be processed is obtained, wherein the experimental information comprises a plurality of reaction tasks; determining constraint conditions for the corresponding experiment plans according to the experiment information; determining an optimization objective of the at least one experimental plan; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition to generate an experiment scheduling strategy; based on the mode that the resources in the intelligent laboratory are automatically called to execute the at least one experimental plan to generate experimental results by the experimental scheduling strategy, the flexible automatic scheduling of man-machine combination can be carried out on chemical experiments, the operation cost of the automatic laboratory is reduced, the working efficiency is improved, and the accurate execution of the experimental plan is ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely examples of the present disclosure and other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of an automated scheduling processing system for an experimental plan, according to an exemplary embodiment.
FIG. 2 is a flow chart illustrating a method of automatically scheduling an experimental plan, according to an exemplary embodiment.
FIG. 3 is a flow chart illustrating a method of automatically scheduling an experimental plan according to another exemplary embodiment.
FIG. 4 is a flow chart illustrating a method of automatically scheduling an experimental plan according to another exemplary embodiment.
FIG. 5 is a schematic diagram illustrating an automated scheduling process for an experimental plan, according to another exemplary embodiment.
FIG. 6 is a schematic diagram illustrating an automated scheduling process for an experimental plan, according to another exemplary embodiment.
FIG. 7 is a block diagram of an automated scheduling processing apparatus for an experimental plan, according to an example embodiment.
Fig. 8 is a block diagram of an electronic device, according to an example embodiment.
Fig. 9 is a block diagram of a computer-readable medium shown according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary 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 concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It will 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 element from another element. Accordingly, a first component discussed below could be termed a second component without departing from the teachings of the concepts 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.
Those skilled in the art will appreciate that the drawings are schematic representations of example embodiments and that the modules or flows in the drawings are not necessarily required to practice the present disclosure, and therefore, should not be taken to limit the scope of the present disclosure.
The applicant has found through studies of the prior art that in the context of chemical experiments, it is difficult for the automation devices to handle uncertainties in chemical operations, and that in actual production, the cost of some automation devices is too high. The efficiency gains are not as good as employment. Therefore, in a man-machine combined scenario, it is not enough to schedule the experimental equipment alone.
The efficiency of execution may be reduced if only chemical equipment is scheduled. Since the arrangement of the machine does not take into account the human task, the execution time or execution status of the task often has uncertainty, especially the task executed by a person, which may lead to a situation of the machine or the robot such as a person (e.g. a certain task start time has been reached, but a task executed by a pre-positioned person has not yet been completed). Scheduling chemical equipment alone can also cause disruption of tasks: for example, a task that is performed by a machine but that needs to be initiated by a human being must begin, the human being is performing other tasks.
In view of the technical defects in the prior art, the application provides an automatic scheduling processing method which can meet the requirements of uncertainty and diversity in a chemical experiment scene, combines manual operation and machine equipment to consider, performs experiment scheduling, meets the requirements in actual operation, and greatly improves experiment efficiency.
FIG. 1 is a schematic diagram of an automated scheduling processing system for an experimental plan, according to an exemplary embodiment.
As shown in fig. 1, in one chemistry experiment scenario, intelligent containers, weighing devices, reactors, post-processing reactors, automated analyzers may be included. Auxiliary equipment such as a material transfer vehicle, a mechanical arm and the like can be further included.
Wherein, the intelligent container is used for loading a reaction substrate;
the weighing device is used for acquiring a preset quantity of reaction substrates, and can further comprise automatic weighing equipment, micro automatic weighing equipment, manual weighing equipment and the like.
And the reactor is used for bearing a reaction substrate and carrying out experimental reaction to generate a reaction result. More specifically, automated reactors, conventional reaction vessels, agitators, and the like may be included.
And the automatic analyzer is used for analyzing the intermediate reactant to generate an analysis result.
And the post-treatment module is used for carrying out subsequent operation or treatment on the intermediate product or the reaction result of the chemical reaction. It is worth mentioning that the treatment of the subsequent reaction can also be continued in the reactor.
The material transfer vehicle is used for automatically conveying materials to corresponding devices according to different flow steps;
a robotic arm may also be included for grasping the reaction substrate or transferring the material to a corresponding device.
In the specific application scenario shown in fig. 1, when a user submits one or more experimental plans, the method in the present application may automatically estimate the reaction time in the one or more experimental plans, then sequence the operation sequence and time of the device or the execution subject (robot or robot) in the intelligent laboratory, and generate an operation instruction so that the execution subject performs the chemical reaction operation according to the instruction.
After one or more experiment plans are started, the system controls the intelligent container to take out the raw material bottles required by the experiment, the raw material bottles are placed on a material taking table by a mechanical arm of the intelligent container,
then, the material can be transported to a batching area by a material transfer vehicle;
the weighing operation is performed by an automatic weighing apparatus or a human being according to the nature of the substance.
Then the material transfer handle conveys the material to the reaction area, sends the raw materials bottle back to intelligent counter for storage.
And selecting a proper reactor according to the type of the reaction, and completing the feeding operation by a mechanical arm.
After the reaction is finished, the mixed solution after the reaction is sampled and automatically pretreated by a mechanical arm, and the automatic analysis is carried out.
According to the operation steps and experimental scheduling strategies in the flowcharts corresponding to the experimental plans, the materials are sent to each subsequent execution main body;
and finally, conveying the material transfer handle product into an intelligent container for warehousing operation.
FIG. 2 is a flow chart illustrating a method of automatically scheduling an experimental plan, according to an exemplary embodiment. The automatic scheduling method 20 of the experimental plan at least includes steps S202 to S208.
As shown in fig. 2, in S202, experimental information corresponding to at least one experimental plan to be processed is obtained, where the experimental information includes a plurality of reaction tasks. The experimental flow chart corresponding to the at least one experimental plan to be processed can be obtained, for example; acquiring a plurality of reaction tasks corresponding to at least one experimental flow chart; task information corresponding to a plurality of reaction tasks is obtained; and generating at least one piece of experimental information through the experimental flow chart, the reaction task and the task information corresponding to the at least one experimental plan.
In the present application, a reaction may include classes of chemical reactions, decomposition reactions, substitution reactions, metathesis reactions, and the like, and an experimental plan may include one or more reaction tasks. The reaction task information may indicate the reaction conditions, environmental information, reaction time, etc. required for the reaction.
It should be noted that, in the present application, the experimental flow chart corresponds to a chemical experimental flow chart. Refers to a directed graph constructed from tasks and task sequences in chemical experiments, each node representing a chemical experiment task (e.g., material-catching, weighing, reaction in fig. 1). The simple arrow indicates that the previous task is done before the latter task is performed after completion. Parallel tasks controlled by parallel task switches: representing that the flow between the start of the parallel task and the end of the parallel task is performed simultaneously.
In S204, constraint conditions are determined for their corresponding experimental plans based on the experimental information. For example, an execution subject set of a plurality of reaction tasks corresponding to an experiment plan is obtained from the experiment information, and constraint conditions are set; obtaining reaction time of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions; and obtaining the reaction sequence of a plurality of reaction tasks corresponding to the experiment plan from the experiment information and setting constraint conditions.
Details of "determining constraints for their corresponding experimental plans based on experimental information" will be described in the corresponding embodiment of fig. 3.
In S206, an optimization objective of the at least one experimental plan is determined. Determining an end time constraint of the at least one experimental plan; setting an experimental weight for each of the at least one experimental plan; an optimization objective of the at least one experimental plan is determined to minimize the completion time.
Details of the "determining the optimization objective of the at least one experimental plan" will be described in the corresponding embodiment of fig. 3.
In S208, the execution sequence of the plurality of reaction tasks corresponding to the at least one experiment plan is optimized based on the optimization objective and the constraint condition, and an experiment scheduling policy is generated. The execution sequence of the plurality of reaction tasks corresponding to the at least one experimental plan may be optimized, for example, by a mixed integer linear programming algorithm based on the optimization objective and the constraint condition; the execution sequence of the plurality of reaction tasks corresponding to the at least one experimental plan may also be optimized, for example, by a constraint planning algorithm based on the optimization objective and the constraint condition.
More specifically, the optimization algorithm described in the present 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, CBC, and the like.
In S210, automatically invoking resources in the intelligent laboratory to execute the at least one experimental plan to generate experimental results based on the experimental scheduling policy.
According to the automatic scheduling processing method of the experimental plan, experimental information corresponding to at least one experimental plan to be processed is obtained, wherein the experimental information comprises a plurality of reaction tasks; determining constraint conditions for the corresponding experiment plans according to the experiment information; determining an optimization objective of the at least one experimental plan; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition to generate an experiment scheduling strategy; based on the mode that the resources in the intelligent laboratory are automatically called to execute the at least one experimental plan to generate experimental results by the experimental scheduling strategy, the flexible automatic scheduling of man-machine combination can be carried out on chemical experiments, the operation cost of the automatic laboratory is reduced, the working efficiency is improved, and the accurate execution of the experimental plan is ensured.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
FIG. 3 is a flow chart illustrating a method of automatically scheduling an experimental plan according to another exemplary embodiment. The flow 30 shown in fig. 3 is a detailed description of S204 "determine constraint conditions for its corresponding experimental plan according to experimental information" in the flow shown in fig. 2.
As shown in fig. 3, in S302, constraints are determined for its corresponding experimental plan based on the experimental information.
It may be assumed, for example, that there are m experimental flowcharts to be arranged:
fl 1 ，fl 2 ，...，fl m ；
for flow fl i There are several tasks on the flow chart:
in S304, a set of execution subjects of a plurality of reaction tasks corresponding to the experiment plan is obtained from the experiment information, and constraint conditions are set. Material properties and reaction properties of the reaction task may be obtained, for example, from the experimental information; determining an execution main body of the reaction task according to the material property and the reaction property; generating the execution subject set corresponding to the experiment plan according to the execution subjects of the plurality of reaction tasks; setting execution constraints for each execution body in the set of execution bodies.
In the present application, the reaction in the same experiment plan may be performed by different automated apparatuses or persons, and for example, the reaction may be performed by an apparatus such as a microwave reactor, a room temperature reactor, or a flow reactor. The choice of the type of device is generally dependent on the nature of the reaction or substance. For example, in the reaction, a microwave reactor (nature of the reaction) is selected for a non-hazardous high temperature reaction; the liquid with good fluidity is an automatic weighing device (substance property) or the like.
In the flow chart, the available devices or operators are selected for each task.
For task t ij With a user-configurable rules engine, define which automation devices can execute, based on the nature of the material and the nature of the reaction, the task to be executed ij Can be operated to define the variablesWherein o is ijc Indicating whether the c-th operator is performing task t ij 。
Having an equation of
In S306, reaction times of a plurality of reaction tasks corresponding to the experiment plan are obtained from the experiment information, and constraint conditions are set. The method comprises the steps that a fixed reaction time can be set for a reaction task according to an execution main body of the reaction task, and a fixed time constraint condition is set; and setting a specified reaction time and an elastic time constraint condition for the reaction task according to the execution main body of the reaction task.
For task t ij Let its start time be s ij The time to end the experiment was e ij Each task has a duration d ij ＝e ij -s ij (2)
Tasks of a preset fixed duration (e.g. tasks of machines)
Let d ij ＝c ij (wherein c) ij Is constant. (3)
Elastic tasks, such as human-operated tasks. Execution time at c ij Andthe task in between means that the execution time of the task must be longer than a constant c ij And less than one variationQuantity->If no task is scheduled subsequently, the execution time of the task can be prolonged (for example, the reaction is to 3 am, the termination is not necessarily performed immediately, and the stirring can be continued until the next day for processing).
Order the
In S308, the reaction sequences of the plurality of reaction tasks corresponding to the experiment plan are obtained from the experiment information, and constraint conditions are set. An experimental flow chart may be extracted from the experimental information, for example; obtaining the reaction sequence of the plurality of reaction tasks according to the experimental flow chart; and setting sequence time constraint conditions for the plurality of reaction tasks according to the reaction sequence.
For two adjacent tasks t on the same flow chart ij ，t ik ；
t ij At t ik Immediately after ending execution e ik ＝s ij (5)
t ij And t ik At the same time start s ik ＝s ij (6)
t ij At t ik A range of times after the start:
s ik +l lb ≤s Ij ＜s ik +l ub wherein l lb And l ub Is constant. (7)
t ij At t ik A range of times after the end begins:
e ik +l lb ≤s ij ＜e ik +l ub wherein l lb And l ub Is constant. (8)
On the same operation object c, tasks do not overlap:
order the
s ij ≥e i′j′ -M 1 ×y iji′j′ -M 2 ×(2-o ijc -o i′j′c ) (9)
s i′j′ ≥e ij -M 1 ×(1-y iji′j′ )-M 2 ×(2-o ijc -o i′j′c ) (10)
Wherein M is 1 ，M 2 In practice 10 can be taken for a very large constant 6 。
Optimization target: it can be set that the last task of each experiment isIts corresponding end time is +.>Constraints were applied to the end time of each experiment:
obj＝minimize∑ i z i (12)
wherein w is i For the weight of the experiment, z i For the completion time of each experiment with a weight, this optimization goal aims to minimize the completion time of all experiments, and the higher the weight the experimental algorithm will induce the best possible end.
FIG. 4 is a flow chart illustrating a method of automatically scheduling an experimental plan according to another exemplary embodiment. The flow 40 shown in fig. 4 is a detailed description of "optimize the execution sequence of the plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization objective and the constraint condition to generate an experiment scheduling policy" when the execution subject of the experiment plan includes an intelligent material transfer vehicle.
When the intelligent material transfer vehicle is required to be scheduled and optimized, the factors of the intelligent material transfer vehicle can be added into the optimization model, and as the running time of the intelligent material transfer vehicle between different working areas is uncertain, the running of the intelligent material transfer vehicle must consider two factors at the same time:
1. Shortest path for intelligent material transfer vehicle to travel
2. And (5) task arrangement.
As shown in fig. 4, in S402, map information of an intelligent laboratory is acquired. Map information of the laboratory may be as shown in fig. 5.
Let the laboratory have a working area: w (w) 1 ，w 2 ，…，w m Intelligent material transfer vehicle is arranged in working area w i ，w j The walking time between the two is d ij 。
Suppose there are six working areas a, b, c, d, e, f, wherein three working areas a, b, c are closer together, and three working areas d, e, f are closer together. But the distance between the two is far.
As shown in fig. 6, assuming that only scheduling is considered and the order of tasks is a, e, b, f, c, g, the intelligent material transfer vehicle is transported far away, wasting much time. If only the shortest travel of the intelligent material transfer cart is considered, the task on the work area may be left idle for personnel or equipment due to waiting for the intelligent material transfer cart, resulting in a delay in task completion time.
In S404, the execution sequence of the plurality of reaction tasks corresponding to the at least one experiment plan is optimized based on the optimization objective and the constraint condition, and an initial scheduling policy is generated. The order of execution of the reaction tasks may be optimized by the method described in fig. 2, generating an initial scheduling policy.
In S406, the experimental scheduling policy is generated by combining the track information of the intelligent material transfer vehicle in a stepwise increasing manner based on the initial scheduling policy and the map information.
In one embodiment, the order of execution of the plurality of reaction tasks may be extracted, for example, from the initial scheduling policy; extracting a plurality of areas 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; and updating the initial scheduling strategy according to the optimized path to generate the experimental scheduling strategy.
For any two tasks t which can be transported by the intelligent material transfer vehicle ij ，t i′j′ Due to t ij ，t i′j′ May be arranged in different working areas, may cause:
s ij ≥e i′j′ +d ww′ -M 3 ×y iji′j′ -M 4 ×(2-a ijc - i′j′c )(13)
s i′j′ ≥e ij +d ww′ -M 3 ×(1-y iji′j′ )
-M 4 ×(2-o ijc - i′j′c ) (14)
wherein M is 3 ，M 4 In practice 10 can be taken for a very large constant 6 。
The formula 9 and the formula 10 are very similar in form, but the same working area has limited tasks within a certain range, but the tasks of any two different working areas can be arranged to be executed before and after each other, so that the intelligent material transfer vehicle can transport the materials. The complexity of the introduction of the 13, 14 equation may be significantly greater than the 9, 10 equation.
Direct solution after the intelligent material transfer vehicle is introduced can lead to a great increase in the solution time of the algorithm. After the algorithm finds the solution based on the current situation of the laboratory, the actual situation of the laboratory may have changed significantly, and the solution of the algorithm cannot be applied to the actual laboratory schedule.
In the present application, this complex problem is solved in two steps.
The problem of intelligent material transfer vehicles is not considered, and a laboratory scheduling algorithm is solved.
The following procedure was repeated:
the tasks corresponding to the solutions are arranged according to the starting and ending time sequenceThe corresponding working area is +.>
Adding constraints to the model in 1 in order of regions
An attempt is made to find a new solution.
If no solution is available and no solution meeting the requirements has been obtained before, the 15 th form is replaced by
Wherein d is * Is a variable and willAdding an optimization target. Due to d * =0 must be a solution that satisfies the constraint. The problem must be solved optimally. Finding the corresponding +.>Will t i And the following tasks are eliminated (namely, the intelligent material transfer vehicle does not transport the materials for the experiment in the current problem, and the next round of transportation is changed, and the work can be completed by a human operator in actual operation).
If a feasible solution exists, setting the latest completion time obj corresponding to the feasible solution p (12) Then add constraints to the optimization objective
obj p ＞minimize∑ i Z i (17)
Attempting to solve again, repeating the operation of (a) if a feasible solution is obtained, and if no feasible solution is available, obtaining the current solution as the optimal solution.
The complexity of solving is greatly reduced by using the algorithm, the 15 expression avoids using a double large M method compared with the 13 and 14 expression, and the number of the equations is from O (n 2 ) O (n), n being the number of tasks, drops down to.
According to the optimization method, the optimization method is innovative, the constraint of material transportation is gradually added into the model from the fact that only laboratory tasks are considered and material transportation is not considered, and the distance between working areas is considered to be 0 because the constraint of material transportation does not exist at the beginning. When the distance between the partial workspaces is added, the process of solving the equation 17 gradually turns a problem that only schedules are considered, but paths are not considered, into a problem that both schedules and paths are considered, driving the model to find a better path to iterate the current solution. Until the remaining solution, even if the other paths are considered not time consuming, the solution is not made better.
Those skilled in the art will appreciate that all or part of the steps implementing the above described embodiments are implemented as a computer program executed by a CPU. The above-described functions defined by the above-described methods provided by the present disclosure are performed when the computer program is executed by a CPU. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic disk or an optical disk, etc.
Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.
FIG. 7 is a block diagram of an automated scheduling processing apparatus for an experimental plan, according to an example embodiment. As shown in fig. 7, the automatic schedule processing apparatus 70 of the experiment plan includes: information module 702, constraint module 704, goal module 706, schedule module 708, and execute module 710.
The information module 702 is configured to obtain experimental information corresponding to at least one experimental plan to be processed, where the experimental information includes a plurality of reaction tasks; the information module 702 is further configured to obtain an experiment flowchart corresponding to the at least one experiment plan to be processed; acquiring a plurality of reaction tasks corresponding to at least one experimental flow chart; task information corresponding to a plurality of reaction tasks is obtained; and generating at least one piece of experimental information through the experimental flow chart, the reaction task and the task information corresponding to the at least one experimental plan.
The constraint module 704 is configured to determine constraint conditions for the corresponding experiment plans according to the experiment information; the constraint module 704 is further configured to obtain an execution subject set of a plurality of reaction tasks corresponding to the experiment plan from the experiment information and set constraint conditions; obtaining reaction time of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions; and obtaining the reaction sequence of a plurality of reaction tasks corresponding to the experiment plan from the experiment information and setting constraint conditions.
The objective module 706 is configured to determine an optimization objective of the at least one experimental plan; the objective module 706 is further configured to determine an end time constraint of the at least one experimental plan; setting an experimental weight for each of the at least one experimental plan; an optimization objective of the at least one experimental plan is determined to minimize the completion time.
The scheduling module 708 is configured to optimize an execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization objective and the constraint condition, and generate an experiment scheduling policy; the scheduling module 708 is further configured to optimize, by using a mixed integer linear programming algorithm, an execution order of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization objective and the constraint condition; and/or optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition through a constraint planning algorithm.
The execution module 710 is configured to automatically invoke resources in the intelligent laboratory to execute the at least one experimental plan to generate experimental results based on the experimental scheduling policy.
According to the automatic scheduling processing device of the experimental plan, experimental information corresponding to at least one experimental plan to be processed is obtained, wherein the experimental information comprises a plurality of reaction tasks; determining constraint conditions for the corresponding experiment plans according to the experiment information; determining an optimization objective of the at least one experimental plan; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition to generate an experiment scheduling strategy; based on the mode that the resources in the intelligent laboratory are automatically called to execute the at least one experimental plan to generate experimental results by the experimental scheduling strategy, the flexible automatic scheduling of man-machine combination can be carried out on chemical experiments, the operation cost of the automatic laboratory is reduced, the working efficiency is improved, and the accurate execution of the experimental plan is ensured.
Fig. 8 is a block diagram of an electronic device, according to an example embodiment.
An electronic device 800 according to such an embodiment of the present disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 8, the electronic device 800 is embodied in the form of 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 memory unit 820, a bus 830 that connects the different system components (including memory unit 820 and processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 810 such that the processing unit 810 performs steps described in the present specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 810 may perform the steps as shown in fig. 2, 3, and 4.
The storage unit 820 may include a readable medium in the form of a volatile memory 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.
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 or some combination of which may include an implementation of a network environment.
Bus 830 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 800 may also communicate with one or more external devices 800' (e.g., keyboard, pointing device, bluetooth device, etc.), devices that enable a user to interact with the electronic device 800, and/or any devices (e.g., routers, modems, etc.) that the electronic device 800 can communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 850. Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 860. Network adapter 860 may communicate with other modules of electronic device 800 via bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 800, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, as shown in fig. 9, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the embodiments of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind 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., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs, which when executed by one of the devices, cause the computer-readable medium to perform the functions of: acquiring experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information comprises a plurality of reaction tasks; determining constraint conditions for the corresponding experiment plans according to the experiment information; determining an optimization objective of the at least one experimental plan; optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition to generate an experiment scheduling strategy; and automatically invoking resources in an intelligent laboratory based on the experiment scheduling policy to execute the at least one experiment plan to generate an experiment result.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and include several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that this disclosure is not limited to the particular arrangements, instrumentalities and methods of implementation described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
Claims (13)
1. An automatic scheduling method for an experimental plan, comprising:
acquiring experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information comprises a plurality of reaction tasks;
determining constraint conditions for the corresponding experiment plans according to the experiment information;
determining an optimization objective of the at least one experimental plan;
optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition to generate an experiment scheduling strategy;
and automatically invoking resources in an intelligent laboratory based on the experiment scheduling policy to execute the at least one experiment plan to generate an experiment result.
2. The method of claim 1, wherein obtaining experimental information corresponding to at least one experimental plan to be processed comprises:
acquiring an experimental flow chart corresponding to the at least one experimental plan to be processed;
Acquiring a plurality of reaction tasks corresponding to at least one experimental flow chart;
task information corresponding to a plurality of reaction tasks is obtained;
and generating at least one piece of experimental information through the experimental flow chart, the reaction task and the task information corresponding to the at least one experimental plan.
3. The method of claim 1, wherein determining constraints for its corresponding experimental plan based on experimental information comprises:
acquiring an execution main body set of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions;
obtaining reaction time of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraint conditions;
and obtaining the reaction sequence of a plurality of reaction tasks corresponding to the experiment plan from the experiment information and setting constraint conditions.
4. The method of claim 3, wherein obtaining the execution subject set of the plurality of reaction tasks corresponding to the experiment plan from the experiment information and setting the constraint condition comprises:
acquiring material properties and reaction properties of a reaction task from the experimental information;
determining an execution main body of the reaction task according to the material property and the reaction property;
Generating the execution subject set corresponding to the experiment plan according to the execution subjects of the plurality of reaction tasks;
setting execution constraints for each execution body in the set of execution bodies.
5. The method of claim 3, wherein obtaining reaction times of a plurality of reaction tasks corresponding to an experiment plan from the experiment information and setting constraints comprises:
setting fixed reaction time and fixed time constraint conditions for the reaction task according to an execution main body of the reaction task;
and setting a specified reaction time and an elastic time constraint condition for the reaction task according to the execution main body of the reaction task.
6. The method of claim 3, wherein obtaining the reaction sequence of the plurality of reaction tasks corresponding to the experiment plan from the experiment information and setting the constraint condition comprises:
extracting an experimental flow chart from the experimental information;
obtaining the reaction sequence of the plurality of reaction tasks according to the experimental flow chart;
and setting sequence time constraint conditions for the plurality of reaction tasks according to the reaction sequence.
7. The method of claim 1, wherein determining the optimization objective of the at least one experimental plan comprises:
Determining an end time constraint of the at least one experimental plan;
setting an experimental weight for each of the at least one experimental plan;
an optimization objective of the at least one experimental plan is determined to minimize the completion time.
8. The method of claim 1, wherein optimizing the order of execution of the plurality of reaction tasks corresponding to the at least one experimental plan based on the optimization objective and the constraint condition, generating an experimental scheduling policy, comprises:
optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experimental plan based on the optimization target and the constraint condition through a mixed integer linear programming algorithm; and/or
And optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition through a constraint planning algorithm.
9. The method of claim 1, wherein the subject of execution of the experiment plan comprises an intelligent material transfer cart;
optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition, and generating an experiment scheduling strategy, wherein the method comprises the following steps:
Acquiring map information of an intelligent laboratory;
optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition, and generating an initial scheduling strategy;
and generating the experimental scheduling strategy by combining the track information of the intelligent material transfer vehicle in a gradual increasing mode based on the initial scheduling strategy and the map information.
10. The method of claim 9, wherein generating the experimental scheduling strategy based on the initial scheduling strategy and the map information in a stepwise increasing manner in combination with trajectory information of an intelligent material transfer vehicle comprises:
extracting the execution sequence of a plurality of reaction tasks from the initial scheduling strategy;
extracting a plurality of areas 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;
and updating the initial scheduling strategy according to the optimized path to generate the experimental scheduling strategy.
11. An automated scheduling apparatus for an experimental plan, comprising:
The information module is used for acquiring experimental information corresponding to at least one experimental plan to be processed, wherein the experimental information comprises a plurality of reaction tasks;
the constraint module is used for determining constraint conditions for the corresponding experiment plans according to the experiment information;
a goal module for determining an optimization goal of the at least one experimental plan;
the scheduling module is used for optimizing the execution sequence of a plurality of reaction tasks corresponding to the at least one experiment plan based on the optimization target and the constraint condition, and generating an experiment scheduling strategy;
and the execution module is used for automatically calling resources in the intelligent laboratory based on the experiment scheduling strategy to execute the at least one experiment plan to generate an experiment result.
12. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-10.
13. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-10.
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