Multi-task scheduling method for multiple scientific constraints of chemical experiment

Abstract

The invention discloses a multi-task scheduling method for chemical experiment multiple scientific constraints, which belongs to the field of chemical experiment task optimization scheduling and comprises the following steps: step 1, experimental task and resource modeling: respectively constructing an experimental task set, an experimental workstation set and each experimental operation parameter according to a chemical experiment process; step 2, operation time interval construction: setting start time and end time of each experimental operation, setting an interval between the start time and the end time as the operation time interval of the experimental operation, and setting a scheduling decision and an auxiliary variable; step 3, multi-scientific constraint joint modeling: in an operation time interval, in combination with a scheduling decision and an auxiliary variable, performing unified description and joint modeling on the multi-scientific constraint to obtain a scheduling model; and 4, scheduling and solving: on the premise of meeting multiple scientific constraints, solving the scheduling model according to a scheduling target to obtain a scheduling scheme for scheduling multiple chemical experiment tasks. According to the method, multiple scientific constraints can be jointly solved, a scheduling scheme meeting all constraints is obtained, and the scheduling efficiency is improved.

Description

A Multi-task Scheduling Method for Chemical Experiments with Multiple Scientific Constraints Technical Field

[0001] This invention relates to the field of chemical experiment task optimization and scheduling technology, and in particular to a multi-task scheduling method for chemical experiments with multiple scientific constraints. Background Technology

[0002] In automated chemical laboratory environments, multiple types of chemical experiments often need to be conducted in parallel within the same laboratory. Unlike traditional manufacturing scheduling problems, chemical experimental tasks are not only subject to engineering constraints, including operation sequence, batch processing, workstation allocation, and consistency of experimental parameters, but also to the experimental mechanism constraints at the chemical experiment level, namely the requirement of zero waiting time between operations. Engineering constraints and experimental mechanism constraints together constitute the scientific constraints of chemical experiments.

[0003] Existing studies on automated chemical experiment scheduling often focus on system implementation or task allocation, typically simplifying only single or partial constraints. They struggle to provide a unified modeling and description of multiple scientific constraints, including zero-wait time, operation order, batch processing, workstation allocation, and experimental parameter consistency, thus limiting the applicability of scheduling methods in complex real-world chemical experimental scenarios.

[0004] Therefore, the problem to be solved is how to provide a standardized model for complex chemical experimental tasks without relying on the specific execution system structure, and generate a scheduling scheme that meets multiple scientific constraints.

[0005] In view of this, the present invention is hereby proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-task scheduling method for chemical experiments with multiple scientific constraints. This method can uniformly model and jointly solve multiple types of scientific constraints involved in chemical experiments, derive a scheduling scheme that satisfies all scientific constraints, improve the scheduling efficiency of multiple chemical experiment tasks, and thus solve the aforementioned technical problems existing in the prior art.

[0007] The objective of this invention is achieved through the following technical solution: a multi-task scheduling method for chemical experiments with multiple scientific constraints, used for scheduling multiple chemical experimental tasks subject to multiple scientific constraints, comprising: Step 1, experimental task and experimental resource modeling: constructing an experimental task set, an experimental workstation set, and parameters for each experimental operation of each experimental task according to the chemical experimental process of the multiple chemical experimental tasks to be scheduled; Step 2, experimental operation time interval construction: setting the start time and end time of each experimental operation of each experimental task, and abstracting the interval between the start time and end time of the operation as the operation time interval of the experimental operation onto the time axis, and providing a time interval for the actual operation. Step 3, Joint Modeling of Multiple Scientific Constraints: Within the operation time interval, combining the set scheduling decision variables and auxiliary variables, a unified description and joint modeling of multiple scientific constraints for various chemical experimental tasks, including operation order constraints, workstation allocation constraints, batch processing constraints, experimental parameter consistency constraints, and zero-wait constraints, is performed to obtain a scheduling model. Step 4, Construction of Scheduling Objectives and Solution of Scheduling: Under the premise of satisfying all constraints of multiple scientific constraints, the scheduling model from Step 3 is optimized and solved according to the set scheduling objectives to obtain an experimental task scheduling scheme that satisfies multiple scientific constraints, and the multiple chemical experimental tasks to be scheduled are then scheduled.

[0008] Compared with existing technologies, the multi-task scheduling method for chemical experiments with multiple scientific constraints provided by this invention has the following advantages: First, it constructs a set of experimental tasks, a set of experimental workstations, and parameters for each experimental operation of each task based on the chemical experimental workflow of the multiple chemical experimental tasks to be scheduled. Then, it determines the operation time interval of the experimental operations and introduces scheduling decision variables and auxiliary variables for experimental scheduling. Next, it uniformly describes and jointly models the multiple scientific constraints of the multiple chemical experimental tasks, including zero-wait constraints, operation order constraints, workstation allocation constraints, batch processing constraints, and experimental parameter consistency constraints, into a scheduling model. Finally, it obtains an experimental task scheduling scheme that satisfies multiple scientific constraints through optimization. This method is independent of specific execution equipment and can be used to guide the scheduling planning of chemical experimental tasks in automation laboratories. It can uniformly describe multiple scientific constraints specific to chemical experiments in the same scheduling model, avoiding the conflicts and infeasibility problems caused by the decentralized processing of multiple constraints in existing methods. The scheduling scheme generated by this invention can significantly improve the experimental efficiency of automation laboratories by setting scheduling optimization objectives. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 is a flowchart of a multi-task scheduling method for multiple scientific constraints in chemical experiments provided by an embodiment of the present invention.

[0011] Figure 2 is a flowchart of a multi-task scheduling method for multiple scientific constraints in chemical experiments provided by an embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them, and do not constitute a limitation on the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0013] First, the following explanation is given for the terms that may be used in this article: The term "and / or" means that either or both can be achieved simultaneously. For example, X and / or Y means that the case includes both "X" or "Y" as well as the three cases of "X and Y".

[0014] The terms "comprising," "including," "containing," "having," or other similar semantic descriptions should be interpreted as non-exclusive inclusion. For example, including a technical feature element (such as raw material, component, ingredient, carrier, dosage form, material, size, part, component, mechanism, device, step, process, method, reaction conditions, processing conditions, parameter, algorithm, signal, data, product or article of manufacture, etc.) should be interpreted as including not only the expressly listed technical feature element, but also other technical feature elements that are not expressly listed and are well-known in the art.

[0015] The term "composed of" excludes any technical features not expressly listed. When used in a claim, it closes the claim to exclude all technical features other than those expressly listed, except for associated conventional impurities. If the term appears only in a clause of a claim, it limits the claim to the elements expressly listed in that clause; elements recited in other clauses are not excluded from the overall claim.

[0016] Unless otherwise explicitly specified or limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this document according to the specific circumstances.

[0017] The terms “center,” “longitudinal,” “lateral,” “length,” “width,” “thickness,” “up,” “down,” “front,” “back,” “left,” “right,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” “outer,” “clockwise,” and “counterclockwise” indicate the current orientation or positional relationship, and are only for the convenience and simplification of description, and do not explicitly or implicitly suggest that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this document.

[0018] The technical solution provided by this invention will be described in detail below. Contents not described in detail in the embodiments of this invention are prior art known to those skilled in the art. Where specific conditions are not specified in the embodiments of this invention, they shall be performed according to conventional conditions in the art or conditions recommended by the manufacturer. Reagents or instruments used in the embodiments of this invention whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0019] As shown in Figures 1 and 2, this invention provides a multi-task scheduling method for chemical experiments with multiple scientific constraints. This method provides a unified modeling approach for multiple scientific constraints in chemical experiments, used for scheduling multiple chemical experimental tasks subject to these constraints. The method includes the following steps: Step 1, Experimental Task and Resource Modeling: Based on the chemical experimental workflow of the multiple chemical experimental tasks to be scheduled, construct a set of experimental tasks, a set of experimental workstations, and parameters for each experimental operation of each task; Step 2, Experimental Operation Time Interval Construction: Set the start and end times for each experimental operation of each task, and use the interval between the start and end times as the time interval for that experimental operation. Step 3: Joint modeling of multiple scientific constraints: Based on the set scheduling decision variables and auxiliary variables, the multiple scientific constraints of the multi-chemical experimental tasks, including operation order constraints, workstation allocation constraints, batch processing constraints, experimental parameter consistency constraints, and zero-wait constraints, are uniformly described and jointly modeled within the operation time interval to obtain the scheduling model; Step 4: Construction of scheduling objectives and solution of scheduling: Under the premise of satisfying all constraints of multiple scientific constraints, the scheduling model of Step 3 is optimized and solved according to the set scheduling objectives to obtain the experimental task scheduling scheme that satisfies multiple scientific constraints, and the multi-chemical experimental tasks to be scheduled are scheduled.

[0020] Preferably, in step 1 of the above method, the constructed experimental task set includes multiple experimental tasks, each experimental task consisting of several experimental operations arranged in a predetermined order; the constructed experimental workstation set includes multiple experimental workstations, each experimental workstation having a corresponding processing capacity, used to characterize the upper limit of the number of experimental operations that can be processed in parallel at the same time.

[0021] Specifically, the experimental task set and experimental workstation set are constructed according to the chemical experimental workflow of the multi-chemical experimental tasks to be scheduled, as follows: the chemical experimental workflow of the multi-chemical experimental tasks to be scheduled is decomposed into an experimental task set consisting of multiple experimental tasks. ,in, This indicates the experimental task, and the experimental task number. =1, 2, ..., n, where n is the total number of experimental tasks; in the set of experimental tasks, each experimental task... An ordered chain of experimental operations, consisting of several experimental operations arranged in a predetermined sequence, is represented as: ,in, This function represents a chain of experiments arranged in a predetermined order. Indicates experimental task The Middle One experimental procedure; This represents the total number of experimental operations in each experimental task; it also represents the number of experimental workstations participating in each experimental task within the experimental task set, forming an experimental workstation set. ,in, This indicates the experimental workstation, and the workstation's serial number. =1, 2, ..., m, where m is the total number of experimental workstations; the set of experimental workstations Each experimental workstation Capacity limit This capacity limit This characterizes the maximum number of experimental operations that the experimental workstation can process in parallel at any given time; it is used for each experimental workstation. Introducing a set of potential experimental batches to characterize the batch processing behavior of experimental workstations. Where the experimental batch number b = 1, 2, ... , For experimental workstations The upper bound of the number of experimental batches that can be formed, and each experimental batch For a given operation time interval, its start time, end time, and duration are denoted as follows: , , By introducing an experimental batch mechanism, the parallel processing capability of the experimental workstation can be effectively characterized, thereby improving the utilization rate of experimental resources.

[0022] The parameters for each experimental operation of each experimental task are constructed as follows: for any experimental operation The parameters to be constructed must include at least: operation duration. Experimental conditions and parameters Operation start time Operation end time A set of candidate experimental workstations capable of performing this experimental operation. and zero wait indicator variable Among them, the zero-wait indicator variable This indicates that there is no time interval between the current experimental operation and the immediately following experimental operation; that is, the completion of the current experimental operation immediately triggers the start of the next immediately following experimental operation. By combining zero-wait constraint and experimental parameter consistency constraint, the scientific feasibility of the experimental process at the physical and chemical levels is guaranteed.

[0023] Preferably, in step 2 of the above method, the length of the operation time interval for constructing the experimental task is equal to the operation duration of the experimental operation. Operation duration =End time of experimental operation -Start time of experimental operation The following scheduling decision variables are set for the experimental scheduling of experimental operations, including: workstation allocation decision variables. Indicates experimental operation Whether or not they were assigned to an experimental workstation If assigned ,otherwise Operation start time , indicating experimental operation Operation start time; Operation end time , indicating experimental operation Operation end time; task completion time This represents the overall completion time of the experimental task; the following auxiliary variables are set for the experimental scheduling of experimental operations, including: batch status auxiliary variable. If the experimental batch If enabled, ,otherwise ; Operation allocation of state auxiliary variables If the experimental operation Assigned to the experimental workstation And it belongs to a batch ,but ,otherwise Batch start time , indicating experimental batch Start time; Batch end time , indicating experimental batch The end time.

[0024] Preferably, in step 3 of the above method, the scheduling decision variables and auxiliary variables set in the operation time interval are combined to uniformly describe and jointly model the multiple scientific constraints of multiple chemical experimental tasks, including operation order constraints, workstation allocation constraints, batch processing constraints, experimental parameter consistency constraints, and zero-wait constraints, to obtain a scheduling model, including: constructing operation order constraint formulas, workstation allocation constraint formulas, batch processing constraint formula groups, experimental parameter consistency constraint formulas, and zero-wait constraint formulas respectively, and combining the constructed operation order constraint formulas, workstation allocation constraint formulas, batch processing constraint formula groups, experimental parameter consistency constraint formulas, and zero-wait constraint formulas as the scheduling model.

[0025] Preferably, in step 3 of the above method, the operation order constraint formula is constructed in the following manner: for multiple experimental operations within the same experimental task, the operation order constraint is constructed such that the start time of a subsequent experimental operation is not earlier than the end time of its preceding experimental operation, that is, adjacent experimental operations satisfy the following operation order constraint formula: ;in, Indicates the end time of the experimental operation; Indicates the start time of the operation immediately following the experimental operation; Indicates experimental task The serial number; Indicates experimental task The Middle The sequence number of each experimental operation; Indicates experimental task The total number of experimental operations. By constraining the order of operations, it can be ensured that the experimental tasks are executed sequentially according to the predetermined experimental procedure.

[0026] Preferably, in step 3 of the above method, the workstation allocation constraint formula is constructed as follows: each experimental operation must be assigned to one and only one available experimental workstation, and the allocation relationship satisfies the following workstation allocation constraint formula: ;in, Indicates the ability to perform experimental operations. The collection of candidate experimental workstations, among which, Indicates experimental task The Middle One experimental procedure; Indicates experimental operation Whether or not they were assigned to an experimental workstation .

[0027] Preferably, in step 3 of the above method, for each experimental workstation, multiple potential experimental batches are introduced to describe the parallel processing batches formed by the experimental workstation during the scheduling process; and the following batch processing constraints are constructed: each experimental operation assigned to the experimental workstation must belong to only one experimental batch on that experimental workstation; multiple experimental operations within the same experimental batch are completely aligned in terms of start and end times, that is, all experimental operations within the same batch start and end at the same time; the number of experimental operations executed in parallel within the same experimental batch does not exceed the capacity limit of the corresponding experimental workstation.

[0028] Specifically, batch constraints are constructed as follows: a set of batch constraint formulas consisting of a batch unique constraint formula, a batch intra-batch synchronization constraint formula, and a capacity limit constraint formula. The batch unique constraint formula states that when an experimental operation is assigned to a workstation, that operation must belong to only one experimental batch on that workstation, satisfying the following batch unique constraint formula: ;in, For experimental workstations Potential experimental batch set, For potential experimental batch set The first in One experimental batch; Indicates experimental operation Whether or not they were assigned to an experimental workstation And it belongs to the experimental batch. ; Indicates experimental operation Whether or not they were assigned to an experimental workstation ; Indicates the ability to perform experimental operations. The set of candidate experimental workstations; the intra-batch synchronization constraint formula is: for experimental operations within the same experimental batch, their start time, end time, and duration must be completely consistent, that is, satisfying the following intra-batch synchronization constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation And it belongs to the experimental batch. ; Indicates experimental operation Operation start time; Indicates experimental batch Start time; Indicates experimental operation The duration of the operation; Indicates each experimental batch The duration; Indicates experimental operation Operation end time; Indicates each experimental batch The end time; the capacity constraint formula is: at any given time, the number of experimental operations executed in parallel on each experimental workstation shall not exceed the capacity limit of that experimental workstation, that is, satisfy the following capacity constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation ; It is an indicator function that takes the value 1 when the condition is true and 0 otherwise. Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; Indicates experimental workstation Maximum capacity; Represents a set of experimental workstations; Indicates time.

[0029] Preferably, in step 3 of the above method, for the same experimental workstation, an experimental parameter consistency constraint is constructed in the time dimension, so that at any given time, the same experimental workstation is only allowed to process experimental operations with consistent experimental condition parameters at the same time; when the experimental condition parameters of two experimental operations are inconsistent, the execution time intervals of the two on the experimental workstation are restricted from overlapping.

[0030] Specifically, the experimental parameter consistency constraint formula is constructed as follows: On the same experimental workstation, experimental operations with different experimental condition parameters are not allowed to overlap in the time dimension, that is, satisfying the following experimental parameter consistency constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation ; Indicates experimental operation Whether or not they were assigned to an experimental workstation ; Indicates experimental operation Experimental condition parameters; Indicates experimental operation Experimental condition parameters; Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; This represents the empty set.

[0031] Preferably, in step 3 of the above method, the zero-wait constraint formula is constructed as follows: The zero-wait constraint formula between adjacent experimental operations is constructed by strictly equaling the end time of the current experimental operation to the start time of its immediately following experimental operation. This zero-wait constraint formula is expressed as: ;in, This represents the zero-wait indicator variable. Zero-wait constraints ensure the continuity of the experimental process, prohibiting any idle time in between.

[0032] Preferably, in step 4 of the above method, the scheduling model obtained in step 3 is optimized and solved according to the set scheduling objective under the premise of satisfying all multiple scientific constraints, to obtain an experimental task scheduling scheme that satisfies multiple scientific constraints, including: defining the overall experimental task completion time as the operation end time of all experimental operations. The maximum value in ,Right now Under the premise of satisfying all multiple scientific constraints, the goal is to minimize the overall experimental task completion time. To set scheduling objectives, the scheduling model obtained in step 3 is optimized and solved using a constraint programming solver to obtain an experimental scheduling scheme that satisfies multiple scientific constraints.

[0033] In summary, the multi-task scheduling method for multiple scientific constraints in chemical experiments provided by the embodiments of the present invention can uniformly describe the unique scientific constraints of multiple chemical experiments in the same scheduling model, avoiding the conflicts and infeasibility problems caused by the decentralized processing of multiple constraints in existing methods. By combining zero-wait constraints and experimental parameter consistency constraints, it ensures the scientific feasibility of the experimental process at the physical and chemical levels and can significantly improve the experimental efficiency of the automation laboratory.

[0034] To more clearly demonstrate the technical solution and its effects provided by the present invention, the following detailed description of the solution provided by the embodiments of the present invention is provided with reference to specific examples.

[0035] Example 1, as shown in Figures 1 and 2, provides a multi-task scheduling method for chemical experiments with multiple scientific constraints. This method is suitable for use in automated chemistry laboratory environments and is used for unified scheduling of multiple chemical experimental tasks. In this example, the automated chemistry laboratory includes multiple experimental workstations, each with a fixed parallel processing capacity to support batch execution of experimental operations. Simultaneously, the laboratory contains multiple experimental tasks to be executed, each consisting of multiple experimental operations with a fixed execution order. It is assumed that the duration and experimental condition parameters of the experimental operations are determined before scheduling and do not change dynamically during the experiment. Different experimental operations are subject to the following scientific constraints during execution: the order of experimental operations, the allocation of experimental workstations, the batch processing capacity of experimental workstations, the consistency of experimental parameters, and the zero-wait constraint between adjacent experimental operations.

[0036] The multi-task scheduling method in this embodiment specifically includes the following steps: Step 1, constructing an experimental task set, an experimental workstation set, and parameters for each experimental operation of each experimental task according to the chemical experimental process of the multi-chemical experimental tasks to be scheduled.

[0037] Specifically, this includes: obtaining the set of experimental tasks and the set of experimental workstations, and modeling them: Let the set of experimental tasks be... The experimental workstations are set up as follows Each of the experimental workstations Capacity limit This describes the maximum number of experimental operations that the experimental workstation can process in parallel at any given time; each experimental task... It consists of an ordered chain of operations, represented as ,in, Indicates task The Middle Each experimental operation; for any experimental operation Its attributes include: operation duration Experimental conditions and parameters Operation start time Operation end time Optional experimental workstation set and zero wait indicator variable ,in, This indicates that no time interval is allowed between this operation and its subsequent operations; to characterize the batch processing behavior of the experimental workstation, for each experimental workstation... Introduce a set of potential experimental batches ,in For experimental workstations The upper bound on the number of batches that can be formed. Each experimental batch. For a given time interval, its start time, end time, and duration are denoted as follows: , , .

[0038] Step 2: Construct the experimental operation time interval: An experimental operation is defined as a time interval determined by the start time and end time of the operation. The length of the time interval is the duration of the experimental operation. ,Depend on limited.

[0039] To achieve experimental scheduling, the following scheduling decision variables are introduced: workstation allocation decision variables. , indicating experimental operation Whether or not they were assigned to an experimental workstation If assigned ,otherwise Operation start time , indicating experimental operation Operation start time; Operation end time , indicating experimental operation Operation end time; task completion time , which represents the overall completion time of the experimental task.

[0040] In addition, to characterize the subordinate relationship between experimental operations and experimental batches, the following auxiliary decision variable is introduced: Batch State Auxiliary Variable If the experimental batch If enabled, ,otherwise ; Operation allocation of state auxiliary variables If the experimental operation Assigned to the experimental workstation And it belongs to the experimental batch. ,but ,otherwise ; , indicating experimental batch Start time; , indicating experimental batch The end time.

[0041] Step 3: Joint Modeling of Multiple Scientific Constraints: Based on the scheduling decision variables and auxiliary variables set in the operation time interval, the multiple scientific constraints of the multi-chemical experimental tasks, including operation order constraints, workstation allocation constraints, batch processing constraints, experimental parameter consistency constraints, and zero-wait constraints, are uniformly described and jointly modeled to obtain the scheduling model. Specifically, the operation order constraints, workstation allocation constraints, batch processing constraints, experimental parameter consistency constraints, and zero-wait constraints in the multiple scientific constraints are constructed respectively, including: (31) Constructing experimental operation order constraints: Experimental operations in the same experimental task must be strictly executed in a predetermined order, and adjacent experimental operations must satisfy the following experimental operation order constraint formula: .

[0042] in, Indicates the end time of the experimental operation; Indicates the start time of the operation immediately following the experimental operation; Indicates experimental task The serial number; Indicates experimental task The Middle The sequence number of each experimental operation; Indicates experimental task The total number of experimental operations. This experimental operation sequence constraint ensures that the order of the experimental procedures is not disrupted.

[0043] (32) Construct workstation allocation constraints (to ensure the uniqueness of experimental workstation allocation): Each experimental operation must be assigned to one and only one available experimental workstation, and the allocation relationship satisfies the following workstation allocation constraint formula: ;in, Indicates the ability to perform experimental operations. The collection of candidate experimental workstations, among which, Indicates experimental task The Middle One experimental procedure; Indicates experimental operation Whether or not they were assigned to an experimental workstation .

[0044] (33) Construct batch processing constraints, which is a set of batch constraint formulas consisting of batch unique constraint formula, intra-batch synchronization constraint formula, and capacity limit constraint formula: The batch unique constraint formula is: When an experimental operation is assigned to a certain experimental workstation, the experimental operation must belong to one and only one experimental batch on that experimental workstation, that is, satisfy the following unique constraint formula: ;in, For experimental workstations Potential experimental batch set, For potential experimental batch set The first in One experimental batch; Indicates experimental operation Whether or not they were assigned to an experimental workstation And it belongs to the experimental batch. ; Indicates experimental operation Whether or not they were assigned to an experimental workstation ; Indicates the ability to perform experimental operations. The set of candidate experimental workstations; the intra-batch synchronization constraint formula is: for experimental operations within the same experimental batch, their start time, end time, and duration must be completely consistent, that is, satisfying the following intra-batch synchronization constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation And it belongs to the experimental batch. ; Indicates experimental operation Operation start time; Indicates experimental batch Start time; Indicates experimental operation The duration of the operation; Indicates each experimental batch The duration; Indicates experimental operation Operation end time; Indicates each experimental batch The end time; the synchronization constraint within this batch is used to characterize the physical characteristics of "simultaneous start and simultaneous end" in experimental batch processing.

[0045] The capacity constraint formula is: at any given time, the number of experimental operations executed in parallel on each experimental workstation must not exceed its capacity limit, that is, satisfy the following capacity constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation ; It is an indicator function that takes the value 1 when the condition is true and 0 otherwise. Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; Indicates experimental workstation Maximum capacity; Represents a set of experimental workstations; Indicates time.

[0046] (34) Constructing experimental parameter consistency constraints: On the same experimental workstation, experimental operations with different experimental condition parameters are not allowed to overlap in the time dimension, that is, satisfying the following experimental parameter consistency constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation ; Indicates experimental operation Whether or not they were assigned to an experimental workstation ; Indicates experimental operation Experimental condition parameters; Indicates experimental operation Experimental condition parameters; Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; This represents the empty set.

[0047] (35) Constructing zero-wait constraints: For experimental operations with zero-wait requirements, there must be no time interval between them and subsequent operations, i.e., the following zero-wait constraint formula must be satisfied: ;in, This represents the zero-wait indicator variable. Zero-wait constraints ensure the continuity of the experimental process, prohibiting any idle time in between.

[0048] Step 4: Construct the scheduling objective and execute the solution: The overall experimental task completion time is defined as the maximum value among the completion times of all experimental operations, i.e., satisfying: Under the premise of satisfying all the above constraints, the goal is to minimize the overall experimental task completion time. With the goal of solving the above scheduling model using a constraint programming solver, an experimental scheduling scheme that satisfies multiple scientific constraints is obtained. The experimental task scheduling scheme is then used to schedule the multiple chemical experimental tasks to be scheduled.

[0049] In summary, the multi-task scheduling method under multiple scientific constraints in this embodiment abstracts the multi-experiment task execution process commonly found in automation laboratories into a multi-task scheduling problem. Combining the physical and scientific characteristics of chemical experiments, it introduces multiple scientific constraints such as operation order, experimental workstation allocation, batch processing, experimental parameter consistency, and zero waiting time, providing a unified, refined, and executable formal description of the complex chemical experiment scheduling problem. Based on this, this embodiment constructs a scheduling rule system containing multiple constraints to jointly optimize the execution timing of experimental operations, transforming the input experimental task flow into a time-based scheduling scheme. Experimental results show that compared to the case without a scheduling strategy, the time required for experiments is reduced by 55.8% after using the method of this invention, effectively improving the execution efficiency of multi-experiment tasks in automation laboratories.

[0050] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0051] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The information disclosed in the background section is intended only to enhance the understanding of the overall background technology of the present invention and should not be construed as an admission or implication in any way that such information constitutes prior art known to those skilled in the art.

Claims

1. A multi-task scheduling method for chemical experiments with multiple scientific constraints, characterized in that, The scheduling of multi-chemical experimental tasks subject to multiple scientific constraints includes: Step 1, experimental task and resource modeling: Based on the chemical experimental procedures of the multi-chemical experimental tasks to be scheduled, construct sets of experimental tasks, sets of experimental workstations, and parameters for each experimental operation of each experimental task; Step 2, experimental operation time interval construction: Set the start time and end time of each experimental operation of each experimental task, and abstract the interval between the start time and end time of the operation as the operation time interval on the time axis, and set scheduling decision variables and auxiliary variables for the experimental scheduling of the experimental operations; Step 3, joint modeling of multiple scientific constraints: Combine the set scheduling decision variables and auxiliary variables in the operation time interval to uniformly describe and jointly model the multiple scientific constraints of the multi-chemical experimental tasks, including operation order constraints, workstation allocation constraints, batch processing constraints, experimental parameter consistency constraints, and zero-wait constraints, to obtain a scheduling model; Step 4, scheduling objective construction and scheduling solution: Under the premise of satisfying all constraints of multiple scientific constraints, optimize and solve the scheduling model of Step 3 according to the set scheduling objective to obtain an experimental task scheduling scheme that satisfies multiple scientific constraints, and schedule the multi-chemical experimental tasks to be scheduled.

2. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 1, characterized in that, In step 1, the experimental task set and the experimental workstation set are constructed according to the chemical experimental process of the multi-chemical experimental task to be scheduled, in the following manner: the chemical experimental process of the multi-chemical experimental task to be scheduled is decomposed into an experimental task set consisting of multiple experimental tasks. ,in, This indicates the experimental task, and the experimental task number. =1, 2, ..., n, where n is the total number of experimental tasks; in the set of experimental tasks, each experimental task... An ordered chain of experimental operations, consisting of several experimental operations arranged in a predetermined sequence, is represented as: ,in, This function represents a chain of experiments arranged in a predetermined order. Indicates experimental task The Middle One experimental procedure; This represents the total number of experimental operations in each experimental task; it also represents the number of experimental workstations participating in each experimental task within the experimental task set, forming an experimental workstation set. ,in, This indicates the experimental workstation, and the workstation's serial number. =1, 2, ..., m, where m is the total number of experimental workstations; the set of experimental workstations Each experimental workstation Capacity limit This capacity limit This characterizes the maximum number of experimental operations that the experimental workstation can process in parallel at any given time; it is used for each experimental workstation. Introducing a set of potential experimental batches to characterize the batch processing behavior of experimental workstations. Where the experimental batch number b = 1, 2, ... ， For experimental workstations The upper bound of the number of experimental batches that can be formed, and each experimental batch For a given operation time interval, its start time, end time, and duration are denoted as follows: ， ， The parameters for each experimental operation of each experimental task are constructed as follows: for any experimental operation The parameters to be constructed must include at least: operation duration. Experimental conditions and parameters Operation start time Operation end time A set of candidate experimental workstations capable of performing this experimental operation. and zero wait indicator variable Among them, the zero-wait indicator variable This indicates that there is no time interval between the current experimental operation and the immediately following experimental operation; that is, the end of the current experimental operation immediately triggers the start of the immediately following experimental operation.

3. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 2, characterized in that, In step 2, the length of the operation time interval for constructing the experimental task is the operation duration of the experimental operation. Operation duration =End time of experimental operation -Start time of experimental operation ； The following scheduling decision variables are set for the experiment scheduling of experimental operations, including: workstation allocation decision variables. Indicates experimental operation Whether or not they were assigned to an experimental workstation If assigned ,otherwise Operation start time , indicating experimental operation Operation start time; Operation end time , indicating experimental operation Operation end time; task completion time This represents the overall completion time of the experimental task; the following auxiliary variables are set for the experimental scheduling of experimental operations, including: batch status auxiliary variable. If batch If enabled, ,otherwise ; Operation allocation of state auxiliary variables If the experimental operation Assigned to the experimental workstation And it belongs to a batch ,but ,otherwise Batch start time , indicating experimental batch Start time; Batch end time , indicating experimental batch The end time.

4. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 3, characterized in that, In step 3, the scheduling decision variables and auxiliary variables set in the operation time interval are combined in the following manner to uniformly describe and jointly model the multiple scientific constraints of multi-chemical experimental tasks, including operation order constraints, workstation allocation constraints, batch processing constraints, experimental parameter consistency constraints, and zero-wait constraints, to obtain a scheduling model. This includes: constructing operation order constraint formulas, workstation allocation constraint formulas, batch processing constraint formula sets, experimental parameter consistency constraint formulas, and zero-wait constraint formulas respectively, and combining the constructed operation order constraint formulas, workstation allocation constraint formulas, batch processing constraint formula sets, experimental parameter consistency constraint formulas, and zero-wait constraint formulas as the scheduling model.

5. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 4, characterized in that, In step 3, the operation order constraint formula is constructed in the following manner: For multiple experimental operations within the same experimental task, the operation order constraint is constructed such that the start time of a subsequent experimental operation is no earlier than the end time of its preceding experimental operation. That is, adjacent experimental operations satisfy the following operation order constraint formula: ;in, Indicates experimental operation Operation end time; Indicates experimental operation The start time of the operation immediately following the experimental operation; Indicates the sequence number of the experimental task; Indicates experimental task The Middle The sequence number of each experimental operation; Indicates experimental task The total number of experimental operations.

6. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 4, characterized in that, In step 3, the workstation allocation constraint formula is constructed as follows: each experimental operation must be assigned to one and only one available experimental workstation, and the allocation relationship satisfies the following workstation allocation constraint formula: ;in, Indicates the ability to perform experimental operations. The candidate experimental workstation set, among which, Indicates experimental task The Middle One experimental procedure; Indicates experimental operation Whether or not they were assigned to an experimental workstation 。 7. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 4, characterized in that, In step 3, batch constraints are constructed as follows: the batch constraint formula set includes: batch uniqueness constraint formula, intra-batch synchronization constraint formula, and capacity limit constraint formula; wherein, when an experimental operation is assigned to a certain experimental workstation, the experimental operation must belong to only one experimental batch on that experimental workstation, that is, satisfy the following batch uniqueness constraint formula: ;in, For experimental workstations Potential experimental batch set, For potential experimental batch set The first in One experimental batch; Indicates experimental operation Whether or not they were assigned to an experimental workstation And it belongs to the experimental batch. ； Indicates experimental operation Whether or not they were assigned to an experimental workstation ； Indicates the ability to perform experimental operations. The set of candidate experimental workstations; for experimental operations within the same experimental batch, the start time, end time, and duration of the operation must be completely consistent, that is, satisfy the following intra-batch synchronization constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation And it belongs to the experimental batch. ； Indicates experimental operation Operation start time; Indicates experimental batch Start time; Indicates experimental operation The duration of the operation; Indicates each experimental batch The duration; Indicates experimental operation Operation end time; Indicates each experimental batch The end time; at any given time, the number of experimental operations executed in parallel on each experimental workstation must not exceed the capacity limit of that experimental workstation, that is, satisfy the following capacity constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation ； It is an indicator function that takes the value 1 when the condition is true and 0 otherwise. Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; Indicates experimental workstation Maximum capacity; Represents a set of experimental workstations; Indicates time.

8. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 4, characterized in that, In step 3, the experimental parameter consistency constraint formula is constructed as follows: on the same experimental workstation, experimental operations with different experimental condition parameters are not allowed to overlap in the time dimension, that is, satisfying the following experimental parameter consistency constraint formula: ;in, Indicates experimental operation Whether or not they were assigned to an experimental workstation ； Indicates experimental operation Whether or not they were assigned to an experimental workstation ； Indicates experimental operation Experimental condition parameters; Indicates experimental operation Experimental condition parameters; Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; Indicates experimental operation Operation start time; Indicates experimental operation Operation end time; This represents the empty set.

9. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to claim 4, characterized in that, In step 3, the zero-wait constraint formula is constructed as follows: The zero-wait constraint formula between adjacent experimental operations is constructed by strictly equipping the end time of the current experimental operation with the start time of its immediately following experimental operation. This zero-wait constraint formula is expressed as: ;in, Indicates a zero-wait indicator variable; Indicates experimental operation Operation end time; Indicates experimental operation The start time of the operation immediately following the experimental operation.

10. The multi-task scheduling method for multiple scientific constraints in chemical experiments according to any one of claims 1-9, characterized in that, In step 4, the scheduling model obtained in step 3 is optimized and solved according to the set scheduling objective, under the premise of satisfying all multiple scientific constraints, to obtain an experimental task scheduling scheme that satisfies multiple scientific constraints. This includes defining the overall experimental task completion time as the operation end time of all experimental operations. The maximum value in ,Right now Under the premise of satisfying all multiple scientific constraints, the goal is to minimize the overall experimental task completion time. To set scheduling objectives, the scheduling model obtained in step 3 is optimized and solved using a constraint programming solver to obtain an experimental scheduling scheme that satisfies multiple scientific constraints.

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