CN116205023B - Design method, device, equipment and storage medium of intelligent robot laboratory - Google Patents
Design method, device, equipment and storage medium of intelligent robot laboratory Download PDFInfo
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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Abstract
The invention provides a design method, a device, computer equipment and a storage medium of a robot intelligent laboratory, wherein the robot intelligent laboratory comprises a plurality of equipment, and the equipment corresponds to at least one experimental step in a plurality of experimental steps; the method comprises the following steps: receiving target experiment requirements; the target experiment requirements comprise target experiment flux and experiment step flow; determining bottleneck equipment according to the target experiment requirements and the number of equipment corresponding to a plurality of experiment steps of the experiment step flow; wherein the bottleneck device represents a device of the plurality of devices that limits experimental throughput of the robotic intelligent laboratory; and adjusting the number of the bottleneck devices so that the experimental flux of the robot intelligent laboratory meets the target experimental flux. The intelligent robot laboratory is designed by adjusting the number of bottleneck devices according to the target experimental flux, so that the efficiency and the accuracy are improved.
Description
Technical Field
The specification relates to the technical field of computer data processing, in particular to a design method, a device, equipment and a storage medium of a robot intelligent laboratory.
Background
In fields such as biological laboratories or physicochemical laboratories, in order to better develop experimental projects, a robot intelligent laboratory is usually designed. The experimental equipment required by the experiment is deployed, and the robot replaces manual sample transfer and other operations, so that the efficiency of experimental development is improved, and the labor cost is reduced.
In the design stage of the robot intelligent laboratory, a certain amount of experimental equipment is required to be selected to deploy the robot intelligent laboratory according to experimental requirements, so that the unification of the equipment amount and experimental flux is realized.
However, in the prior art, a certain number of devices are often required to be selected, and after the robot intelligent laboratory is actually deployed, the experimental flux of the robot intelligent laboratory can be accurately determined, and further, the device use cost is reduced on the premise that the number of the devices of the robot intelligent laboratory is adjusted to meet the target experimental flux. Experimental throughput the method for constructing the intelligent laboratory of the robot has relatively low efficiency.
Disclosure of Invention
In view of this, various embodiments of the present specification are directed to providing a design method, apparatus, computer device, and storage medium for a robot smart laboratory, which improve the design efficiency of the robot smart laboratory to some extent in terms of experimental throughput.
The embodiment of the specification provides a design method of a robot intelligent laboratory, wherein the robot intelligent laboratory is used for executing a plurality of experimental steps and comprises a plurality of devices, and the devices correspond to at least one experimental step in the plurality of experimental steps; the method comprises the following steps: receiving target experiment requirements; wherein the target experiment requirement comprises a target experiment flux and an experiment step flow of the plurality of experiment steps; determining bottleneck equipment according to the target experiment requirements and the number of equipment corresponding to a plurality of experiment steps; wherein the bottleneck device represents a device of the plurality of devices that limits the robotic intelligent laboratory experimental flux; and adjusting the number of the bottleneck devices so that the experimental flux of the robot intelligent laboratory meets the target experimental flux.
The embodiment of the specification provides a design device of a robot intelligent laboratory, wherein the robot intelligent laboratory is used for executing a plurality of experimental steps and comprises a plurality of devices, and the devices correspond to at least one experimental step in the plurality of experimental steps; the device comprises: the receiving module is used for receiving target experiment requirements; wherein the target experiment requirement comprises a target experiment flux and an experiment step flow of the plurality of experiment steps; the bottleneck determining module is used for determining bottleneck equipment according to the target experiment requirements and the number of the equipment corresponding to the plurality of experiment steps; wherein the bottleneck device represents a device of the plurality of devices that limits the robotic intelligent laboratory experimental flux; and the adjusting module is used for adjusting the quantity of the bottleneck devices so that the experimental flux of the intelligent robot laboratory meets the target experimental flux.
The present description provides a computer device comprising a memory storing a computer program and a processor implementing the method of any one of the claims when executing the computer program.
The present description embodiment provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method of any one of the claims.
The present description embodiment calculates experimental flux of a robot smart laboratory. In the case where the experimental flux does not meet the target experimental flux requirement, the experimental flux can be increased by adjusting the number of bottleneck devices. The design efficiency of the intelligent laboratory of the robot is improved to a certain extent, and the design cost is reduced.
Drawings
Fig. 1 is a schematic flow chart of a design method of a robot intelligent laboratory according to an embodiment.
Fig. 2 is a schematic flow chart of a design method of a robot intelligent laboratory according to an embodiment.
Fig. 3 is a schematic diagram illustrating an operation sequence according to an embodiment.
Fig. 4 is a schematic diagram of a bottleneck device confirmation method according to an embodiment.
Fig. 5 is a block diagram of a design apparatus of a robot intelligent laboratory according to an embodiment.
Detailed Description
In order to make the technical solution of the present specification better understood by those skilled in the art, the technical solution of the present specification embodiment will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present specification, and it is apparent that the described embodiment is only a part of the embodiment of the present specification, but not all the embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
The robot intelligent laboratory can comprise at least one robot workstation, and the robot workstation can automatically prepare experimental samples through intelligent control inside the robot workstation. In particular, the robotic workstation may include a plurality of instruments for performing experiments and a robot for transferring experimental samples between the plurality of instruments. The robot may be a robotic arm. The mechanical arm can be fixed or arranged on the guide rail. Of course, a plurality of robot workstations included in the robot smart laboratory may also constitute an experimental island, between which experimental samples can be transferred by a mobile robot.
Please refer to fig. 1. In the example of the scenario of the design system of the robotic intelligent laboratory provided in the present specification, the user may be a staff member of the biomedical field. The user may wish to design a robotic intelligent laboratory in the computer device according to the user configured target experimental requirements. The experimental flux of the robot intelligent laboratory can be determined through the expected operation time sequence of a plurality of devices in the robot intelligent laboratory. According to the robot intelligent laboratory, the corresponding robot intelligent laboratory is arranged in the real world, so that the cost for adjusting instrument equipment in the robot intelligent laboratory can be reduced, and the efficiency of designing the robot intelligent laboratory is improved.
In the process of designing the intelligent robot laboratory through computer equipment, a user can provide corresponding experiment requirements to form target experiment requirements. The experimental requirements can include information of target samples required to be prepared by the intelligent robot laboratory, experimental step flows of a plurality of experimental steps required to prepare the target samples, constraints of the plurality of experimental steps, experimental flux requirements for preparing the target samples, equipment and the number of equipment of corresponding experimental steps for preparing the experimental samples, and the like, wherein the information is included in the intelligent robot laboratory. Wherein the robotic intelligent laboratory may include multiple categories of devices. The apparatus may correspond to at least one of a plurality of experimental steps required to prepare the target sample. Multiple devices of the same class may represent simultaneous execution of corresponding experimental steps on a number of samples of the device.
The computer, upon receiving the user provided experimental requirements, may input the experimental requirements into a planning solution module to determine an expected experimental flux for the robotic intelligent laboratory. Specifically, the computer may construct a mathematical model of a robotic intelligent laboratory that prepares the target sample. Wherein the mathematical model may comprise a plurality of parameters. At least some of the plurality of parameters may be used to represent start-stop times of a plurality of experimental steps corresponding to execution of the target sample by a device in the robotic intelligent laboratory. And, an objective function that can be used to adjust parameters of the mathematical model can be established for the mathematical model and the preset target performance. Specifically, the computer may construct mathematical models and objective functions based on information such as the initial number of devices and constraints of the number of experimental steps required to prepare the target experimental sample. The objective function may be set such that the total time for all experimental steps to complete is also minimal. The objective function can be optimized through an optimization method, and the running time sequence of the intelligent robot laboratory can be planned according to a plurality of parameters in the optimized objective function, so that the expected experimental flux of the intelligent robot laboratory based on the construction of the initial number of devices is obtained.
In the case where the predicted experimental flux is less than the target experimental flux, the computer may invoke a feedback adjustment module to adjust the robotic intelligent laboratory. Specifically, the feedback adjustment module can adjust the number of devices in the intelligent laboratory of the robot, and send the adjusted device number information to the planning solution module to re-solve the predicted experimental flux. The feedback adjustment module will first determine the bottleneck device. Specifically, the computer may calculate an average interval time between execution of a plurality of experimental steps by different classes of equipment according to the operation time sequence of the robot intelligent laboratory. The device corresponding to the minimum average interval time is determined as the bottleneck device. The feedback adjustment module then adjusts the number of devices. Specifically, the feedback adjustment module may calculate the expected experimental flux of the intelligent robot laboratory again, and may set the start-stop time of the experimental step corresponding to the bottleneck device when the previous experimental step of the experimental step corresponding to the bottleneck device is completed, thereby obtaining the start-stop time of the experimental step corresponding to the bottleneck device. The maximum number of experimental steps to be performed in parallel at the same time is determined according to the start-stop time of the experimental steps, and the number of bottleneck devices can be adjusted to the maximum number. After the feedback adjustment module completes adjustment, the adjusted information can be sent to the planning solution module to be solved again, and the expected experimental flux of the intelligent robot laboratory is determined again. Under the condition that the expected experimental flux of the robot intelligent laboratory meets the target experimental flux, the designed robot intelligent laboratory can be obtained. In case the predicted experimental flux of the robot smart laboratory does not meet the target experimental flux, the above steps may be repeated, and the number of bottleneck devices is adjusted so that the predicted experimental flux of the robot smart laboratory meets the target experimental flux.
The above description is merely provided as an example of the present invention and is not intended to limit the invention, but any modifications, equivalents, etc. within the spirit and principles of the invention should be included in the scope of the invention.
The embodiment of the specification provides a design system of a robot intelligent laboratory. The design system of the robot intelligent laboratory may include a client and a server. The client may be an electronic device with network access capabilities. Specifically, for example, the client may be a desktop computer, a tablet computer, a notebook computer, a smart phone, a digital assistant, a smart wearable device, a shopping guide terminal, a television, a smart speaker, a microphone, and the like. Wherein, intelligent wearable equipment includes but is not limited to intelligent bracelet, intelligent wrist-watch, intelligent glasses, intelligent helmet, intelligent necklace etc.. Alternatively, the client may be software that can be run in the electronic device. The server may be an electronic device with some arithmetic processing capability. Which may have a network communication module, a processor, memory, and the like. Of course, the server may also refer to software running in the electronic device. The server may be a distributed server, or may be a system having a plurality of processors, memories, network communication modules, etc. that cooperate. Alternatively, the server may be a server cluster formed for several servers. Or, with the development of science and technology, the server may also be a new technical means capable of realizing the corresponding functions of the embodiment of the specification. For example, a new form of "server" based on quantum computing implementation may be possible.
Referring to fig. 2, the present description provides a method for designing a robot smart laboratory that may be used to perform a plurality of experimental steps, the robot smart laboratory may include a plurality of devices corresponding to at least one of the plurality of experimental steps; the design method of the robot intelligent laboratory may include the following steps.
Step S110: receiving target experiment requirements; wherein the target experiment requirement includes a target experiment flux and an experiment step flow of the plurality of experiment steps.
In the process of designing the robot intelligent laboratory, the experimental requirements of the robot intelligent laboratory can be set, so that the design system of the robot intelligent laboratory can determine the number of the devices and the corresponding operation time sequence of the robot intelligent laboratory with better experimental requirements, and the unification of experimental flux and device cost is ensured.
The experiment requirement may be constraint information set for the robot intelligent laboratory according to a requirement of the robot intelligent laboratory for preparing the target experimental sample, which is designed as expected in the real world, by a user in designing the robot intelligent laboratory. The experimental requirements may be an experimental flux requirement of the robot intelligent laboratory, an experimental step requirement of a plurality of experimental steps corresponding to the preparation of the target experimental sample in the real world set for the robot intelligent laboratory, and the like. The experimental flux requirement can be the target experimental flux to be achieved by the intelligent robot laboratory after the design is finished. The target experimental flux may be an experimental flux of a robot smart laboratory preparing a target sample corresponding to the experimental flux of the target experimental sample in the real world. The experiment step requirements can comprise information such as the dependence of a plurality of experiment steps, equipment corresponding to the plurality of experiment steps, execution time of the experiment steps, interval time limitation among the plurality of experiment steps and the like. Wherein, the experimental requirements can also include no conflict between experimental steps of the same equipment. For example, one device for the same time period can only perform one experimental step. And under the condition that the experimental flux of the robot intelligent laboratory for preparing the target sample is smaller than the target experimental flux, the design parameters of the robot intelligent laboratory can be adjusted.
The experimental step flow may represent a plurality of experimental steps and corresponding precedence relation that are required to be involved in a robot intelligent laboratory in order to prepare a certain sample.
The design method of the robot intelligent laboratory can be simulation of the robot intelligent laboratory in the real world. With reference to design parameters of the robot intelligent laboratory obtained by simulation, a corresponding robot intelligent laboratory with corresponding parameters can be arranged in the real world. Specifically, the design parameters may include the number of robot intelligent laboratory devices, the operating time sequence, and the like. And, according to the simulation, the production index of the sample of the robot intelligent laboratory can be predicted to estimate the production index of the sample of the robot intelligent laboratory in the real world. The robot intelligent laboratory can be adjusted in advance based on experimental requirements by arranging the robot intelligent laboratory in the real world and performing simulation, so that when the robot intelligent laboratory is arranged with reference to simulation results, the robot intelligent laboratory in the real world can be better ensured to meet the experimental requirements set by users. The robotic intelligent laboratory may be used to perform experimental tasks. In particular, the experimental task may be an experiment in the biomedical field. For example, a cell culture experiment may be used. Multiple devices of the robotic intelligent laboratory may be used to perform multiple experimental steps of the cell culture experiment. Wherein the cultured cells can be used as a target experimental sample. Accordingly, the robotic intelligent laboratory may be configured to simulate a cell culture experiment, and the plurality of devices included in the robotic intelligent laboratory may correspond to the plurality of devices included in the robotic intelligent laboratory for performing a plurality of experimental steps of the cell culture experiment. Wherein the cultured cells can be used as a target sample.
The plurality of devices may each belong to a different class. There may be multiple numbers of devices of the same class. The same class of equipment may perform at least one of the plurality of experimental steps required to prepare the target sample.
The device may represent a plurality of devices included in a robotic intelligent laboratory. The device may correspond to a device in a real world robotic intelligent laboratory. Accordingly, the apparatus may correspond to at least one of a plurality of experimental steps involved in the experimental sample preparation process. For example, three experimental steps are required to prepare a sample. Wherein the device 1 may be used to perform experimental step 1. Device 2 may be used to perform experimental steps 2 and 3. The experimental step may be an experimental processing step of the experimental sample, for example, culturing cells through a greenhouse. Of course, the experimental step may also be a step of transferring the experimental sample, for example, a time of transferring the experimental sample to another device by a robot arm or a mobile robot. In some embodiments, as the number of devices increases, it may be indicative of an increase in the laboratory space occupied by the actual devices, and thus, the time consuming process of transferring the experimental sample may also be indicative of an automatic calculation of the adjustment.
The plurality of devices may each belong to a different class. The same class of equipment may be used to perform the same experimental steps. There may be multiple numbers of devices of the same class. Multiple devices of the same class may be used to perform the same experimental procedure on multiple samples at the same time. In some embodiments, the same class of equipment may also perform multiple experimental steps required to prepare the equipment. For example, a plurality of experimental steps such as ratio dilution and reagent dispensing may be designated for the equipment corresponding to the pipetting station in cell culture. In addition, consistent with real world devices, the devices may also provide parameter adjustment and other functions, for example, the corresponding run time may be adjusted according to the execution time of the experimental steps. And adjusting a plurality of setting parameters of the intelligent robot laboratory according to the configuration information set by the user. Specifically, the user can adjust information such as the number of devices, the positions of the devices, and the like of the robot intelligent laboratory. The robot intelligent laboratory is adjusted and the adjusted index is estimated, so that a large amount of cost consumed in the real world adjustment laboratory can be reduced, the equipment adjustment efficiency is improved, and the robot intelligent laboratory has a good reference effect on the design of the real world robot intelligent laboratory. In some embodiments, where different devices are capable of performing the same experimental step, the time or other indicator of performing the experimental step may be different, but the same effect may be achieved.
The method for receiving the target experiment requirement can be directly receiving the experiment requirement input by the user. Such as the experimental step dependence of the target sample entered by the user and the target experimental flux. Of course, the experimental request input request may be sent to the user by the design system of the robot intelligent laboratory, and then received according to the input of the user. In some embodiments, the user may also be provided with multiple experimental requirements. Wherein the experimental requirement input items can include a mandatory and optional experimental requirement. In the event that the field of the experiment requirement must be filled in is blank, the system may send an experiment requirement input request to the user and wait for the user to input the experiment requirement.
Step S120: determining bottleneck equipment according to the target experiment requirements and the number of equipment corresponding to a plurality of experiment steps; wherein the bottleneck device represents a device of the plurality of devices that limits the robotic intelligent laboratory experimental throughput.
After inputting the target test requirements, the experimental flux of the robot intelligent laboratory can be calculated. Under the condition that the experimental flux of the robot intelligent laboratory is smaller than the target experimental flux, the experimental flux of the robot intelligent laboratory can be improved to a greater extent by adjusting the number of bottleneck devices. Correspondingly, the experimental flux of the prepared experimental sample of the robot intelligent laboratory designed by referring to the simulation result of the robot intelligent laboratory can also approach the experimental requirement to a certain extent. The method for determining the bottleneck device can be integrated into the feedback adjustment module.
The bottleneck device may represent a device having a large influence on experimental throughput of the robot smart laboratory among a plurality of kinds of devices required to prepare the target sample. The bottleneck device may be one of a plurality of classes of devices. Wherein the bottleneck device may perform at least one experimental step. Of course, in some embodiments, the bottleneck device may also be multiple categories of devices.
The method for determining the bottleneck equipment according to the experimental requirements and the number of the equipment corresponding to the experimental steps of the target experimental sample can be to simulate the intelligent robot laboratory to obtain the time sequence relation of each experimental step and the corresponding equipment. Based on the timing relationship, a bottleneck device may be determined. Specifically, for example, the bottleneck device may be determined by calculating the idle rate of each class of devices. In some embodiments, the bottleneck device may be a device with a small number of devices or a lengthy execution time of the bottleneck experimental step.
In some embodiments, the method for determining bottleneck devices may also adjust the number of devices for each class of devices separately. The bottleneck device is determined by comparing experimental throughput of the robot intelligent laboratory for different adjustment schemes. Specifically, for example, a copy of a device class number of robotic intelligent laboratories may be created. The number of devices in one of the categories is then increased by 1 for each copy. By comparing the expected experimental fluxes of the copies of the plurality of robot intelligent laboratories, the device added by the copy with the highest increase of the experimental fluxes can be used as the bottleneck device. Accordingly, by adjusting the number of the devices, the experimental flux of the robot intelligent laboratory can be improved to meet the target experimental flux. In some embodiments, the method for determining the bottleneck device may also consider the price of the bottleneck device to select a device with a larger limited experimental throughput and a lower price.
Step S130: and adjusting the number of the bottleneck devices so that the experimental flux of the robot intelligent laboratory meets the target experimental flux.
The corresponding robot intelligent laboratory is designed based on a set number of devices, and the experimental flux of the robot intelligent laboratory can have a certain upper limit. On the basis, the experimental flux of the intelligent robot laboratory can be improved by adjusting the number of bottleneck devices. In addition, the effect of improving higher experimental flux by adding fewer devices can be achieved to a certain extent by adjusting the number of bottleneck devices.
The method of adjusting the number of bottleneck devices may be integrated into the feedback adjustment module. The method for adjusting the number of the bottleneck devices can be adding 1 to the number of the bottleneck devices at a time. After the number of bottleneck devices is adjusted, the experimental flux adjusted by the intelligent robot laboratory can be calculated again. Under the condition that the adjusted experimental flux is still smaller than the target experimental flux, the bottleneck equipment can be determined again, and the relationship between the adjusted experimental flux and the target experimental flux can be judged again after adding 1 to the number of the bottleneck equipment. Of course, the method of adjusting the number of the bottleneck devices may be to calculate the number of devices required to adjust the bottleneck devices to non-bottleneck devices, and then directly adjust the number of devices to the corresponding number. The bottleneck device is then determined again. Wherein the re-determined bottleneck device may not be identical to the original bottleneck device. In the case that the experimental flux of the robot intelligent laboratory does not reach the target experimental flux, the number of bottleneck devices determined again can be adjusted to improve the experimental flux of the robot intelligent laboratory.
In some embodiments, the step of determining a bottleneck device according to the target experimental requirement and the number of devices corresponding to the plurality of experimental steps includes: calculating the expected experimental flux of the intelligent robot laboratory according to the target experimental requirement and the number of the equipment corresponding to the plurality of experimental steps; in the case where the predicted experimental flux is less than the target experimental flux, a bottleneck device is determined among a plurality of devices.
In determining the bottleneck device, the expected experimental flux of the robot intelligent laboratory may be calculated first. And determining bottleneck equipment in the case that the predicted experimental flux is smaller than the target experimental flux. In the case that the predicted experimental flux is greater than the target experimental flux, the robot intelligent laboratory can be designed and completed. That is, when the expected experimental flux is greater than the target experimental flux, the corresponding design parameters of the robot smart laboratory, such as information of the number of devices and operation timings, etc., can be constructed in the real world.
The predicted experimental flux may represent an experimental flux that can be achieved when a robot intelligent laboratory constituted by a set number of devices uses a target sample set by a user as a target experimental sample. The experimental flux of the robot intelligent laboratory can have certain differences for the experimental steps of configuring different time sequences for the set number of devices. For example, the number of devices of experimental step 1 may be 2. Accordingly, the experimental flux of the robot intelligent laboratory is inconsistent when the experimental step 1 is submitted to the first device for execution and the two devices for execution respectively. In some embodiments, according to a set number of devices, the time arrangement of the experimental steps operated by each device can be optimized according to the experimental requirements, so as to improve the experimental flux of the robot intelligent laboratory. Thus, the predicted experimental flux may also represent the experimental flux that can be achieved after optimizing a robot intelligent laboratory that is built up of a set number of devices.
The method of calculating the predicted experimental flux of the robot smart laboratory may be integrated into the planning solution module. The method for calculating the expected experimental flux of the intelligent robot laboratory can be to configure corresponding experimental step tasks for equipment according to the number of the equipment and the experimental requirements corresponding to a plurality of experimental steps set by a user. The run time sequence is then initialized and optimized by the objective function to obtain the predicted experimental flux. Of course, the method for calculating the expected experimental flux of the intelligent robot laboratory can also obtain the preparation time sequence of a plurality of target experimental samples according to the task of the experimental steps configured for each device. In particular, the start-stop times of multiple experimental steps of each sample during the preparation process can be obtained. The predicted experimental throughput may be determined by counting the number of samples that complete the plurality of experimental steps.
In some embodiments, the step of calculating the predicted experimental flux of the robotic intelligent laboratory according to the target experimental requirements and the number of devices corresponding to the plurality of experimental steps comprises: planning the operation time sequence of the intelligent robot laboratory according to the experiment requirements and the number of the equipment corresponding to the experiment steps; the operation time sequence comprises time arrangement of corresponding experiment steps executed by equipment along with time progress; based on the run time sequence, the predicted experimental flux of the robot intelligent laboratory is calculated.
Different predicted experimental fluxes can be obtained by configuring different experimental step tasks for the device. Therefore, according to the experimental requirements and the number of the devices corresponding to the experimental steps of the target experimental sample, the operation time sequence can be planned for the intelligent robot laboratory in the step of calculating the expected experimental flux of the intelligent robot laboratory, so that the expected experimental flux corresponding to the operation time sequence can be determined. Based on the predicted experimental flux, adjustments can be made to the robotic intelligent laboratory.
The operation time sequence may represent time arrangement of the equipment for executing the corresponding experimental steps along with the time schedule in the process that the robot intelligent laboratory repeatedly executes the plurality of experimental steps set by the user. Referring to fig. 3, the operation timing may be presented in the form of a timing diagram. The operation timing may include timing of sample generation and timing of corresponding work performed by the device. The start-stop time of each experimental step in the generated time sequence of the sample can be in one-to-one correspondence with the start-stop time of the experimental step in the time sequence of the corresponding work performed by the equipment. Wherein, the experimental sample needs to be obtained by sequentially executing four experimental steps. The apparatus 1 can be used to perform experimental step 1 and experimental step 4. Device 2 and device 3 may be used to perform step 2 and step 3, respectively. In the example of the operation sequence shown in fig. 3, the apparatus can only execute one experimental step at a time, and there is a dependency relationship among multiple experimental steps of the sample, that is, the previous experimental step needs to be executed to complete before the next experimental step can be executed. In this embodiment, the generation timing of the sample and the execution timing of the device may be estimated results of computer operations.
In particular, the operation sequence of the robot intelligent laboratory may include a sequence formed by a plurality of experimental steps performed by a plurality of devices in a time dimension. The same device may perform a plurality of corresponding experimental steps over time. A time interval limit may be set between a plurality of experimental steps performed by the apparatus according to experimental requirements. In addition, where the device corresponds to multiple experimental steps, it is meant that the device may have the ability to perform multiple experimental steps. In this case, the device may also comprise different experimental steps in a plurality of experimental steps in the time dimension. The time for the same device to execute different experimental steps may or may not have an intersection according to the target experimental requirements. For example, in the case where two experimental functions are integrated in the same apparatus and can be executed simultaneously, experimental steps corresponding to the experimental functions may be set and may be executed simultaneously.
The method for planning the operation time sequence of the intelligent robot laboratory can be integrated into the planning solving module. Specifically, the method for planning the operation time sequence of the intelligent robot laboratory may be to configure corresponding experimental steps for different time periods of the equipment according to the target experimental requirement. Specifically, corresponding experimental steps can be configured for the equipment according to the dependency relationship among a plurality of experimental steps set by the user in the experimental requirement. Wherein, a plurality of experimental steps set by the user can correspondingly generate one sample. For a plurality of experimental steps corresponding to the same sample, it may be set that the next experimental step can be configured for the corresponding device only after the previous experimental step is executed and completed, and after the ending time point of the previous experimental step. In addition, when the experimental steps are configured for the equipment, the execution time of the experimental steps configured for the same equipment may not be coincident.
Specifically, for example, preparing a sample requires two experimental steps. The first experimental step of the first sample to be prepared may be assigned to the corresponding device. Then, during a period of time after the first experimental step is performed, a second experimental step required for preparing the first sample is allocated to the corresponding apparatus. The two experimental steps required to prepare the second sample can then be dispensed. In the course of the two experimental steps for preparing the second sample, the device which is in an idle state before the time point or the device which requires less waiting time for executing the experimental steps can be prioritized, and the experimental steps are allocated to the samples according to the dependency relationship of the two experimental steps. The experimental steps of a plurality of target samples to be prepared are repeatedly distributed to corresponding equipment, and the operation time sequence of the intelligent robot laboratory can be obtained.
Of course, the method of planning the device may also construct an objective function. Wherein the objective function may be considered as a mathematical model of the robot intelligence laboratory. The parameters of the objective function may comprise parameters representing the start-stop times of the device for performing the respective experimental steps. The parameters of the objective function may also include the number of devices of different categories. The objective function may also include constraints for a plurality of experimental steps for preparing the target sample. And each group of parameters of the objective function conforming to the experimental step constraint of the target sample can be planned to obtain the operation time sequence of the intelligent robot laboratory. In some embodiments, the method of planning the operation timing of the robot smart laboratory may be to determine a preferred operation timing by optimizing an objective function. Wherein, the optimization objective of the objective function may be to prepare the largest number of experimental samples per unit time. And optimizing the objective function according to an optimization method to obtain the start-stop time of a plurality of experimental steps of the equipment, thereby obtaining the operation time sequence of the intelligent robot laboratory.
In some embodiments, the target experimental requirement may include a time constraint between at least two adjacent experimental steps of the plurality of experimental steps; and planning the operation time sequence of the intelligent robot laboratory according to the experiment requirements and the number of the devices corresponding to the experiment steps, wherein the step comprises the following steps: and configuring a plurality of corresponding experimental steps for the equipment according to the target experimental requirement to obtain the operation time sequence of the intelligent robot laboratory, and enabling the execution time of the experimental steps to meet the time constraint.
In some application scenarios, there may be a time constraint between at least two adjacent experimental steps of the plurality of experimental steps of preparing the target sample. For example, in a robotic intelligent laboratory designed to perform cell culture experiments, cultured cells may be used as a target sample. In the cell culture experiment, a certain time constraint can be provided between two culture experiment steps. For example, cells are transferred to a greenhouse within a certain time period after adding a substance to the cells. There is a set time constraint between the transfer experimental step and the experimental step of adding the substance. Therefore, in the step of planning the operation time sequence of the intelligent robot laboratory, a plurality of corresponding experimental steps can be configured for the equipment according to the time constraint, so that the operation time sequence of the intelligent robot laboratory is obtained, and the execution time of the experimental steps can meet the time constraint.
In some embodiments, the run schedule includes corresponding performance; the target experiment requirements include target performance of an operation time sequence of the robot intelligent laboratory; the method further comprises the steps of: and adjusting the operation time sequence of the intelligent robot laboratory according to the target performance so that the performance of the operation time sequence of the intelligent robot laboratory tends to the target performance.
The intelligent laboratory of the robot formed according to the set number of equipment can also have different experimental fluxes according to different operation time sequences. In some embodiments, optimizing the temporal arrangement of the robotic intelligent laboratory may also improve the experimental throughput of the robotic intelligent laboratory to meet the target experimental throughput.
The performance may be an indicator for evaluating a specific objective of the robotic intelligent laboratory that is sought. Specifically, for example, the performance may be a device idle rate of the robot smart laboratory, an experimental flux of a target sample, and the like. According to the robot intelligent laboratory formed by the set number of equipment, the performance also has a certain difference according to the different operation time sequences. For example, when the number of devices performing the same experimental step is large, the operation time sequences formed by the average allocation of the corresponding experimental step to a plurality of devices and the allocation of the corresponding experimental step to only one device are different, and the target experimental fluxes of the corresponding robot intelligent laboratories are also different. The target performance may be a user-set direction of optimization of one performance. For example, the target performance may represent a maximum experimental throughput of the robot smart laboratory, a minimum idle rate of the device, or the like. According to the target performance, the operation time sequence of the robot intelligent laboratory formed based on the set number of devices can be adjusted so that the performance of the operation time sequence of the robot intelligent laboratory tends to the target performance.
The method for adjusting the operation time sequence of the intelligent robot laboratory can be integrated in a planning solving module. Specifically, the method for adjusting the operation time sequence of the intelligent robot laboratory may plan a plurality of operation time sequences according to the number of the devices. Next, the performance of the robot smart laboratory at the plurality of operation timings may be calculated, respectively, and an operation timing corresponding to the performance closest to the target performance may be selected as the adjusted operation timing. In some embodiments, an objective function may be constructed from the target performance, and the operating sequence of the robot smart laboratory is planned by constructing the objective function. Under the condition that the parameters of the objective function are initial parameters, the initial operation time sequence of the intelligent robot laboratory can be obtained. The method for adjusting the operation time sequence of the intelligent robot laboratory can be to continuously improve and adjust the initial parameters of the objective function through an optimization method so as to obtain better parameters. Wherein the parameters of different objective functions may correspond to different operation timings. The operation time sequence corresponding to the initial parameter of the objective function can be adjusted to the operation time sequence corresponding to the optimal parameter of the objective function, so that the experimental flux of the intelligent robot laboratory is improved.
In some embodiments, the step of determining a bottleneck device according to the target experimental requirement and the number of devices corresponding to the plurality of experimental steps includes: determining experimental step interval time between two adjacent experimental steps executed by equipment according to the operation time sequence of the intelligent robot laboratory; and determining bottleneck equipment based on the experimental step interval time corresponding to the equipment.
The bottleneck device may be a device that limits the robot smart laboratory lab throughput. By adjusting the number of bottleneck devices, the effect of better improving the experimental flux of the robot intelligent laboratory by adjusting fewer devices can be achieved to a certain extent. Thus, the determination of the bottleneck device is important.
The method for determining the bottleneck device may be integrated in the feedback adjustment module. Specifically, the method for determining the bottleneck device may be determined based on the interval time between different experimental steps in the time sequence of performing a plurality of experimental steps for each device. In some embodiments, the time interval between experimental steps of the same apparatus may be multiple. At this time, an average value of a plurality of interval times may be taken as the interval time corresponding to the device. The device with longer interval time can indicate that after the device completes a previous experimental step, the device continuously waits for another device to execute the experimental step of the previous step depending on the subsequent experimental step needed to be executed by the device. The device may not perform the subsequent experimental step until another device performs the experimental step that completes the previous step that the subsequent experimental step of the device depends on, resulting in an interval of time between the device performing the plurality of corresponding experimental steps. This shows that the longer-spaced devices are not devices limiting the experimental throughput of the robotic intelligent laboratory. Conversely, a device with a shorter interval, meaning that experimental steps are performed continuously, may act as a bottleneck device. In some embodiments, the idle rate of each device may be determined by an interval time. Bottleneck devices may also be determined based on the idle rate of each device. The device with smaller device idle rate is busy and can be used as bottleneck device. In some embodiments, there may be multiple devices of the same class, and accordingly, the average idle rate of the class of devices may be calculated, or the time interval between multiple experimental steps of the devices may be used to determine the bottleneck device.
In some embodiments, the same class of equipment may correspond to performing multiple ones of multiple experimental steps for preparing a target sample. After determining that the device is a bottleneck device, the bottleneck experimental step may also be determined in a plurality of experimental steps corresponding to the bottleneck device. Accordingly, the device capable of executing the bottleneck experimental steps can be added to the robot intelligent laboratory, the device capable of completing a plurality of experimental steps does not need to be provided, and the device can be adjusted more flexibly. For example, the number of devices may be adjusted in consideration of the cost of the devices. The method for determining the bottleneck experimental step may be to compare the durations of a plurality of experimental steps executed by the bottleneck equipment, and take the experimental step with a longer duration as the bottleneck experimental step.
In some embodiments, the step of adjusting the number of bottleneck devices comprises: under the condition that the previous experimental step of the experimental step corresponding to the bottleneck equipment is completed, the experimental step corresponding to the bottleneck equipment is executed immediately, and start-stop time of the experimental steps corresponding to the bottleneck equipment is obtained; determining the maximum number of experimental steps executed in parallel at the same time according to the start-stop time of the experimental steps corresponding to the bottleneck equipment; and adjusting the number of the bottleneck devices to the maximum number of the parallel executed experimental steps.
In the case that the experimental flux of the robot intelligent laboratory does not reach the target experimental flux, the experimental flux of the robot intelligent laboratory can be improved by adding the number of bottleneck devices. In some embodiments, after adding a bottleneck device, the experimental flux of the robot intelligent laboratory is calculated again, and the bottleneck device may not change. The intelligent laboratory of the robot needs to be repeatedly added with the equipment of the same category and simulated to operate, so that the efficiency of adjusting the number of bottleneck equipment is reduced. The efficiency of the bottleneck device adjustment can be improved by first determining the number of bottleneck devices required and adjusting accordingly. The method of the number of the bottleneck devices required can be expressed as that after adding the corresponding number of devices, the device class with the largest experimental flux limit on the intelligent robot laboratory is changed.
The method of determining the number of bottleneck devices required may be integrated in the feedback adjustment module. The method of determining the number of bottleneck devices required may be without limiting the number of bottleneck devices currently. In other words, the run time sequence may be adjusted in the process of calculating the predicted experimental flux of the robot smart laboratory. Specifically, the experimental step corresponding to the bottleneck device may be executed immediately after the previous experimental step of the experimental step corresponding to the bottleneck device is executed, and it may be considered that there is always an idle bottleneck device to execute the corresponding experimental step. Thus, the start-stop time of a plurality of bottleneck experimental steps can be obtained. According to the start-stop time of the bottleneck experimental steps, the maximum number of experimental steps which are executed in parallel at the same time can be determined. Please refer to fig. 4. In fig. 4, the experimental step n may be an experimental step corresponding to the bottle apparatus. Wherein the maximum number of experimental steps performed in parallel at the same time is 3. The method for adjusting the number of bottleneck devices may be to directly adjust the number of bottleneck devices to the maximum number of parallel execution experimental steps.
Referring to fig. 5, the present disclosure provides a design apparatus of a robot smart laboratory, the robot smart laboratory including a plurality of devices, the devices corresponding to at least one of a plurality of experimental steps; the apparatus may include a receiving module, a bottleneck determining module, and an adjusting module.
The receiving module is used for receiving target experiment requirements; wherein the target experiment requirement includes a target experiment flux and an experiment step flow of the plurality of experiment steps.
The bottleneck determining module is used for determining bottleneck equipment according to the target experiment requirements and the number of the equipment corresponding to the plurality of experiment steps; wherein the bottleneck device represents a device of the plurality of devices that limits the robotic intelligent laboratory experimental throughput.
And the adjusting module is used for adjusting the quantity of the bottleneck devices so that the experimental flux of the intelligent robot laboratory meets the target experimental flux.
Specific functions and effects achieved by the design device of the intelligent robot laboratory can be explained in reference to other embodiments of the present specification, and are not described herein. The various modules in the design apparatus of the robot intelligent laboratory may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in hardware or independent of a processor in the computer equipment, and can also be stored in a memory in the computer equipment in a software mode, so that the processor can call and execute the operations corresponding to the modules.
In some embodiments a computer device may be provided comprising a memory having a computer program stored therein and a processor, which when executing the computer program implements the method of the embodiments.
In some embodiments a computer readable storage medium may be provided, on which a computer program is stored which, when executed by a processor, implements the method in the embodiments.
Those skilled in the art will appreciate that implementing all or part of the processes in the methods of the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise processes of embodiments of the methods as described herein. Any reference to memory, storage, database, or other medium used in the implementations provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The various embodiments of the present disclosure are described in a progressive manner. The different embodiments focus on describing different portions compared to other embodiments. Those skilled in the art will appreciate, after reading the present specification, that a plurality of embodiments of the present specification and a plurality of technical features disclosed in the embodiments may be combined in a plurality of ways, and for brevity of description, all of the possible combinations of the technical features in the embodiments are not described. However, as long as there is no contradiction between the combinations of these technical features, it should be considered as the scope described in the present specification.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely illustrative of the present invention and is not intended to limit the scope of the claims. Various modifications and changes may occur to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which fall within the spirit and principles of the present invention are intended to be included within the scope of the claims.
Claims (8)
1. A method of designing a robotic intelligent laboratory, wherein the robotic intelligent laboratory is configured to perform a plurality of experimental steps, the robotic intelligent laboratory comprising a plurality of devices, the devices corresponding to at least one of the plurality of experimental steps; the method comprises the following steps:
Receiving target experiment requirements; wherein the target experiment requirement comprises a target experiment flux and an experiment step flow of the plurality of experiment steps; determining bottleneck equipment according to the target experiment requirements and the number of equipment corresponding to a plurality of experiment steps; wherein the bottleneck device represents a device of the plurality of devices that limits experimental throughput of the robotic intelligent laboratory; the operation time sequence of the intelligent robot laboratory is planned according to the target experiment requirements and the number of the equipment corresponding to the plurality of experiment steps; determining the expected interval time between two adjacent experimental steps executed by equipment according to the operation time sequence of the intelligent robot laboratory; determining the bottleneck equipment based on the expected interval time corresponding to the equipment;
adjusting the number of bottleneck devices so that the experimental flux of the robot intelligent laboratory meets the target experimental flux; under the condition that the previous experimental step of the experimental step corresponding to the bottleneck equipment is completed, executing the experimental step corresponding to the bottleneck equipment to obtain start-stop time of the experimental steps corresponding to the bottleneck equipment; determining the maximum number of experimental steps executed in parallel at the same time according to the start-stop time of the experimental steps corresponding to the bottleneck devices; and adjusting the number of the bottleneck devices to the maximum number of the parallel executed experimental steps.
2. The method of claim 1, wherein the step of determining a bottleneck device in accordance with the target experimental requirement and the number of devices corresponding to the plurality of experimental steps comprises:
calculating the expected experimental flux of the intelligent robot laboratory according to the target experimental requirement and the number of the equipment corresponding to the plurality of experimental steps;
in the case where the predicted experimental flux is less than the target experimental flux, a bottleneck device is determined among a plurality of devices.
3. The method according to claim 2, wherein the step of calculating the expected experimental flux of the intelligent robot laboratory in accordance with the target experimental requirement and the number of devices corresponding to the plurality of experimental steps comprises:
planning the operation time sequence of the intelligent robot laboratory according to the target experiment requirement and the number of the equipment corresponding to the plurality of experiment steps; the operation time sequence represents the time arrangement of the equipment for executing corresponding experimental steps along the time progress;
based on the run time sequence, a predicted experimental flux of the robotic intelligent laboratory is calculated.
4. A method according to claim 3, wherein the target experimental requirement comprises a time constraint between at least two adjacent experimental steps of the plurality of experimental steps; and planning the operation time sequence of the intelligent robot laboratory according to the experiment requirements and the number of the devices corresponding to the experiment steps, wherein the step comprises the following steps:
And configuring a plurality of corresponding experimental steps for the equipment according to the target experimental requirement to obtain the operation time sequence of the intelligent robot laboratory, and enabling the execution time of the experimental steps to meet the time constraint.
5. A method according to claim 3, wherein the target experimental requirements include a target performance of the operating sequence of the robotic intelligent laboratory; the operation time sequence comprises corresponding performances; the method further comprises the steps of:
and adjusting the operation time sequence of the intelligent robot laboratory according to the target performance so that the performance of the operation time sequence of the intelligent robot laboratory tends to the target performance.
6. A design apparatus of a robot intelligent laboratory, wherein the robot intelligent laboratory is configured to perform a plurality of experimental steps, the robot intelligent laboratory including a plurality of devices, the devices corresponding to at least one experimental step of the plurality of experimental steps; the device comprises:
the receiving module is used for receiving target experiment requirements; wherein the target experiment requirement comprises a target experiment flux and an experiment step flow of the plurality of experiment steps;
the bottleneck determining module is used for determining bottleneck equipment according to the target experiment requirements and the number of the equipment corresponding to the plurality of experiment steps; wherein the bottleneck device represents a device of the plurality of devices that limits experimental throughput of the robotic intelligent laboratory; the operation time sequence of the intelligent robot laboratory is planned according to the target experiment requirements and the number of the equipment corresponding to the plurality of experiment steps; determining the expected interval time between two adjacent experimental steps executed by equipment according to the operation time sequence of the intelligent robot laboratory; determining the bottleneck equipment based on the expected interval time corresponding to the equipment;
The adjusting module is used for adjusting the number of the bottleneck devices so that the experimental flux of the intelligent robot laboratory meets the target experimental flux; under the condition that the previous experimental step of the experimental step corresponding to the bottleneck equipment is completed, executing the experimental step corresponding to the bottleneck equipment to obtain start-stop time of the experimental steps corresponding to the bottleneck equipment; determining the maximum number of experimental steps executed in parallel at the same time according to the start-stop time of the experimental steps corresponding to the bottleneck devices; and adjusting the number of the bottleneck devices to the maximum number of the parallel executed experimental steps.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any one of claims 1 to 5.
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