Experimental object processing control method and device, equipment and storage medium

Abstract

The invention provides an experimental object processing control method and device, equipment and a storage medium. The method comprises the steps that according to the states of all existing experimental objects on an experimental platform, the experimental objects are processed; predicting and calculating execution time consumed by a single experiment object at each target equipment resource to complete each target experiment step corresponding to the target equipment resource; obtaining time consumed in the process that a single experiment object arrives at the target equipment resource to execute each target experiment step; according to execution time consumption corresponding to execution of each target experiment step corresponding to each target equipment resource in all the target equipment resources and the process time consumption, determining a maximum time consumption difference value corresponding to execution of the target experiment step by the target equipment resource in the experiment platform; according to the maximum time consumption difference value and a preset threshold value, whether the next batch of experiment objects are obtained and added into the experiment platform to execute the target experiment process or not is determined, and the high-throughput experiment processing process is achieved.

Description

Technical Field

[0001] The present application relates to the field of system control technology, and in particular to an experimental object processing control method, device, equipment and storage medium. Background Art

[0002] Pharmacokinetics (PK) is the science of studying the absorption, distribution, metabolism and excretion of drugs in the body. Pharmacokinetic research is crucial for drug development, efficacy evaluation and drug safety assessment. With the advancement of science and technology, especially the rapid development of high-throughput screening (HTS) technology, pharmacokinetic research is gradually moving towards a new stage of automation, high throughput and high precision.

[0003] The pharmacokinetic automation system realizes various pharmacokinetic testing needs by integrating sample preparation, liquid handling, data acquisition and analysis. The efficient operation of the pharmacokinetic automation system is inseparable from the support of the experimental object processing control algorithm. The experimental object processing control algorithm is responsible for deciding whether to add experimental objects to the automation system. How to ensure that the experimental tasks can be performed in an orderly and efficient manner on the platform with existing equipment resources and improve the high throughput of the platform system has become a research hotspot. Summary of the invention

[0004] In view of this, the present application provides an experimental object processing control method, device, equipment and storage medium.

[0005] The first aspect provides an experimental object processing control method, which is applied to an experimental platform including multiple device resources, each device resource is used to execute at least one experimental step of an experimental process on the experimental object, and the method may include: according to the status of all existing experimental objects on the experimental platform, predicting and calculating the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource; wherein the target device resources at least include bottleneck device resources; obtaining the process time required for a single experimental object to reach the target device resource to execute each target experimental step; according to the execution time corresponding to each target device resource among all the target device resources to execute each corresponding target experimental step and the process time, determining the maximum time difference corresponding to the target device resource executing the target experimental step in the experimental platform, wherein the time difference refers to the difference between the execution time and the corresponding process time; according to the maximum time difference and a preset threshold, determining whether to obtain the next batch of the experimental objects to be added to the experimental platform to execute the target experimental process; wherein each batch of experimental objects includes at least one experimental object.

[0006] The second aspect provides a high-throughput experimental platform verification method, which includes: obtaining equipment resource configuration information of the current experimental platform and experimental configuration information of the target experimental process associated with the equipment resource configuration information; obtaining experimental objects and executing the target experimental process according to the equipment resource configuration information and the experimental configuration information, and when obtaining the next batch of experimental objects, using any one of the experimental object processing control methods provided in the embodiments of the present application to determine the time for obtaining the next batch of experimental objects; repeating the above process to complete the processing of all target experimental objects, and adjusting the equipment resource configuration information and the corresponding experimental configuration information according to the completion status of the experiment.

[0007] The third aspect provides an experimental object processing control device, which includes: a prediction and calculation module, which is used to predict and calculate the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource according to the status of all existing experimental objects on the experimental platform; wherein the target device resources at least include bottleneck device resources; a process time acquisition module, which is used to obtain the process time for each of the experimental objects to reach the target device resource to execute each target experimental step; a difference determination module, which is used to determine the maximum time difference corresponding to the target device resources in the experimental platform executing the target experimental step according to the execution time and process time corresponding to each target device resource in all target device resources executing each target experimental step corresponding to it, wherein the time difference refers to the difference between the execution time and the corresponding process time; an object acquisition control module, which is used to determine whether to acquire the next batch of experimental objects to be added to the experimental platform to execute the target experimental process according to the maximum time difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

[0008] The fourth aspect provides a high-throughput experimental platform verification device, which includes: an information acquisition module, used to obtain the equipment resource configuration information of the current experimental platform and the experimental configuration information of the target experimental process associated with the equipment resource configuration information; an object acquisition module, used to obtain the experimental object and execute the target experimental process according to the equipment resource configuration information and the experimental configuration information, when obtaining the next batch of target experimental objects, any one of the experimental object processing control methods provided in the embodiments of the present application is used to determine the time to obtain the next batch of experimental objects; repeat the above process and complete the processing of all target experimental objects; an information adjustment module, used to adjust the equipment resource configuration information and the corresponding experimental configuration information according to the completion status of the experiment.

[0009] The fifth aspect provides an electronic device, which may include a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the program implements the steps of any one of the experimental object processing control methods provided in the embodiments of the present application, or the steps of the high-throughput experimental platform verification method provided in the embodiments of the present application.

[0010] The sixth aspect provides a computer-readable storage medium, which stores instructions. When the instructions are executed by a processor, the steps of any one of the experimental object processing control methods provided in the embodiments of the present application, or the steps of the high-throughput experimental platform verification method provided in the embodiments of the present application are implemented.

[0011] In summary, the experimental object processing control method, device, equipment and storage medium provided by the present application have the following beneficial effects:

[0012] The embodiment of the present application predicts and calculates the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource based on the status of all existing experimental objects on the experimental platform; and obtains the process time required for a single experimental object to reach the target device resource to execute each target experimental step; determines the maximum time difference in the experimental platform based on all execution times and the corresponding process times, and determines whether to obtain the next batch of experimental objects to add to the experimental platform to execute the target experimental process based on the maximum time difference and a preset threshold, thereby dynamically determining the acquisition time of the next batch of experimental objects, and then ensuring that each device resource can run efficiently and orderly in the experimental platform (especially bottleneck resources), so that the experimental platform achieves high throughput and ensures that device resources are used in the most reasonable way, while avoiding the occurrence of excessive load or idleness, thereby enhancing the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 A schematic diagram showing the structure of an experimental platform provided in an embodiment of the present application is shown;

[0015] Figure 2 A schematic diagram showing a flow chart of an experimental object processing control method provided in an embodiment of the present application;

[0016] Figure 3A schematic diagram showing a flow chart of a high-throughput experimental platform verification method provided in an embodiment of the present application;

[0017] Figure 4 A schematic diagram showing the structure of an experimental object processing control device provided in an embodiment of the present application is shown;

[0018] Figure 5 A schematic diagram of the structure of a high-throughput experimental platform verification device provided by an embodiment of the present application is shown;

[0019] Figure 6 A schematic structural diagram of an electronic device provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] In order to make the above and other features and advantages of the present application more clear, the present application is further described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explaining to those skilled in the art and are only exemplary and not restrictive.

[0021] In the following description, many specific details are set forth to provide a thorough understanding of the present application. However, it is apparent to those skilled in the art that specific details need not be adopted to practice the present application. In other cases, well-known steps or operations are not described in detail to avoid blurring the present application.

[0022] On the one hand, an embodiment of the present application provides an experimental platform. Figure 1 A schematic diagram of the structure of an experimental platform provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the experimental platform 10 includes multiple device resources 11 and a control system 12 .

[0023] In some embodiments of the present application, each device resource 11 is used to perform at least one experimental step of the experimental process on the experimental object. Among them, each batch of experimental objects added to the experimental platform 10 can be one experimental object or multiple experimental objects. One experimental object corresponds to one experimental process, and one experimental process corresponds to multiple experimental steps.

[0024] Each device resource 11 communicates with the control system 12 , that is, the control system 12 can interact with each device resource 11 through the calling interface of each device resource 11 .

[0025] In some embodiments, the control system 12 may further include a visualization display module. The visualization display module is used to display task dynamic information and equipment dynamic information.

[0026] In some embodiments of the present application, the equipment resources 11 may include but are not limited to experimental equipment such as micropipette pipettes, liquid workstations, film sealing machines, refrigerators, rotating plate stations, lid opening and closing machines, humidity-controlled refrigerators, oscillators, centrifuges, film tearing machines, PCR instruments, solid phase extractors, dispensers, labeling machines, and barcode scanners.

[0027] Another aspect of the present application provides an experimental object processing control method, which is applied to Figure 1 The experimental platform shown in the figure. The experimental object processing control method can be executed by an experimental object processing control device, and the experimental object processing control device can be configured on the control system 12.

[0028] Figure 2 A flow chart of a method for controlling an experimental object processing provided by an embodiment of the present application is shown as follows: Figure 2 As shown, the experimental object processing control method may include the following steps.

[0029] S21, based on the status of all existing experimental objects on the experimental platform, predict and calculate the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource.

[0030] The experimental object involved in the embodiment of the present application may be an experimental plate or an experimental tube. The state of an existing experimental object refers to the experimental step in which the experimental object that has been added to the experimental platform is located. The target experimental step may refer to the experimental step in the experimental process that has not been executed by the experimental object, and the target device resource may refer to the device resource defined by the user or the device resource customized by the system. The target experimental step may be an experimental step corresponding to the target device resource. Different target device resources correspond to one or more different target experimental steps. For example, when the third and fifth experimental steps in the experimental process are both centrifugation steps, the target device resource is a centrifuge device, and a target experimental step corresponding to the centrifuge device may be the third centrifugation step, and another corresponding target experimental step may be the fifth centrifugation step.

[0031] The execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource involved in the embodiments of the present application may refer to: the total time required for all experimental objects on the experimental platform that have not executed the corresponding target experimental steps to execute and complete the target experimental steps at the corresponding target device resources in the future, the number of experimental objects targeted by the target experimental steps executed each time by the target device resources (i.e., the number of carriers referred to below), and the execution time equal to the ratio of the total time to the number of carriers; in other words, the execution time can be understood as the unit time required for the target device resources to execute the target experimental operation on the existing experimental objects on the experimental platform in the future, and the unit time can be understood as the time for a single experimental object.

[0032] In one embodiment of the present application, the experimental object processing control device can predict the execution time required for each target device resource on the computing experimental platform to complete each corresponding target experimental step of a single experimental object based on the prediction model.

[0033] It should be noted that, for each type of target device resource, a target experimental step corresponds to an execution time.

[0034] For example, three different types of equipment resources (equipment resource A, equipment resource B, and equipment resource C) on the experimental platform are taken as target equipment resources, the three equipment resources correspond to three experimental steps, and the experimental platform already has four experimental objects. When the four experimental objects have not yet executed the experimental steps corresponding to the three equipment resources, and the four experimental objects are in the same experimental position, the experimental object processing and control device calculates the execution time required for the equipment resource A to complete the target experimental step for a single experimental object, the execution time required for the equipment resource B to complete the target experimental step for a single experimental object, and the execution time required for the equipment resource C to complete the target experimental step for a single experimental object.

[0035] For another example, two different types of equipment resources (equipment resource A and equipment resource B) on the experimental platform are used as target equipment resources, and the experiment of experimental resource A is performed first and then the experiment of experimental resource B. Experimental object 1 has passed through equipment resource A, but has not yet reached equipment resource B, and implementation object 2 has not yet passed through equipment resource A. The experimental object processing control device calculates the execution time required for equipment resource B to complete the target experimental step for a single experimental object according to the status of experimental object 1 and experimental object 2; and calculates the execution time required for a single experimental object at equipment resource A to complete the target experimental step according to the status of experimental object 2.

[0036] In the embodiments of the present application, if two device resources of the same type execute the same experimental step, when calculating the execution time consumption, these two device resources are regarded as a target device resource, that is, the experimental object processing control device calculates an execution time consumption for these two device resources.

[0037] For example, device resource C and device resource D are the same type of device resource and execute the same experimental step, and the purpose is to achieve the throughput of this experimental step. For example, both execute the 4th experimental step. When the experimental object has not yet executed the 4th experimental step, the experimental object processing control device calculates an execution time consumption required for device resource C and device resource D to jointly execute and complete the 4th experimental step for a single experimental object.

[0038] In the embodiments of the present application, if a device resource executes two experimental steps, it is necessary to calculate the execution time consumption corresponding to each experimental step of this device resource.

[0039] For example, device resource C executes the 3rd experimental step and the 5th experimental step. When the experimental object has not yet executed the two experimental steps corresponding to device resource C, the experimental object processing control device calculates an execution time consumption required for device resource C to execute and complete the 3rd experimental step for a single experimental object, and calculates an execution time consumption required for device resource C to execute and complete the 5th experimental step for a single experimental object.

[0040] In some embodiments, the target device resource may include a bottleneck device resource in the experimental platform. The bottleneck device resource includes a device resource that restricts the throughput capacity of the experimental platform, such as a centrifuge, an oscillator, etc.

[0041] S22, obtain the process time consumption for a single experimental object to reach the target device resource to execute each target experimental step.

[0042] The process time consumption involved in the embodiments of the present application may refer to the time consumed by a single experimental object from the moment of entering the starting position of the experimental platform to reaching the target device resource corresponding to each target experimental step. Each target experimental step executed by a target device resource corresponds to a process time consumption.

[0043] In an embodiment of the present application, the process time consumption can be calculated based on existing parameters. For example, the experimental object processing control device can accumulate the experimental step execution time of each device resource before the target device resource and the transportation time between device resources to obtain the process time consumption for reaching the target device resource.

[0044] For example, the execution time of device resource A to perform the experimental steps is 5 minutes and the transportation time to reach device resource B is 2 minutes. The execution time of device resource B to perform the experimental steps is 3 minutes and the transportation time to reach device resource C is 1 minute. The time taken for a single experimental object to reach device resource C is 5+2+3+1=11 minutes.

[0045] In another embodiment, the process time can be obtained by actual measurement of the experimental platform. When the experiment is officially started, a batch of experimental samples are taken for experimentation, and the experimental object processing control device records the time when a batch of experimental samples reaches each device resource, thereby obtaining the process time of a single experimental object reaching the target resource. Here, the process time only represents the time required to reach the target device resource for executing the target experimental step.

[0046] S23, determining the maximum time difference corresponding to the target device resources in the experimental platform executing the target experimental step according to the execution time and process time corresponding to each target experimental step executed by each target device resource among all the target device resources.

[0047] The time difference refers to the difference between the execution time and the corresponding process time. Specifically, the time difference is equal to the execution time minus the process time, and the time difference may be positive or negative according to actual conditions.

[0048] The time difference involved in the embodiment of the present application refers to the difference between the execution time and the corresponding process time. The maximum time difference corresponding to the target device resource executing the target experimental step in the experimental platform can be understood as the maximum value of the time difference corresponding to each target device resource executing each corresponding target experimental step in the current experimental platform.

[0049] In one embodiment of the present application, the experimental object processing control device calculates the execution time and process time corresponding to each target experimental step executed by each target device resource on a single experimental object, and determines the maximum time difference for executing the target experimental step by a target device resource in the experimental platform at the current moment based on the time difference between the execution time and the process time corresponding to each target experimental step corresponding to the single experimental object.

[0050] For example, there are three experimental objects on the experimental platform, as well as target device resources A, target device resources B, and target device resources C. The time difference between target device resource A and a single experimental object that is about to execute experimental step A is 5 minutes, the time difference between target device resource B and a single experimental object that is about to execute experimental step B is 6 minutes, and the time difference between target device resource C and a single experimental object that is about to execute experimental step C is 7 minutes. At this time, the maximum time difference corresponding to the target device resources in the experimental platform executing the target experimental steps for a single experimental object is 7 minutes.

[0051] S24, determining whether to obtain the next batch of experimental objects to be added to the experimental platform to execute the target experimental process according to the maximum time difference and the preset threshold; wherein each batch of experimental objects includes at least one experimental object.

[0052] The preset threshold involved in the embodiment of the present application is a parameter that can be pre-set. The threshold is determined based on the experimental object and the experimental process executed by the experimental object. The size of the threshold mainly affects the experimental effect of the experimental object on the experimental platform. Optionally, the preset threshold can be 5 minutes. In an embodiment of the present application, the experimental object processing control device can determine whether adding the next batch of experimental objects to the experimental platform to execute the target experimental process will affect the experimental effect based on the comparison result of the maximum time difference and the preset threshold, thereby determining whether to obtain the next batch of experimental objects from the experimental platform.

[0053] Specifically, when the maximum time difference is less than the preset threshold, adding the next batch of experimental objects will not affect the experimental effect, so it is determined that the equipment resources of the experimental platform support adding the next batch of experimental objects, so it is determined that the next batch of experimental objects can be obtained and added to the experimental platform to execute the target experimental process. Among them, the maximum time difference is less than the preset threshold, including the case where the maximum time difference is a negative number.

[0054] When the maximum time difference is greater than the preset threshold, adding the next batch of experimental objects will affect the experimental effect, so it is determined that the device resources of the experimental platform do not support adding the next batch of experimental objects, so it is determined that the next batch of experimental objects cannot be obtained.

[0055] In the above embodiment, the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource is predicted and calculated according to the status of all existing experimental objects on the experimental platform; the process time required for a single experimental object to reach the target device resource to execute each target experimental step is obtained; the maximum time difference in the experimental platform is determined based on all execution times and the corresponding process times, and according to the maximum time difference and the preset threshold, it is determined whether to obtain the next batch of experimental objects to join the experimental platform to execute the target experimental process, so that the acquisition time of the next batch of task objects can be determined according to the status of the target device resources and the status of each existing experimental object on the experimental platform, thereby ensuring that each device resource can run efficiently and orderly in the experimental platform, realizing a high-throughput experimental processing process and ensuring that the device resources are used in the most reasonable way, while avoiding the occurrence of excessive load or idleness, and enhancing the stability and reliability of the system.

[0056] In some embodiments, the number of experimental objects in the next batch can be limited according to the plate capacity of the bottleneck equipment resource of the experimental platform. For example, if the bottleneck equipment resource in the experimental platform is a centrifuge, and the minimum plate capacity of the centrifuge is 2, then the number of experimental objects in each batch can be 2. The plate capacity can be understood as the number of experimental objects carried by the target equipment resource when executing the target step once.

[0057] In some embodiments, S23, according to the execution time corresponding to each target experimental step and the corresponding process time of each target device resource in all target device resources, the maximum time difference corresponding to the target experimental step executed by the target device resources in the experimental platform is determined, including: for each target device resource, according to the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource, and the process time of a single experimental object to reach the target device resource to execute each target experimental step, a group of execution times and process times for the target device resource to execute the corresponding target experimental step are determined; according to each group of execution times and process times, the time difference for a single experimental object to execute each corresponding target experimental step on the target device resource is calculated; and the maximum time difference is determined among all the calculated time differences.

[0058] In one embodiment of the present application, the experimental object processing control device counts the execution time required for each resource device to complete each corresponding target experimental step for a single experimental object, as well as the process time for a single experimental object to reach each target device resource, and extracts a group of execution time and process time corresponding to each target device resource executing each target experimental step. The execution time of each group is subtracted from the process time to obtain the time difference of each target experimental step corresponding to each target device resource of a single experimental object, and the size of all time difference values ​​is compared to determine the maximum time difference value.

[0059] In an embodiment of the present application, the maximum time difference can be obtained by the following formula:

[0060] Tb=max(Ti-Tsi) (1)

[0061] Among them, Tb represents the maximum time difference, Ti represents the execution time corresponding to the target device resources executing the i-th target experimental step, i represents the sequence number of the target experimental step, and Tsi represents the process time corresponding to the i-th target experimental step.

[0062] For example, there are target device resources A, target device resources B, and target device resources C on the experimental platform. The execution time of target device resource A for executing experimental step A on a single experimental object is 8 minutes, the process takes 1 minute, and the time difference is 7 minutes. The execution time of target device resource B for executing experimental step B on a single experimental object is 12 minutes, the process takes 10 minutes, and the time difference is 2 minutes. The execution time of target device resource C for executing experimental step C on a single experimental object is 15 minutes, the process takes 6 minutes, and the time difference is 9 minutes. At this time, the maximum time difference corresponding to the target device resources in the experimental platform executing the target experimental steps on a single experimental object is 9 minutes.

[0063] In the above embodiment, by calculating the time difference of each target experimental step corresponding to each target device resource and determining the maximum time difference among all calculated time differences, the maximum time difference of a target device resource in the current experimental platform can be accurately determined, providing a decision-making basis for acquiring the next batch of experimental objects.

[0064] In some embodiments, S23, according to the execution time and process time corresponding to each target experimental step executed by each target device resource among all target device resources, the maximum time difference corresponding to the target experimental step executed by the target device resources in the experimental platform is determined, including: determining the maximum execution time according to all predicted and calculated execution times; determining the process time corresponding to the first target experimental step executed by the first target device resource corresponding to the maximum execution time; calculating the time difference according to the maximum execution time and the process time determined in the previous step, as the maximum time difference.

[0065] The first target device resource involved in the embodiment of the present application is the target device resource corresponding to the maximum execution time consumption. The first target experimental step is the target experimental step corresponding to the maximum execution time consumption.

[0066] In one embodiment of the present application, the experimental object processing control device traverses all execution times, finds out the maximum execution time, and determines the first target device resource and the first target experimental step corresponding to the maximum execution time. The corresponding process time is found according to the first target device resource and the first target experimental step, and the maximum execution time is subtracted from the corresponding process time to obtain the maximum time difference.

[0067] In an embodiment of the present application, the maximum time difference can be expressed by the following formula:

[0068] Tb=max(Ti)-Ts 1 (2)

[0069] Among them, Tb represents the maximum time difference, Ti represents the execution time corresponding to the target device resource executing the i-th target experimental step, max(Ti) represents the maximum execution time among all execution times, i represents the sequence number of the target experimental step, and Ts 1 It indicates the time taken by the first target device resource to execute the process corresponding to the first target experimental step.

[0070] For example, there are target device resources A, target device resources B, and target device resources C on the experimental platform. The execution time of target device resource A to execute experimental step A on a single experimental object is 8 minutes, and the process time is 1 minute. The execution time of target device resource B to execute experimental step B on a single experimental object is 12 minutes, and the process time is 10 minutes. The execution time of target device resource C to execute experimental step C on a single experimental object is 15 minutes, and the process time is 6 minutes. At this time, the maximum execution time corresponding to the target device resources executing the target experimental steps in the experimental platform is 15 minutes, and the corresponding process time is 6 minutes. Therefore, the maximum time difference corresponding to the target device resources executing the target experimental steps in the experimental platform is 15-6=9 minutes.

[0071] In the above embodiment, the current maximum execution time of the experimental platform is first determined, and then the corresponding process time is determined based on the first target device resources corresponding to the maximum execution time by executing the first target experimental step corresponding to it. Finally, the current maximum time difference of the experimental platform is obtained, thereby providing a decision-making basis for obtaining the next batch of experimental objects.

[0072] In some embodiments, S21, based on the status of all existing experimental objects on the experimental platform, predicts and calculates the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource, including: periodically predicting and calculating the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource according to the status of all existing experimental objects on the experimental platform at a preset frequency.

[0073] The preset frequency involved in the embodiment of the present application can be set based on factors such as the operating status of the experimental platform, the number of experimental objects, the availability of target device resources, and the real-time requirements of experimental data. Optionally, the preset frequency can be 5 seconds.

[0074] In one embodiment of the present application, when the preset frequency is reached, the experimental object processing control device uses the prediction model to predict and calculate the execution time required for each target device resource to complete the corresponding target experimental step for a single experimental object based on the status of all existing experimental objects in the experimental platform. In this way, the above steps are repeated periodically according to the preset frequency to ensure that the execution time required for each target device resource in the experimental platform to complete each target experimental step for a single experimental object is updated and predicted in a timely manner, and whether to obtain the next batch of the experimental objects to be added to the experimental platform is determined in a timely manner based on the prediction results.

[0075] In some embodiments, the prediction and calculation of the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource is based on the status of all existing experimental objects on the experimental platform, including at least one of the following influencing factors: the time required for a single experimental object to execute the target experimental step at each target device resource (or the time required to execute the target experimental step in the future); the average remaining time required for the current target experimental step that the target device resource is currently executing on at least one experimental object; and the historical average waiting time of a single experimental object waiting to execute the target experimental step at the target device resource.

[0076] The time required for a single experimental object to execute a target experimental step at each target device resource involved in the embodiments of the present application may refer to the ratio of the total time required for all experimental objects that have not executed the target experimental step to complete the target experimental step at each target device resource and the load capacity of the target device resource.

[0077] The average remaining time required for the current target experimental step currently being executed by the target device resource on at least one experimental object may be an average of the remaining time required for multiple experimental objects at the target device resource to complete the current target experimental step. The remaining time may be obtained by subtracting the executed time from the theoretical execution time of the current target experimental step.

[0078] For example, in a certain device resource, the first experimental object still needs 2 minutes to complete the target experimental step, the second experimental object still needs 2 minutes to complete the target experimental step, the third experimental object still needs 4 minutes to complete the target experimental step, and the fourth experimental object still needs 4 minutes to complete the target experimental step. It can be determined that the average remaining time required for the target device resource to execute the current target experimental step on at least one experimental object is 3 minutes (the total remaining time divided by the total number of experimental objects).

[0079] The historical average waiting time of a single experimental subject waiting at the target device resource to execute the target experimental step can reflect the waiting situation of the experimental subject at the target device resource. It can be determined based on the average waiting time of the previous batch of experimental subjects at the target device or based on the average waiting time of several historical batches of experimental subjects at the target device.

[0080] For example, the previous batch contains four experimental subjects. The first experimental subject waits for 2 minutes to start executing the target experimental steps, the second experimental subject waits for 2 minutes to execute the target experimental steps, the third experimental subject waits for 4 minutes to start executing the experimental steps, and the fourth experimental subject waits for 4 minutes to start executing the experimental steps. It can be determined that the historical average waiting time of the experimental subjects waiting to execute the target experimental steps at the target device resources is 3 minutes (the total waiting time divided by the total number of experimental subjects).

[0081] In one embodiment of the present application, the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource can be: the time required for the single experimental object to execute the target experimental step at each target device resource, the average remaining time of the current target experimental step currently being executed by the target device resource, and the historical average waiting time of the single experimental object waiting to execute the target experimental step at the target device resource.

[0082] The execution time Ti required for a single experimental subject to complete each target experimental step corresponding to the target device resource at each target device resource can be calculated by the following formula.

[0083]

[0084] Where N is the number of target device resources executing the i-th target experimental step, B is the number of boards of target device resources executing the i-th target experimental step, T Z i represents the time required for a single experimental subject to execute the i-th target experimental step at the target device resource, E represents the remaining time of the i-th target experimental step currently being executed by the target resource device corresponding to the i-th target experimental step, and Wt represents the historical waiting time of a single experimental subject waiting to execute the i-th target experimental step at the target device resource.

[0085] In some embodiments, the experimental subject processing control method further includes: a step of calculating the time required for a single experimental subject to perform a target experimental step at each target device resource.

[0086] The step of calculating the time required for a single experimental object to perform a target experimental step at each target device resource specifically includes: obtaining the number of experimental objects that have not reached the target device resource to perform the target experimental step, and the number of experimental objects that have reached the target device resource but have not performed the corresponding target experimental step among the experimental objects already in the experimental platform; determining the time for a single experimental object to perform the corresponding target experimental step at the target device resource; determining the total time required for the target device resource to perform the corresponding target experimental step based on the number of experimental objects that have not reached the target device resource to perform the target experimental step, the number of experimental objects that have reached the target device resource but have not performed the corresponding target experimental step, and the time for a single experimental object to perform the corresponding target experimental step at the target device resource; obtaining the number of target device resources to perform the target experimental step and the number of carriers for each target device resource; calculating the time required for a single experimental object to perform the target experimental step at each target device resource based on the total time required for the target device resource to perform the corresponding target experimental step, the number of target device resources to perform the target experimental step and the number of carriers for each target device resource.

[0087] The load board quantity involved in the embodiment of the present application may be the number of experimental objects that the target device resource carries at one time. The time for a single experimental object to execute the corresponding target experimental step at the target device resource may be determined according to the operation process of the target experimental step.

[0088] The number of experimental subjects that have not reached the target device resources to execute the target experimental steps involved in the embodiment of the present application may refer to the number of experimental subjects that include the target experimental steps executed by the target device resources in the experimental process but have not reached the location of the target device resources.

[0089] The number of experimental subjects that have arrived at the target device resources but have not executed the corresponding target experimental steps involved in the embodiment of the present application may refer to the number of experimental subjects that have reached the location of the target device resources but have not yet started to execute the corresponding target experimental steps.

[0090] In one embodiment of the present application, the experimental object processing control device can add the number of experimental objects that have not reached the target device resources to execute the target experimental steps and the number of experimental objects that have reached the target device resources but have not executed the corresponding target experimental steps, to obtain the total number of experimental objects that have not executed the target steps but are about to execute the target steps at the target device resources, and then multiply the total number of experimental objects that execute the target steps at the device resources by the time it takes a single experimental object to execute the corresponding target experimental steps at the target device resources, to obtain the total time required for the target device resources on the experimental platform to execute the corresponding target experimental steps.

[0091] Since the board capacity of each target device resource is different and there can be multiple target device resources of the same type in the experimental platform to perform the same experimental step to increase the throughput of the step, the time required for a single experimental object to perform the target experimental step at each target device resource can be obtained by dividing the total time required for the target device resource to perform the corresponding target experimental step by the product of the number of target device resources that perform the target experimental step and the board capacity of each target device resource. The number of target device resources can be 1 or more.

[0092] Specifically, the time Tzi required for a single experimental subject to perform the target experimental step at each of the target device resources can be obtained by the following formula.

[0093]

[0094] Among them, Wi is the number of experimental objects that have not reached the target device resources to execute the i-th target experimental step, R is the execution time of the i-th target experimental step, Ai is the number of experimental objects that have reached the target device resources but have not executed the i-th target experimental step, N is the number of target device resources to execute the i-th target experimental step, and B is the number of carriers of the target device resources to execute the i-th target experimental step.

[0095] For example, the experimental platform has two equipment resources for executing experimental step A, and each can run three experimental objects. For the experimental object, the total time required for the target equipment resources to execute the corresponding experimental step A is 24 minutes, so the time required for a single experimental object to execute experimental step A at the target equipment resources is 24÷(2×3)=4 minutes.

[0096] Another aspect of the present application embodiment provides a high-throughput experimental platform verification method, which can be used to verify the following: Figure 1 The high-throughput experimental platform verification method can be performed by a high-throughput experimental platform verification device, which is configured as follows: Figure 1 The control system shown. Figure 3 A schematic diagram of the process of the high-throughput experimental platform verification method provided in the embodiment of the present application is shown as follows: Figure 3 As shown, the high-throughput experimental platform verification method may include the following steps.

[0097] S31, obtaining device resource configuration information of the current experimental platform and experimental configuration information of the target experimental process associated with the device resource configuration information.

[0098] The device resource configuration information involved in the embodiments of the present application may include, but is not limited to, the device resource type, the number of device resources, the status of the device resources, the number of boards on which the device resources are loaded, the attribute tags of the device resources, and the connection relationship between the device resources. The experimental configuration information may include experimental parameters, the types of device resources required, the order of experimental steps, and the time required for each experimental step.

[0099] In one embodiment of the present application, the high-throughput experimental platform verification device configuration can obtain the latest equipment resource configuration information through the equipment resource calling interface, and can retrieve the experimental configuration information of the relevant experimental process from the experimental process database according to the experimental instructions input by the user.

[0100] S32, acquiring experimental objects and executing the target experimental process according to the equipment resource configuration information and the experimental configuration information. When acquiring the next batch of target experimental objects, any one of the above experimental object processing control methods is used to determine the time for acquiring the next batch of experimental objects.

[0101] The experimental objects involved in the embodiments of the present application can be selected from the sample storage library. In one embodiment of the present application, the high-throughput experimental platform verification device can execute the target experimental process for all experimental objects according to the equipment resource configuration information and the experimental configuration information, according to the predetermined steps and experimental parameters. In addition, when scheduling the next batch of target experimental objects to enter the experimental platform to execute the experimental process, the aforementioned experimental object processing control method is used to determine the time to obtain the next batch of experimental objects, thereby reducing waiting time and resource waste.

[0102] S33, repeat the above process to complete the processing of all target experimental objects, and adjust the device resource configuration information and the corresponding experimental configuration information according to the completion status of the experiment.

[0103] The experiment completion status involved in the embodiment of the present application may include experiment process data and experiment completion time.

[0104] In one embodiment of the present application, the high-throughput experimental platform verification device continues to process the remaining experimental objects according to steps S31 and S32 until all target experimental objects have been processed. And during the experiment, the experimental data is recorded.

[0105] In one embodiment of the present application, adjusting the device resource configuration information and the corresponding experimental configuration information according to the completion status of the experiment can be specifically performed by comparing the experiment completion time with the expected completion time after the experiment is completed. If the experiment completion time is greater than the expected completion time, then parsing the experimental process data to analyze the potential problems or improvement points of the experimental platform, thereby adjusting the device resource configuration information and the corresponding experimental configuration information to improve the working efficiency of the experimental platform, verify the experimental process, maximize the use of experimental resources, and improve experimental efficiency.

[0106] It should be noted that the expected completion time can be set according to user needs and is not limited in the embodiments of the present application.

[0107] Another aspect of the embodiments of the present application provides an experimental object processing control device. Figure 4 A schematic diagram showing the structure of an experimental object processing control device provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the experimental object processing control device 40 may include the following modules.

[0108] The prediction and calculation module 41 is used to predict and calculate the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource based on the status of all existing experimental objects on the experimental platform; wherein the target device resources include at least bottleneck device resources.

[0109] The process time acquisition module 42 is used to acquire the process time taken for a single experimental subject to reach a target device resource to execute each target experimental step.

[0110] The difference determination module 43 is used to determine the maximum time difference corresponding to the target device resources in the experimental platform executing the target experimental steps based on the execution time and the process time corresponding to each target experimental step executed by each target device resource among all target device resources. The time difference refers to the difference between the execution time and the corresponding process time.

[0111] The object acquisition control module 44 is used to determine whether to acquire the next batch of experimental objects to add to the experimental platform to execute the target experimental process according to the maximum time-consuming difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

[0112] In some embodiments, the difference determination module 43 is specifically used to determine, for each target device resource, a set of execution times and process times for the target device resource to execute the corresponding target experimental steps based on the execution time required for a single experimental subject to complete each target experimental step corresponding to the target device resource, and the process time for the experimental subject to reach the target device resource to execute each target experimental step; calculate the time difference for a single experimental subject to execute each corresponding target experimental step on the target device resource based on each set of execution time and process time; and determine the maximum time difference among all calculated time differences.

[0113] In some embodiments, the difference determination module 43 is specifically used to determine the maximum execution time based on all the predicted and calculated execution times; determine the process time corresponding to the first target device resources executing the first target experimental step corresponding to the maximum execution time; calculate the time difference based on the maximum execution time and the determined process time as the maximum time difference.

[0114] In some embodiments, the prediction and calculation module 41 is specifically used to periodically predict and calculate the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource according to the status of all existing experimental objects on the experimental platform at a preset frequency.

[0115] In some embodiments, based on the status of all existing experimental objects on the experimental platform, the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource is predicted and calculated, including at least one of the following influencing factors: the time required for a single experimental object to execute the target experimental step at each target device resource; the average remaining time required for the current target experimental step that the target device resource is currently executing on at least one experimental object; and the historical average waiting time of a single experimental object waiting to execute the target experimental step at the target device resource.

[0116] In some embodiments, the experimental object processing control apparatus may further include an execution time module for calculating the time required for a single experimental object to execute a target experimental step at each of the target device resources.

[0117] The execution time module is specifically used to obtain the number of experimental objects that have not reached the target device resources to execute the target experimental steps, and the number of experimental objects that have reached the target device resources but have not executed the corresponding target experimental steps; determine the time for a single experimental object to execute the corresponding target experimental steps at the target device resources; determine the total time required for the target device resources to execute the corresponding target experimental steps based on the number of experimental objects that have not reached the target device resources to execute the target experimental steps, the number of experimental objects that have reached the target device resources but have not executed the corresponding target experimental steps, and the time for a single experimental object to execute the corresponding target experimental steps at the target device resources; obtain the number of target device resources to execute the target experimental steps and the number of carriers for each target device resource; calculate the time required for a single experimental object to execute the target experimental steps at each of the target device resources based on the total time required for the target device resources to execute the corresponding target experimental steps, the number of target device resources to execute the target experimental steps and the number of carriers for each target device resource.

[0118] Another aspect of the embodiments of the present application provides a high-throughput experimental platform verification device. Figure 5 The schematic diagram of the structure of a high-throughput experimental platform verification device provided by the embodiment of the present application is shown as follows: Figure 5 As shown, the high-throughput experimental platform verification device 50 may include the following modules.

[0119] The information acquisition module 51 is used to acquire the device resource configuration information of the current experiment platform and the experiment configuration information of the target experiment process associated with the device resource configuration information.

[0120] The object acquisition module 52 is used to acquire the experimental objects and execute the target experimental process according to the device resource configuration information and the experimental configuration information. When acquiring the next batch of target experimental objects, any one of the experimental object processing control methods provided in the embodiments of the present application is used to determine the time for acquiring the next batch of experimental objects; repeat the above process and complete the processing of all target experimental objects.

[0121] The information adjustment module 53 is used to adjust the device resource configuration information and the corresponding experiment configuration information according to the completion status of the experiment.

[0122] It should be understood that the specific features, operations and details described hereinabove with respect to the method of the present application may also be similarly applied to the device and system of the present application, or vice versa. In addition, each step of the method of the present application described above may be performed by a corresponding component or unit of the device or system of the present application.

[0123] It should be understood that each module / unit of the device of the present application can be implemented in whole or in part by software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of the electronic device in the form of hardware or firmware, or independent of the processor, or stored in the memory of the electronic device in the form of software for the processor to call to execute the operations of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0124] In another aspect of the present application, an electronic device is provided. Figure 6 The schematic structural diagram of an electronic device provided according to an embodiment of the present application is shown, as Figure 6 shown, the electronic device 60 includes a processor 61, a memory 62, and a program stored on the memory and executable on the processor. When the program is executed by the processor, it implements the steps of the experimental object processing control method provided in any of the above embodiments, or the steps of the high-throughput experimental platform verification method provided in the above embodiments.

[0125] The electronic device 60 can be generally a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities.

[0126] In one embodiment, the electronic device 60 may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the electronic device 60 can be used to provide necessary computing, processing, and / or control capabilities. The memory of the electronic device 60 can include a non-volatile storage medium and an internal memory. The non-volatile storage medium can store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the electronic device 60 can be used to connect and communicate with external devices through a network.

[0127] In another aspect of the present application, a computer-readable storage medium is provided. Instructions are stored on the computer-readable storage medium. When the instructions are executed by the processor, they implement the steps of the experimental object processing control method provided in any of the above embodiments, or the steps of the high-throughput experimental platform verification method provided in the above embodiments.

[0128] Those skilled in the art will appreciate that the method steps of the present application can be completed by instructing related hardware such as electronic devices or processors through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the computer program causes the steps of the present application to be executed when it is executed. Depending on the circumstances, any reference to memory, storage or other media herein may include non-volatile or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0129] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling experimental object processing, characterized in that: Applied to an experimental platform including a plurality of equipment resources, each equipment resource is used to perform at least one experimental step of an experimental process on an experimental object, the method comprising: According to the status of all existing experimental objects on the experimental platform, predict and calculate the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource; wherein the target device resource at least includes a bottleneck device resource; The time taken to obtain a single experimental object to arrive at the target device resource to perform each target experimental step; According to the execution time and process time corresponding to each target experimental step executed by each target device resource among all target device resources, determine the maximum time difference corresponding to the target experimental step executed by the target device resources in the experimental platform, wherein the time difference refers to the difference between the execution time and the corresponding process time; According to the maximum time-consuming difference and a preset threshold, determine whether to obtain the next batch of experimental objects to join the experimental platform to execute the target experimental process; wherein each batch of experimental objects includes at least one experimental object.

2. The method according to claim 1, characterized in that The step of determining the maximum time difference corresponding to the target device resources in the experimental platform executing the target experimental step according to the execution time and the process time corresponding to each target experimental step executed by each target device resource in all the target device resources comprises: For each target device resource, a set of execution times and process times for the target device resource to execute the corresponding target experimental steps is determined based on the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource and the process time required for a single experimental object to reach the target device resource to execute each target experimental step; According to each group of execution time and process time, calculate the time difference of each target experimental step executed by a single experimental object on the target device resource; The maximum time difference is determined from all calculated time differences.

3. The method according to claim 1, characterized in that The step of determining the maximum time difference corresponding to the target device resources in the experimental platform executing the target experimental step according to the execution time and the process time corresponding to each target experimental step executed by each target device resource in all the target device resources comprises: Determine the maximum execution time from among all the predicted and calculated execution times; Determine the process time consumed by the first target device resource to execute the first target experimental step corresponding to the first target device resource corresponding to the maximum execution time consumed; The time difference is calculated according to the maximum execution time and the process time determined in the previous step to serve as the maximum time difference.

4. The method according to claim 1, characterized in that: The predicting and calculating, based on the status of all existing experimental objects on the experimental platform, the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource comprises: The execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource is predicted and calculated periodically according to the status of all existing experimental objects on the experimental platform at a preset frequency.

5. The method according to claim 1, characterized in that The predicting and calculating, based on the status of all existing experimental objects on the experimental platform, the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource includes at least one of the following influencing factors: The time required for a single experimental subject to perform a target experimental step at each of the target device resources; The average remaining time required for the current target experimental step currently being executed by the target device resource on at least one of the experimental objects; The historical average waiting time of a single experimental subject waiting to execute the target experimental step at the target device resource.

6. The method according to claim 5, characterized in that The method comprises: calculating the time required for a single experimental subject to perform a target experimental step at each target device resource; The calculating of the time required for a single experimental subject to perform a target experimental step at each target device resource specifically includes: Obtain the number of experimental objects that have not arrived at the target device resources to execute the target experimental steps, and the number of experimental objects that have arrived at the target device resources but have not executed the corresponding target experimental steps. Determine the time for a single experimental subject to perform a corresponding target experimental step at the target device resource; Determine the total time required for the target device resource to execute the corresponding target experimental step based on the number of experimental subjects who have not reached the target device resource to execute the target experimental step, the number of experimental subjects who have arrived at the target device resource but have not executed the corresponding target experimental step, and the time it takes for a single experimental subject to execute the corresponding target experimental step at the target device resource; Obtaining the number of the target device resources for executing the target experimental steps and the number of boards for each target device resource; Based on the total time required for the target device resources to execute the corresponding target experimental steps, the number of the target device resources that execute the target experimental steps, and the number of boards for each target device resource, the time required for a single experimental object to execute the target experimental steps at each target device resource is calculated.

7. A high-throughput experimental platform verification method, characterized in that: The steps include: Acquire device resource configuration information of the current experimental platform and experimental configuration information of the target experimental process associated with the device resource configuration information; Acquire the experimental objects and execute the target experimental process according to the device resource configuration information and the experimental configuration information, and when acquiring the next batch of experimental objects, use the experimental object processing control method according to any one of claims 1 to 6 to determine the time for acquiring the next batch of experimental objects; The above process is repeated to complete the processing of all target experimental objects, and the device resource configuration information and the corresponding experimental configuration information are adjusted according to the completion status of the experiment.

8. An experimental object processing control device, characterized in that: The device comprises: A prediction and calculation module is used to predict and calculate the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource at each target device resource according to the status of all existing experimental objects on the experimental platform; wherein the target device resources at least include bottleneck device resources; A process time acquisition module, used to acquire the process time consumed by a single experimental object to reach the target device resource to execute each target experimental step; A difference determination module is used to determine the maximum time difference corresponding to the target experimental step executed by the target device resources in the experimental platform according to the execution time and process time corresponding to each target experimental step executed by each target device resource in all target device resources, wherein the time difference refers to the difference between the execution time and the corresponding process time; The object acquisition control module is used to determine whether to acquire the next batch of experimental objects to add to the experimental platform to execute the target experimental process according to the maximum time-consuming difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

9. A high-throughput experimental platform verification device, characterized in that: The device comprises: An information acquisition module, used to acquire the device resource configuration information of the current experimental platform and the experimental configuration information of the target experimental process associated with the device resource configuration information; An object acquisition module, used to acquire experimental objects and execute the target experimental process according to the device resource configuration information and the experimental configuration information, and when acquiring the next batch of target experimental objects, the experimental object processing control method according to any one of claims 1 to 6 is used to determine the time for acquiring the next batch of experimental objects; repeat the above process and complete the processing of all target experimental objects; The information adjustment module is used to adjust the device resource configuration information and the corresponding experiment configuration information according to the completion status of the experiment.

10. An electronic device, characterized in that: It comprises a processor, a memory and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the experimental object processing control method as described in any one of claims 1 to 6 or the steps of the high-throughput experimental platform verification method as described in claim 7 are implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by the processor, the steps of the experimental object processing control method described in any one of claims 1 to 6, or the steps of the high-throughput experimental platform verification method described in claim 7 are implemented.

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