Experimental subject processing control method and apparatus, and device and storage medium

Abstract

An experimental subject processing control method and apparatus, and a device and a storage medium. The method comprises: on the basis of the states of all existing experimental subjects on an experimental platform, predicting and calculating an execution time required for a single experimental subject at each target device resource to complete each target experimental step corresponding to the target device resource; acquiring a process time for a single experimental subject to reach the target device resource to execute each target experimental step; determining a corresponding maximum time difference for the target device resources on the experimental platform to execute the target experimental steps; and on the basis of the maximum time difference and a preset threshold, determining whether to acquire a subsequent batch of experimental subjects and add same to the experimental platform to execute a target experimental flow.

Description

Experimental object processing control methods, devices, equipment, and storage media

[0001] This application claims priority to Chinese Patent Application No. 202411958289.8, filed on December 27, 2024, entitled "Experimental Object Processing Control Method, Apparatus, Device and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of system control technology, and in particular to an experimental object processing control method, apparatus, equipment, and storage medium. Background Technology

[0003] Pharmacokinetics (PK) is the science that studies the absorption, distribution, metabolism, and excretion of drugs in the body. Pharmacokinetic studies are crucial for drug development, efficacy evaluation, and drug safety assessment. With advancements in technology, particularly 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.

[0004] Automated pharmacokinetic systems integrate sample preparation, liquid handling, data acquisition, and analysis to meet various pharmacokinetic detection needs. The efficient operation of such systems relies heavily on experimental subject handling control algorithms. These algorithms determine whether to add experimental subjects to the automated system. Ensuring the orderly and efficient execution of experimental tasks on a platform with existing equipment resources, and improving the system's high throughput, has become a research hotspot. Summary of the Invention

[0005] In view of this, this application provides an experimental object processing control method, apparatus, device, and storage medium.

[0006] A first aspect provides an experimental object processing control method, applied to an experimental platform including multiple device resources, each device resource being used to execute at least one experimental step of an experimental process on the experimental object. The method may include: predicting and calculating the execution time required for a single experimental object at each target device resource to complete each target experimental step corresponding to that 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; obtaining the process time for a single experimental object to reach the target device resource to execute each target experimental step; determining the maximum time difference between the execution time and the corresponding process time for each target device resource executing each target experimental step in the experimental platform, based on the execution time and the process time of each target device resource among all target device resources; wherein the time difference refers to the difference between the execution time and the corresponding process time; and determining whether to add the next batch of experimental objects to the experimental platform to execute the target experimental process based on the maximum time difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

[0007] The second aspect provides a high-throughput experimental platform verification method, which includes: acquiring the current experimental platform's device resource configuration information and the experimental configuration information of the target experimental process associated with the device resource configuration information; acquiring experimental objects and executing the target experimental process according to the device resource configuration information and the experimental configuration information; when acquiring the next batch of experimental objects, using any of the experimental object processing control methods provided in the embodiments of this application to determine the time for acquiring the next batch of experimental objects; repeating the above process to complete the processing of all target experimental objects; and adjusting the device resource configuration information and the corresponding experimental configuration information according to the experimental completion status.

[0008] A third aspect provides an experimental object processing control device, comprising: a prediction calculation module, configured to predict and calculate the execution time required for a single experimental object at each target device resource to complete each target experimental step corresponding to that target device resource, based on the status of all existing experimental objects on the experimental platform; wherein the target device resource includes at least a bottleneck device resource; a process time acquisition module, configured to acquire the process time for each experimental object to reach the target device resource to execute each target experimental step; a difference determination module, configured to determine the maximum time difference corresponding to the execution of the target experimental steps by the target device resources on the experimental platform based on the execution time and the process time of each target device resource executing each target experimental step, wherein the time difference refers to the difference between the execution time and the corresponding process time; and an object acquisition control module, configured to determine whether to acquire the next batch of experimental objects to be added to the experimental platform to execute the target experimental process based on the maximum time difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

[0009] The fourth aspect provides a high-throughput experimental platform verification device, which includes: an information acquisition module, used to acquire the current experimental platform's equipment resource configuration information and the experimental configuration information of the target experimental process associated with the equipment resource configuration information; an object acquisition module, used to acquire experimental objects and execute the target experimental process according to the equipment resource configuration information and the experimental configuration information, and when acquiring the next batch of target experimental objects, to determine the time for acquiring the next batch of experimental objects using any of the experimental object processing control methods provided in the embodiments of this application; repeating the above process and completing the processing of all target experimental objects; and an information adjustment module, used to adjust the equipment resource configuration information and the corresponding experimental configuration information according to the experimental completion status.

[0010] The fifth aspect provides an electronic device that may include a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the steps of any of the experimental object processing control methods provided in the embodiments of this application, or the steps of the high-throughput experimental platform verification method provided in the embodiments of this application.

[0011] A sixth aspect provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of any of the experimental object processing control methods provided in the embodiments of this application, or the steps of the high-throughput experimental platform verification method provided in the embodiments of this application.

[0012] In summary, the experimental object processing control method, apparatus, equipment, and storage medium provided in this application have the following beneficial effects:

[0013] This application embodiment predicts and calculates the execution time required for a single experimental object to complete each target experimental step corresponding to that 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 for a single experimental object to arrive at the target device resource to execute each target experimental step; based on all execution times and their corresponding process times, it determines the maximum time difference in the experimental platform; and based on the maximum time difference and a preset threshold, it determines whether to acquire the next batch of experimental objects to add to the experimental platform to execute the target experimental process, thereby dynamically determining the acquisition time of the next batch of experimental objects, thus ensuring that each device resource can operate efficiently and orderly in the experimental platform (especially bottleneck resources), thereby enabling the experimental platform to achieve high throughput and ensuring that device resources are used most rationally, while avoiding overload or idle phenomena, and enhancing the stability and reliability of the system. Attached Figure Description

[0014] To more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 shows a schematic diagram of the structure of an experimental platform provided in an embodiment of this application;

[0016] Figure 2 shows a flowchart of an experimental object processing control method provided in an embodiment of this application;

[0017] Figure 3 shows a flowchart illustrating the high-throughput experimental platform verification method provided in an embodiment of this application;

[0018] Figure 4 shows a schematic diagram of the structure of an experimental object processing control device provided in an embodiment of this application;

[0019] Figure 5 shows a schematic diagram of the structure of a high-throughput experimental platform verification device provided in an embodiment of this application;

[0020] Figure 6 shows a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] To make the above and other features and advantages of this application clearer, the application is further described below with reference to the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explanation to those skilled in the art, and are exemplary only, not restrictive.

[0022] In the following description, numerous specific details are set forth to provide a thorough understanding of this application. However, it will be apparent to those skilled in the art that the specific details are not required to practice this application. In other instances, well-known steps or operations have not been described in detail to avoid obscuring this application.

[0023] This application provides an experimental platform. Figure 1 shows a schematic diagram of the structure of an experimental platform provided in this application embodiment. As shown in Figure 1, the experimental platform 10 includes multiple device resources 11 and a control system 12.

[0024] In some embodiments of this application, each device resource 11 is used to perform at least one experimental step of an experimental process on the experimental object. Each batch of experimental objects added to the experimental platform 10 can consist of one or more experimental objects. One experimental object corresponds to one experimental process, and one experimental process corresponds to multiple experimental steps.

[0025] Each device resource 11 communicates with the control system 12, meaning that the control system 12 can interact with each device resource 11 through the calling interface of each device resource 11.

[0026] 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.

[0027] In some embodiments of this application, the equipment resource 11 may include, but is not limited to, experimental equipment such as micro-pipettes, liquid workstations, sealing machines, refrigerators, rotary plate stations, capping machines, humidity-controlled refrigerators, shakers, centrifuges, film peeling machines, PCR instruments, solid-phase extraction instruments, liquid separators, marking machines, and barcode scanners.

[0028] Another aspect of this application provides an experimental object processing control method applied to the experimental platform shown in Figure 1. This experimental object processing control method can be executed by an experimental object processing control device, which can be configured on the control system 12.

[0029] Figure 2 shows a flowchart of an experimental object processing control method provided in an embodiment of this application. As shown in Figure 2, the experimental object processing control method may include the following steps.

[0030] 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 that target device resource at each target device resource.

[0031] The experimental objects involved in this application embodiment can be experimental boards or experimental tubes. The status of existing experimental objects refers to the experimental steps that the experimental objects already added to the experimental platform are currently in. The target experimental step can refer to the experimental step in the experimental process that the experimental object has not yet been executed, and the target device resource can refer to user-defined device resources or system-defined device resources. The target experimental step can be an experimental step corresponding to the target device resource. Different target device resources correspond to different one or more target experimental steps. For example, when steps 3 and 5 in the experimental process are both centrifugation steps, the target device resource is a centrifuge device, and one target experimental step corresponding to the centrifuge device can be step 3 (centrifugation step), and the other corresponding target experimental step can be step 5 (centrifugation step).

[0032] 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, as described in the embodiments of this application, can refer to: the total time required for all experimental objects on the experimental platform that have not yet executed the corresponding target experimental steps to complete the target experimental steps at the corresponding target device resource in the future, and the number of experimental objects (i.e., the number of boards in the following text) targeted by each target experimental step executed by the target device resource. The execution time is equal to the ratio of the total time to the number of boards. In other words, the execution time can be understood as the unit time required for the target device resource to perform the target experimental operation for 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.

[0033] In one embodiment of this application, the experimental object processing control device can predict and calculate the execution time required for each target experimental step that a single experimental object is about to complete based on the resources of each target device on the experimental platform, according to the prediction model.

[0034] It should be noted that each type of target device resource has a corresponding execution time for each target experimental step.

[0035] For example, three different types of equipment resources (equipment resource A, equipment resource B, and equipment resource C) on the experimental platform are used as target equipment resources. The three equipment resources correspond to three experimental steps. The experimental platform already has four experimental objects. When none of the four experimental objects have 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 control device calculates the execution time required for equipment resource A for a single experimental object to complete the target experimental step, the execution time required for equipment resource B for a single experimental object to complete the target experimental step, and the execution time required for equipment resource C for a single experimental object to complete the target experimental step.

[0036] For example, consider two different types of equipment resources (equipment resource A and equipment resource B) on an experimental platform as target equipment resources. The experiment is performed first on equipment resource A, followed by the experiment on equipment resource B. Experiment object 1 has passed through equipment resource A but has not yet reached equipment resource B, while experiment object 2 has not yet passed through equipment resource A. The experiment object processing control device calculates the execution time required for a single experiment object to complete the target experimental step at equipment resource B based on the states of experiment object 1 and experiment object 2; and calculates the execution time required for a single experiment object at equipment resource A to complete the target experimental step based on the state of experiment object 2.

[0037] In this embodiment of the application, if two device resources of the same type perform the same experimental step, when calculating the execution time, the two device resources are regarded as a target device resource, that is, the experimental object processing control device calculates an execution time for the two device resources.

[0038] For example, if equipment resources C and D are the same type of equipment resources and perform the same experimental step to achieve the throughput of that experimental step, such as both performing step 4, the experimental object's processing control device calculates the execution time required for equipment resources C and D to jointly complete step 4 on a single experimental object before the experimental object has performed step 4.

[0039] In this embodiment of the application, if a device resource performs two experimental steps, it is necessary to calculate the execution time of the device resource for each experimental step.

[0040] For example, when device resource C is executing experimental steps 3 and 5, and the experimental object has not yet executed the two experimental steps corresponding to device resource C, the experimental object processing control device calculates the execution time required when device resource C is about to complete experimental step 3 on a single experimental object, and calculates the execution time required when device resource C is about to complete experimental step 5 on a single experimental object.

[0041] In some embodiments, the target equipment resources may include bottleneck equipment resources in the experimental platform. Bottleneck equipment resources include equipment resources that limit the throughput capacity of the experimental platform, such as centrifuges and shakers.

[0042] S22, Obtain the time taken for a single experimental object to reach the target device resource to execute each target experimental step.

[0043] The process time involved in the embodiments of this application can refer to the time consumed by a single experimental object from the moment it enters the starting position of the experimental platform to the moment it reaches 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.

[0044] In one embodiment of this application, the process time can be calculated based on existing parameters. For example, the experimental object processing control device can accumulate the execution time of the experimental steps of each device resource before the target device resource and the transportation time between device resources to obtain the process time to reach the target device resource.

[0045] For example, the execution time of the experimental steps by equipment resource A is 5 minutes and the transportation time to equipment resource B is 2 minutes. The execution time of the experimental steps by equipment resource B is 3 minutes and the transportation time to equipment resource C is 1 minute. The process time for a single experimental object to reach equipment resource C is 5+2+3+1=11 minutes.

[0046] In another embodiment, the process time can be obtained through actual measurement on the experimental platform. At the start of the experiment, a batch of experimental samples is taken for testing. The experimental object processing control device records the time it takes for a batch of experimental samples to reach each device resource, thus obtaining the process time for a single experimental object to reach the target resource. Here, the process time only represents the time required to reach the target device resource where the target experimental step is performed.

[0047] S23, based on the execution time and process time of each target experimental step performed by each target device resource in the experimental platform, determine the maximum time difference of the target experimental steps performed by the target device resources.

[0048] The time difference refers to the difference between the execution time and the corresponding process time. Specifically, the time difference equals the execution time minus the process time, and the time difference may be positive or negative depending on the actual situation.

[0049] The time difference involved in the embodiments of this application refers to the difference between the execution time and the corresponding process time. The maximum time difference corresponding to the execution of the target experimental step by the target device resources in the experimental platform can be understood as the maximum value of the time difference corresponding to the execution of each target experimental step by each target device resource in the current experimental platform.

[0050] In one embodiment of this application, the experimental object processing control device calculates the execution time and process time corresponding to each target experimental step performed by each target device resource on a single experimental object. Based on the time difference between the execution time and process time corresponding to each target experimental step for a single experimental object, the maximum time difference for a certain target device resource in the experimental platform to perform the target experimental step at the current moment is determined.

[0051] For example, an experimental platform contains three experimental objects, and target device resources A, B, and C. The time difference for target device resource A to execute experimental step A on a single experimental object is 5 minutes, for target device resource B it's 6 minutes, and for target device resource C it's 7 minutes. In this case, the maximum time difference for each target device resource to execute the target experimental step on a single experimental object is 7 minutes.

[0052] S24. Based on the maximum time difference and the preset threshold, determine whether to acquire the next batch of experimental subjects to add to the experimental platform to execute the target experimental process; wherein, each batch of experimental subjects includes at least one experimental subject.

[0053] The preset threshold involved in this application embodiment is a pre-settable parameter. This threshold is determined based on the experimental subjects and the experimental procedures performed on them. The size of this threshold mainly affects the experimental effect of the experimental subjects on the experimental platform. Optionally, the preset threshold can be 5 minutes. In one embodiment of this application, the experimental subject processing control device can determine whether adding the next batch of experimental subjects to the experimental platform to execute the target experimental procedure will affect the experimental effect based on the comparison result between the maximum time difference and the preset threshold, thereby determining whether to obtain the next batch of experimental subjects from the experimental platform.

[0054] Specifically, if the maximum time difference is less than a preset threshold, adding the next batch of experimental subjects will not affect the experimental results. This confirms that the experimental platform's equipment resources support adding the next batch of experimental subjects, thus allowing the acquisition of the next batch of experimental subjects. The next batch of experimental subjects can then be added to the experimental platform to execute the target experimental process. The maximum time difference being less than the preset threshold includes cases where the maximum time difference is negative.

[0055] If the maximum time difference exceeds the preset threshold, adding the next batch of experimental subjects will affect the experimental results. Therefore, it is determined that the experimental platform's equipment resources do not support adding the next batch of experimental subjects, and thus it is determined that it is not possible to obtain the next batch of experimental subjects.

[0056] In the above 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 that target device resource at each target device resource is predicted and calculated; the process time for a single experimental object to arrive at the target device resource to execute each target experimental step is obtained; based on all execution times and their corresponding process times, the maximum time difference in the experimental platform is determined; based on the maximum time difference and a preset threshold, it is determined whether to acquire the next batch of experimental objects to add to the experimental platform to execute the target experimental process. Thus, the acquisition time of the next batch of task objects can be determined based on 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 operate efficiently and orderly in the experimental platform, realizing a high-throughput experimental processing flow and ensuring that device resources are used most reasonably, while avoiding overload or idleness, and enhancing the stability and reliability of the system.

[0057] In some embodiments, the number of experimental subjects in the next batch can be limited by the board capacity of the bottleneck device resource of the experimental platform. For example, if the bottleneck device resource in the experimental platform is a centrifuge, and the minimum board capacity of the centrifuge is 2, then the number of experimental subjects in each batch can be 2. The board capacity can be understood as the number of experimental subjects carried by the target device resource in a single execution of the target step.

[0058] In some embodiments, S23, determining the maximum time difference for each target experimental step performed by a target device resource in the experimental platform based on the execution time and process time corresponding to each target experimental step performed by each target device resource in all target device resources includes: for each target device resource, determining a set of execution time and process time for the target device resource to perform the corresponding target experimental step based on the execution time required for a single experimental object to complete each target experimental step performed by the target device resource, and the process time for a single experimental object to arrive at the target device resource to perform each target experimental step; calculating the time difference for a single experimental object to perform each target experimental step performed by the target device resource based on each set of execution time and process time; and determining the maximum time difference among all calculated time differences.

[0059] In one embodiment of this application, the experimental object processing control device calculates the execution time required for each resource device to complete each 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. It then extracts a set of execution time and process time corresponding to each target experimental step performed by each target device resource. By subtracting the process time from each set of execution time, the device obtains the time difference for each target experimental step for each target device resource. Finally, it compares the magnitude of all time differences to determine the maximum time difference.

[0060] In one embodiment of this application, the maximum time difference can be obtained by the following formula: T b =max(T) i -T si (1)

[0061] Among them, T b T represents the maximum time difference. i T represents the execution time for the target device resources to perform the i-th target experimental step, where i represents the sequence number of the target experimental step. si This represents the process time corresponding to the i-th objective experimental step.

[0062] For example, the experimental platform contains target device resources A, B, and C. Target device resource A takes 8 minutes to execute experimental step A on a single experimental object, with the process taking 1 minute, resulting in a time difference of 7 minutes. Target device resource B takes 12 minutes to execute experimental step B on a single experimental object, with the process taking 10 minutes, resulting in a time difference of 2 minutes. Target device resource C takes 15 minutes to execute experimental step C on a single experimental object, with the process taking 6 minutes, resulting in a time difference of 9 minutes. In this case, the maximum time difference for each target device resource executing the target experimental step on a single experimental object on the experimental platform is 9 minutes.

[0063] In the above embodiments, by calculating the time difference for 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 for a certain target device resource in the current experimental platform can be accurately determined, providing a decision-making basis for the acquisition of the next batch of experimental objects.

[0064] In some embodiments, S23, based on the execution time and process time of each target device resource in all target device resources executing each corresponding target experimental step, the maximum time difference corresponding to the execution of the target experimental step by the target device resources in the experimental platform is determined, including: determining the maximum execution time among all the predicted and calculated execution times; executing the corresponding first target experimental step by the first target device resource corresponding to the maximum execution time, and determining the process time corresponding to the execution of the corresponding first target experimental step by the first target device resource; calculating the time difference based on 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 this application embodiment is the target device resource corresponding to the maximum execution time. The first target experimental step is the target experimental step corresponding to the maximum execution time.

[0066] In one embodiment of this application, the experimental object processing control device traverses all execution times, finds the maximum execution time, and determines the first target device resource and the first target experimental step corresponding to the maximum execution time. It then finds the corresponding process time based on the first target device resource and the first target experimental step, and subtracts the maximum execution time from the corresponding process time to obtain the maximum time difference.

[0067] In one embodiment of this application, the maximum time difference can be expressed by the following formula: T b =max(T) i )-T s1 (2)

[0068] Among them, T b T represents the maximum time difference. i Max(T) represents the execution time of the target device resources for executing the i-th target experimental step. i ) represents the maximum execution time among all execution times, i represents the sequence number of the target experimental step, and T s1 This indicates the time taken for the first target equipment resources to execute the corresponding first target experimental step.

[0069] For example, the experimental platform contains target device resources A, B, and C. Target device resource A takes 8 minutes to execute experimental step A on a single experimental object, with a process time of 1 minute. Target device resource B takes 12 minutes to execute experimental step B on a single experimental object, with a process time of 10 minutes. Target device resource C takes 15 minutes to execute experimental step C on a single experimental object, with a process time of 6 minutes. Therefore, the maximum execution time for the target experimental steps by the target device resources on the experimental platform is 15 minutes, and the corresponding process time is 6 minutes. Thus, the maximum time difference for the target experimental steps by the target device resources on the experimental platform is 15 - 6 = 9 minutes.

[0070] In the above embodiment, by first determining the maximum execution time of the experimental platform, and then determining the corresponding process time by executing the corresponding first target experimental step based on the first target device resources corresponding to the maximum execution time, the current maximum time difference of the experimental platform is finally obtained, thereby providing a decision basis for obtaining the next batch of experimental objects.

[0071] In some embodiments, S21, based on the state 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, includes: 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 at each target device resource based on the state of all existing experimental objects on the experimental platform at a preset frequency.

[0072] The preset frequency involved in this application embodiment can be set based on factors such as the operating status of the experimental platform, the number of experimental objects, the availability of target equipment resources, and the real-time requirements of the experimental data. Optionally, the preset frequency can be 5 seconds.

[0073] In one embodiment of this application, when a preset frequency is reached, the experimental object processing control device uses a 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. This process is repeated periodically at 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 to determine whether to add the next batch of experimental objects to the experimental platform based on the prediction results.

[0074] In some embodiments, the step of 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, based on the status of all existing experimental objects on the experimental platform, includes 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 currently being executed by the target device resource on at least one experimental object; and the historical average waiting time for a single experimental object at the target device resource to wait for the execution of the target experimental step.

[0075] The time required for a single experimental object to perform the target experimental step at each target device resource in the embodiments of this application may refer to the ratio of the total time required for all experimental objects that have not yet performed the target experimental step to complete the target experimental step at each target device resource to the amount of the target device resource's carrier board.

[0076] The average remaining time required for the current target experimental step, which is currently being performed on at least one experimental object by the target device resource, can be the average of the remaining times required for multiple experimental objects at the target device resource to complete the current target experimental step. The remaining time can be obtained by subtracting the already executed time from the theoretical execution time of the current target experimental step.

[0077] For example, in a certain device resource, the first experimental subject needs 2 minutes to complete the target experimental step, the second experimental subject needs 2 minutes to complete the target experimental step, the third experimental subject needs 4 minutes to complete the target experimental step, and the fourth experimental subject needs 4 minutes to complete the target experimental step. Then, 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 subject is 3 minutes (the sum of the remaining time divided by the total number of experimental subjects).

[0078] The historical average waiting time of a single experimental subject at the target device resource for executing the target experimental step can reflect the waiting status 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.

[0079] For example, if the previous batch contained four experimental subjects, the first experimental subject waited 2 minutes to start executing the target experimental step, the second experimental subject waited 2 minutes to start executing the target experimental step, the third experimental subject waited 4 minutes to start executing the experimental step, and the fourth experimental subject waited 4 minutes to start executing the experimental step, then it can be determined that the historical average waiting time for the experimental subjects to wait at the target device resource to execute the target experimental step is 3 minutes (total waiting time divided by the total number of experimental subjects).

[0080] In one embodiment of this 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 a 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 for a single experimental object to wait to execute the target experimental step at the target device resource.

[0081] The execution time Ti required for a single experimental object to complete each target experimental step corresponding to that target device resource at each target device resource can be calculated using the following formula.

[0082] Where N is the number of target equipment resources for executing the i-th target experimental step, B is the number of carrier boards for the target equipment resources for executing the i-th target experimental step, and T Zi Let W represent the time required for a single experimental object at the target device resource to execute the i-th target experimental step, E represent the remaining time for the target resource device corresponding to the i-th target experimental step to currently execute the i-th target experimental step, and W represent the remaining time for the target resource device to currently execute the i-th target experimental step. t This represents the historical waiting time for a single experimental object to wait at the target device resource to execute the i-th target experimental step.

[0083] In some embodiments, the experimental object processing control method further includes the step of calculating the time required for a single experimental object to perform a target experimental step at each target device resource.

[0084] The step of calculating the time required for a single experimental object to execute the target experimental step at each target device resource specifically includes: obtaining the number of experimental objects on the experimental platform that have not yet reached the target device resource to execute the target experimental step, and the number of experimental objects that have reached the target device resource but have not executed the corresponding target experimental step; determining the time for a single experimental object to execute the corresponding target experimental step at the target device resource; determining the total time required for the target device resource to execute the corresponding target experimental step based on the number of experimental objects that have not reached the target device resource to execute the target experimental step, the number of experimental objects that have reached the target device resource but have not executed the corresponding target experimental step, and the time for a single experimental object to execute the corresponding target experimental step at the target device resource; obtaining the number of target device resources executing the target experimental step and the number of carrier boards for each target device resource; and calculating the time required for a single experimental object to execute the target experimental step at each target device resource based on the total time required for the target device resource to execute the corresponding target experimental step, the number of target device resources executing the target experimental step, and the number of carrier boards for each target device resource.

[0085] The number of carrier boards involved in this application embodiment can be the number of experimental objects carried by the target device resource in a single instance. The time for a single experimental object to perform the corresponding target experimental step at the target device resource can be determined according to the operation process of the target experimental step.

[0086] The number of experimental objects that did not reach the target device resource to execute the target experimental steps in the embodiments of this application can refer to the number of experimental objects that include the target experimental steps executed by the target device resource in the experimental process, but did not reach the location of the target device resource.

[0087] The number of experimental subjects that have arrived at the target device resource but have not yet performed the corresponding target experimental steps in the embodiments of this application may refer to the number of experimental subjects that have arrived at the location of the target device resource but have not yet started performing the corresponding target experimental steps.

[0088] In one embodiment of this application, the experimental object processing control device can add the number of experimental objects that have not reached the target device resource to execute the target experimental step to the number of experimental objects that have reached the target device resource but have not executed the corresponding target experimental step, to obtain the total number of experimental objects that have not executed the target device resource but are about to execute the target step. Then, the total number of experimental objects that execute the target step at the device resource is multiplied by the time it takes for a single experimental object to execute the corresponding target experimental step at the target device resource, to obtain the total time required for the target device resource on the experimental platform to execute the corresponding target experimental step.

[0089] Since the carrying capacity of each target device resource differs, and multiple identical target device resources can be used in the experimental platform to perform the same experimental step, increasing the throughput of that 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 to perform the corresponding target experimental step on the target device resource by the product of the number of target device resources performing the target experimental step and the carrying capacity of each target device resource. The number of target device resources can be one or more.

[0090] Specifically, the time T required for a single experimental object to perform the target experimental steps at each of the target device resources. zi It can be obtained using the following formula.

[0091] Among them, W i Let R be the number of experimental subjects that did not reach the target equipment resource level to execute the i-th target experimental step, and let A be the execution time of the i-th target experimental step. i N represents the number of experimental objects that have reached the target device resource but have not yet executed the i-th target experimental step, N represents the number of target device resources that have executed the i-th target experimental step, and B represents the number of carrier boards of the target device resources that have executed the i-th target experimental step.

[0092] For example, the experimental platform has two devices that can execute experimental step A, and each device can run three experimental subjects. For each experimental subject, the total time required for the target device to execute the corresponding experimental step A is 24 minutes. Therefore, the time required for a single experimental subject to execute experimental step A at the target device is 24 ÷ (2 × 3) = 4 minutes.

[0093] This application provides another aspect of a high-throughput experimental platform verification method, which can be applied to verify the working effect of the experimental platform shown in Figure 1. This high-throughput experimental platform verification method can be executed by a high-throughput experimental platform verification device, which is configured in the control system shown in Figure 1. Figure 3 shows a flowchart of the high-throughput experimental platform verification method provided in this application embodiment. As shown in Figure 3, the high-throughput experimental platform verification method may include the following steps.

[0094] S31, obtain the current experimental platform's equipment resource configuration information and the experimental configuration information of the target experimental process associated with the equipment resource configuration information.

[0095] The device resource configuration information involved in this application embodiment may include, but is not limited to, device resource type, device resource quantity, device resource status, device resource board size, device resource attribute tags, and connection relationships between device resources. Experiment configuration information may include experimental parameters, required device resource types, experimental step sequence, and the time required for each experimental step.

[0096] In one embodiment of this application, the high-throughput experimental platform verification device configuration can obtain the latest device resource configuration information through the device resource call interface, and can retrieve the experimental configuration information of relevant experimental processes from the experimental process database according to the experimental instructions input by the user.

[0097] S32, acquire experimental objects and execute the target experimental process according to the equipment resource configuration information and experimental configuration information. When acquiring the next batch of target experimental objects, use any of the above-mentioned experimental object processing control methods to determine the time for acquiring the next batch of experimental objects.

[0098] The experimental subjects involved in this application's embodiments can be selected from a sample storage library. In one embodiment of this application, the high-throughput experimental platform verification device can execute the target experimental process on all experimental subjects according to predetermined steps and experimental parameters based on equipment resource configuration information and experimental configuration information. Furthermore, when scheduling the next batch of target experimental subjects to enter the experimental platform to execute the experimental process, the aforementioned experimental subject processing control method is used to determine the time for acquiring the next batch of experimental subjects, thereby reducing waiting time and resource waste.

[0099] S33, Repeat the above process to complete the processing of all target experimental objects, and adjust the equipment resource configuration information and corresponding experimental configuration information according to the experimental completion status.

[0100] The experimental completion status involved in the embodiments of this application may include experimental process data and experimental completion time.

[0101] In one embodiment of this 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. Experimental data is also recorded during the experiment.

[0102] In one embodiment of this application, adjusting the equipment resource configuration information and the corresponding experimental configuration information according to the completion status of the experiment can be specifically performed as follows: after the experiment is completed, compare the experimental completion time with the expected completion time. If the experimental completion time is greater than the expected completion time, then analyze the experimental process data, identify potential problems or improvement points of the experimental platform, and adjust the equipment resource configuration information and the corresponding experimental configuration information to improve the working efficiency of the experimental platform, verify the experimental process, maximize the utilization of experimental resources, and improve experimental efficiency.

[0103] It should be noted that the expected completion time can be set according to user needs, and is not limited in this embodiment.

[0104] This application provides another aspect of an experimental object processing control device. Figure 4 shows a schematic diagram of the structure of an experimental object processing control device provided in this application embodiment. As shown in Figure 4, the experimental object processing control device 40 may include the following modules.

[0105] The prediction 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 resource includes at least the bottleneck device resource.

[0106] The process time acquisition module 42 is used to acquire the process time of a single experimental object arriving at the target device resource to execute each target experimental step.

[0107] The difference determination module 43 is used to determine the maximum time difference for the target experimental steps executed by the target device resources in the experimental platform based on the execution time and process time of each target experimental step executed by each target device resource in all target device resources. The time difference refers to the difference between the execution time and the corresponding process time.

[0108] The object acquisition control module 44 is used to determine whether to acquire the next batch of experimental objects and add them to the experimental platform to execute the target experimental process based on the maximum time difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

[0109] In some embodiments, the difference determination module 43 is specifically used for each target device resource to determine a set of execution time and process time for the target device resource to execute the corresponding target experimental step, 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 for the experimental object to arrive at the target device resource to execute each target experimental step; to calculate the time difference for a single experimental object to execute each target experimental step corresponding to the target device resource based on each set of execution time and process time; and to determine the maximum time difference among all the calculated time differences.

[0110] In some embodiments, the difference determination module 43 is specifically used to determine the maximum execution time among all the predicted and calculated execution times; to execute the corresponding first target experimental step according to the first target device resource corresponding to the maximum execution time, and to determine the process time corresponding to the execution of the corresponding first target experimental step by the first target device resource; and to calculate the time difference based on the maximum execution time and the determined process time as the maximum time difference.

[0111] In some embodiments, the prediction 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, based on the state of all existing experimental objects on the experimental platform at a preset frequency.

[0112] 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 currently being executed by the target device resource on at least one experimental object; and the historical average waiting time for a single experimental object at the target device resource to wait to execute the target experimental step.

[0113] In some embodiments, the experimental object processing control device may further include an execution time module for calculating the time required for a single experimental object to perform a target experimental step at each of the target device resources.

[0114] The execution time module is specifically used to obtain the number of experimental objects in the existing experimental platform that have not yet reached the target device resource to execute the target experimental step, and the number of experimental objects that have reached the target device resource but have not executed the corresponding target experimental step; determine the time for a single experimental object to execute the corresponding target experimental step at the target device resource; based on the number of experimental objects that have not yet reached the target device resource to execute the target experimental step, the number of experimental objects that have reached the target device resource but have not executed the corresponding target experimental step, and the time for a single experimental object to execute the 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; obtain the number of target device resources executing the target experimental step and the amount of board space for each target device resource; based on the total time required for the target device resource to execute the corresponding target experimental step, the number of target device resources executing the target experimental step, and the amount of board space for each target device resource, calculate the time required for a single experimental object to execute the target experimental step at each target device resource.

[0115] Another aspect of this application provides a high-throughput experimental platform verification device. Figure 5 shows a schematic diagram of the structure of a high-throughput experimental platform verification device provided in this application embodiment. As shown in Figure 5, the high-throughput experimental platform verification device 50 may include the following modules.

[0116] The information acquisition module 51 is used to acquire 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.

[0117] The object acquisition module 52 is used to acquire experimental objects and execute the target experimental process according to the equipment resource configuration information and experimental configuration information. When acquiring the next batch of target experimental objects, the time for acquiring the next batch of experimental objects is determined by any experimental object processing control method provided in the embodiments of this application. The above process is repeated and the processing of all target experimental objects is completed.

[0118] The information adjustment module 53 is used to adjust the equipment resource configuration information and the corresponding experimental configuration information according to the completion status of the experiment.

[0119] It should be understood that the specific features, operations, and details described herein with respect to the methods of this application can also be similarly applied to the apparatus and system of this application, or vice versa. Furthermore, each step of the methods of this application described above can be performed by a corresponding component or unit of the apparatus or system of this application.

[0120] It should be understood that the various modules / units of the device of this application can be implemented wholly or partially through software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of the electronic device in hardware or firmware form or independent of the processor, or it can be stored in the memory of the electronic device in software form for the processor to call to execute the operation 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.

[0121] In another aspect, this application provides an electronic device. Figure 6 shows a schematic diagram of the structure of an electronic device according to an embodiment of this application. As shown in Figure 6, the electronic device 60 includes a processor 61, a memory 62, and a program stored in 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.

[0122] The electronic device 60 can be broadly defined as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities.

[0123] In one embodiment, the electronic device 60 may include a processor, memory, network interface, communication interface, etc., connected via 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 may include non-volatile storage media and internal memory. The non-volatile storage media may 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 media. The network interface and communication interface of the electronic device 60 can be used to connect and communicate with external devices via a network.

[0124] In another aspect, this application provides a computer-readable storage medium storing instructions, wherein when executed by a processor, the instructions 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.

[0125] Those skilled in the art will understand that the method steps of this application can be performed by a computer program instructing related hardware, such as electronic devices or processors. The computer program can be stored in a non-transitory computer-readable storage medium, and its execution causes the steps of this application to be performed. Depending on the context, any reference herein to memory, storage, or other media 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 drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0126] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0127] Example

[0128] Example 1: A method for controlling the processing of experimental objects, characterized in that it is applied to an experimental platform including multiple device resources, each device resource being used to execute at least one experimental step of an experimental procedure on the experimental object, the method comprising:

[0129] 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 that target device resource at each target device resource is predicted and calculated; wherein the target device resource includes at least the bottleneck device resource.

[0130] The time taken for a single experimental object to reach the target device resource to perform each target experimental step;

[0131] Based on the execution time and process time of each target experimental step performed by each target device resource in all target device resources, the maximum time difference for the target experimental steps performed by the target device resources in the experimental platform is determined. The time difference refers to the difference between the execution time and the corresponding process time.

[0132] Based on the maximum time difference and a preset threshold, it is determined whether to acquire the next batch of experimental subjects to add to the experimental platform to execute the target experimental process; wherein each batch of experimental subjects includes at least one experimental subject.

[0133] Example 2: The method according to Example 1, characterized in that, determining the maximum time difference for the target experimental steps performed by the target device resources in the experimental platform based on the execution time and process time corresponding to each target experimental step performed by each target device resource in all target device resources, includes:

[0134] For each target device resource, 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 for a single experimental object to arrive at the target device resource to execute each target experimental step, a set of execution time and process time for the target device resource to execute the corresponding target experimental step is determined.

[0135] Based on the execution time and process time of each group, calculate the time difference of a single experimental object in executing each target experimental step on the target device resources;

[0136] The maximum time difference is determined from all the calculated time differences.

[0137] Example 3: The method according to Example 1 or 2, characterized in that, determining the maximum time difference for the target experimental steps performed by the target device resources in the experimental platform based on the execution time and process time corresponding to each target experimental step performed by each target device resource in all target device resources, includes:

[0138] The maximum execution time is determined from all the predicted and calculated execution times.

[0139] Based on the first target device resources corresponding to the longest execution time, execute the corresponding first target experimental step, and determine the process time corresponding to the first target device resources executing the corresponding first target experimental step;

[0140] The time difference is calculated based on the maximum execution time and the process time determined in the previous step, and is used as the maximum time difference.

[0141] Example 4. The method according to any one of Examples 1 to 3, characterized in that, based on the state 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 includes:

[0142] According to a preset frequency, the execution time required for a single experimental object to complete each target experimental step corresponding to the target device resource is predicted and calculated periodically based on the status of all existing experimental objects on the experimental platform.

[0143] Example 5: The method according to any one of Examples 1 to 4, characterized in that, based on the state 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 that target device resource at each target device resource includes at least one of the following influencing factors:

[0144] The time required for a single experimental object to perform the target experimental steps at each of the target device resources;

[0145] The target device resources are currently performing the current target experimental step on at least one of the experimental objects, with the average remaining time required.

[0146] The historical average waiting time for a single experimental object to perform the target experimental step at the target device resource.

[0147] Example 6. The method according to any one of Examples 1 to 5, characterized in that the method includes: calculating the time required for a single experimental object to perform the target experimental steps at each of the target device resources;

[0148] The calculation of the time required for a single experimental object to perform the target experimental steps at each of the target device resources specifically includes:

[0149] The number of experimental objects that have not yet reached the target device resource to execute the target experimental step among the existing experimental objects of the experimental platform, and the number of experimental objects that have reached the target device resource but have not executed the corresponding target experimental step;

[0150] Determine the time required for a single experimental subject to perform the corresponding target experimental steps at the target device resource;

[0151] Based on the number of experimental subjects that have not reached the target device resource to execute the target experimental step, the number of experimental subjects that have reached the target device resource but have not executed the corresponding target experimental step, and the time for a single experimental subject to execute the corresponding target experimental step at the target device resource, the total time required for the target device resource to execute the corresponding target experimental step is determined.

[0152] Obtain the number of target device resources for performing the target experimental steps and the number of carrier boards for each target device resource;

[0153] Based on the total time required for the target device resources to execute the corresponding target experimental steps, the number of target device resources executing the target experimental steps, and the number of carrier boards for each target device resource, calculate the time required for a single experimental object to execute the target experimental steps at each target device resource.

[0154] Example 7: A method for verifying a high-throughput experimental platform, characterized by comprising the following steps:

[0155] Obtain the current experimental platform's equipment resource configuration information and the experimental configuration information of the target experimental process associated with the equipment resource configuration information;

[0156] The experimental subjects are acquired and the target experimental process is executed according to the equipment resource configuration information and experimental configuration information. When acquiring the next batch of experimental subjects, the time for acquiring the next batch of experimental subjects is determined by the experimental subject processing control method described in any one of Examples 1-6.

[0157] Repeat the above process to complete the processing of all target experimental objects, and adjust the equipment resource configuration information and the corresponding experimental configuration information according to the experimental completion status.

[0158] Example 8: An experimental object processing control device, characterized in that the device comprises:

[0159] The prediction 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, based on the status of all existing experimental objects on the experimental platform; wherein the target device resource includes at least the bottleneck device resource.

[0160] The process time acquisition module is used to acquire the process time of a single experimental object arriving at the target device resource to execute each target experimental step.

[0161] The difference determination module is used to determine the maximum time difference for the target experimental steps performed by the target device resources in the experimental platform based on the execution time and process time corresponding to each target experimental step performed by each target device resource in all target device resources. The time difference refers to the difference between the execution time and the corresponding process time.

[0162] The object acquisition control module is used to determine whether to acquire the next batch of experimental objects and add them to the experimental platform to execute the target experimental process based on the maximum time difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

[0163] Example 9: A high-throughput experimental platform verification device, characterized in that the device comprises:

[0164] The information acquisition module is used to acquire 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;

[0165] The object acquisition module is used to acquire experimental objects and execute the target experimental process according to the equipment resource configuration information and experimental configuration information. When acquiring the next batch of target experimental objects, the time for acquiring the next batch of experimental objects is determined by the experimental object processing control method described in any one of Examples 1-6. The above process is repeated and the processing of all target experimental objects is completed.

[0166] The information adjustment module is used to adjust the equipment resource configuration information and the corresponding experiment configuration information according to the completion status of the experiment.

[0167] Example 10: An electronic device, characterized in that it includes 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, it implements the steps of the experimental object processing control method as described in any one of Examples 1-6, or the steps of the high-throughput experimental platform verification method as described in Example 7.

[0168] Example 11: A computer-readable storage medium, characterized in that instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, they implement the steps of the experimental object processing control method as described in any one of Examples 1-6, or the steps of the high-throughput experimental platform verification method as described in Example 7.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A subject handling control method, characterized by, The method, applied to an experimental platform comprising multiple device resources, each device resource being used to perform at least one experimental step of an experimental procedure on an experimental object, includes: 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 that target device resource at each target device resource is predicted and calculated; wherein the target device resource includes at least the bottleneck device resource. The time taken for a single experimental object to reach the target device resource to perform each target experimental step; Based on the execution time and process time of each target experimental step performed by each target device resource in all target device resources, the maximum time difference for the target experimental steps performed by the target device resources in the experimental platform is determined. The time difference refers to the difference between the execution time and the corresponding process time. Based on the maximum time difference and a preset threshold, it is determined whether to acquire the next batch of experimental subjects to add to the experimental platform to execute the target experimental process; wherein each batch of experimental subjects includes at least one experimental subject.

2. The method of claim 1, wherein, The step of determining the maximum time difference for executing target experimental steps on the experimental platform based on the execution time and process time of each target experimental step for each target device resource among all target device resources includes: For each target device resource, 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 for a single experimental object to arrive at the target device resource to execute each target experimental step, a set of execution time and process time for the target device resource to execute the corresponding target experimental step is determined. Based on the execution time and process time of each group, calculate the time difference of a single experimental object in executing each target experimental step on the target device resources; The maximum time difference is determined from all the calculated time differences.

3. The method of claim 1, wherein, The step of determining the maximum time difference for executing target experimental steps on the experimental platform based on the execution time and process time of each target experimental step for each target device resource among all target device resources includes: The maximum execution time is determined from all the predicted and calculated execution times. Based on the first target device resources corresponding to the longest execution time, execute the corresponding first target experimental step, and determine the process time corresponding to the first target device resources executing the corresponding first target experimental step; The time difference is calculated based on the maximum execution time and the process time determined in the previous step, and is used as the maximum time difference.

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

5. The method of claim 1, wherein, The step of predicting and calculating the execution time required for a single experimental object to complete each target experimental step corresponding to each target device resource at each target device resource, based on the status of all existing experimental objects on the experimental platform, includes at least one of the following influencing factors: The time required for a single experimental object to perform the target experimental steps at each of the target device resources; The target device resources are currently performing the current target experimental step on at least one of the experimental objects, with the average remaining time required. The historical average waiting time for a single experimental object to perform the target experimental step at the target device resource.

6. The method of claim 5, wherein, The method includes the step of calculating the time required for a single experimental object to perform a target experimental step at each of the target device resources; The calculation of the time required for a single experimental object to perform the target experimental steps at each of the target device resources specifically includes: The number of experimental objects that have not yet reached the target device resource to execute the target experimental step among the existing experimental objects of the experimental platform, and the number of experimental objects that have reached the target device resource but have not executed the corresponding target experimental step; Determine the time required for a single experimental subject to perform the corresponding target experimental steps at the target device resource; Based on the number of experimental subjects that have not reached the target device resource to execute the target experimental step, the number of experimental subjects that have reached the target device resource but have not executed the corresponding target experimental step, and the time for a single experimental subject to execute the corresponding target experimental step at the target device resource, the total time required for the target device resource to execute the corresponding target experimental step is determined. Obtain the number of target device resources for performing the target experimental steps and the number of carrier 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 target device resources executing the target experimental steps, and the number of carrier boards for each target device resource, calculate the time required for a single experimental object to execute the target experimental steps at each target device resource.

7. A high-throughput experimental platform validation method, characterized in that, Includes the following steps: Obtain the current experimental platform's equipment resource configuration information and the experimental configuration information of the target experimental process associated with the equipment resource configuration information; The experimental subjects are acquired and the target experimental process is executed according to the equipment resource configuration information and experimental configuration information. When acquiring the next batch of experimental subjects, the experimental subject processing control method according to any one of claims 1-6 is used to determine the time for acquiring the next batch of experimental subjects. Repeat the above process to complete the processing of all target experimental objects, and adjust the equipment resource configuration information and the corresponding experimental configuration information according to the experimental completion status.

8. An experimental subject processing control apparatus characterized by comprising: The device includes: The prediction 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, based on the status of all existing experimental objects on the experimental platform; wherein the target device resource includes at least the bottleneck device resource. The process time acquisition module is used to acquire the process time of a single experimental object arriving at the target device resource to execute each target experimental step. The difference determination module is used to determine the maximum time difference for the target experimental steps performed by the target device resources in the experimental platform based on the execution time and process time corresponding to each target experimental step performed by each target device resource in all target device resources. 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 and add them to the experimental platform to execute the target experimental process based on the maximum time difference and a preset threshold; wherein each batch of experimental objects includes at least one experimental object.

9. A high-throughput experimental platform validation apparatus, characterized by, The device includes: The information acquisition module is used to acquire 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; The object acquisition module is used to acquire experimental objects and execute the target experimental process according to the equipment resource configuration information and experimental configuration information. When acquiring the next batch of target experimental objects, the experimental object processing control method according to any one of claims 1-6 is used to determine the time for acquiring the next batch of experimental objects. The above process is repeated and the processing of all target experimental objects is completed. The information adjustment module is used to adjust the equipment resource configuration information and the corresponding experiment configuration information according to the completion status of the experiment.

10. An electronic device, comprising: It includes 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, it implements the steps of the experimental object processing control method as described in any one of claims 1-6, or the steps of the high-throughput experimental platform verification method as described in claim 7.

11. A computer-readable storage medium, characterized in that, Instructions are stored on the computer-readable storage medium, which, when executed by a processor, implement the steps of the experimental object processing control method as described in any one of claims 1-6, or the steps of the high-throughput experimental platform verification method as described in claim 7.

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