Method for transferring target liquid and liquid transfer device

Abstract

The invention provides a method for transferring target liquid and a liquid transferring device. In a method according to one embodiment, an excitation is applied to a target liquid. Feedback information related to the performance of the target liquid and the surrounding medium under excitation is measured. A set of liquid characteristic parameters related to a characteristic of the target liquid is determined by the computing module based at least on the measured feedback information. And based on the determined set of liquid characteristic parameters, determining motion parameters of the liquid transfer component in the process of driving the liquid transfer component of the liquid transfer device to transfer the target volume of the target liquid by a calculation module. Movement of the liquid transfer member is controlled by the control module based on the determined movement parameter.

Description

Technical Field

[0001] The present disclosure relates to liquid handling required for scientific research and related product development in fields such as life science, environmental science, chemistry, materials science, medicine, etc., and more particularly to a method for transferring a target liquid and a liquid transfer device. Background Art

[0002] In application fields such as life science, environmental science, chemistry, materials science, medicine, pharmacy, food science, etc., in order to conduct scientific research and product development, various liquids need to be processed. One important process is to transfer a specific volume of liquid from one container to another.

[0003] Currently, a commonly used liquid transfer method is to use an automated liquid transfer instrument (hereinafter referred to as a pipetting instrument), such as an automated pipetting workstation, for liquid transfer. When using existing pipetting instruments, an operator needs to manually set pipetting parameters such as pipetting speed on the pipetting instrument. For different types of liquids, the most suitable pipetting parameters are often different. Since whether the pipetting parameters are set appropriately for the liquid to be transferred depends on the operator's level, the existing pipetting instruments cannot stably obtain optimal pipetting effects, including the accuracy, precision, speed, and efficiency of liquid transfer, and the setting of the above parameters greatly affects the reliability, accuracy of scientific research in fields such as life science, environmental science, chemistry, materials science, medicine, etc., and the quality and efficiency of product development. In addition, due to the need to manually set pipetting parameters, the pipetting efficiency is greatly reduced, and the efficiency of using the pipetting instrument for the above scientific research and product development is also greatly reduced. Summary of the Invention

[0004] This section is provided to introduce a selected set of concepts that are further described below in the Detailed Description section in a simplified form. This section is not intended to identify the essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter.

[0005] One objective of the present disclosure is to provide an improved method for transferring a target liquid and a liquid transfer device. In particular, one of the technical problems to be solved by the present disclosure is that existing pipetting instruments cannot stably obtain high-precision and high-accuracy pipetting effects and result in low efficiency and lack of intelligent features due to the need for operators to manually set pipetting parameters such as pipetting speed according to manual experience for different properties of liquids, thus seriously affecting the quality and efficiency of product development in fields such as life science, medicine, chemistry, materials science, etc.

[0006] According to a first aspect of the present disclosure, a method for transferring a target liquid is provided. The method includes: applying an excitation to the target liquid. The method further includes: measuring feedback information related to the performance of the target liquid and the surrounding medium under the excitation. The method further includes: determining, by a calculation module, a set of liquid characteristic parameters related to the characteristics of the target liquid based at least on the measured feedback information. The method further includes: determining, by the calculation module, the motion parameters of the liquid transfer component of the liquid transfer device during the process of driving the liquid transfer component to transfer a target volume of the target liquid based on the determined set of liquid characteristic parameters. The method further includes: controlling, by a control module, the motion of the liquid transfer component based on the determined motion parameters.

[0007] According to the above first aspect, since the motion parameters of the liquid transfer component are determined based on the set of liquid characteristic parameters, and the set of liquid characteristic parameters is obtained from measurement, it is possible to automatically determine the motion parameters suitable for the liquid to be transferred without relying on an operator, improving the accuracy, precision and efficiency of liquid transfer. Since this method integrates sensing, calculation and driving, it is an intelligent method.

[0008] In an embodiment of the present disclosure, the method further includes: measuring the environmental parameters of the environment where the target liquid is located. Based on the measured feedback information and environmental parameters, the set of liquid characteristic parameters is determined. According to this embodiment, since the set of liquid characteristic parameters reflects the influence of the environment, the motion parameters of the liquid transfer component determined based on the set of liquid characteristic parameters can change with the change of the environment, ensuring the accuracy and precision of liquid transfer regardless of the environment.

[0009] In an embodiment of the present disclosure, the set of liquid characteristic parameters is determined based on the measured feedback information and the parameters of the liquid transfer device.

[0010] In an embodiment of the present disclosure, the excitation applied to the target liquid includes a physical excitation.

[0011] In an embodiment of the present disclosure, the excitation applied to the target liquid includes: a predetermined initial motion trajectory of the liquid transfer device.

[0012] In an embodiment of the present disclosure, the method further includes: measuring feedback information related to the performance of the target liquid and the surrounding medium during the transfer process.

[0013] In an embodiment of the present disclosure, the feedback information includes: information directly or indirectly feedback by the target liquid under the excitation or during the transfer process.

[0014] In one embodiment of the present disclosure, the feedback information includes: information reflecting the excitation, or information reflecting the motion state of the liquid transfer component during the transfer process.

[0015] In one embodiment of the present disclosure, the measured feedback information includes one or more of the following: a signal acquired using a pressure sensor; the power of the motor driving the liquid transfer component; the current signal of the motor driving the liquid transfer component; the voltage signal of the motor driving the liquid transfer component.

[0016] In one embodiment of the present disclosure, the environmental parameters include one or more of the following parameters: liquid temperature; ambient temperature; humidity; and ambient air pressure.

[0017] In one embodiment of the present disclosure, the set of liquid characteristic parameters determined based on the measured feedback information includes one or more of the following: calculating the change amount of the feedback information as a member of the set; calculating the change rate of the feedback information over time as a member of the set; calculating the integral of the feedback information over time as a member of the set; and determining the feedback information as a member of the set.

[0018] In one embodiment of the present disclosure, the set of liquid characteristic parameters determined based on the measured feedback information and environmental parameters includes: determining the environmental parameters as members of the set.

[0019] In one embodiment of the present disclosure, the measured feedback information or environmental parameters are time series that change over time, and the determined set of liquid characteristic parameters is a time series that changes over time.

[0020] In one embodiment of the present disclosure, the liquid transfer component includes one or more of the following components: a piston; a pump; and a moving mechanism for moving a tip or nozzle used to aspirate liquid. The motion parameters include one or more of the following parameters: the speed of the piston; the acceleration of the piston; the rotation speed of the pump; the delay time between two adjacent motions of the piston or pump; the volume of the air column aspirated after aspirating the target volume of the target liquid; the volume of the air column aspirated before starting the transfer process; the speed at which the tip or nozzle enters the liquid surface of the target liquid; the speed at which the tip or nozzle leaves the liquid surface of the target liquid; the identification information of the action of the tip or nozzle contacting the wall of the container where the target liquid is located; and the identification information of the action of rinsing the tip or nozzle.

[0021] In one embodiment of the present disclosure, determining the set of liquid characteristic parameters based on the measured feedback information related to the performance of the target liquid and the surrounding medium during the transfer process includes: re-determining the set of liquid characteristic parameters based on the feedback information measured in real time. Determining the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters includes: re-determining the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters. Controlling the motion of the liquid transfer component based on the determined motion parameters includes: controlling the motion of the liquid transfer component based on the re-determined motion parameters of the liquid transfer component.

[0022] In one embodiment of the present disclosure, the set of liquid characteristic parameters is re-determined based on the feedback information measured in real time and the environmental parameters measured in real time.

[0023] In one embodiment of the present disclosure, the re-determination of the set of liquid characteristic parameters and the re-determination of the motion parameters of the liquid transfer component are performed in real time.

[0024] In one embodiment of the present disclosure, determining the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters includes: determining a set of optimal initial motion parameters suitable for the determined set of liquid characteristic parameters from a pre-determined multiple sets of optimal initial motion parameters.

[0025] In one embodiment of the present disclosure, determining the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters includes: determining a motion strategy model suitable for the determined set of liquid characteristic parameters from a pre-determined multiple motion strategy models; and using the determined motion strategy model to predict the motion parameters of the liquid transfer component with the control target of the liquid transfer component and the determined set of liquid characteristic parameters as inputs.

[0026] In one embodiment of the present disclosure, re-determining the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters includes: using the previously determined motion strategy model to predict the motion parameters of the liquid transfer component with the control target of the liquid transfer component and the re-determined set of liquid characteristic parameters as inputs.

[0027] In one embodiment of the present disclosure, determining a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters includes: determining the liquid characteristic category to which the target liquid belongs based on the determined set of liquid characteristic parameters; and determining a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters based on the determined liquid characteristic category and a motion parameter library that pre-determines the correspondence between different liquid characteristic categories and different sets of optimal initial motion parameters or different motion strategy models.

[0028] In one embodiment of the present disclosure, using the determined set of liquid characteristic parameters as input, a classification model is used to determine the liquid characteristic category. The classification model is configured to be able to determine that liquids that behave similarly under the same excitation belong to the same liquid characteristic category.

[0029] In one embodiment of the present disclosure, the classification model is configured to be able to determine that liquids that behave similarly under the same environmental conditions and under the same excitation belong to the same liquid characteristic category.

[0030] In one embodiment of the present disclosure, the classification model is a trained machine learning model.

[0031] According to a second aspect of the present disclosure, a liquid transfer device is provided. The liquid transfer device includes: a control module, a liquid sensing module, and a calculation module. The control module is configured to apply an excitation to the target liquid. The liquid sensing module is configured to measure feedback information related to the performance of the target liquid and the surrounding medium under the excitation. The calculation module is configured to: determine a set of liquid characteristic parameters related to the characteristics of the target liquid based at least on the measured feedback information; and determine the motion parameters of the liquid transfer component during the process of driving the liquid transfer component to transfer a target volume of the target liquid based on the determined set of liquid characteristic parameters. The control module is further configured to control the motion of the liquid transfer component based on the determined motion parameters.

[0032] According to the above second aspect, since the motion parameters of the liquid transfer component are determined based on the set of liquid characteristic parameters, and the set of liquid characteristic parameters is derived from measurement, it is possible to automatically determine the motion parameters suitable for the liquid to be transferred without relying on an operator, improving the accuracy, precision, and efficiency of liquid transfer. Since this liquid transfer device integrates sensing, calculation, and driving, it is an intelligent device.

[0033] In one embodiment of the present disclosure, the liquid transfer device further includes: an environmental sensing module configured to measure environmental parameters of the environment where the target liquid is located. The calculation module is configured to determine a set of liquid characteristic parameters based on the measured feedback information and environmental parameters. According to this embodiment, since the set of liquid characteristic parameters reflects the influence of the environment, the motion parameters of the liquid transfer component determined based on the set of liquid characteristic parameters can vary with the change of the environment, so as to ensure the accuracy and precision of liquid transfer regardless of the environment.

[0034] In one embodiment of the present disclosure, the calculation module is configured to determine a set of liquid characteristic parameters based on the measured feedback information and the parameters of the liquid transfer device.

[0035] In one embodiment of the present disclosure, the excitation applied to the target liquid includes physical excitation.

[0036] In one embodiment of the present disclosure, the excitation applied to the target liquid includes: a predetermined initial motion trajectory of the liquid transfer device.

[0037] In one embodiment of the present disclosure, the liquid sensing module is configured to measure feedback information related to the performance of the target liquid and the surrounding medium during the transfer process.

[0038] In one embodiment of the present disclosure, the feedback information includes: information directly or indirectly feedback by the target liquid under the excitation or during the transfer process.

[0039] In one embodiment of the present disclosure, the feedback information includes: information reflecting the excitation, or information reflecting the motion state of the liquid transfer component during the transfer process.

[0040] In one embodiment of the present disclosure, the measured feedback information includes one or more of the following: a signal obtained using a pressure sensor; the power of the motor driving the liquid transfer component; the current signal of the motor driving the liquid transfer component; the voltage signal of the motor driving the liquid transfer component.

[0041] In one embodiment of the present disclosure, the environmental parameters include one or more of the following parameters: liquid temperature; environmental temperature; humidity; and environmental air pressure.

[0042] In one embodiment of the present disclosure, the calculation module is configured to determine a set of the liquid characteristic parameters based on the measured feedback information by performing one or more of the following operations: calculating a change amount of the feedback information as a member of the set; calculating a rate of change of the feedback information over time as a member of the set; calculating an integral of the feedback information over time as a member of the set; and determining the feedback information as a member of the set.

[0043] In one embodiment of the present disclosure, the calculation module is configured to determine a set of the liquid characteristic parameters based on the measured feedback information and environmental parameters by determining the environmental parameters as members of the set.

[0044] In one embodiment of the present disclosure, the measured feedback information or environmental parameters are time series that vary over time, and the determined set of the liquid characteristic parameters is a time series that varies over time.

[0045] In one embodiment of the present disclosure, the liquid transfer component includes one or more of the following components: a piston; a pump; and a moving mechanism for moving a tip or nozzle used to aspirate a liquid. The motion parameters include one or more of the following parameters: the speed of the piston; the acceleration of the piston; the rotation speed of the pump; the delay time between two adjacent motions of the piston or the pump; the volume of an air column aspirated after aspirating the target volume of the target liquid; the volume of an air column aspirated before starting the transfer process; the speed at which the tip or nozzle enters the liquid surface of the target liquid; the speed at which the tip or nozzle leaves the liquid surface of the target liquid; identification information of an action in which the tip or nozzle contacts the wall of the container where the target liquid is located; and identification information of an action for rinsing the tip or nozzle.

[0046] In one embodiment of the present disclosure, the calculation module is configured to: re-determine a set of the liquid characteristic parameters based on the feedback information measured in real time and related to the performance of the target liquid and the surrounding medium during the transfer process; and re-determine the motion parameters of the liquid transfer component based on the re-determined set of the liquid characteristic parameters. The control module is configured to control the motion of the liquid transfer component based on the re-determined motion parameters of the liquid transfer component.

[0047] In one embodiment of the present disclosure, the calculation module is configured to re-determine a set of the liquid characteristic parameters based on the feedback information measured in real time and the environmental parameters measured in real time.

[0048] In one embodiment of the present disclosure, the computing module is configured to re-determine the set of liquid characteristic parameters and re-determine the motion parameters of the liquid transfer component in real time.

[0049] In one embodiment of the present disclosure, the computing module is configured to determine the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters by performing the following operations: determining a set of optimal initial motion parameters suitable for the determined set of liquid characteristic parameters from a pre-determined multiple sets of optimal initial motion parameters.

[0050] In one embodiment of the present disclosure, the computing module is configured to determine the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters by performing the following operations: determining a motion strategy model suitable for the determined set of liquid characteristic parameters from a pre-determined multiple motion strategy models; and using the determined motion strategy model to predict the motion parameters of the liquid transfer component with the control objective of the liquid transfer component and the determined set of liquid characteristic parameters as inputs.

[0051] In one embodiment of the present disclosure, the computing module is configured to re-determine the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters by performing the following operations: using the previously determined motion strategy model to predict the motion parameters of the liquid transfer component with the control objective of the liquid transfer component and the re-determined set of liquid characteristic parameters as inputs.

[0052] In one embodiment of the present disclosure, the computing module is configured to determine a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters by performing the following operations: determining the liquid characteristic category to which the target liquid belongs based on the determined set of liquid characteristic parameters; and determining a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters based on the determined liquid characteristic category and a pre-determined motion parameter library reflecting the correspondence between different liquid characteristic categories and different multiple sets of optimal initial motion parameters or different motion strategy models.

[0053] In one embodiment of the present disclosure, the computing module is configured to use a classification model to determine the liquid characteristic category with the determined set of liquid characteristic parameters as an input. The classification model is configured to be able to determine liquids that exhibit similar behavior under the same excitation as belonging to the same liquid characteristic category.

[0054] In one embodiment of the present disclosure, the classification model is configured to be able to determine liquids that exhibit similar behavior under the same environmental conditions and under the same excitation as belonging to the same liquid characteristic category.

[0055] In one embodiment of the present disclosure, the classification model is a trained machine learning model.

[0056] According to the third aspect of the present disclosure, a computer-readable storage medium is provided. Program instructions are stored on the computer-readable storage medium. When executed by at least one processor, the program instructions cause the at least one processor to perform the operations of the calculation module or the control module according to the second aspect described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the technical solutions of the present disclosure, the accompanying drawings of the embodiments will be briefly introduced below. Apparently, the schematic structural diagrams in the following drawings are not necessarily drawn to scale, but present each feature in a simplified form. Moreover, the following drawings only relate to some embodiments of the present disclosure and do not limit the present disclosure.

[0058] Figure 1A is a flowchart showing a method for transferring a target liquid according to the first embodiment of the present disclosure;

[0059] Figure 1B is a flowchart showing a method for transferring a target liquid according to the second embodiment of the present disclosure;

[0060] Figure 2 is a schematic diagram showing an exemplary scenario of the transfer of the target liquid;

[0061] Figure 3A and Figure 3B is for illustrating Figure 1A and Figure 1B a flowchart of the method;

[0062] Figure 4 is for illustrating Figure 3A and Figure 3B a flowchart of the method;

[0063] Figure 5A and Figure 5B are graphs showing liquid characteristic parameters obtained in response to excitation for multiple liquids;

[0064] Figure 6A is a flowchart showing a method for transferring a target liquid according to the third embodiment of the present disclosure;

[0065] Figure 6B is a flowchart showing a method for transferring a target liquid according to the fourth embodiment of the present disclosure;

[0066] Figure 7 is for illustrating Figure 6A and Figure 6B a flowchart of the method; and

[0067] Figure 8 is a block diagram showing a liquid transfer device according to an embodiment of the present disclosure. Detailed implementation manners

[0068] For the purpose of explanation, some details are set forth in the following description to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to those skilled in the art that the embodiments can be implemented without these specific details or with equivalent configurations.

[0069] As described above, when using an existing pipetting instrument for liquid transfer, an operator needs to manually set pipetting parameters such as pipetting speed on the pipetting instrument. Since whether the pipetting parameters are suitable for the liquid to be transferred depends on the level of the operator, the existing pipetting instrument cannot stably obtain the optimal pipetting effect. Moreover, since the pipetting parameters need to be manually set, the pipetting efficiency is reduced, and the pipetting precision and accuracy for various liquids with different properties cannot be guaranteed. Due to relying on manual experience, it also cannot support the intelligent scenario of continuous 24-hour operation.

[0070] The present disclosure provides an improved method for transferring a target liquid and a liquid transfer device. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0071] I. Method for Transferring Target Liquid

[0072] First Embodiment

[0073] Figure 1A is a flowchart showing a method for transferring a target liquid according to the first embodiment of the present disclosure. For example, the target liquid may be a liquid that needs to be at least partially transferred from one container to another container for purposes such as scientific research and product development. The transfer of the target liquid may cover the entire process from the aspiration of a specific volume (which may also be referred to as the target volume) of the target liquid to the discharge of the target liquid of the specific volume, or may only refer to a part of the entire process (for example, the aspiration process or the discharge process).

[0074] As Figure 1AAs shown, the method includes steps 102, 104, 108, 110, and 112. In step 102, an excitation is applied to the target liquid. In step 104, feedback information related to the performance of the target liquid and the surrounding medium under the excitation is measured. In step 108, a set of liquid characteristic parameters related to the characteristics of the target liquid is determined by a calculation module based at least on the measured feedback information. In step 110, based on the determined set of liquid characteristic parameters, the calculation module determines the motion parameters of the liquid transfer component during the process of driving the liquid transfer component of the liquid transfer device to transfer a target volume of the target liquid. In step 112, the control module controls the motion of the liquid transfer component based on the determined motion parameters.

[0075] Using Figure 1A the method, since the motion parameters of the liquid transfer component are determined based on a set of liquid characteristic parameters, and the set of liquid characteristic parameters is derived from measurement, it is possible to automatically determine the motion parameters suitable for the liquid to be transferred without relying on an operator, improving the accuracy, precision, and efficiency of liquid transfer. Since this method integrates sensing, calculation, and driving, it is an intelligent method.

[0076] Second Embodiment

[0077] The inventors of the present application have found that Figure 1A the method can achieve good pipetting effects in some cases (for example, when the environmental conditions of the usage environment of the liquid transfer device do not change much), but cannot achieve optimal pipetting effects in some cases. After research, the inventors of the present application have found that Figure 1A the reason why the method cannot achieve optimal pipetting effects in some cases is that it does not realize the influence of environmental conditions on liquid transfer. For example, the motion parameters that can achieve good pipetting effects in a city located in a low-altitude area cannot achieve good pipetting effects in a city located in a high-altitude area. This is because the air pressure in the high-altitude area is lower than that in the low-altitude area, and the environmental air pressure affects the transfer of the liquid, as will be described in detail later. Another example is that the motion parameters that can achieve good pipetting effects in a southern city in China cannot achieve good pipetting effects in a northern city in China. This is because the humidity in the northern city is lower than that in the southern city, and the environmental humidity also affects the transfer of the liquid, as will be described in detail later.

[0078] It is precisely realizing this problem that the inventors of the present application propose a method for transferring a target liquid according to the second embodiment of the present disclosure. Figure 1B It is a flowchart showing the method. As Figure 1BAs shown, the method includes steps 102, 104, 106, 109, 110, and 112. In step 102, an excitation is applied to the target liquid. In step 104, feedback information related to the performance of the target liquid and the surrounding medium under the excitation is measured. In step 106, environmental parameters of the environment where the target liquid is located are measured. In step 109, a set of liquid characteristic parameters related to the characteristics of the target liquid is determined by a calculation module based at least on the measured feedback information and environmental parameters. In step 110, based on the determined set of liquid characteristic parameters, the calculation module determines the motion parameters of the liquid transfer component during the process of driving the liquid transfer component of the liquid transfer device to transfer a target volume of the target liquid. In step 112, the control module controls the motion of the liquid transfer component based on the determined motion parameters.

[0079] Using Figure 1B the method of Figure 1A In addition to being able to obtain the effects of the method of

[0080] Next, each step of Figure 1A and Figure 1B will be described in detail.

[0081] Step 102

[0082] In step 102, an excitation is applied to the target liquid. For example, the excitation may include a physical excitation. As a first option, the physical excitation may be a predetermined initial motion trajectory of a piston of the liquid transfer device. For this first option, as a first example thereof, the predetermined initial motion trajectory may be that the piston is driven to suck a small amount of the target liquid and discharge it. Here, the meaning of "a small amount" is less than a predetermined percentage (e.g., 10%) of the target volume. For ease of understanding, Figure 2An exemplary scenario showing the transfer of a target liquid is presented. In this exemplary scenario, the target liquid 22 is located in the target container 21, and the liquid transfer device is a pipetting head, which at least includes a sleeve 23, a piston 25, and a motor 26. The sleeve 23 is used to connect with the pipette tip 24 (for example, the sleeve 23 can be hermetically sleeved in the pipette tip 24). The pipette tip 24 is used to contact the target liquid 22 to aspirate the target liquid 22. The pipette tip 24 can be a disposable component or a component that can be reused multiple times. The piston 25 is connected to the sleeve 23. The motor 26 is used to drive the piston 25 to move in the sleeve 23, thereby aspirating the target liquid 22 through the pipette tip 24, or discharging the target liquid 22, or aspirating and discharging the target liquid 22, where "aspirating and discharging" can also be referred to as "pipetting". The motor 26 can be connected to the piston 25 through a piston rod or other suitable transmission means.

[0083] As Figure 2 shown, the pipetting head may further include: a pipeline 27 for guiding a small amount of air inside the sleeve 23 to the outside, and a pressure sensor 28 for measuring the pressure of the air guided by the pipeline 27. For example, the pressure sensor 28 can be a differential pressure sensor that measures the difference between the air pressure in the pipeline 27 and the ambient atmospheric pressure. The use of the pressure sensor 28 will be described later with reference to step 104.

[0084] It should be noted that Figure 2 the exemplary scenario shown is only for illustrative purposes. As another example, the liquid transfer device can be a pipetting workstation including a pipetting head. In addition to including a pipetting head having a sleeve 23, a piston 25, a motor 26, a pipeline 27, and a pressure sensor 28, the pipetting workstation may further include a moving mechanism for moving the pipetting head. As yet another example, the liquid transfer device is not limited to a pipetting head and a pipetting workstation, and can also be other liquid transfer devices. As yet another example, the sleeve and the pipette tip can be an integrated component with a nozzle, where the function of the nozzle is similar to that of the pipette tip.

[0085] In the first example of the above first option, the piston can be driven to move at a predetermined initial speed, thereby aspirating and discharging a small amount of the target liquid. For example, in Figure 2In the exemplary scenario shown, the piston can first move upward at a predetermined initial movement speed to aspirate the target liquid, and then move downward at the same predetermined initial movement speed to discharge the target liquid. Optionally, the initial movement speed used to aspirate the target liquid (which can also be referred to as, for example, the first initial movement speed) can be different from the initial movement speed used to discharge the target liquid (which can also be referred to as, for example, the second initial movement speed). Optionally, the piston can be driven to reach a predetermined initial speed at a predetermined initial positive acceleration (which can also be referred to as, for example, the first initial acceleration). Optionally, the piston can be driven to reduce the movement speed from the predetermined initial speed to zero at a predetermined initial negative acceleration (which can also be referred to as, for example, the second initial acceleration). Optionally, the piston can be driven to wait for a predetermined delay time after aspirating the target liquid before discharging it. Alternatively, the piston can be driven to move according to a predetermined speed curve to aspirate and discharge a small amount of the target liquid. For example, when aspirating and discharging the target liquid, the piston can first perform uniformly accelerated motion and then uniformly decelerated motion.

[0086] For example, in order to identify various different target liquids, the same physical excitation can be applied to these liquids. As an exemplary example, the piston can be driven to move to a first fixed position (for example, at a distance of W0 from the lower end of the sleeve 23) at a fixed movement speed. Then, the moving mechanism is driven to move the pipette tip to a fixed depth below the liquid surface (for example, at a distance of Z0 from the liquid surface) at a fixed movement speed. Then, the piston is driven to aspirate the liquid (or suck the liquid), and the piston is moved to a second fixed position (for example, at a distance of W1 from the lower end of the sleeve 23) at a fixed movement speed. Finally, the piston is driven to discharge the liquid (or inject the liquid), and the piston is moved to the above-mentioned first fixed position at a fixed movement speed.

[0087] As a second example of the above first option, the predetermined initial movement trajectory can be that the piston is driven to aspirate only a small amount of the target liquid. In this case, it is only necessary to subsequently aspirate the remaining volume of the target liquid to complete the aspiration of the target liquid with the target volume. For example, in Figure 2 In the exemplary scenario shown, the piston can move upward at a predetermined initial movement speed to aspirate the target liquid. Optionally, the piston can be driven to reach a predetermined initial speed at a predetermined initial positive acceleration (which can also be referred to as, for example, the first initial acceleration). Optionally, the piston can be driven to reduce the movement speed from the predetermined initial speed to zero at a predetermined initial negative acceleration (which can also be referred to as, for example, the second initial acceleration). Optionally, the piston can be driven to wait for a predetermined delay time after aspirating the target liquid. Alternatively, the piston can be driven to move according to a predetermined speed curve to aspirate only a small amount of the target liquid. For example, when aspirating the target liquid, the piston can first perform uniformly accelerated motion and then uniformly decelerated motion.

[0088] As a second option, the physical excitation can be a predetermined initial movement trajectory of the pump of the liquid transfer device. For this second option, as its first example, the predetermined initial movement trajectory can be that the pump is driven to suck a small amount of the target liquid and discharge it. The function of the pump is similar to that of the piston 25 in Figure 2 . Therefore, Figure 2 The exemplary scenario shown can be modified by replacing the piston 25 with a pump to correspond to this third example. In this modified scenario, the pump can suck air from inside the sleeve to suck the target liquid, and then suck air from outside into the sleeve to discharge the target liquid. For example, the pump can be driven to move at a predetermined initial rotational speed to suck and discharge a small amount of the target liquid. Optionally, the initial rotational speed (which can also be referred to as, for example, the first initial rotational speed) used to suck the target liquid can be different from the initial rotational speed (which can also be referred to as, for example, the second initial rotational speed) used to discharge the target liquid. Optionally, the pump can be driven to wait for a predetermined delay time after sucking the target liquid before discharging it. Alternatively, the pump can be driven to move according to a predetermined rotational speed curve to suck and discharge a small amount of the target liquid.

[0089] As a second example of the above second option, the predetermined initial movement trajectory can be that the pump is driven to only suck a small amount of the target liquid. For example, the pump can be driven to move at a predetermined initial rotational speed to only suck a small amount of the target liquid. Optionally, the pump can be driven to wait for a predetermined delay time after sucking the target liquid. Alternatively, the pump can be driven to move according to a predetermined rotational speed curve to only suck a small amount of the target liquid.

[0090] As a third option, the physical excitation can be to only drive the moving mechanism to move the suction head to a predetermined depth below the liquid level of the target liquid. For example, the moving mechanism can move the suction head at a constant speed to the predetermined depth below the liquid level. Optionally, the moving mechanism can accelerate the suction head to this predetermined speed with a predetermined positive acceleration. Optionally, the moving mechanism can decelerate this predetermined speed to zero with a predetermined negative acceleration. Optionally, a predetermined time can be waited after moving the suction head to the predetermined depth below the liquid level. Optionally, the moving mechanism can move the suction head to the predetermined depth below the liquid level according to a predetermined speed curve.

[0091] It should be noted that the physical excitation can include a predetermined initial movement trajectory of the liquid transfer device and is not limited to the examples described above.

[0092] Step 104

[0093] In step 104, feedback information related to the performance of the target liquid and the surrounding medium under the excitation is measured. For example, the feedback information may include information directly or indirectly fed back by the target liquid under the excitation. The feedback information may also include information reflecting the excitation.

[0094] In the case where the excitation is the excitation regarding the first option / second option / third option mentioned in step 102 (the predetermined initial movement trajectory of the piston / pump / moving mechanism), as a first example, the feedback information may include the gas pressure information of the air in the cavity where the aspirated target liquid is located. Figure 2 In the exemplary scenario shown, the gas pressure information is the gas pressure information of the air in the sleeve 23, which can be measured or obtained by using the pressure sensor 28. For example, the pressure sensor 28 may be a differential pressure sensor, and the gas pressure information measured by it may be a differential pressure signal (or differential pressure information) representing the difference between the air pressure in the sleeve 23 and the ambient atmospheric pressure. For this first example, the above-mentioned "surrounding medium" is the air in the cavity where the aspirated target liquid is located. Since this air is adjacent to the aspirated target liquid, the gas pressure information of this air is indirectly fed back by the target liquid.

[0095] As a second example, the feedback information may include the current signal, or voltage signal, or power of the motor that drives the liquid transfer component (such as a piston, a pump, a moving mechanism, etc.). Figure 2 In the exemplary scenario shown, the current signal / voltage signal / power of the motor 26 can be measured by the corresponding current sensor / voltage sensor / power sensor (not shown). Similarly, the current signal, or voltage signal, or power of the motor of the moving mechanism can be measured by the corresponding current sensor / voltage sensor / power sensor. For this second example, the above-mentioned "surrounding medium" is the air in the cavity where the aspirated target liquid is located. The magnitude of the current / voltage / power of the motor can reflect the fluidity of the target liquid. For example, a liquid with relatively low fluidity consumes relatively more power, and a liquid with relatively high fluidity consumes relatively less power. Therefore, the measured current signal / voltage signal / power is indirectly fed back by the target liquid.

[0096] As described above, the feedback information may also include information reflecting the predetermined initial movement trajectory of the liquid transfer device (such as the position of the piston, the movement speed / acceleration, the rotation speed of the pump, the movement speed of the moving mechanism, etc.). The information of the initial movement trajectory can be measured by the driver of the motor, where the driver is a component of the control module of the liquid transfer device described later.

[0097] As a third example, the feedback information may include a combination of the feedback information of the above two examples. It should be noted that the feedback information is not limited to the examples described above, and other measurable information related to the performance of the target liquid and the surrounding medium under the excitation can also be used as the feedback information.

[0098] Step 106

[0099] Step 106 is Figure 1B a component of the method. In step 106, the environmental parameters of the environment where the target liquid is located are measured. As a first option, the environmental parameter may be the environmental air pressure, which can be measured using a pressure sensor. Taking the Figure 2 exemplary scenario shown (for other scenarios, the following description can be similarly applied) as an example, when sucking the liquid, the piston moves upward to expand the closed cavity inside the suction head, resulting in a decrease in the air pressure inside the cavity, generating a negative pressure relative to the environmental air pressure, and causing the liquid to flow into the suction head. When discharging the liquid, the piston moves downward to reduce the closed cavity inside the suction head, resulting in an increase in the air pressure inside the cavity, generating a positive pressure relative to the environmental air pressure, and causing the liquid to flow out of the suction head. Therefore, the amount of liquid suction and discharge is closely related to the environmental air pressure. For the same volume of liquid suction or discharge, the movement stroke of the piston is different under different environmental air pressures. Therefore, considering the environmental air pressure can more accurately reflect the performance of the target liquid during the liquid transfer process.

[0100] As a second option, the environmental parameter can be humidity, which can be measured using a humidity sensor. Environmental humidity affects the volatility of a liquid. At the same temperature, the evaporation rate of a liquid is inversely proportional to the environmental humidity. When the environmental humidity approaches saturation, the liquid hardly evaporates. A large volatility of the liquid will cause the actual transferred volume of the liquid to be smaller than the target volume (or target capacity). Therefore, considering the environmental humidity can more accurately reflect the performance of the target liquid during liquid transfer. For example, during liquid transfer, the volatility of the target liquid can be judged based on changes in environmental humidity and the air pressure inside the chamber. When the environmental humidity is low and the air pressure inside the chamber increases significantly, it can be judged that the volatility of the target liquid is strong. In this case, operations such as rinsing and aspirating an air column can be performed when transferring the target liquid. Rinsing means sucking and discharging the liquid into and out of the pipette tip several times before sucking the liquid. Since rinsing can increase the humidity inside the chamber and thus reduce liquid evaporation, this operation can avoid the problem that the liquid evaporation amount is large due to the dryness of the inner wall of the pipette tip, resulting in the actual liquid transferred volume being less than the target volume. Aspirating an air column means aspirating an air column after sucking the liquid. This operation can prevent the liquid from dripping during transfer. Because the evaporation of a liquid with strong volatility in the chamber of the pipette tip will cause the air pressure inside the chamber to increase. If the liquid stays at the nozzle of the pipette tip after sucking the liquid, the increased air pressure will cause the liquid to drip. However, if the operation of aspirating an air column is performed, even if the increased air pressure pushes the liquid out of the chamber, the liquid will not drip.

[0101] As a third option, the environmental parameter can be the liquid temperature, which can be measured using a non-contact temperature sensor. The liquid temperature reflects the intensity of the movement of liquid molecules. As the temperature rises, the viscosity of a liquid generally decreases. This is because when the temperature increases, the movement of liquid molecules intensifies, the internal frictional force decreases, the fluidity increases, so the viscosity decreases. Therefore, considering the liquid temperature can more accurately reflect the performance of the target liquid during liquid transfer.

[0102] As a fourth option, the environmental parameter can be the environmental temperature, which can be measured using a temperature sensor. When no additional temperature control is applied to the liquid, the temperature of the liquid can be considered equal to the environmental temperature. As mentioned above, as the temperature of the liquid rises, the viscosity of the liquid generally decreases. Therefore, the environmental temperature affects the fluidity of the liquid, resulting in different movement parameters (such as speed, delay time) being required for the movement of the piston when transferring the liquid at different environmental temperatures. Therefore, considering the environmental temperature can more accurately reflect the performance of the target liquid during liquid transfer.

[0103] As a fifth option, the environmental parameter can be a combination of two or more of the environmental parameters in the above four options. By using such a combination, the influence of the environment on the performance of the target liquid during liquid transfer can be comprehensively reflected.

[0104] Step 108

[0105] Step 108 is Figure 1A a component of the method. In step 108, a computing module determines a set of liquid property parameters related to the properties of the target liquid, at least based on the measured feedback information. The computing module can be a component of the liquid transfer device. It should be noted that the set can consist of multiple liquid property parameters or only one liquid property parameter. For a certain or certain feedback information, the feedback information can be determined as a member of the set. As an example, for the differential pressure signal obtained by using the differential pressure sensor mentioned above, the value of the differential pressure signal can be determined as a member of the set. As another example, for the current signal / voltage signal / power of the motor mentioned above, the value of the current signal / voltage signal / power can be determined as a member of the set.

[0106] As a second option, for a certain or certain feedback information, the change amount of the feedback information can be calculated as a member of the set. As an example, for the differential pressure signal obtained by using the differential pressure sensor mentioned above, the change amount of the differential pressure signal can be determined as a member of the set. As another example, for the current signal / voltage signal / power of the motor mentioned above, the change amount of the current signal / voltage signal / power can be determined as a member of the set.

[0107] As a third option, for a certain or certain feedback information, the rate of change of the feedback information with time can be calculated as a member of the set. As an example, for the differential pressure signal obtained by using the differential pressure sensor mentioned above, the rate of change of the differential pressure signal with time can be calculated as a member of the set. Assuming that the air in the cavity where the target liquid is aspirated is an ideal gas, according to the ideal gas state equation PV / T = c (where P is the pressure of the gas, V is the volume of the gas, T is the absolute temperature of the gas, and c is a constant), assuming that the temperature of the target liquid remains unchanged during the aspiration of the target liquid, then if the volume of the gas remains unchanged, the pressure of the gas also remains unchanged. In Figure 2In the exemplary scenario shown, assuming that the piston moves in the sleeve and displaces a certain volume, and the same volume of the target liquid is aspirated (i.e., the target liquid completely synchronously follows the movement of the piston), then the volume of the gas in the sleeve will remain unchanged, which means that the pressure of the gas will also remain unchanged, such that the differential pressure signal or the change amount of the differential pressure signal remains unchanged, or the rate of change of the differential pressure signal with respect to time is zero. As the situation where the target liquid follows the movement of the piston varies, the differential pressure signal, or the change amount of the differential pressure signal, or the rate of change of the differential pressure signal with respect to time will change accordingly. Therefore, the differential pressure signal, or the change amount of the differential pressure signal, or the rate of change of the differential pressure signal with respect to time can reflect the following characteristics of the target liquid following the movement of the piston. Regarding the differential pressure signal, the change amount of the differential pressure signal, and the rate of change of the differential pressure signal with respect to time, the set may include one of the three, or may include two or all of the three.

[0108] As another example, for the current signal / voltage signal / power of the motor mentioned above, the rate of change of the current signal / voltage signal / power with respect to time can be calculated as a member of the set. As mentioned before, the magnitude of the current / voltage / power of the motor can reflect the magnitude of the fluidity of the target liquid. Correspondingly, the change amount or the rate of change with respect to time of the current signal / voltage signal / power can reflect the change in the fluidity of the liquid. This fluidity affects the determination of the liquid aspiration speed. For example, for a target liquid with a relatively low fluidity, the liquid aspiration speed needs to be relatively slow; for a target liquid with a relatively high fluidity, the liquid aspiration speed needs to be relatively fast. Regarding the current signal / voltage signal / power, its change amount, and its rate of change with respect to time, the set may include one of the three, or may include two or all of the three.

[0109] As a fourth option, for a certain or some feedback information, the integral of the feedback information with respect to time can be calculated as a member of the set. As an example, for the differential pressure signal obtained by using the differential pressure sensor mentioned above, the integral of the differential pressure signal with respect to time can be calculated as a member of the set. As another example, for the current signal / voltage signal / power of the motor mentioned above, the integral of the current signal / voltage signal / power with respect to time can be calculated as a member of the set. Including the integral value in the set helps to avoid the influence of interference or other unknown factors during the measurement process and thus characterize the liquid properties in multiple dimensions. In the case where the applied excitation is the predetermined initial movement trajectory of the liquid transfer device mentioned above, since the feedback information can be measured at each predetermined time interval during the process of completing the initial movement trajectory, the measured feedback information can be a time series that changes with time. Correspondingly, the set of determined liquid property parameters can also be a time series that changes with time.

[0110] Optionally, in step 108, a set of liquid characteristic parameters can be determined based on the measured feedback information and the parameters of the liquid transfer device. As an example, the parameters of the liquid transfer device can be the material of the tip used to aspirate the liquid. This is mainly because the surface finish of different materials may be different, resulting in different hydrophilic or hydrophobic properties of the tip, and further affecting the performance of the target liquid during the liquid transfer process. As another example, the parameters of the liquid transfer device can be the appearance of the tip. This is mainly because for tips with different appearances (such as different shapes, or different sizes, or different pore diameters), the volume of the transferred liquid corresponding to when the liquid transfer component (such as a piston) moves to a certain stroke during the liquid transfer process will be different, thus affecting the performance of the target liquid during the liquid transfer process. As yet another example, the parameters of the liquid transfer device can be the tip model that comprehensively reflects the material and appearance of the tip. It should be noted that the present disclosure is not limited to the above examples, and other liquid transfer device parameters that affect the performance of the target liquid during the liquid transfer process are also possible.

[0111] Accordingly, when determining the set of liquid characteristic parameters, the parameters of the liquid transfer device can be determined as members of the set. In step 110 described later, for different liquid transfer device parameters included in the set of liquid characteristic parameters, different pre-determined sets of optimal initial motion parameters or different motion strategy models can be used to determine the motion parameters of the liquid transfer component.

[0112] Step 109

[0113] Step 109 is Figure 1B a component of the method. In step 109, a set of liquid characteristic parameters is determined based on at least the measured feedback information and environmental parameters. Regarding the measured feedback information, the details of determining the set of liquid characteristic parameters based on the measured feedback information have been described above with respect to step 108, and will not be elaborated here.

[0114] For certain environmental parameters, the environmental parameters can be determined as members of the set. As mentioned above, including environmental parameters in the set can more accurately reflect the performance of the target liquid during the liquid transfer process.

[0115] In the case where the applied excitation is the predetermined initial motion trajectory of the above-mentioned liquid transfer device, since the feedback information and environmental parameters can be measured at each predetermined time interval during the process of completing the initial motion trajectory, the measured feedback information and environmental parameters can both be time series that vary with time. Accordingly, the determined set of liquid characteristic parameters can also be a time series that varies with time.

[0116] Optionally, in step 109, a set of the liquid characteristic parameters may be determined based on the measured feedback information, environmental parameters, and the parameters of the liquid transfer device. When determining the set of the liquid characteristic parameters, the parameters of the liquid transfer device may be determined as members of the set. Accordingly, in step 110 described later, for different liquid transfer device parameters included in the set of the liquid characteristic parameters, different predetermined sets of optimal initial motion parameters or different motion strategy models may be used to determine the motion parameters of the liquid transfer component.

[0117] Step 110

[0118] In step 110, the computing module determines the motion parameters of the liquid transfer component during the process of driving the liquid transfer component of the liquid transfer device to transfer the target liquid of the target volume based on the determined set of the liquid characteristic parameters. Below, for ease of description, step 110 is described by taking the liquid transfer component as a piston as an example. It should be noted that the following description is applicable to both Figure 1A the method of Figure 1B and the method of

[0119] As a first option, step 110 may be implemented as Figure 3AStep 314. In step 314, a set of optimal initial motion parameters suitable for the determined set of liquid characteristic parameters is determined from a pre-determined multiple sets of optimal initial motion parameters. For example, regarding the multiple sets of optimal initial motion parameters, they can be obtained in the manner described below before the liquid transfer device leaves the factory.

[0120] First, for a variety of different known liquids (such as multiple liquids with different fluidities), the same excitation can be applied to these liquids under the same environmental conditions, such as the predetermined initial motion trajectory of the liquid transfer device described in step 102. When the same excitation is applied to each liquid, the feedback information described in step 104 and the environmental parameters described in step 106 are measured in real time. Furthermore, a set of liquid characteristic parameters of this liquid can be obtained, as described in step 109. Since the measurement is carried out in real time, the obtained set of liquid characteristic parameters is a time series that changes with time, which comprehensively reflects the performance of this liquid under this excitation. Then, the environmental conditions (such as environmental temperature, air pressure, humidity) can be changed multiple times, and the above experiment is repeated for these various different known liquids to obtain their respective corresponding time series. In this way, the corresponding multiple time series groups obtained for a variety of different known liquids (where the different time series in each time series group correspond to different environmental conditions) can be built into the liquid transfer device or downloaded to the liquid transfer device when needed as a comparison standard. It should be noted that in order to reduce the number of time series included in each time series group, for the same environmental parameter, for example, taking temperature as an example, the above experiment can be repeated at several temperature values with a relatively large interval within the normal operating temperature range of the liquid transfer device. In addition, for Figure 1A the method, the above experiment can also be carried out only under a certain predetermined environmental condition without changing the environmental conditions and repeating the experiment multiple times.

[0121] Additionally, under the same environmental conditions, for each of these multiple different known liquids, the piston can be moved at a certain speed for a certain distance to aspirate that liquid. The moving speed of the piston can vary within a certain range, and the moving distance of the piston can vary within a certain range. For example, the piston can be moved at a certain speed for different distances, then the speed can be increased by a certain increment and the piston can be moved for different distances again until both the moving speed and the moving distance have traversed within the corresponding ranges. At the same time, the liquid aspirated each time is weighed and measured. The moving speed used when aspirating to the target volume in the shortest time is determined as a member of a set of optimal initial motion parameters suitable for that liquid. The moving distance used when aspirating to the target volume in the shortest time can also be determined as a member of a set of optimal initial motion parameters suitable for that liquid. Optionally, for this optimal initial moving speed, the piston can reach this optimal initial moving speed with different accelerations to aspirate that liquid. The acceleration corresponding to the best liquid transfer effect (aspirating to the target volume in the shortest time) is determined as a member of a set of optimal initial motion parameters suitable for that liquid. Optionally, for each experiment of moving the piston to aspirate the liquid, the pressure difference of the air in the cavity can be observed when the piston finally stops moving, and the time interval from when the piston stops moving to when this pressure difference reaches stability can be recorded. For example, the average value of this time interval obtained from all experiments can be taken as the optimal initial delay time, and this optimal initial delay time is taken as a member of a set of optimal initial motion parameters suitable for that liquid. Optionally, if the surface tension of this liquid is small or it is volatile, the minimum volume of the post-aspirated air column that prevents this liquid from falling can be found from experiments of aspirating different volumes of the post-aspirated air column, and this minimum volume is taken as a member of a set of optimal initial motion parameters suitable for that liquid. Optionally, if the surface tension of this liquid is large, the minimum volume of the pre-aspirated air column that can drain this liquid completely can be found from experiments of aspirating different volumes of the pre-aspirated air column, and this minimum volume is taken as a member of a set of optimal initial motion parameters suitable for that liquid. Optionally, if this liquid is volatile, the identification information of the action of rinsing the pipette tip can be determined as a member of a set of optimal initial motion parameters suitable for that liquid. Then, the environmental conditions (such as environmental temperature, air pressure, humidity) can be changed multiple times, and the above experiments can be repeated for these multiple different known liquids to obtain a set of optimal initial motion parameters corresponding to each changed environmental condition. In this way, the corresponding multiple sets of optimal initial motion parameters obtained for these multiple different known liquids (each liquid has several sets of optimal initial motion parameters, corresponding to different environmental conditions respectively) can be built into the liquid transfer device or downloaded to the liquid transfer device for selection when needed.Similarly, as described above, in order to reduce the number of sets of optimal initial motion parameters corresponding to different environmental conditions, for the same environmental parameter, for example, taking temperature as an example, the above experiment can be repeated at several temperature values with a relatively large interval within the normal operating temperature range of the liquid transfer device. Additionally, for Figure 1A the method, the above experiment can also be conducted only under a certain predetermined environmental condition without repeatedly changing the environmental conditions for the experiment.

[0122] In this way, in step 314, from multiple time series groups corresponding to a variety of different known liquids, which are either built-in or downloaded, the time series that is closest to the determined set of liquid characteristic parameters can be selected, and the set of optimal initial motion parameters corresponding to this time series can be determined as the set of optimal initial motion parameters suitable for the determined set of liquid characteristic parameters. For example, techniques for calculating the similarity between two time series (such as the dynamic time warping DTW algorithm) can be used to calculate the similarity between the determined set of liquid characteristic parameters and each time series, and the time series with the highest similarity can be selected as the closest time series. It should be noted that for Figure 1A the method, since the determined set of liquid characteristic parameters may not include environmental parameters, when selecting the time series closest to it, the determined set of liquid characteristic parameters can be compared with each time series after removing the environmental parameters from each time series. Moreover, based on which liquid the selected closest time series corresponds to and the environmental parameters in this closest time series, a corresponding set of optimal initial motion parameters can be determined. Although Figure 1A the method may not measure the current environmental parameters, the environmental parameters in the closest time series can be used as an estimated value of the current environmental parameters.

[0123] As a second option, step 110 can be implemented as Figure 3B steps 316 and 318. In step 316, from a plurality of predetermined motion strategy models, a motion strategy model suitable for the determined set of liquid characteristic parameters is determined. For example, regarding the plurality of motion strategy models, they can be obtained in the manner described below before the liquid transfer device leaves the factory.

[0124] First, similar to step 314, for a variety of different known liquids, corresponding multiple time series groups can be obtained (different time series in each time series group correspond to different environmental conditions). These multiple time series groups can be built-in in the liquid transfer device or downloaded to the liquid transfer device as a comparison standard when needed.

[0125] In addition, under the same environmental conditions, for each of these multiple different known liquids, the piston can be moved at a constant speed for a certain distance to suck up the liquid. The moving speed of the piston can vary within a large range, and the moving distance of the piston can vary within a large range. The large range of the moving speed refers to the range covering various speeds that the piston may use during the liquid suction process. The large range of the moving distance refers to the range covering various distances that the piston may move during the liquid suction process. For example, the moving speed can randomly vary within this large range, and the moving distance can randomly vary within this large range. During each movement of the piston, the feedback information and environmental parameters are measured in real time (Note: For the method of Figure 1A , in the stage of preparing relevant data or information in advance, the feedback information can be measured in real time without measuring the environmental parameters, or the feedback information and environmental parameters can be measured in real time). Furthermore, a set of liquid characteristic parameters of this liquid can be obtained. For the sake of convenience in explanation, as an exemplary example, the feedback information includes the pressure difference ΔP of the air in the cavity where the sucked liquid is located, the moving speed v of the piston, and the moving distance L of the piston (for example, with the lower end of the sleeve as the origin), and the environmental parameter is the environmental temperature Ts (Note: For the method of Figure 1A , this environmental parameter can be omitted or not). The obtained set of liquid characteristic parameters is (ΔP, v, L, Ts). Then, change the environmental conditions and repeat the above experiment (Note: For the method of Figure 1A , the experiment can be conducted only under a certain predetermined environmental condition, or the environmental conditions can be changed and the experiment can be repeated).

[0126] Express the pressure difference of the air during the uniform motion of the piston as ΔP1, the moving speed of the piston as v1, the moving distance of the piston as L1, and the environmental temperature as Ts1. Express the total distance moved by the piston during this liquid suction process as L2, and the final stable value of the pressure difference of the air in the cavity after the piston stops moving as ΔP2. Then, according to the set of liquid characteristic parameters measured for each movement of the piston, a series of input vectors (ΔP1 i , L1 i , Ts1 i , v1 i , L2) can be obtained, where the subscript i represents the index of the sampling time point. These input vectors all correspond to the output data ΔP2. Use these input vectors and the corresponding output data as training data to train the first machine learning model (such as a neural network model). In this way, for this liquid, the trained first machine learning model can, according to the current environmental conditions (Note: For the method of Figure 1AThe method, the current environmental conditions can be omitted or not; in the case where they are not omitted, the environmental parameters in the closest time series can be used as the estimated value of the current environmental parameters as described above), the current moving distance of the piston, the corresponding current pressure difference, and the current moving speed to predict the target pressure difference corresponding to the piston moving to a certain target distance at the current moving speed. Accordingly, for these multiple different known liquids, multiple corresponding first machine learning models can be obtained.

[0127] As an exemplary example, the number of neurons in the input layer of the adopted neural network model is 5 (note: for Figure 1A the method, the number of neurons in the input layer is 4 when the environmental parameters are omitted, and the number of neurons in the input layer is 5 when the environmental parameters are not omitted), the number of neurons in the output layer is 1, and 3 hidden layers are set. Regarding the number of neurons in each hidden layer, in order to facilitate training and avoid overfitting, start with a relatively small number of neurons first and then gradually increase. The input vector and output data can be normalized to between [0, 1]. Define parameters such as the optimization algorithm (such as the Adam optimization algorithm), the maximum number of training times (such as epoch = 300), and the learning rate (such as rate = 0.01) to perform model training. Initially, use the initial weights of the neural network model for prediction. Then, calculate the error value according to the loss function using the predicted value and the actual value. Then, use the magnitude of the error value calculated according to the loss function to adjust the parameter values of the neural network model to make the predicted value approach the measured value. After several cycles (adjustment of model parameters), reach the state where the predicted value can accurately predict the true value. When inputting the input vector into the trained model for prediction, the output result can be de-normalized.

[0128] For each of the multiple different known liquids, its motion strategy model can call the first machine learning model corresponding to this liquid to predict the motion parameters. Regarding the specific prediction process, it will be described later with reference to step 318. In this way, for these multiple different known liquids, multiple corresponding motion strategy models can be obtained. These multiple motion strategy models can be built into the liquid transfer device or downloaded to the liquid transfer device for selection when needed.

[0129] In this way, in step 316, the time series closest to the determined set of liquid characteristic parameters can be selected from the multiple time series groups corresponding to the multiple different known liquids that are built-in or downloaded, and the motion strategy model corresponding to this time series is determined as the motion strategy model suitable for the determined set of liquid characteristic parameters.

[0130] In step 318, using the control target of the liquid transfer component and the determined set of liquid characteristic parameters as inputs, the motion parameters of the liquid transfer component are predicted using the determined motion strategy model. As an example, the control target of the liquid transfer component can be the target pressure difference between the air pressure in the cavity where the target liquid is located and the ambient atmospheric pressure when the target liquid of the target capacity is aspirated. As an exemplary example, the target pressure difference can be calculated through formula derivation. Assume that in Figure 2 the shown scenario, when the sleeve and the tip just start to contact the target liquid, the air pressure in the cavity is the ambient atmospheric pressure P 0 , the volume of the cavity (or the volume of the air in the cavity) is V 0 , the temperature of the air in the cavity is T 0 ; when the target liquid of the target capacity V T is aspirated, the piston moves a distance L T , the air pressure in the cavity is P 2 , the volume is V 2 , the temperature is T 2 , and the difference between the air pressure in the cavity and the ambient atmospheric pressure at this time, i.e., the target pressure difference, is P 2 - P 0 = ΔP 2 . It is also assumed that the air in the cavity is an ideal gas. Then, according to the state equation of the ideal gas, the following equation (1) can be obtained:

[0131]

[0132] By transforming equation (1), the following equation (2) can be obtained:

[0133]

[0134] When the piston moves a distance L T (i.e., the target liquid of the target capacity V T is aspirated), the total volume of the cavity becomes V 0 + L T S 0 (where S 0 is the cross-sectional area of the piston), and this total volume is the sum of the target capacity V T of the target liquid and the volume V 2 of the air in the cavity. This can be expressed as the following equation (3):

[0135]

[0136] By transforming equation (3), the following expression (4) for the target pressure difference ΔP can be obtained:

[0137]

[0138] In expression (4), the ambient atmospheric pressure P 0 can be measured by a pressure sensor; the volume V of the cavity 0 is determined by the volume of the suction tip and the initial position of the piston within the sleeve, and thus can be calculated; the temperature T of the air within the cavity 2 and T 0 can be obtained by measuring Figure 2 the temperature of the air guided by pipeline 27 in; the target volume V T is a known quantity. Therefore, the target pressure difference can be calculated according to expression (4). It should be noted that the principle of the above-mentioned derivation process can be similarly applied to other application scenarios.

[0139] The initial motion parameters predicted by the determined motion strategy model in response to the set of input control objectives and liquid characteristic parameters may include one or more of the determined set of optimal initial motion parameters described in step 314. As an exemplary example, the initial motion parameters predicted by the motion strategy model may include the optimal initial moving speed. Additionally, the motion strategy model may also predict the subsequent moving distance that the piston still needs to move when sucking the target liquid to the target volume, as one of the motion parameters of the piston. For the case where the excitation applied in step 102 is suction-beating or only sucking a small amount of the target liquid, assume that the distance of the piston from the lower end of the sleeve is L0 at the end of the application of this excitation. Then, if the piston is made to continue moving a predetermined unit distance ΔL, the motion strategy model can form an input vector with the most recently acquired pressure difference, piston moving distance, and ambient temperature (note: for the Figure 1A method, this ambient temperature may or may not be omitted), as well as the optimal initial moving speed, (L0 + ΔL), and input this input vector into the first machine learning model mentioned above to predict the pressure difference of the air within the cavity when the piston moves to the distance (L0 + ΔL). If the predicted pressure difference is less than the target pressure difference, then (L0 + ΔL) in the input vector can be successively replaced with (L0 + 2ΔL), (L0 + 3ΔL), (L0 + 4ΔL), etc., to predict the pressure difference respectively, and the moving distance corresponding to when the predicted pressure difference is closest to the target pressure difference is determined as the subsequent moving distance of the piston. If the predicted pressure difference is greater than the target pressure difference, then (L0 + ΔL) in the input vector can be successively replaced with (L0 + ΔL / 2), (L0 + ΔL / 4), (L0 + ΔL / 8), etc., to predict the pressure difference respectively, and the moving distance corresponding to when the predicted pressure difference is closest to the target pressure difference is determined as the subsequent moving distance of the piston.

[0140] As another example, the control target of the liquid transfer component can be the target volume of the target liquid to be aspirated. Similar to the foregoing example, the initial motion parameters predicted by the determined motion strategy model in response to the input set of control targets and liquid characteristic parameters may include one or more of the determined set of optimal initial motion parameters described in step 314. As an exemplary example, the initial motion parameters predicted by the motion strategy model may include the optimal initial moving speed. Additionally, the motion strategy model may also predict the subsequent moving distance that the piston still needs to move when aspirating the target liquid to the target volume, as one of the motion parameters of the piston. Similar to the above equation (3), during the process when the distance that the piston moves reaches L T During the process when the distance that the piston moves is L 1 At this time, the total volume of the cavity is the volume V of the target liquid aspirated L And the volume V of the air in the cavity 1 The sum of. This can be expressed as the following equation (5):

[0141]

[0142] Where T 1 Is the temperature of the air in the cavity at this time, ΔP 1 Is the difference between the air pressure in the cavity and the ambient atmospheric pressure at this time. By transforming equation (5), the following equation (6) can be obtained:

[0143]

[0144] For the case where the excitation applied in step 102 is suction or only aspirating a small amount of the target liquid, assume that the distance between the piston and the lower end of the sleeve is L0 at the end of the application of this excitation. Then, if the piston is made to continue moving a predetermined unit distance ΔL, the motion strategy model can use the most recently collected differential pressure, piston moving distance, and ambient temperature in the set of input liquid characteristic parameters (note: for Figure 1AThe method, the ambient temperature can be omitted or not omitted), as well as the optimal initial moving speed, (L0 + ΔL) form an input vector, and this input vector is input into the first machine learning model mentioned above to predict the pressure difference of the air in the cavity when the piston moves to a distance of (L0 + ΔL). Substituting the predicted pressure difference into Equation (6) can obtain the predicted volume of the target liquid to be aspirated. If the predicted volume is less than the target volume, then (L0 + ΔL) in the input vector can be successively replaced with (L0 + 2ΔL), (L0 + 3ΔL), (L0 + 4ΔL), etc., to predict the pressure difference respectively, calculate the predicted volume corresponding to the predicted pressure difference, and determine the subsequent moving distance of the piston as the moving distance corresponding to when the predicted volume is closest to the target volume. If the predicted volume is greater than the target volume, then (L0 + ΔL) in the input vector can be successively replaced with (L0 + ΔL / 2), (L0 + ΔL / 4), (L0 + ΔL / 8), etc., to predict the pressure difference respectively, calculate the predicted volume corresponding to the predicted pressure difference, and determine the subsequent moving distance of the piston as the moving distance corresponding to when the predicted volume is closest to the target volume. It should be noted that regarding the first machine learning model called by the motion strategy model, after the liquid transfer device leaves the factory, after each time the user uses the liquid transfer device to transfer liquid, training data as described above can be obtained based on the measured feedback information. Optionally, these training data can be used to continue training the first machine learning model to optimize its prediction ability.

[0145] Step 314 or 316 above can also be implemented as Figure 4 Steps 420 and 422. In step 420, based on the determined set of liquid characteristic parameters, determine the liquid characteristic category to which the target liquid belongs. As an option, the determined set of liquid characteristic parameters can be used as input, and a classification model can be used to determine the liquid characteristic category. The classification model can be configured to be able to determine liquids that behave similarly under the same excitation as belonging to the same liquid characteristic category. Optionally, the classification model can be configured to be able to determine liquids that behave similarly under the same environmental conditions and under the same excitation as belonging to the same liquid characteristic category. For example, regarding the classification model, it can be obtained in the manner described below before the liquid transfer device leaves the factory.

[0146] First, as described above regarding step 314, for a variety of different known liquids (such as a variety of liquids with different fluidities), corresponding multiple time series groups can be obtained, and different time series in each time series group correspond to different environmental conditions (note: for Figure 1AThe method can obtain corresponding multiple time series for multiple different known liquids only under a certain predetermined environmental condition. Corresponding labels can be added to these time series to serve as input vectors. For example, the label can be the name or descriptive information of the liquid. The corresponding output data is the classification label of the liquid. For example, for N liquids with different fluidities, their classification labels are 1, 2, …, N respectively. Use these input vectors and the corresponding output data as training data to train a second machine learning model (such as a neural network model). In this way, the trained second machine learning model can determine the liquid property category to which the target liquid belongs based on the set of liquid property parameters of the target liquid.

[0147] As an exemplary example, assume the length of the time series is K, the number of types of liquid property parameters obtained based on the measured feedback information is J, and the number of types of measured environmental parameters is M (note: for Figure 1A the method, the number M of environmental parameters can be zero). Considering that the environmental parameters are basically unchanged during the measurement process, the number of neurons in the input layer of the adopted neural network model is K*J + M. For example, if a sample is taken every 10 ms, then 50 samples (K = 50) can be obtained in 500 ms of applying the excitation. Additionally, if the liquid property parameter obtained based on the measured feedback information is the pressure difference and the measured environmental parameter is the environmental temperature. Then, the number of neurons in the input layer is 51. The number of neurons in the output layer is 1, and 3 hidden layers are set. Examples of hidden layers include but are not limited to long short-term memory network (LSTM) layers, gated recurrent unit (GRU) layers, and so on. Regarding the number of neurons in each hidden layer, to facilitate training and avoid overfitting, start with a relatively small number of neurons first and then gradually increase. The input vector can be normalized to the range [0, 1]. Define parameters such as an optimization algorithm (such as the Adam optimization algorithm), the maximum number of training times (such as epoch = 300), the learning rate (such as rate = 0.01), etc., and perform model training. Initially, classification is performed using the initial weights of the neural network model. Then, the error value is calculated according to the loss function using the classification value and the actual value. Then, the parameter values of the neural network model are adjusted using the magnitude of the error value calculated according to the loss function to make the classification value approach the actual value. After several cycles (adjustment of model parameters), a state is reached where the classification value can accurately predict the actual value.

[0148] Figure 5A and Figure 5B Liquid property parameters obtained in response to excitation are shown for multiple different liquids. In Figure 5A and Figure 5B the excitation is to move the piston 2 mm for liquid suction, stay stationary for 4.5 seconds, and then move the piston 2 mm for liquid injection. Figure 5AThe liquid characteristic parameter therein is the pressure difference of the air in the cavity, which presents as a time series. In Figure 5A , the abscissa is time. A sample is obtained every 3 ms, so the abscissa 8000 corresponds to 24 s. For Liquids 1 to 8, the ordinate is the pressure difference in hPa (hectopascal). For the excitation curve 9, the ordinate is the moving distance of the piston, and this moving distance is normalized to between [0, 1] in the figure. From Figure 5A , it can be seen that for liquids with different fluidities, the time series of their pressure differences are different. Therefore, this time series can be used as a classification criterion. It should be noted that the eight liquids in the figure are only representative liquids in these eight liquid characteristic categories.

[0149] Figure 5B The liquid characteristic parameter therein is the integral of the pressure difference of the air in the cavity over time, which also presents as a time series. In Figure 5B , the abscissa is also time, the same as Figure 5A . For Liquids 1 to 8, the ordinate is the pressure difference in hPa (hectopascal). For the excitation curve 9, the ordinate is the moving distance of the piston, and this moving distance is magnified by 5000 times in the figure so that readers can clearly observe this excitation. Figure 5B The time series of the pressure difference integral in

[0150] can increase the dimension for differentiating liquids and help avoid misjudgment.

[0151] It should be noted that the present disclosure is not limited to the examples described above. As another example, other machine learning algorithms or non-machine learning algorithms (such as support vector machines, K-Means, decision trees, etc.) can also be used to construct a classification model. As yet another example, the liquid transfer component can be a pump similar to the function of the piston. The motion parameters of the liquid transfer component can include but are not limited to: the rotation speed of the pump; the delay time between two adjacent motions of the pump; the volume of the air column sucked after sucking the target volume of the target liquid; the volume of the air column sucked before starting the transfer process; the identification information of the action of rinsing the suction head (or nozzle); or a combination of two or more of the above motion parameters.

[0152] In this example of a pump (e.g., a plunger pump, which changes the internal and external pressure difference by changing the volume of an internal closed cavity), the determination of the optimal initial motion parameters can be similar to the example of a piston. Regarding the first machine learning model called by the motion strategy model, the input vector (ΔP1 i ,L1 i ,Ts1 i ,v1 i ,L2) is replaced by the input vector (ΔP1 i ,t1 i ,Ts1 i ,v1 i ,t2), where t1 is the rotation time of the pump, v1 is the speed of the pump, and t2 is the total rotation time when the pump stops rotating. The output data is still ΔP2. Regarding the control target of the motion strategy model, the target pressure difference is adopted, which can be approximately estimated using equation (4).

[0153] As another example, the liquid transfer component may be a moving mechanism for moving a suction head (or nozzle) for sucking liquid. The motion parameters of the liquid transfer component may include, but are not limited to: the speed at which the suction head (or nozzle) enters the liquid surface of the target liquid; the speed at which the suction head (or nozzle) leaves the liquid surface of the target liquid; the identification information of the action of the suction head (or nozzle) contacting the wall of the container where the target liquid is located; or a combination of two or more of the above motion parameters. Since the moving mechanism can move the suction head (or nozzle), the motion parameters of the above suction head (or nozzle) are actually the motion parameters of the moving mechanism. For example, for liquids with large surface tension, when the suction head enters the liquid, the slower the speed, the longer the liquid molecules are in contact with the surface of the suction head, and the greater the amount of adhesion. This is because when the suction head enters the liquid, a depression will be formed on the surface of the liquid. The liquid with large surface tension flows slowly and has no time to fill the depression, so it is easier to stick to the suction head. Therefore, for liquids with large surface tension, the suction head needs to enter the liquid faster, and the speed when leaving the liquid needs to be faster (to utilize inertia), which can reduce the amount of liquid adhering to the outer wall of the suction head and improve the accuracy of pipetting. Regarding the action of the suction tip (or nozzle) contacting the wall of the container where the target liquid is located, for liquids with higher viscosity, this action can be used to make the residual liquid on the suction tip stick to the wall of the container.

[0154] For the example of the moving mechanism, its corresponding motion parameters can be included in the optimal initial motion parameters described in step 314. That is, when the optimal initial motion parameters of the piston (or pump) are determined by experiment, the optimal initial motion parameters of the moving mechanism can be determined incidentally. For example, for each liquid, the suction head can be allowed to enter / leave the liquid surface of the target liquid at various speeds, and the speed that obtains the best effect (so that the liquid does not adhere to the outer wall of the suction head) is determined as a member of the corresponding optimal initial motion parameters.

[0155] As yet another example, the liquid transfer component can be a combination of a piston (or a pump) and a moving mechanism. Accordingly, the motion parameters of the liquid transfer component can include, but are not limited to, combinations of the corresponding motion parameters mentioned above.

[0156] As yet another example, step 110 is also applicable to the discharge of liquid. The determination of its optimal initial motion parameters can be similar to the example of liquid aspiration described above. Regarding the first machine learning model for invoking the motion strategy model, the input vector (ΔP1 i , L1 i , Ts1 i , v1 i , L2) can be replaced with the input vector (ΔP1 i , D1 i , Ts1 i , v1 i , D3), where D1 is the distance between the piston and the lower end of the sleeve during the discharge of the liquid, and D3 is the distance between the piston and the lower end of the sleeve when the liquid is completely discharged. The output data ΔP2 can be replaced with ΔP3, which is the pressure difference of the air in the cavity when the liquid is completely discharged. When predicting the moving distance of the piston during liquid discharge, for the case of completely discharging the aspirated liquid, the target pressure difference being zero or the liquid volume in the cavity being zero can be set as the control target; for the case of discharging only a part of the aspirated liquid, the remaining liquid volume after discharging a part of the liquid can be used as the control target, or the target pressure difference calculated using equation (4) can be used as the control target (note that when using equation (4), V T refers to the remaining liquid volume after discharging a part of the liquid). Other details can be similar to the example of liquid aspiration described above.

[0157] Step 112

[0158] In step 112, the control module controls the motion of the liquid transfer component based on the determined motion parameters. The control module can be a component of the liquid transfer device. For example, when the liquid transfer component is a piston or a pump, the control module can include a first driver for driving the motor to operate. When the liquid transfer component is a moving mechanism, the control module can include a second driver for driving the motor of the moving mechanism to operate.

[0159] Third Embodiment and Fourth Embodiment

[0160] Figure 6A is a flowchart showing a method for transferring a target liquid according to a third embodiment of the present disclosure. This method can be executed in response to step 112 in the method of the first embodiment. As Figure 6A shown, this method includes steps 624, 608, 610, and 612.Figure 6B is a flowchart showing a method for transferring a target liquid according to a fourth embodiment of the present disclosure. This method can be executed in response to step 112 in the method of the second embodiment. As Figure 6B shown, this method includes steps 624, 106, 609, 610, and 612. Below, Figure 6A and Figure 6B each step of the method will be described.

[0161] In step 624, feedback information related to the performance of the target liquid and the surrounding medium during the transfer process is measured. For example, the feedback information may include information directly or indirectly fed back by the target liquid during the transfer process. The feedback information may also include information reflecting the motion state of the liquid transfer component during the transfer process. Step 624 may be similar to step 104, the difference being that step 624 is executed in response to step 112, so its corresponding excitation (which may be referred to as a subsequent excitation to distinguish it from the excitation of step 102) is the motion of the liquid transfer component (such as a piston or a pump).

[0162] Step 106 is Figure 6B a component of the

[0163] method. In step 106, environmental parameters of the environment where the target liquid is located are measured. Figure 6A Step 608 is

[0164] a component of the Figure 6B method. In step 608, at least based on the feedback information measured in real time, the set of liquid characteristic parameters is re - determined. Step 608 may be similar to step 108.

[0165] Optionally, since the motion strategy model / liquid characteristic category of the target liquid has been determined previously, in step 608 / step 609, the feedback information (such as differential pressure) can be directly determined as a member of the set, without including the result after operating on this feedback information (such as change amount, rate of change / integral over time).

[0166] In step 610, based on the re-determined set of the liquid characteristic parameters, re-determine the motion parameters of the liquid transfer component. For example, step 610 can be implemented as step 718. In step 718, using the control objective of the liquid transfer component and the re-determined set of the liquid characteristic parameters as inputs, predict the motion parameters of the liquid transfer component by using the previously determined motion strategy model. Below, for the sake of simplicity of description, it is still described by taking the liquid transfer component as a piston. As previously mentioned, the initial motion parameters predicted by the motion strategy model in response to the input control objective and the set of the liquid characteristic parameters may include one or more of the determined set of optimal initial motion parameters described in step 314. As an exemplary example, the initial motion parameters predicted by the motion strategy model may include the optimal initial moving speed. In step 610, in response to the input control objective of the liquid transfer component and the re-determined set of the liquid characteristic parameters, the motion strategy model can adjust the moving speed of the piston. For example, during the second prediction, a predetermined first increment can be added to the moving speed of the piston as the predicted moving speed. Additionally, predict the subsequent moving distance of the piston as one of the motion parameters of the piston.

[0167] In step 612, based on the re-determined motion parameters of the liquid transfer component, control the motion of the liquid transfer component. Then, for Figure 6A the method, steps 624, 608, 610, 612 can continue to be executed in real time. For Figure 6B the method, steps 624, 106, 609, 610, 612 can continue to be executed in real time. When the piston has not moved the subsequent moving distance, due to the real-time measurement of the feedback information and the environmental parameters (note: for Figure 6A the method, the environmental parameters may not be measured in real time), a third prediction can be made in response to the newly input re-determined set of the liquid characteristic parameters. The difference between the feedback pressure difference after speed increase and the feedback pressure difference before speed increase can be calculated. If the difference is within the predetermined threshold range, a predetermined first increment can be added to the moving speed of the piston as the predicted moving speed. On the contrary, if the difference is outside the predetermined threshold range, a predetermined second increment can be subtracted from the moving speed of the piston as the predicted moving speed. The second increment may be equal to or unequal to the first increment. In this way, by controlling the difference (or change amount) of the feedback pressure difference within the predetermined threshold range during the moving speed adjustment process, the moving speed of the piston can be adaptively adjusted. Additionally, predict the subsequent moving distance of the piston as one of the motion parameters of the piston.

[0168] Next, as long as a set of re-determined liquid characteristic parameters is input when the piston has not completed this subsequent movement distance, the above adjustment process can be repeated until the piston completes the final subsequent movement distance to finish liquid suction. Similarly, for the process of discharging the target liquid, the process described above can be similarly applied.

[0169] In the example of the motion strategy model described above, a trained first machine learning model is called to predict the pressure difference obtained after the piston continues to move a specific distance. However, it should be noted that the present disclosure is not limited to this example. As another example, the target volume of the target liquid can be used as the control target, and the volume of the aspirated liquid can be estimated by multiplying the distance that the piston moves for pipetting by the cross-sectional area of the cavity. The total distance that the piston needs to move is calculated according to the equation that the volume of the aspirated liquid is equal to the target volume, and the subsequent movement distance of the piston is calculated by subtracting the distance that the piston has moved from the total distance. This example can simplify the motion strategy model at the cost of a certain pipetting accuracy.

[0170] Based on the descriptions of the above first / second embodiments and third / fourth embodiments, at least one embodiment of the present disclosure provides a method for transferring a target liquid. The method includes: applying an excitation to the target liquid (step 102); measuring feedback information related to the performance of the target liquid and the surrounding medium under the excitation (step 104); determining, by a calculation module, at least based on the measured feedback information, a set of liquid characteristic parameters related to the characteristics of the target liquid (step 108); determining, by the calculation module, based on the determined set of liquid characteristic parameters, the motion parameters of a liquid transfer component of a liquid transfer device during the process of driving the liquid transfer component to transfer a target volume of the target liquid (step 110); controlling, by a control module, the motion of the liquid transfer component based on the determined motion parameters (step 112); and measuring feedback information related to the performance of the target liquid and the surrounding medium during the transfer process (step 630).

[0171] In the above method, for the first stage of determining the initial motion parameters in response to the initially applied excitation and the second stage of re-determining the subsequent motion parameters in response to the motion of the liquid transfer component according to the initial motion parameters, steps 108, 110, and 112 are all executed, that is, they are shared by the first stage and the second stage. In the first stage, the determinations in steps 108 and 110 are initial determinations, and the motion parameters used in step 112 are the initially determined motion parameters. In the second stage, the determinations in steps 108 and 110 are re-determinations, and the motion parameters used in step 112 are the re-determined motion parameters. Specifically, step 108 is implemented as step 608, step 110 is implemented as step 610, and step 112 is implemented as step 612.

[0172] In addition, based on the descriptions of the above first / second embodiments and third / fourth embodiments, at least one other embodiment of the present disclosure provides a method for transferring a target liquid. The method includes: applying an excitation to the target liquid (step 102); measuring feedback information related to the performance of the target liquid and the surrounding medium under the excitation (step 104); measuring environmental parameters of the environment where the target liquid is located (step 106); determining, by a calculation module, a set of liquid characteristic parameters related to the characteristics of the target liquid based at least on the measured feedback information and environmental parameters (step 109); determining, by the calculation module, motion parameters of a liquid transfer component of a liquid transfer device during the process of transferring a target volume of the target liquid based on the determined set of liquid characteristic parameters (step 110); controlling, by a control module, the motion of the liquid transfer component based on the determined motion parameters (step 112); and measuring feedback information related to the performance of the target liquid and the surrounding medium during the transfer process (step 630).

[0173] In the above method, for the first stage of determining initial motion parameters in response to an initially applied excitation and the second stage of re-determining subsequent motion parameters in response to the motion of the liquid transfer component according to the initial motion parameters, steps 106, 109, 110, and 112 are all executed, that is, shared by the first stage and the second stage. In the first stage, the determinations in steps 109 and 110 are initial determinations, and the motion parameters used in step 112 are the initially determined motion parameters. In the second stage, the determinations in steps 109 and 110 are re-determinations, and the motion parameters used in step 112 are the re-determined motion parameters. Specifically, step 109 is implemented as step 609, step 110 is implemented as step 610, and step 112 is implemented as step 612.

[0174] In the above third / fourth embodiments, subsequent motion parameters are re-determined in response to the liquid transfer component moving according to the initial motion parameters. As an alternative to the third / fourth embodiments, the liquid transfer component may also always move according to the initial motion parameters until the target liquid of the target volume is aspirated. The inventors of the present application have found that this alternative can achieve good pipetting effects in some cases (for example, when the target liquid to be transferred exactly matches a certain built-in liquid category), but cannot achieve optimal pipetting effects in some cases. After research, the inventors of the present application have found that the reason why this alternative cannot achieve optimal pipetting effects in some cases is that it does not realize that the properties of liquids are complex. For example, when a liquid changes from a static state to a flowing state, surface tension plays a dominant role. When the liquid starts to flow, since the molecular attraction on the liquid surface is broken, the viscosity of the liquid starts to play a dominant role. Moreover, the properties of liquids are not limited to surface tension and viscosity. Other properties such as the density and volatility of liquids will affect the performance of liquids during transfer. For example, for a liquid with strong volatility, during the entire transfer process of the liquid, due to the increase in the air pressure in the cavity caused by the evaporation of the liquid, the following characteristics of the liquid will change, especially. Therefore, the set of liquid property parameters that comprehensively reflects the performance of the liquid during transfer (especially the parameters reflecting the following characteristics of the liquid) usually changes during the entire liquid transfer process. Due to such changes, corresponding adjustment of the motion parameters can help achieve better pipetting effects. It is precisely realizing this problem that in the third / fourth embodiments, in response to the liquid transfer component moving according to the initial motion parameters, the set of liquid property parameters is still re-determined based on the real-time measured feedback information (and optionally, environmental parameters), and the subsequent motion parameters are re-determined based on the re-determined set of liquid property parameters. Since the determination of the subsequent motion parameters takes into account the changes in the set of liquid property parameters during the liquid transfer process, the initial motion parameters can be corrected so that the liquid transfer component moves with more suitable motion parameters. In particular, when steps 608, 610, and 612 are executed in real time in the third embodiment or when steps 609, 610, and 612 are executed in real time in the fourth embodiment, even if the target liquid is an unknown liquid whose properties are unknown to the operator (although the optimal initial motion parameters obtained according to the closest built-in liquid category are relatively good solutions to a certain extent, as mentioned above, there is still room for improvement), since the motion parameters are adaptively determined in real time according to the real-time measurement results in both the first stage of the initial excitation and the subsequent second stage, the optimal transfer effect can also be achieved for this unknown liquid.

[0175] II. Liquid Transfer Device

[0176] Figure 8is a block diagram showing a liquid transfer device according to an embodiment of the present disclosure. Examples of liquid transfer devices include, but are not limited to: pipette tips, pipetting workstations, and the like. As Figure 8 shown, the liquid transfer device 80 includes at least: a control module 88, a liquid sensing module 82, and a calculation module 86.

[0177] The control module 88 is configured to apply an excitation to the target liquid, as described above with respect to step 102. The liquid sensing module 82 is configured to measure feedback information related to the performance of the target liquid and the surrounding medium under the excitation, as described above with respect to step 104. The calculation module 86 is configured to: determine a set of liquid characteristic parameters related to the characteristics of the target liquid based at least on the measured feedback information; and determine the motion parameters of the liquid transfer component during the process of driving the liquid transfer component to transfer a target volume of the target liquid based on the determined set of liquid characteristic parameters, as described above with respect to steps 108 and 110. Optionally, the calculation module 86 may be configured to determine the set of liquid characteristic parameters based on the measured feedback information and the parameters of the liquid transfer device. The control module 88 is further configured to control the motion of the liquid transfer component based on the determined motion parameters, as described above with respect to step 112.

[0178] As described above, in response to the control module 88 controlling the motion of the liquid transfer component based on the determined motion parameters in the first stage corresponding to the initially applied excitation, the liquid sensing module 82 may be configured to measure feedback information related to the performance of the target liquid and the surrounding medium during the transfer process, as described above with respect to step 624. The calculation module 86 may be configured to: re-determine the set of liquid characteristic parameters based on the feedback information measured in real time; re-determine the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters, as described above with respect to steps 608 and 610. The control module 88 may be configured to control the motion of the liquid transfer component based on the re-determined motion parameters of the liquid transfer component, as described above with respect to step 612. Optionally, the calculation module 86 may be configured to perform the re-determination of the set of liquid characteristic parameters and the re-determination of the motion parameters of the liquid transfer component in real time. Accordingly, the control module 88 may be configured to control the motion of the liquid transfer component in real time based on the motion parameters of the liquid transfer component re-determined in real time.

[0179] As Figure 8As shown, the liquid transfer device 80 optionally includes an environmental sensing module 84 configured to measure environmental parameters of the environment in which the target liquid is located, as described above with respect to step 106. Accordingly, the calculation module 86 may be configured to: determine a set of liquid characteristic parameters related to the characteristics of the target liquid based at least on the measured feedback information and environmental parameters; and determine the motion parameters of the liquid transfer member during the process of driving the liquid transfer member to transfer the target volume of the target liquid based on the determined set of liquid characteristic parameters, as described above with respect to steps 109 and 110. Optionally, the calculation module 86 may be configured to determine the set of liquid characteristic parameters based on the measured feedback information, the environmental parameters, and the parameters of the liquid transfer device. The control module 88 is further configured to control the motion of the liquid transfer member based on the determined motion parameters, as described above with respect to step 112.

[0180] As previously described, in response to the control module 88 controlling the motion of the liquid transfer member based on the determined motion parameters in the first stage corresponding to the initially applied excitation, the liquid sensing module 82 may be configured to measure feedback information related to the performance of the target liquid and the surrounding medium during the transfer process, as described above with respect to step 624. The environmental sensing module 84 may be configured to measure environmental parameters of the environment in which the target liquid is located, as described above with respect to step 106. The calculation module 86 may be configured to: re-determine the set of liquid characteristic parameters based on the feedback information measured in real time and the environmental parameters measured in real time; re-determine the motion parameters of the liquid transfer member based on the re-determined set of liquid characteristic parameters, as described above with respect to steps 609 and 610. The control module 88 may be configured to control the motion of the liquid transfer member based on the re-determined motion parameters of the liquid transfer member, as described above with respect to step 612. Optionally, the calculation module 86 may be configured to perform the re-determination of the set of liquid characteristic parameters and the re-determination of the motion parameters of the liquid transfer member in real time. Accordingly, the control module 88 may be configured to control the motion of the liquid transfer member in real time based on the motion parameters of the liquid transfer member re-determined in real time.

[0181] As an example, the computing module 86 or the control module 88 can be implemented as at least one processor and at least one memory storing program instructions. The program instructions, when executed by the at least one processor, cause the at least one processor to perform the operations of the computing module 86 or the control module 88 described above. Examples of processors include, but are not limited to, general-purpose computers, special-purpose computers, microprocessors, digital signal processors (DSPs), processors based on multi-core processor architectures, microcontroller units (MCUs), and the like. The memory can be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory, and the like. As another example, the computing module 86 or the control module 88 can be implemented as a hardware circuit, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), etc.

[0182] Accordingly, at least one aspect of the present disclosure provides a computer-readable storage medium. Program instructions are stored on the computer-readable storage medium. The program instructions, when executed by at least one processor, cause the at least one processor to perform the operations of the computing module 86 or the control module 88 described above. Examples of computer-readable storage media include, but are not limited to, hard disks, optical disks, removable storage media, solid-state memories, random access memories (RAM), and the like.

[0183] References in the present disclosure to "one embodiment", "an embodiment", etc. mean that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment must include that particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. Additionally, when a particular feature, structure, or characteristic is described in connection with one embodiment, it is within the knowledge of those skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described. It should be noted that two consecutively shown boxes (or steps) in the drawings can actually be executed substantially in parallel, or these boxes (or steps) can sometimes also be executed in the reverse order, depending on the functions involved.

[0184] It should be understood that although the terms "first", "second", etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the present disclosure. In the present disclosure, the term "and / or" includes any and all combinations of any one of the associated listed terms. It should also be understood that when the terms "comprising", "having", and / or "including" are used herein, they refer to the presence of the stated features, elements, and / or components, and do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof. The term "connected" as used herein encompasses direct and / or indirect connection between two elements.

[0185] The present disclosure includes any novel feature or combination of features explicitly or in any general form disclosed herein. Various modifications and adaptations of the above exemplary embodiments of the present disclosure will become apparent to those skilled in the art when read in conjunction with the accompanying drawings. However, any and all modifications and adaptations will still fall within the scope of the non-limiting and exemplary embodiments of the present disclosure.

Claims

1. A method for transferring a target liquid, comprising: applying an excitation to the target liquid; measuring feedback information related to the performance of the target liquid and the surrounding medium under the excitation; determining, by a calculation module, a set of liquid characteristic parameters related to the characteristics of the target liquid based at least on the measured feedback information; determining, by the calculation module, the motion parameters of the liquid transfer component during the process of driving the liquid transfer component of the liquid transfer device to transfer a target volume of the target liquid based on the determined set of liquid characteristic parameters; and controlling, by a control module, the motion of the liquid transfer component based on the determined motion parameters.

2. The method according to claim 1, further comprising: measuring environmental parameters of the environment where the target liquid is located; and wherein, based on the measured feedback information and environmental parameters, determining the set of liquid characteristic parameters.

3. The method according to claim 1 or 2, wherein determining the set of liquid characteristic parameters based on the measured feedback information and the parameters of the liquid transfer device.

4. The method according to claim 1, wherein the excitation applied to the target liquid includes a physical excitation.

5. The method according to claim 4, wherein the excitation applied to the target liquid includes: a predetermined initial motion trajectory of the liquid transfer device.

6. The method according to claim 1, further comprising: measuring feedback information related to the performance of the target liquid and the surrounding medium during the transfer process.

7. The method according to claim 1 or 6, wherein the feedback information includes: information directly or indirectly fed back by the target liquid under the excitation or during the transfer process.

8. The method according to claim 7, wherein the feedback information includes: information reflecting the excitation, or information reflecting the motion state of the liquid transfer component during the transfer process.

9. The method according to claim 1 or 6, wherein the measured feedback information includes one or more of the following: a signal obtained using a pressure sensor; the power of the motor driving the liquid transfer component; the current signal of the motor driving the liquid transfer component; the voltage signal of the motor driving the liquid transfer component.

10. The method according to claim 2, wherein the environmental parameters include one or more of the following parameters: liquid temperature; environmental temperature; humidity; and environmental air pressure.

11. The method according to claim 1, wherein determining the set of liquid characteristic parameters based on the measured feedback information includes one or more of the following: calculating the change amount of the feedback information as a member of the set; calculating the change rate of the feedback information with respect to time as a member of the set; calculating the integral of the feedback information with respect to time as a member of the set; and determining the feedback information as a member of the set.

12. The method according to claim 2, wherein determining the set of liquid characteristic parameters based on the measured feedback information and environmental parameters includes: determining the environmental parameters as members of the set.

13. The method according to claim 11 or 12, wherein, the measured feedback information or environmental parameters are a time series that varies with time, and the determined set of liquid characteristic parameters is a time series that varies with time.

14. The method according to claim 1, wherein, the liquid transfer component includes one or more of the following components: a piston; a pump; and a moving mechanism for moving a tip or nozzle used for sucking the liquid; wherein the motion parameters include one or more of the following parameters: the speed of the piston; the acceleration of the piston; the rotational speed of the pump; the delay time between two adjacent motions of the piston or the pump; the volume of the air column sucked after sucking the target volume of the target liquid; the volume of the air column sucked before starting the transfer process; the speed at which the tip or nozzle enters the liquid level of the target liquid; the speed at which the tip or nozzle leaves the liquid level of the target liquid; the identification information of the action of the tip or nozzle contacting the wall of the container where the target liquid is located; and the identification information of the action of rinsing the tip or nozzle.

15. The method according to claim 6, wherein, determining the set of liquid characteristic parameters based on the measured feedback information related to the performance of the target liquid and the surrounding medium during the transfer process includes: re-determining the set of liquid characteristic parameters based on the feedback information measured in real time; wherein determining the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters includes: re-determining the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters; and wherein controlling the motion of the liquid transfer component based on the determined motion parameters includes: controlling the motion of the liquid transfer component based on the re-determined motion parameters of the liquid transfer component.

16. The method according to claim 15, wherein, re-determine the set of liquid characteristic parameters based on the feedback information measured in real time and the environmental parameters measured in real time.

17. The method according to claim 15 or 16, wherein, re-determining the set of liquid characteristic parameters and re-determining the motion parameters of the liquid transfer component are performed in real time.

18. The method according to claim 1, wherein, determining the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters includes: determining a set of optimal initial motion parameters suitable for the determined set of liquid characteristic parameters from a pre-determined multiple sets of optimal initial motion parameters; or wherein determining the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters includes: determining a motion strategy model suitable for the determined set of liquid characteristic parameters from a pre-determined multiple motion strategy models; and using the determined motion strategy model to predict the motion parameters of the liquid transfer component with the control target of the liquid transfer component and the determined set of liquid characteristic parameters as inputs.

19. The method according to claim 15 or 16, wherein, Redetermining the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters includes: using the control objective of the liquid transfer component and the re-determined set of liquid characteristic parameters as inputs, and predicting the motion parameters of the liquid transfer component using the previously determined motion strategy model.

20. The method according to claim 18, wherein, determining a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters includes: based on the determined set of liquid characteristic parameters, determining the liquid characteristic category to which the target liquid belongs; and based on the determined liquid characteristic category and a motion parameter library that pre-determines the correspondence between different liquid characteristic categories and different sets of optimal initial motion parameters or different motion strategy models, determining a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters.

21. The method according to claim 20, wherein, using the determined set of liquid characteristic parameters as an input, and determining the liquid characteristic category using a classification model, wherein the classification model is configured to be able to determine that liquids that behave similarly under the same excitation belong to the same liquid characteristic category.

22. The method according to claim 21, wherein, the classification model is a trained machine learning model.

23. A liquid transfer device, comprising: a control module configured to apply an excitation to a target liquid; a liquid sensing module configured to measure feedback information related to the performance of the target liquid and the surrounding medium under the excitation; and a calculation module configured to: at least based on the measured feedback information, determine a set of liquid characteristic parameters related to the characteristics of the target liquid; and based on the determined set of liquid characteristic parameters, determine the motion parameters of the liquid transfer component during the process of driving the liquid transfer component to transfer a target volume of the target liquid; wherein the control module is further configured to control the motion of the liquid transfer component based on the determined motion parameters.

24. The liquid transfer device according to claim 23, further comprising: an environment sensing module configured to measure the environmental parameters of the environment where the target liquid is located; and wherein the calculation module is configured to determine the set of liquid characteristic parameters based on the measured feedback information and the environmental parameters.

25. The liquid transfer device according to claim 23 or 24, wherein, the calculation module is configured to determine the set of liquid characteristic parameters based on the measured feedback information and the parameters of the liquid transfer device.

26. The liquid transfer device according to claim 23, wherein, the excitation applied to the target liquid includes a physical excitation.

27. The liquid transfer device according to claim 26, wherein, the excitation applied to the target liquid includes: a predetermined initial motion trajectory of the liquid transfer device.

28. The liquid transfer device according to claim 23, wherein, The liquid sensing module is configured to measure feedback information related to the performance of the target liquid and the surrounding medium during the transfer process.

29. The liquid transfer device according to claim 23 or 28, wherein, the feedback information includes: information directly or indirectly fed back by the target liquid under the excitation or during the transfer process.

30. The liquid transfer device according to claim 29, wherein, the feedback information includes: information reflecting the excitation, or information reflecting the motion state of the liquid transfer component during the transfer process.

31. The liquid transfer device according to claim 23 or 28, wherein, the measured feedback information includes one or more of the following: a signal obtained using a pressure sensor; the power of the motor driving the liquid transfer component; the current signal of the motor driving the liquid transfer component; the voltage signal of the motor driving the liquid transfer component.

32. The liquid transfer device according to claim 24, wherein, the environmental parameters include one or more of the following parameters: liquid temperature; ambient temperature; humidity; and ambient air pressure.

33. The liquid transfer device according to claim 23, wherein, the calculation module is configured to determine a set of the liquid characteristic parameters based on the measured feedback information by performing one or more of the following operations: calculating the change amount of the feedback information as a member of the set; calculating the rate of change of the feedback information over time as a member of the set; calculating the integral of the feedback information over time as a member of the set; and determining the feedback information as a member of the set.

34. The liquid transfer device according to claim 24, wherein, the calculation module is configured to determine a set of the liquid characteristic parameters based on the measured feedback information and environmental parameters by determining the environmental parameters as members of the set.

35. The liquid transfer device according to claim 33 or 34, wherein, the measured feedback information or environmental parameters are time series that change over time, and the determined set of the liquid characteristic parameters is a time series that changes over time.

36. The liquid transfer device according to claim 23, wherein, the liquid transfer component includes one or more of the following components: a piston; a pump; and a moving mechanism for moving a tip or nozzle used for sucking liquid; wherein the motion parameters include one or more of the following parameters: the speed of the piston; the acceleration of the piston; the rotational speed of the pump; the delay time between adjacent two motions of the piston or the pump; the volume of the air column sucked after sucking the target volume of the target liquid; the volume of the air column sucked before starting the transfer process; the speed at which the tip or nozzle enters the liquid surface of the target liquid; the speed at which the tip or nozzle leaves the liquid surface of the target liquid; the identification information of the action that the tip or nozzle contacts the wall of the container where the target liquid is located; and the identification information of the action of rinsing the tip or nozzle.

37. The liquid transfer device according to claim 28, wherein, the calculation module is configured to: re-determine the set of liquid characteristic parameters based on the feedback information measured in real time and related to the performance of the target liquid and the surrounding medium during the transfer process; and re-determine the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters; and wherein, the control module is configured to control the motion of the liquid transfer component based on the re-determined motion parameters of the liquid transfer component.

38. The liquid transfer device according to claim 37, wherein, the calculation module is configured to re-determine the set of liquid characteristic parameters based on the feedback information measured in real time and the environmental parameters measured in real time.

39. The liquid transfer device according to claim 37 or 38, wherein, the calculation module is configured to re-determine the set of liquid characteristic parameters and re-determine the motion parameters of the liquid transfer component in real time.

40. The liquid transfer device according to claim 23, wherein, the calculation module is configured to determine the motion parameters of the liquid transfer component based on the determined set of liquid characteristic parameters by performing the following operations: determine a set of optimal initial motion parameters suitable for the determined set of liquid characteristic parameters from a pre-determined multiple sets of optimal initial motion parameters; or determine a motion strategy model suitable for the determined set of liquid characteristic parameters from a pre-determined multiple motion strategy models; and use the determined motion strategy model to predict the motion parameters of the liquid transfer component with the control target of the liquid transfer component and the determined set of liquid characteristic parameters as inputs.

41. The liquid transfer device according to claim 37 or 38, wherein, the calculation module is configured to re-determine the motion parameters of the liquid transfer component based on the re-determined set of liquid characteristic parameters by performing the following operations: use the previously determined motion strategy model to predict the motion parameters of the liquid transfer component with the control target of the liquid transfer component and the re-determined set of liquid characteristic parameters as inputs.

42. The liquid transfer device according to claim 40, wherein, the calculation module is configured to determine a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters by performing the following operations: determine the liquid characteristic category to which the target liquid belongs based on the determined set of liquid characteristic parameters; and based on the determined liquid characteristic category and a motion parameter library that pre-determines the correspondence between different liquid characteristic categories and different multiple sets of optimal initial motion parameters or different motion strategy models, determine a set of optimal initial motion parameters or a motion strategy model suitable for the determined set of liquid characteristic parameters.

43. The liquid transfer device according to claim 42, wherein, The calculation module is configured to use a classification model to determine the liquid property category with the determined set of liquid property parameters as input, wherein the classification model is configured to be able to determine liquids that exhibit similar behavior under the same excitation as belonging to the same liquid property category.

44. The liquid transfer device according to claim 43, wherein, the classification model is a trained machine learning model.

45. A computer-readable storage medium having program instructions stored thereon, the program instructions causing the at least one processor to perform the operations of the calculation module or the control module according to any one of claims 23 to 44 when executed by the at least one processor.

Images