Mechanically-actuated flow cytometry sample preparation

Abstract

This application discloses a flow cytometry blood sample preprocessing method and system based on a robotic arm, relating to the field of flow cytometry technology. The method includes: acquiring operational and layout information of the sample to be processed, and encoding this information to obtain sample features; generating an initial chromosome population based on the sample features; performing genetic mutation processing on the initial chromosome population to obtain a target optimized population, and selecting target chromosomes that meet preset fitness conditions from the target optimized population; determining the trajectory planning scheme corresponding to the target chromosome as the target trajectory scheme, and controlling the robotic arm to perform sample preprocessing operations according to the target trajectory scheme. This application acquires sample information and performs feature encoding, thereby optimizing the trajectory using a genetic evolution algorithm based on the sample features. This allows the robotic arm's trajectory scheme to fully consider the characteristics and layout of the sample, improving the synchronization and overall efficiency of sample processing.

Description

Technical Field

[0001] This application relates to the field of flow cytometry technology, and in particular to a flow cytometry blood sample pretreatment method and system based on a robotic arm. Background Technology

[0002] In the pretreatment of flow cytometry blood samples, individual differences often lead to variations in the order of reagent addition, centrifugation, and other operational steps within the same batch of samples. Traditional methods rely on manual processing, which requires significant human resources for large-scale sample pretreatment. However, with the automation of flow cytometry blood sample processing, robotic arms can replace manual labor to process large batches of samples, thereby reducing labor costs.

[0003] However, existing robotic arm trajectory planning methods typically employ fixed processing sequences and paths. When preprocessing large batches of samples, individual differences lead to variations in the order of reagent addition, centrifugation, and other operational steps, resulting in different processing times for different samples. This leads to problems such as excessively long waiting times and redundant operating trajectories when processing different samples, ultimately impacting sample processing efficiency. Summary of the Invention

[0004] The main objective of this application is to provide a flow cytometry blood sample preprocessing method and system based on a robotic arm, aiming to solve the technical problem of low efficiency in the existing robotic arm trajectory planning method when performing batch sample preprocessing.

[0005] To achieve the above objectives, this application proposes a flow cytometry blood sample pretreatment method based on a robotic arm, the method comprising:

[0006] Obtain the operation information and layout information of the sample to be processed, and encode the sample features based on the operation information and layout information;

[0007] An initial chromosome population is generated based on the sample features. The initial chromosome population includes several chromosomes obtained by initializing the population from the first generation chromosomes. Each chromosome represents a trajectory planning scheme, and the gene segments in each of the first generation chromosomes are the sample features.

[0008] Genetic mutation processing is performed on the initial chromosome population to obtain a target optimized population, and target chromosomes that meet preset fitness conditions are selected from the target optimized population.

[0009] The trajectory planning scheme corresponding to the target chromosome is determined as the target trajectory scheme of the robotic arm, and the robotic arm is controlled to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme.

[0010] In one embodiment, the step of encoding based on the operation information and layout information to obtain sample features includes:

[0011] The operation process and corresponding process time of the sample to be processed are determined based on the operation information, and the spatial coordinates of the sample to be processed are determined based on the layout information.

[0012] The spatial coordinates, the operation process, and the corresponding process time are encoded using real number encoding to obtain the sample features corresponding to the sample to be processed.

[0013] In one embodiment, the step of performing genetic mutation processing on the initial chromosome population to obtain the target optimized population includes:

[0014] In the current iteration round, a tournament selection method is used to determine the next generation chromosome population from the initial chromosome population;

[0015] Gene crossover and gene mutation operations are performed on the chromosomes in the next generation population to obtain an updated chromosome population.

[0016] The updated chromosome population is used as the initial chromosome for the next iteration round, and the process returns to the step of determining the next generation chromosome population from the initial chromosome population using the tournament selection method.

[0017] The target optimized population is obtained when the current iteration round meets the preset maximum iteration round.

[0018] In one embodiment, the operation information includes each operation process and its corresponding process time, the layout information includes the regional location of the sample processing area corresponding to each operation process, and the step of determining the next generation chromosome population from the initial chromosome population using the tournament selection method includes:

[0019] A fitness function is constructed based on the process time and the location of each region.

[0020] The initial chromosome population is divided into a predetermined number of groups, and the fitness of chromosomes in each group is evaluated according to the fitness function.

[0021] Based on the evaluation results, chromosomes within a preset fitness range are selected from each group to form the next generation chromosome population.

[0022] In one embodiment, the step of controlling the robotic arm to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme includes:

[0023] The real-time position of the robotic arm is obtained, and the next target processing area of ​​the robotic arm is determined based on the real-time position and the target trajectory scheme.

[0024] The robotic arm is controlled to move to the next target processing area so that the robotic arm can perform sample preprocessing operations on the sample to be processed. The sample preprocessing operations are the operation flow corresponding to the next target processing area.

[0025] In one embodiment, the step of acquiring the real-time position of the robotic arm and determining the next target processing area of ​​the robotic arm based on the real-time position and the target trajectory scheme includes:

[0026] The real-time position and real-time operating status of the robotic arm are obtained, wherein the real-time operating status is either a stationary state or a moving state.

[0027] When the robotic arm is stationary, the current target processing area of ​​the robotic arm is determined according to the target trajectory scheme, and the positional deviation between the real-time position and the current target processing area is calculated.

[0028] When the position deviation is within the preset position error range, the next target processing area of ​​the robotic arm is determined based on the real-time position and the target trajectory scheme.

[0029] In one embodiment, after the step of obtaining the real-time position and real-time operating status of the robotic arm, the method further includes:

[0030] When the robotic arm is in motion, determine the speed data of the robotic arm, and generate a real-time motion trajectory based on the real-time position and the speed data;

[0031] The expected motion trajectory of the robotic arm is determined according to the target trajectory scheme, and the trajectory deviation between the real-time motion trajectory and the expected motion trajectory is calculated.

[0032] When the trajectory deviation is within the preset trajectory error range, the next target processing area of ​​the robotic arm is determined based on the real-time motion trajectory.

[0033] In one embodiment, the operation information further includes: operation requirement information, and the step of controlling the robotic arm to move to the next target processing area includes:

[0034] Determine whether the reagent information and equipment information at the next target processing area meet the operational requirements.

[0035] When the operational requirements are met, the robotic arm is controlled to move to the next target processing area.

[0036] In one embodiment, after the step of determining whether the reagent information and equipment information at the next target processing area meet the operational requirements, the method further includes:

[0037] If the operation requirements are not met, the updated operation information and updated layout information of the sample to be processed are determined based on the real-time location and the target trajectory scheme.

[0038] Based on the updated operation information and the updated layout information, the updated target trajectory scheme of the robotic arm is determined, and the robotic arm is controlled to perform sample preprocessing operations on the sample to be processed in accordance with the updated target trajectory scheme.

[0039] In addition, to achieve the above objectives, this application also proposes a flow cytometry blood sample pretreatment system, the system comprising: a controller and a robotic arm;

[0040] The controller is used to acquire operation information and layout information of the sample to be processed, and to encode the sample features based on the operation information and layout information.

[0041] The controller is further configured to generate an initial chromosome population based on the sample features. The initial chromosome population includes several chromosomes obtained by initializing the population from the first generation chromosomes. Each chromosome represents a trajectory planning scheme, and the gene segments in each of the first generation chromosomes are the sample features.

[0042] The controller is also used to perform genetic mutation processing on the initial chromosome population to obtain a target optimized population, and to select target chromosomes that meet preset fitness conditions from the target optimized population.

[0043] The controller is further configured to determine the trajectory planning scheme corresponding to the target chromosome as the target trajectory scheme of the robotic arm, and send the control command corresponding to the target trajectory scheme to the robotic arm;

[0044] The robotic arm is used to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme.

[0045] This application discloses a flow cytometry blood sample preprocessing method based on a robotic arm. The method involves acquiring the operation and layout information of the sample to be processed, encoding this information, and obtaining sample features. An initial chromosome population is generated based on these features. This initial chromosome population includes several chromosomes obtained from initializing the population using first-generation chromosomes. Each chromosome represents a trajectory planning scheme, and gene segments in each first-generation chromosome serve as sample features. Genetic mutation processing is performed on the initial chromosome population to obtain a target optimized population. Target chromosomes that meet preset fitness conditions are selected from the target optimized population. The trajectory planning scheme corresponding to the target chromosome is determined as the target trajectory scheme for the robotic arm, and the robotic arm is controlled to perform sample preprocessing operations according to the target trajectory scheme.

[0046] Because this application can accurately acquire sample information and encode it effectively, it provides comprehensive and accurate sample features for subsequent trajectory planning. Then, based on the sample features, a genetic evolution algorithm is used to optimize the trajectory of the robotic arm. This allows the robotic arm's trajectory plan to fully consider the characteristics and layout of the samples, improve the synchronicity and overall efficiency of sample processing, reduce sample waiting time, and avoid affecting the detection results due to untimely sample processing. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating the first embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm according to this application.

[0050] Figure 2 This is a flowchart illustrating the second embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm according to this application.

[0051] Figure 3 This is a flowchart illustrating the third embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm in this application.

[0052] Figure 4 This is a flowchart illustrating the fourth embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm according to this application.

[0053] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0055] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0056] This application provides a flow cytometry blood sample preprocessing method based on a robotic arm, referencing... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm according to this application. In this embodiment, the method includes steps S10 to S40:

[0057] Step S10: Obtain the operation information and layout information of the sample to be processed, and encode the sample features based on the operation information and layout information.

[0058] It should be noted that the execution entity in this embodiment can be the controller in the streaming sample preprocessing system. The controller can be a computing electronic device with data processing, network communication and program execution capabilities, such as a mobile phone or tablet.

[0059] It should be understood that the sample to be processed can be from the same batch of samples that need to be processed in a medical laboratory, and this same batch may include one or more flow cytometry blood samples. The operational information for the sample to be processed may be the sequence of operational procedures determined according to the experimental task, the estimated time for each step, and may even include the operational requirements for each step.

[0060] For example, the procedure for sample A is "mixing reaction (15 minutes) - adding BC hemolysin and letting it stand for reaction (3 minutes) - centrifugation (5 minutes) - and other steps (omitted)", and the procedure for sample B is "adding BD hemolysin and letting it stand for reaction (2 minutes) - centrifugation (5 minutes) - adding fetal bovine serum washing solution (1 minute) - and other steps (omitted)".

[0061] In addition, the above-mentioned operational information can be obtained from the information label attached to the test tube of the sample to be processed, or from the laboratory's information management system, through information collection tools such as barcode scanners.

[0062] The layout information of the sample to be processed can be the specific location of the sample within the sample processing area, which can be represented by spatial layout coordinates. This layout information can be obtained through the controller's interactive interface by the user clicking to select the sample to be processed, thereby acquiring the coordinates of the sample's spatial layout. For example, sample A is placed at coordinates (X... a Y a Sample B is placed at coordinate (X) b Y b The position of ).

[0063] In addition, the layout information may also include the location of the sample processing area corresponding to each operation process of the sample, and the location of the area may also be represented by spatial layout coordinates.

[0064] Understandably, the operation steps and time information of each sample obtained above can be encoded, and a suitable encoding method (such as real number encoding) can be used to convert them into a specific code form, so that this information of each sample can be presented in the form of digital gene fragments.

[0065] For example, sample A can be encoded as [X] using real number encoding. a Y a [1, 15, 2, 3, 3, 5], where X a Y a The coordinates are: 1 indicates mixing reaction, 15 indicates mixing reaction for 15 minutes, 2 indicates adding BC hemolysin and allowing the reaction to stand, 3 indicates adding BC hemolysin and allowing the reaction to stand for 3 minutes, 3 indicates centrifugation, and 5 indicates centrifugation for 5 minutes. Sample B is coded as [X]. b Y b [4, 2, 3, 5, 5, 1] ​​where X b Y b The coordinates are: 4 indicates the addition of BD hemolysin and allowing the reaction to stand; 3 indicates centrifugation; and 5 indicates the addition of fetal bovine serum washing solution.

[0066] In a specific implementation, the operation flow and corresponding process time of the sample to be processed can be determined based on the operation information, and the spatial coordinates of the sample to be processed can be determined based on the layout information; the spatial coordinates, the operation flow and the corresponding process time are feature-encoded using a real number encoding method to obtain the sample features, i.e., the sample code, corresponding to the sample to be processed.

[0067] Step S20: Generate an initial chromosome population based on the sample features. The initial chromosome population includes several chromosomes obtained by initializing the population from the first generation chromosomes. Each chromosome represents a trajectory planning scheme, and the gene segments in each of the first generation chromosomes are the sample features.

[0068] It should be noted that the sample codes corresponding to multiple samples in the same batch can be used as gene fragments to form primary chromosomes. These gene fragments are arranged and combined on the chromosome in a certain order, which reflects the specific order and trajectory of the robotic arm in manipulating the samples. For example, the gene fragments of a certain chromosome can be the sample code corresponding to sample A and the sample code corresponding to sample B. That is, the chromosome can be represented as [sample A gene fragment, sample B gene fragment].

[0069] It should be understood that, based on the sample size and the controller's computing resources, the population can be initialized using the initial chromosomes: a random number generation algorithm is used to randomly generate a number of chromosomes (e.g., 100) to form an initial chromosome population. Each chromosome in this population can represent an operation sequence and trajectory planning scheme for the robotic arm to process that batch of samples.

[0070] For example, if gene segments of a chromosome are the sample characteristics of sample A, sample characteristics of sample B, etc., then in the context of robotic arm operation, this means that the robotic arm first processes sample A according to its operation process and position information, then processes sample B, and so on, thus forming a complete trajectory planning scheme.

[0071] Step S30: Perform genetic mutation processing on the initial chromosome population to obtain the target optimized population, and select target chromosomes that meet the preset fitness conditions from the target optimized population.

[0072] It should be understood that the fitness function can be constructed based on the following principles: the total time taken by the robotic arm to process all samples (the shorter the total time, the higher the fitness), the length of the robotic arm's trajectory (the shorter the trajectory length, the higher the fitness), and the synchronicity of the completion times of all samples (the smaller the sum of the differences between the completion times of each sample and the latest completion time, the higher the fitness).

[0073] The fitness value of each chromosome in the population is calculated based on the fitness function. The higher the fitness value, the better the trajectory planning scheme corresponding to that chromosome.

[0074] It should be noted that in handling genetic variation within a population, a method such as tournament selection can be used. Each time, a number of individuals are randomly selected from the population to form a tournament group. The fitness values ​​of the individuals in the group are compared, and the individual with the highest fitness is selected to enter the next generation of the population. This process is repeated until the number of individuals in the new population reaches a preset value (e.g., the number of chromosomes in the initial chromosome population).

[0075] In addition, selection, crossover, and mutation operations, such as those used in genetic algorithms, can be applied to the individuals forming the tournament group to generate new chromosomes and gene fragments. These selection, crossover, and mutation operations are repeated until a preset maximum number of iterations (e.g., 200) is reached, or the fitness value of the best individual in the population no longer significantly improves in consecutive iterations (e.g., 10 consecutive iterations) (the rate of change of fitness value is less than 0.01). At this point, the iteration operation is stopped, and the final target optimized population is obtained.

[0076] Finally, from the obtained target optimized population, the target chromosome that satisfies the preset fitness condition (highest fitness value) can be selected.

[0077] Step S40: Determine the trajectory planning scheme corresponding to the target chromosome as the target trajectory scheme of the robotic arm, and control the robotic arm to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme.

[0078] It should be understood that once the target chromosome is determined, its corresponding trajectory planning scheme can be used as the target trajectory scheme to be executed by the robotic arm. Based on this target trajectory scheme, the order of preprocessing operations performed by the robotic arm on each sample and its running trajectory can be determined, clarifying key information such as the robotic arm's movement path and the sequence of operation steps within the sample processing area.

[0079] In practice, based on the target trajectory scheme determined above, the controller can send the corresponding control commands to the robotic arm, so that the robotic arm can perform sample preprocessing operations on each sample to be processed in sequence according to the received commands.

[0080] For example, the robotic arm can move to the position of the corresponding sample to be processed in the order specified in the plan, and complete various operation steps such as sample mixing reaction, addition of hemolysin, centrifugation, etc., thereby realizing an efficient and accurate pre-processing procedure for convective blood samples.

[0081] This embodiment can accurately acquire sample information and encode it effectively, providing comprehensive and accurate sample features for subsequent trajectory planning. Then, based on the sample features, a genetic evolution algorithm is used to optimize the robot arm trajectory, so that the robot arm's trajectory planning can fully consider the characteristics and layout of the samples, improve the synchronicity and overall efficiency of sample processing, reduce sample waiting time, and avoid the impact of untimely sample processing on the detection results.

[0082] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2This is a flowchart illustrating the second embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm according to this application.

[0083] In this embodiment, to specifically illustrate how to use an evolutionary algorithm to plan the target trajectory of the robotic arm, step S30 specifically includes: steps S301 to S304:

[0084] Step S301: In the current iteration round, the next generation chromosome population is determined from the initial chromosome population using the tournament selection method.

[0085] It should be noted that, firstly, a fitness function can be constructed, which can be:

[0086] Fitness = a × 1 / T total +b×1 / L+r×1 / S

[0087] Among them, T total The total processing time for the robotic arm to process all samples can be calculated by simulating the robotic arm running according to the trajectory planning scheme corresponding to the chromosome, calculating the processing time of each sample and summing them up. The processing time of each sample can be the sum of the process time corresponding to each operation procedure of the sample. L is the length of the robotic arm's running trajectory, which can be calculated using the Euclidean distance formula based on the distance between each operation point of the robotic arm in the sample processing area. S is the synchronicity index of the completion time of all sample processing, which can be calculated by summing the differences between the completion time of each sample and the latest completion time of the sample. a, b, and r are weighting coefficients, with values ​​of 0.4, 0.3, and 0.3, respectively.

[0088] Next, the initial chromosome population can be divided into multiple groups according to a preset number requirement. For each chromosome in each group, the fitness function constructed above is used to evaluate their fitness, and the fitness value of each chromosome is calculated. The higher the fitness value, the better the trajectory planning scheme corresponding to that chromosome.

[0089] Based on the fitness assessment results, chromosomes with fitness levels within a preset range can be selected from each group and combined to form the next generation chromosome population. This process performs an initial screening and optimization of the population, ensuring that chromosomes entering the next generation meet certain fitness standards. This gradually improves the overall optimization of the population, moving it towards a higher-quality trajectory planning scheme that is more suitable for practical applications.

[0090] In a specific implementation, the controller can construct a fitness function based on the time consumption of each process and the location of each region; divide the initial chromosome population into a preset number of groups, and evaluate the fitness of chromosomes in each group according to the fitness function; based on the evaluation results, select chromosomes in each group that are within a preset fitness range to form the next generation chromosome population.

[0091] Step S302: Perform gene crossover and gene mutation operations on the chromosomes in the next generation population to obtain an updated chromosome population.

[0092] It should be understood that gene crossover and gene mutation operations can be performed on the chromosomes in the next generation population, i.e., the selected chromosomes.

[0093] Gene crossing operation can be performed by randomly selecting two chromosomes (parent chromosomes), randomly determining one or more crossover points on their gene sequences, and exchanging gene segments after the crossover points to generate new chromosomes (offspring chromosomes).

[0094] For example, if parent chromosome 1 is [sample A gene fragment, sample B gene fragment] and parent chromosome 2 is [sample C gene fragment, sample D gene fragment], after selecting a crossover point, the gene fragments are exchanged to generate offspring chromosome 1 with [sample A gene fragment, sample D gene fragment] and offspring chromosome 2 with [sample C gene fragment, sample B gene fragment].

[0095] Gene mutation operations can involve randomly selecting a gene segment on a chromosome and altering the gene information (operation flow or time) on that segment. For example, changing the operation step identifier in a gene segment from 3 (centrifugation) to 2 (adding BC hemolysin and allowing the reaction to stand), or adjusting the time information by ±1 minute. This simulates gene mutation phenomena in biological evolution, introducing new genetic diversity into the population and avoiding the optimization process from getting trapped in local optima.

[0096] Step S303: Use the updated chromosome population as the initial chromosome for the next iteration round, and return to the step of determining the next generation chromosome population from the initial chromosome population using the tournament selection method.

[0097] Step S304: Obtain the target optimized population when the current iteration round meets the preset maximum iteration round.

[0098] It should be understood that after gene crossover and gene mutation are completed, an updated chromosome population is obtained. This updated population serves as the initial chromosome population for the next iteration, and then the process returns to the aforementioned step S301.

[0099] This process is repeated continuously, allowing the population to be optimized and improved through selection, crossover, and mutation, until the current iteration reaches the pre-set maximum iteration number (e.g., 200), or the fitness value of the best individual in the population no longer significantly improves in 10 consecutive iterations (fitness value change rate is less than 0.01). At this point, the population is identified as the target optimization population, and the best chromosome in this population, i.e., the target chromosome, is output. The trajectory planning scheme corresponding to the target chromosome is then the better trajectory planning scheme after sufficient evolution and optimization.

[0100] This embodiment employs a tournament selection method combined with a genetic evolutionary algorithm for fitness screening. This continuously optimizes the trajectory planning scheme, enabling simultaneous start and finish processing of samples in the same batch. This significantly improves the synchronicity and overall efficiency of sample processing, reduces sample waiting time, and prevents samples from affecting test results due to untimely processing. Furthermore, it minimizes the robotic arm's trajectory and time consumption, thereby reducing energy consumption and wear, extending equipment lifespan, and improving the timeliness of sample preprocessing. It also possesses strong versatility and flexibility, making it suitable for various types and scales of medical hematological sample preprocessing scenarios.

[0101] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm according to this application.

[0102] In this embodiment, to facilitate controlling the robotic arm to perform sample preprocessing operations according to the target trajectory scheme at any time, step S40 further includes: steps S401 to S402:

[0103] Step S401: Obtain the real-time position of the robotic arm, and determine the next target processing area of ​​the robotic arm based on the real-time position and the target trajectory scheme.

[0104] It should be understood that during the operation of the robotic arm, the operating status (e.g., stationary or moving state) of the robotic arm can be monitored in real time through position sensors on the robotic arm or monitoring devices (e.g., cameras, liquid level sensors) at the sample areas corresponding to each operation process.

[0105] Furthermore, to specifically illustrate how to control the robotic arm to execute the target trajectory when it is stationary, step S401 specifically includes: steps S4011 to S4013:

[0106] Step S4011: Obtain the real-time position and real-time operating status of the robotic arm, wherein the real-time operating status is either a stationary state or a moving state.

[0107] In practical implementation, the current spatial coordinates of the robotic arm can be determined by the position sensor on the robotic arm, while the operating status can be determined by the feedback information from the control module of the robotic arm. For example, when all the joint motors of the robotic arm stop operating and the position of the end effector remains unchanged for a period of time, the control module can determine that the robotic arm is in a stationary state; conversely, if a joint motor is driving and the position of the end effector is constantly changing, the robotic arm is determined to be in a moving state.

[0108] Step S4012: When the robotic arm is stationary, determine the current target processing area of ​​the robotic arm according to the target trajectory scheme, and calculate the positional deviation between the real-time position and the current target processing area.

[0109] It should be understood that when the robotic arm is stationary, it can be considered that the robotic arm has either completed or has not yet started a certain (sample) operation process. At this time, according to the processing sequence planned in the target trajectory scheme, the target processing area where the robotic arm is currently located can be determined.

[0110] For example, if, according to the target trajectory scheme, after the robotic arm completes the processing of sample A, it should next process sample B, then when the robotic arm is stationary, its current target processing area can be determined to be the processing area where sample A is located, because at this time the robotic arm may have just completed the processing operation of sample A.

[0111] In practice, after determining the current target processing area, the real-time position of the robotic arm and the position coordinates of the current target processing area can be obtained, and the positional deviation between the two can be calculated.

[0112] For example, if the current target processing area's location coordinates are (X... c Y c The real-time position coordinates of the robotic arm are (X... r Y r If the positional deviation in the X direction is X, then the positional deviation is X. c -X r In the Y direction, Y c -Y r By calculating the positional deviation, we can determine whether the robotic arm has accurately reached the target processing area, or whether there is a positional error.

[0113] Step S4013: When the position deviation is within the preset position error range, determine the next target processing area of ​​the robotic arm based on the real-time position and the target trajectory scheme.

[0114] It should be noted that the preset position error range can be a pre-set threshold range, used to determine whether the deviation between the real-time position of the robotic arm and the position of the target processing area is within an acceptable error range.

[0115] In practical applications, the preset position error range can be set based on factors such as the precision requirements of the robotic arm, the size of the sample processing area, and the allowable error in actual operation. This embodiment does not impose any restrictions on this. For example, the preset position error range can be set to approximately ±0.5 mm in both the X and Y directions.

[0116] In practice, if the calculated position deviation is within the preset position error range, the robotic arm has reached the current target processing area relatively accurately. At this point, the next target processing area of ​​the robotic arm can be determined according to the sequence planned in the target trajectory scheme.

[0117] Furthermore, to specifically illustrate how to control the robotic arm to execute the target trajectory scheme when it is in motion, after step S4011, the following steps are also included: steps S4012' to S4014':

[0118] Step S4012': When the robotic arm is in motion, determine the speed data of the robotic arm, and generate a real-time motion trajectory based on the real-time position and the speed data.

[0119] It should be understood that when a robotic arm is in motion, its current speed data can be obtained through speed sensors on the arm or feedback information from the motors in its control module. This speed data can include information such as linear velocity and angular velocity. For example, linear velocity represents the speed of the robotic arm's end effector in the straight line direction, while angular velocity represents its speed during rotational motion.

[0120] It should be noted that after obtaining the real-time position and velocity data of the robotic arm, kinematic formulas and other related algorithms, combined with time parameters, can be used to generate the real-time motion trajectory of the robotic arm. Specifically, starting from the real-time position, the displacement change of the robotic arm in each tiny time interval can be determined based on the velocity data, thereby depicting the real-time motion trajectory of the robotic arm in space.

[0121] If at a certain time t1, the real-time position of the robotic arm is (X1, Y1), and its velocity data is (V... x1 V y1 After a small time interval Δt, its new position can be approximated as (X1+V). x1 ×Δt,Y1+V y1By continuously calculating multiple such position points (×Δt), the real-time motion trajectory of the robotic arm can be generated.

[0122] Step S4013': Determine the expected motion trajectory of the robotic arm according to the target trajectory scheme, and calculate the trajectory deviation between the real-time motion trajectory and the expected motion trajectory.

[0123] It should be noted that the target trajectory scheme may include the various target processing areas of the robotic arm and their order, as well as the ideal path for the robotic arm to move between these areas and the corresponding motion parameters and other planned trajectory information.

[0124] Based on the planned trajectory information, the expected motion trajectory of the robotic arm at the current moment can be determined, that is, the motion route that the robotic arm should follow according to the target trajectory plan.

[0125] For example, if the target trajectory scheme specifies that the path for the robotic arm to move from sample processing area A to area B is a specific straight line trajectory, and specifies the corresponding speed, acceleration and other parameters, then this straight line trajectory and its corresponding motion parameter combination can constitute the expected motion trajectory of the robotic arm.

[0126] In practical implementation, the real-time motion trajectory generated by the signing page can be compared with the expected motion trajectory to calculate the trajectory deviation between the two. This trajectory deviation can be measured in various ways, such as calculating the distance difference between each point on the real-time motion trajectory and the corresponding point on the expected motion trajectory, or comparing the differences in motion parameters such as speed and acceleration. Simultaneously, speed deviation, direction deviation, etc., can also be calculated. Combining these deviation information yields the trajectory deviation of the robotic arm.

[0127] Step S4014': When the trajectory deviation is within the preset trajectory error range, determine the next target processing area of ​​the robotic arm based on the real-time motion trajectory.

[0128] It should be understood that the preset trajectory error range can also be a pre-set threshold range, used to determine whether the deviation between the actual movement trajectory of the robotic arm and the expected trajectory is within an acceptable range.

[0129] In practical applications, the preset trajectory error range can be set by considering factors such as the precision requirements of the robotic arm, the allowable error in sample processing, and various interference factors in the actual working environment. For example, in some high-precision sample processing scenarios, the preset trajectory error range may be small, requiring trajectory deviations to be at the millimeter level or even smaller; while in some scenarios with relatively lower precision requirements, this range can be appropriately widened to the centimeter level, etc.

[0130] In practical implementation, if the calculated trajectory deviation is within the preset trajectory error range, the actual motion trajectory of the robotic arm is close to the expected trajectory, and it can operate normally according to the requirements of the target trajectory scheme. At this time, the movement of the robotic arm can be considered controllable and in line with expectations. Then, based on the current real-time motion trajectory of the robotic arm and information such as the planned processing area sequence in the target trajectory scheme, the next target processing area of ​​the robotic arm can be determined.

[0131] Step S402: Control the robotic arm to move to the next target processing area so that the robotic arm performs a sample preprocessing operation on the sample to be processed. The sample preprocessing operation is the operation flow corresponding to the next target processing area.

[0132] In practice, the controller can move the robotic arm to the next target processing area. Once the robotic arm reaches the next target processing area, it can perform sample preprocessing operations on the corresponding sample according to the operation procedure of that area.

[0133] For example, if the next target processing area involves adding a certain hemolysin and allowing it to react, the robotic arm can precisely control the amount and time of adding the reagent after it arrives, complete the hemolysin addition operation, and then keep it in a static state to wait for the reaction to complete, ensuring the accuracy and standardization of the sample pretreatment operation.

[0134] This embodiment can avoid operational errors caused by the robotic arm not accurately reaching the target processing area when the robotic arm is stationary by calculating and judging the position deviation, thus laying the foundation for subsequent correct sample processing. At the same time, when the robotic arm is in motion, by monitoring the speed data and generating and comparing the real-time motion trajectory, it can promptly detect trajectory deviations during operation and flexibly adjust the next action of the robotic arm based on whether the trajectory deviation is within the preset error range. This ensures that the robotic arm can operate strictly according to the planned trajectory scheme, further enhancing the reliability and stability of the robotic arm in the process of performing tasks, and ensuring the coherent, efficient and high-quality completion of the sample preprocessing process.

[0135] Based on the first, second, and third embodiments of this application, the content in the fourth embodiment that is the same as or similar to the first, second, and third embodiments described above can be referred to the above description and will not be repeated hereafter. Please refer to [the relevant documentation] for further details. Figure 4 , Figure 4 This is a flowchart illustrating the fourth embodiment of the flow cytometry blood sample pretreatment method based on a robotic arm according to this application.

[0136] In this embodiment, in order to promptly detect and handle abnormal situations during the operation of the robotic arm and ensure the stability and reliability of the sample preprocessing process, step S402 further includes: steps S4021 to S4024:

[0137] Step S4021: Determine whether the reagent information and equipment information at the next target processing area meet the operational requirements.

[0138] It should be understood that this operational requirement information corresponds to each operational procedure and is used to ensure the accuracy and reliability of sample processing. This operational requirement information may include reagent information such as the type, specifications, and concentration of reagents, as well as equipment information such as the type, status, and parameter settings of the equipment.

[0139] It should be noted that before controlling the robotic arm to move to the next target processing area, it is possible to determine whether the reagent and equipment information at the next target processing area meets the operational requirements. This ensures that the robotic arm can smoothly and accurately perform the corresponding sample pretreatment operations after arriving at the area, avoiding adverse consequences such as operation failure or sample damage due to reagents or equipment not meeting the requirements.

[0140] Step S4022: When the operation requirement information is met, control the robotic arm to move to the next target processing area.

[0141] In practice, if the reagent information or equipment information at the next target processing area meets the operational requirements, it means that the currently planned target trajectory can be executed smoothly, and the robotic arm can be directly controlled to move to the next target processing area.

[0142] Step S4023: When the operation requirements are not met, determine the updated operation information and updated layout information of the sample to be processed based on the real-time location and the target trajectory scheme.

[0143] It should be understood that if the reagent or equipment information in the next target processing area does not meet the operational requirements, it means that the currently planned target trajectory scheme cannot be executed smoothly. The relevant operational and layout information needs to be updated and adjusted, and an abnormal alarm should be issued to inform the user.

[0144] It should be noted that the laboratory's resource management system and real-time monitoring data can be used to query the currently available reagent and equipment information, and then the location of the target processing area corresponding to the next operation procedure can be re-determined based on this information.

[0145] For example, if the reagent required for a certain operation of a sample is insufficient in the original next target processing area, the operation sequence of the sample can be updated by finding other areas that store sufficient reagents, adjusting the operation to other suitable areas, and updating the layout information such as the coordinates of the sample in the spatial layout accordingly. At the same time, the operation time of each operation step can be re-estimated.

[0146] In practice, the updated operation information and updated layout information of the sample to be processed can be re-evaluated and determined based on the real-time position of the robotic arm and the original target trajectory scheme.

[0147] Step S4024: Determine the updated target trajectory scheme of the robotic arm based on the updated operation information and the updated layout information, and control the robotic arm to perform sample preprocessing operations on the sample to be processed according to the updated target trajectory scheme.

[0148] It should be understood that after obtaining the updated operation and layout information, the updated target trajectory scheme for the robotic arm can be redefined based on this new information. This process of redefined the updated target trajectory scheme can be similar to the planning process of the initial target trajectory scheme described above, and will not be elaborated upon in this embodiment.

[0149] In practical implementation, after determining the updated target trajectory scheme, the robotic arm can be controlled to perform sample pretreatment operations according to the updated target trajectory scheme: the robotic arm moves to the new operation area according to the updated target trajectory scheme, and performs corresponding processing on the sample according to the updated operation procedures and requirements, such as adding the correct reagents and performing the correct centrifugation operation, to ensure that the sample pretreatment process can be completed smoothly and accurately.

[0150] Furthermore, when the position deviation of the aforementioned robotic arm exceeds the preset position deviation range when it is stationary, or when the trajectory deviation exceeds the preset trajectory error range when it is in motion, an alarm can also be issued, and an updated target trajectory scheme can be regenerated. This enables real-time monitoring and adjustment of the robotic arm's trajectory, ensuring the stability and reliability of the entire preprocessing process.

[0151] This embodiment ensures that the sample pretreatment process can be completed smoothly and accurately by checking the reagents and equipment in the area before controlling the robotic arm to move to the next target processing area. Even if the reagents or equipment do not meet the original operating requirements, the sample can be effectively processed by updating and adjusting them, thus ensuring the stability and reliability of the entire pretreatment process.

[0152] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the flow cytometry blood sample pretreatment method based on the robotic arm in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0153] This application also provides a flow cytometry blood sample pretreatment system, the system comprising: an electrically connected controller and a robotic arm.

[0154] The controller is used to acquire operation information and layout information of the sample to be processed, and to encode the sample features based on the operation information and layout information.

[0155] The controller is further configured to generate an initial chromosome population based on the sample features. The initial chromosome population includes several chromosomes obtained by initializing the population from the first generation chromosomes. Each chromosome represents a trajectory planning scheme, and the gene segments in each of the first generation chromosomes are the sample features.

[0156] The controller is also used to perform genetic mutation processing on the initial chromosome population to obtain a target optimized population, and to select target chromosomes that meet preset fitness conditions from the target optimized population.

[0157] The controller is further configured to determine the trajectory planning scheme corresponding to the target chromosome as the target trajectory scheme of the robotic arm, and send the control command corresponding to the target trajectory scheme to the robotic arm;

[0158] The robotic arm is used to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme.

[0159] In this embodiment, the controller can accurately acquire sample information and encode it effectively, providing comprehensive and accurate sample features for subsequent trajectory planning. Then, based on the sample features, a genetic evolution algorithm is used to optimize the robot arm trajectory, so that the robot arm's trajectory planning can fully consider the characteristics and layout of the samples, improve the synchronicity and overall efficiency of sample processing, reduce sample waiting time, and avoid the sample affecting the detection results due to untimely processing.

[0160] Other embodiments or specific implementations of the flow cytometry blood sample pretreatment system of this application can be found in the above-described method embodiments, and will not be repeated here.

[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other elements in the process, method, article, or system that includes that element.

[0162] The sequence numbers of the above embodiments of the present invention are merely for description and do not represent the superiority or inferiority of the embodiments. They are only some embodiments of this application and are not intended to limit the scope of this application. All equivalent structural transformations made under the technical concept of this application and based on the content of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the protection scope of this application.

Claims

1. A flow cytometry blood sample pretreatment method based on a robotic arm, characterized in that, The method includes: Obtain the operation information and layout information of the sample to be processed, and encode the sample features based on the operation information and layout information; An initial chromosome population is generated based on the sample features. The initial chromosome population includes several chromosomes obtained by initializing the population from the first generation chromosomes. Each chromosome represents a trajectory planning scheme, and the gene segments in each of the first generation chromosomes are the sample features. Genetic mutation processing is performed on the initial chromosome population to obtain a target optimized population, and target chromosomes that meet preset fitness conditions are selected from the target optimized population. The trajectory planning scheme corresponding to the target chromosome is determined as the target trajectory scheme of the robotic arm, and the robotic arm is controlled to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme; The step of encoding based on the operation information and layout information to obtain sample features includes: The operation process and corresponding process time of the sample to be processed are determined according to the operation information, and the spatial coordinates of the sample to be processed are determined according to the layout information. The operation information includes each operation process and the corresponding process time, and the layout information includes the regional position of the sample processing area corresponding to each operation process. The spatial coordinates, the operation process, and the corresponding process time are feature-encoded using a real number encoding method to obtain the sample features corresponding to the sample to be processed. The step of performing genetic mutation processing on the initial chromosome population to obtain the target optimized population includes: In the current iteration round, the tournament selection method is used to determine the next generation chromosome population from the initial chromosome population, including: constructing a fitness function based on the time consumption of each process and the location of each region; The initial chromosome population is divided into a predetermined number of groups, and the fitness of chromosomes in each group is evaluated according to the fitness function. Based on the evaluation results, chromosomes within a preset fitness range are selected from each group to form the next generation chromosome population; Gene crossover and gene mutation operations are performed on the chromosomes in the next generation chromosome population to obtain an updated chromosome population. The updated chromosome population is used as the initial chromosome for the next iteration round, and the process returns to the step of determining the next generation chromosome population from the initial chromosome population using the tournament selection method. The target optimized population is obtained when the current iteration round meets the preset maximum iteration round.

2. The method as described in claim 1, characterized in that, The step of controlling the robotic arm to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme includes: The real-time position of the robotic arm is obtained, and the next target processing area of ​​the robotic arm is determined based on the real-time position and the target trajectory scheme. The robotic arm is controlled to move to the next target processing area so that the robotic arm can perform sample preprocessing operations on the sample to be processed. The sample preprocessing operations are the operation flow corresponding to the next target processing area.

3. The method as described in claim 2, characterized in that, The step of obtaining the real-time position of the robotic arm and determining the next target processing area of ​​the robotic arm based on the real-time position and the target trajectory scheme includes: The real-time position and real-time operating status of the robotic arm are obtained, wherein the real-time operating status is either a stationary state or a moving state. When the robotic arm is stationary, the current target processing area of ​​the robotic arm is determined according to the target trajectory scheme, and the positional deviation between the real-time position and the current target processing area is calculated. When the position deviation is within the preset position error range, the next target processing area of ​​the robotic arm is determined based on the real-time position and the target trajectory scheme.

4. The method as described in claim 3, characterized in that, After the step of obtaining the real-time position and real-time operating status of the robotic arm, the method further includes: When the robotic arm is in motion, determine the speed data of the robotic arm, and generate a real-time motion trajectory based on the real-time position and the speed data; The expected motion trajectory of the robotic arm is determined according to the target trajectory scheme, and the trajectory deviation between the real-time motion trajectory and the expected motion trajectory is calculated. When the trajectory deviation is within the preset trajectory error range, the next target processing area of ​​the robotic arm is determined based on the real-time motion trajectory.

5. The method as described in claim 2, characterized in that, The operation information also includes: operation requirement information, and the step of controlling the robotic arm to move to the next target processing area includes: Determine whether the reagent information and equipment information at the next target processing area meet the operational requirements. When the operational requirements are met, the robotic arm is controlled to move to the next target processing area.

6. The method as described in claim 5, characterized in that, After the step of determining whether the reagent information and equipment information at the next target processing area meet the operational requirements, the method further includes: If the operation requirements are not met, the updated operation information and updated layout information of the sample to be processed are determined based on the real-time location and the target trajectory scheme. Based on the updated operation information and the updated layout information, the updated target trajectory scheme of the robotic arm is determined, and the robotic arm is controlled to perform sample preprocessing operations on the sample to be processed in accordance with the updated target trajectory scheme.

7. A flow cytometry blood sample pretreatment system, characterized in that, The system is used to perform the flow cytometry blood sample pretreatment method based on a robotic arm as described in any one of claims 1 to 6, the system comprising: a controller and a robotic arm; The controller is used to acquire operation information and layout information of the sample to be processed, and to encode the sample features based on the operation information and layout information. The controller is further configured to generate an initial chromosome population based on the sample features. The initial chromosome population includes several chromosomes obtained by initializing the population from the first generation chromosomes. Each chromosome represents a trajectory planning scheme, and the gene segments in each of the first generation chromosomes are the sample features. The controller is also used to perform genetic mutation processing on the initial chromosome population to obtain a target optimized population, and to select target chromosomes that meet preset fitness conditions from the target optimized population. The controller is further configured to determine the trajectory planning scheme corresponding to the target chromosome as the target trajectory scheme of the robotic arm, and send the control command corresponding to the target trajectory scheme to the robotic arm; The robotic arm is used to perform sample preprocessing operations on the sample to be processed according to the target trajectory scheme.

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