Carotid artery ultrasonic scanning body position optimization method and device, storage medium and equipment

Abstract

The invention provides a carotid artery ultrasonic scanning body position optimization method and device, a storage medium and equipment. The method comprises the steps that a historical task pose data set under a human body coordinate system is acquired; obtaining a representative task pose set according to the historical task data set; constructing a position performance evaluation model according to the representative task position set; according to the body position performance evaluation model, body position parameters conforming to preset body position performance evaluation indexes are determined, and a scanned body position is obtained according to the body position parameters. According to the method, the optimal body position of the detected object human body can be determined when the carotid artery is automatically scanned, repeated operation is reduced, and scanning efficiency and imaging quality are improved.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and more specifically, to a method, apparatus, storage medium, and device for optimizing body position during carotid ultrasound scanning. Background Technology

[0002] Currently, there is a lack of methods for comprehensive optimization of the patient's overall positioning in carotid artery scanning using ultrasound robots. Clinical positioning is often based on experience, making it difficult to predict in advance whether the carotid artery's workspace will exceed the robot's joint travel, singular configurations, or collision constraints. If the scan exceeds the robotic arm's working area or exhibits abnormal posture, it may force an interruption and repositioning, requiring repeated scanning and posing safety hazards. Furthermore, even in seemingly achievable positions, poor motion performance may occur during actual scanning, affecting fit stability and imaging quality. Summary of the Invention

[0003] To address the issue of body positioning optimization, this application provides a method for optimizing body positioning during carotid artery ultrasound scanning. This method can determine the optimal body position of the subject during autonomous carotid artery scanning, reducing repetitive operations and improving scanning efficiency and imaging quality.

[0004] This application proposes a method for optimizing the patient's position during carotid artery ultrasound scanning, applicable to autonomous ultrasound robot scanning, including:

[0005] Obtain the historical task pose dataset in human coordinate system;

[0006] Based on the historical task dataset, a representative task pose set is obtained;

[0007] A body posture performance evaluation model is constructed based on the representative task pose set;

[0008] Based on the posture performance evaluation model, determine the posture parameters that meet the predetermined posture performance evaluation indicators, and obtain the scanning posture based on the posture parameters.

[0009] In some embodiments, prior to the step of obtaining the historical task pose dataset in the human coordinate system, the method further includes:

[0010] The historical pose data of the actuators of the ultrasonic robot are obtained, and a task pose dataset is constructed based on the historical pose data.

[0011] Construct a human body coordinate system;

[0012] The task pose dataset is converted to a historical task pose dataset in the human coordinate system.

[0013] In some embodiments, the step of constructing the human coordinate system includes:

[0014] Based on the base coordinate system of the ultrasonic robot, obtain the origin position of the human body coordinate system;

[0015] The rotation matrix of the human body relative to the base coordinate system is obtained based on bounding box and principal component analysis;

[0016] Based on the origin position and the rotation matrix, obtain the transformation matrix of the human body coordinate system relative to the base coordinate system;

[0017] The human body coordinate system is constructed based on the transformation matrix.

[0018] In some embodiments, the step of obtaining a representative task pose set based on the historical task dataset includes:

[0019] Obtain the pose matrix of the task points in the historical task dataset under the human coordinate system, and convert the pose matrix into a six-dimensional pose vector;

[0020] The six-dimensional pose vector is clustered into multiple clusters using a clustering algorithm. The center vector of each cluster is used as a representative of a typical task pose to obtain multiple cluster center vectors.

[0021] The multiple cluster center vectors constitute a cluster center set, which is used as a representative task pose set.

[0022] In some embodiments, the step of constructing a postural performance evaluation model based on the representative task pose set includes:

[0023] Obtain representative poses and their pose matrices from the representative task pose set, and obtain the pose of the actuator in the base coordinate system based on the representative poses and the pose matrix.

[0024] Obtain the joint angle vectors of the given pose;

[0025] Based on the joint angle vector, the postural performance objective function is obtained, and the objective function is used as the postural performance evaluation model.

[0026] In some embodiments, the step of obtaining representative poses and their pose matrices from a representative task pose set, and obtaining the pose of the actuator in the base coordinate system based on the representative poses and the pose matrix, includes:

[0027] The body position parameterization of the object under test is converted into a three-degree-of-freedom vector in the base coordinate system;

[0028] The human coordinate system is transformed into a reference task coordinate system using three degrees of freedom.

[0029] Obtain representative poses from the representative task pose set, and obtain the pose matrix of the representative poses in the reference task coordinate system according to the reference task coordinate system;

[0030] The pose of the ultrasonic robot's actuator in the base coordinate system is obtained based on the pose matrix and the transformation matrix.

[0031] In some embodiments, the step of obtaining the postural performance objective function based on the joint angle vector includes:

[0032] The maneuverability of the ultrasonic robot's actuator is determined based on the joint angle vector;

[0033] Based on the maneuverability, the average maneuverability of all representative task poses is obtained, and the average maneuverability is used as the objective function of the postural performance. The average maneuverability is calculated by the following formula.

[0034]

[0035] in, This represents the three-degree-of-freedom vector of the measured object's position in the base coordinate system. For the first The maneuverability of a representative pose, where N is the number of all representative task poses.

[0036] In some embodiments, a carotid ultrasound scanning positioning optimization device is also provided, comprising:

[0037] The data acquisition module is used to acquire historical task pose datasets in the human coordinate system.

[0038] The representative task pose acquisition module is used to obtain a set of representative task poses based on the historical task dataset.

[0039] The performance evaluation module is used to construct a body position performance evaluation model based on the representative task pose set.

[0040] The body position optimization module is used to determine the body position parameters that meet the predetermined body position performance evaluation indicators according to the body position performance evaluation model, and to obtain the scanning position according to the body position parameters.

[0041] In some embodiments, a computer-readable storage medium is also provided, the computer-readable storage medium storing a computer program that performs the aforementioned method for optimizing the positioning of a carotid ultrasound scan.

[0042] In some embodiments, a carotid ultrasound scanning positioning optimization device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the carotid ultrasound scanning positioning optimization method as described above.

[0043] The beneficial technical effects of this application are as follows:

[0044] The carotid artery ultrasound scanning posture optimization method provided in this application involves: acquiring a historical task pose dataset in a human coordinate system; obtaining a representative task pose set based on the historical task dataset; constructing a posture performance evaluation model based on the representative task pose set; determining posture parameters that meet predetermined posture performance evaluation indicators based on the posture performance evaluation model; and obtaining the scanning posture based on the posture parameters. This method quickly determines the optimal posture for the subject during autonomous carotid artery scanning, improving safety, reducing repetitive operations, and enhancing scanning efficiency and imaging quality. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the method for optimizing the body position during carotid artery ultrasound scanning in the embodiments of this application;

[0047] Figure 2 This is a schematic diagram of multiple historical task points and human coordinate systems in an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the distribution of historical task points in the human coordinate system in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of representative task pose extraction in the embodiments of this application;

[0050] Figure 5 This is a schematic diagram of the carotid artery ultrasound scanning position optimization device in the embodiments of this application. Detailed Implementation

[0051] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0052] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0053] It should be noted that the executing entity of each embodiment of this application can be a computing service system with data processing, network communication, and program execution functions, such as an electronic system capable of realizing the above functions, an ultrasonic robot autonomous scanning system, etc. The following description uses an ultrasonic robot autonomous scanning system (hereinafter referred to as the "system") as an example to illustrate the following embodiments. The ultrasonic robot autonomous scanning system includes a robot with a robotic arm. An actuator, i.e., an ultrasonic probe, is installed at the end of the robotic arm. The system controls the movement of the robotic arm to perform ultrasonic autonomous scanning on the object being tested. The system also includes other components, such as a memory and ultrasonic imaging equipment, which will not be described in detail here. The object being tested can be a patient or a testing personnel; this is not limited here.

[0054] The core of the technical solution provided in this application lies in constructing a human coordinate system using the end-effector trajectory data of the actuator (ultrasound probe) collected in historical autonomous carotid artery scanning tasks. Based on the constructed human coordinate system, a clustering algorithm is used to extract a set of representative (general and universal) task poses. Under the given joint angle constraints of the ultrasound robot, the maneuverability assessment and optimization search of the subject's position are performed to determine the optimal position and orientation that maximizes the overall maneuverability of the representative task poses. This provides a quantitative basis for position recommendations and placement for autonomous carotid artery ultrasound robot scanning.

[0055] Reference Figure 1 As shown in the figure, this application provides a method for optimizing the body position during carotid ultrasound scanning, including:

[0056] Step 101: Obtain the historical task pose dataset in the human coordinate system.

[0057] In some embodiments, prior to the step of obtaining the historical task pose dataset in the human coordinate system, the method further includes:

[0058] (1) Obtain the historical pose data of the actuator of the ultrasonic robot, and construct a task pose dataset based on the historical pose data.

[0059] The historical pose data (trajectory data) of the end effector of the ultrasound robot collected during the historical autonomous carotid artery scanning task is obtained, and the task pose dataset is constructed based on the historical pose data.

[0060] (2) Construct the human body coordinate system.

[0061] In order to unify the autonomous carotid artery scanning task for different subjects and in different lying positions into the same reference system, it is necessary to establish a human coordinate system corresponding to the actual body position of the subject.

[0062] (3) Convert the task pose dataset to the historical task pose dataset in the human coordinate system.

[0063] The human coordinate system established through the above steps converts the task pose dataset into a set of historical task poses of the reference task within the human coordinate system. In this embodiment, the reference task can be the first task or can be set according to requirements. This unifies the task pose dataset under the same coordinate system, facilitating subsequent search for the optimal body position.

[0064] Step 102: Obtain a representative set of task poses based on the historical task dataset.

[0065] Clustering algorithms are used to perform cluster analysis on historical task datasets to extract a representative set of task poses.

[0066] Step 103: Construct a body position performance evaluation model based on the representative task pose set.

[0067] In this embodiment, under the given joint angle constraints of the ultrasonic robot, a model for evaluating the positional performance of the tested object is established to assess its maneuverability.

[0068] Step 104: Determine the positional parameters that meet the predetermined positional performance evaluation indicators according to the positional performance evaluation model, and obtain the scanning position according to the positional parameters.

[0069] In this embodiment, the body position performance evaluation model is optimized and searched to determine the body position parameters that make the overall maneuverability of the representative task pose optimal. Based on the body position parameters, the scanning body position, i.e., the body position and orientation, is obtained, providing a quantitative body position recommendation and positioning basis for the autonomous scanning of the carotid ultrasound robot.

[0070] The method described in the above embodiments involves: acquiring a historical task pose dataset in a human coordinate system; obtaining a representative task pose set based on the historical task dataset; constructing a postural performance evaluation model based on the representative task pose set; determining postural parameters that meet predetermined postural performance evaluation indicators based on the postural performance evaluation model; and obtaining the scanning position based on the postural parameters. This method rapidly determines the optimal position of the subject during autonomous carotid artery scanning, improving safety, reducing repetitive operations, and enhancing scanning efficiency and imaging quality.

[0071] In some embodiments, the method further includes the step of constructing a human coordinate system, which includes:

[0072] Step 201: Obtain the origin position of the human body coordinate system based on the base coordinate system of the ultrasonic robot.

[0073] (1) For any carotid artery autonomous scanning task, collect the position points of all actuator ends in the base coordinate system in the task, and obtain the pose matrix and position vector of the position points.

[0074] In this embodiment, for each autonomous carotid artery scanning task j, the position points of the ends of all actuators (i.e., ultrasound probes) in the task are collected in the base coordinate system {B} of the ultrasound robot. Let the pose matrix of the k-th point in the base coordinate system be... The position vector is Find the minimum and maximum values ​​in each of the three coordinate axes in the base coordinate system. , , , , , .

[0075] (2) Taking the midpoint of each direction as the position vector of the origin of the human body coordinate system in the base coordinate system, that is:

[0076]

[0077] in, Represents the human coordinate system in the j-th task { The origin is located at the geometric center of the base coordinate system {B}.

[0078] Step 202: Obtain the rotation matrix of the human body relative to the base coordinate system based on bounding box and principal component analysis.

[0079] First, construct the covariance moments. And for the covariance matrix Perform eigenvalue decomposition to obtain three pairwise orthogonal principal component direction vectors for the j-th task. , , Its corresponding eigenvalues, , These correspond to the two direction vectors with larger eigenvalues. The covariance matrix is ​​represented as follows: :

[0080]

[0081] in, Let j be the number of task points for the j-th task. Let be the translation vector of the i-th point in the j-th task relative to the base coordinate system {B}.

[0082] Then, the directions of the first and second principal components are selected as the human body coordinate system. The z-axis direction is determined by the right-hand rule, and then the rotation matrix of the human body relative to the base coordinate system in the j-th task is constructed. .

[0083] Step 203: Based on the origin position and the rotation matrix, obtain the transformation matrix of the human body coordinate system relative to the base coordinate system.

[0084] Based on the origin position determined in the above steps, construct the j-th task human coordinate system. Transformation matrix relative to the base coordinate system :

[0085]

[0086] Step 204: Construct the human body coordinate system based on the transformation matrix.

[0087] Based on the above process, a human coordinate system corresponding to the actual body position of the measured object can be established using the transformation matrix, such as... Figure 2 As shown, this allows for the unification of autonomous carotid artery scanning tasks across different subjects and lying positions within the same reference frame. This not only reduces system deployment and calibration costs but also ensures repeatability and robustness in clinical settings, providing a foundation for subsequent data-driven position optimization.

[0088] Furthermore, in some embodiments, the step of converting the task pose dataset to a historical task pose dataset in the human coordinate system includes:

[0089] Using the transformation matrix of the above embodiments The poses of all end points recorded in the scanning task are unified to the human coordinate system of the reference task. In this embodiment, this can be the human coordinate system of the first task. Below, as follows Figure 3As shown. As in the following formula:

[0090]

[0091] in, This is the pose matrix of the k-th point in the j-th task after unifying the coordinate system. Let be the pose matrix of the k-th point in the base coordinate system for the j-th task. This is the transformation matrix (pose matrix) between the human coordinate system of the first task and the base coordinate system. Using these two pose matrices and the transformation matrix, the position of the k-th point in the j-th task is transformed from the base coordinate system to the position in the human coordinate system of the first task. This unification of the coordinate system facilitates subsequent searching for the optimal body position parameters.

[0092] In some embodiments, the step of obtaining a representative task pose set based on the historical task dataset includes:

[0093] Step 301: Obtain the pose matrix of the task points in the historical task dataset under the human coordinate system, and convert the pose matrix into a six-dimensional pose vector.

[0094] pose matrix of all task points after unifying coordinate system This is then converted into a six-dimensional pose vector consisting of three translations and three rotations. This first unifies all task points in the task dataset to the reference task coordinate system, and then converts them into six-dimensional pose vectors, facilitating cluster analysis of the tasks.

[0095] Step 302: The six-dimensional pose vector is clustered into multiple clusters using a clustering algorithm. The center vector of each cluster is used as a representative of a typical task pose to obtain multiple cluster center vectors.

[0096] The K-means clustering algorithm is used to divide a large number of discrete task points into several clusters in the six-dimensional pose space, and the center vector of each cluster is... As a representative of a typical task pose, namely:

[0097]

[0098] in, These are the coordinate systems relative to the human body in the first task. The x, y, and z axes are translated. Represents rotation along the x, y, and z axes.

[0099] Step 303: Multiple cluster center vectors form a cluster center set, which is then used as a representative task pose set.

[0100] Each The representative pose simultaneously reflects a typical spatial position and actuator attitude, the cluster center set { This constitutes a representative set of task poses, such as Figure 4 As shown.

[0101] Furthermore, for subsequent calculations, these six-dimensional pose vectors are converted into pose matrices. While ensuring coverage of different anatomical regions of the carotid artery and the diversity of common scanning postures, this dataset significantly reduces the amount of data involved in position optimization assessment, thereby improving the efficiency of subsequent optimization solutions.

[0102] In some embodiments, the step of constructing a postural performance evaluation model based on the representative task pose set includes:

[0103] Step 401: Obtain the representative pose and its pose matrix from the representative task pose set, and obtain the pose of the actuator in the base coordinate system based on the representative pose and the pose matrix.

[0104] (1) The position parameter of the object to be measured is converted into a three-degree-of-freedom vector in the base coordinate system.

[0105] In this embodiment, the position of the object under test relative to the ultrasonic robot base coordinate system is parameterized as a three-degree-of-freedom vector. :

[0106]

[0107] in, , Represents the human body coordinate system { } On the examination bed, translation coordinates This indicates the rotation angle around the Z-axis.

[0108] (2) The human coordinate system is converted into a reference task coordinate system through three degrees of freedom.

[0109] { } is the reference (first) mission coordinate system obtained after three-degree-of-freedom transformation.

[0110] (3) Obtain the representative poses in the representative task pose set, and obtain the pose matrix of the representative poses in the reference task coordinate system according to the reference task coordinate system.

[0111] For each representative pose of the actuator end effector This allows us to obtain representative poses. exist{ Pose matrix in coordinate system .

[0112] (4) Based on the pose matrix and the transformation matrix, the pose of the actuator of the ultrasonic robot in the base coordinate system is obtained.

[0113] The position of the actuator in the base coordinate system is further obtained. :

[0114]

[0115] Step 402: Obtain the joint angle vector of the pose.

[0116] In this embodiment, the maneuverability of the robotic arm is used as the core evaluation index, and an optimization search is performed within the body position parameter space of the tested object. For each pose point... The joint angle vectors are obtained by solving the robot's inverse kinematics.

[0117] Step 403: Based on the joint angle vector, obtain the postural performance objective function, and use the objective function as the postural performance evaluation model.

[0118] (1) Determine the maneuverability of the actuator of the ultrasonic robot based on the joint angle vector.

[0119] Maneuverability is the ability of a robotic arm to move in all directions in its current posture. The larger this value is, the better the robotic arm's ability to move in all directions.

[0120] In this embodiment, based on the joint angle vector Solving for the maneuverability of a robotic arm (actuator):

[0121]

[0122] in For the first Maneuverability of a representative pose, For the Jacobian matrix of the robotic arm, It is a determinant operation.

[0123] (2) Based on the maneuverability, obtain the average maneuverability of all representative task poses, and use the average maneuverability as the objective function of the body position performance. The average maneuverability is calculated using the following formula:

[0124]

[0125] in, This represents the three-degree-of-freedom vector of the measured object's position in the base coordinate system. For the first The maneuverability of a representative pose, where N is the number of all representative task poses.

[0126] Furthermore, in some embodiments, positional parameters that meet predetermined positional performance evaluation indicators are determined according to the positional performance evaluation model, and the scanning position is obtained according to the positional parameters.

[0127] Based on this objective function, intelligent optimization algorithms (such as genetic algorithms, particle swarm optimization, simulated annealing, etc.) are used to solve the problem, with the body position vector as... The search variables automatically find the robotic arm position parameters that optimize average maneuverability while satisfying all joint angle constraints.

[0128] Further, based on optimal body positioning parameters, the subject is guided to lie in the optimal scanning position relative to the robot base before the scan, i.e., the best position and orientation. Medical personnel position the subject accordingly and then control the ultrasound robot to perform autonomous carotid artery scanning in this optimal position. While ensuring the safety margin of the ultrasound robot's joints, the overall maneuverability and operational stability during autonomous carotid artery ultrasound scanning are improved.

[0129] Based on the same inventive concept, embodiments of this application also provide a carotid artery ultrasound scanning positioning optimization device, such as... Figure 5 As shown, it includes:

[0130] Data acquisition module 501 is used to acquire historical task pose datasets in human coordinate system;

[0131] The representative task pose acquisition module 502 is used to obtain a representative task pose set based on the historical task dataset.

[0132] Performance evaluation module 503 is used to construct a body position performance evaluation model based on the representative task pose set;

[0133] The body position optimization module 504 is used to determine the body position parameters that meet the predetermined body position performance evaluation indicators according to the body position performance evaluation model, and to obtain the scanning body position according to the body position parameters.

[0134] In this embodiment, the carotid ultrasound scanning position optimization device is a device corresponding to the carotid ultrasound scanning position optimization method provided in the above embodiments. Its specific implementation can be found in the detailed description of the carotid ultrasound scanning position optimization method in the above embodiments, and will not be repeated here. Based on the same inventive concept, this embodiment also provides a computer-readable storage medium storing a computer program that executes the carotid ultrasound scanning position optimization method as described in the embodiments.

[0135] Based on the same inventive concept, embodiments of this application also provide a carotid ultrasound scanning positioning optimization device, including the carotid ultrasound scanning positioning optimization method described in the above embodiments. Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein.

[0136] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0137] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for optimizing patient positioning during carotid artery ultrasound scanning, applied to autonomous ultrasound robot scanning, characterized in that, include: Obtain the historical task pose dataset in human coordinate system; Based on the historical task dataset, a representative task pose set is obtained; A body posture performance evaluation model is constructed based on the representative task pose set; Based on the posture performance evaluation model, determine the posture parameters that meet the predetermined posture performance evaluation indicators, and obtain the scanning posture based on the posture parameters.

2. The method according to claim 1, characterized in that, Before the step of obtaining the historical task pose dataset in the human coordinate system, the method further includes: The historical pose data of the actuators of the ultrasonic robot are obtained, and a task pose dataset is constructed based on the historical pose data. Construct a human body coordinate system; The task pose dataset is converted to a historical task pose dataset in the human coordinate system.

3. The method according to claim 2, characterized in that, The steps for constructing the human coordinate system include: Based on the base coordinate system of the ultrasonic robot, obtain the origin position of the human body coordinate system; The rotation matrix of the human body relative to the base coordinate system is obtained based on bounding box and principal component analysis; Based on the origin position and the rotation matrix, obtain the transformation matrix of the human body coordinate system relative to the base coordinate system; The human body coordinate system is constructed based on the transformation matrix.

4. The method according to claim 1, characterized in that, The step of obtaining a representative set of task poses based on the historical task dataset includes: Obtain the pose matrix of the task points in the historical task dataset under the human coordinate system, and convert the pose matrix into a six-dimensional pose vector; The six-dimensional pose vector is clustered into multiple clusters using a clustering algorithm. The center vector of each cluster is used as a representative of a typical task pose to obtain multiple cluster center vectors. The multiple cluster center vectors constitute a cluster center set, which is used as a representative task pose set.

5. The method according to claim 1, characterized in that, The step of constructing a postural performance evaluation model based on the representative task pose set includes: Obtain representative poses and their pose matrices from the representative task pose set, and obtain the pose of the actuator in the base coordinate system based on the representative poses and the pose matrix. Obtain the joint angle vectors of the given pose; Based on the joint angle vector, the postural performance objective function is obtained, and the objective function is used as the postural performance evaluation model.

6. The method according to claim 5, characterized in that, The step of obtaining representative poses and their pose matrices from a representative task pose set, and obtaining the pose of the actuator in the base coordinate system based on the representative poses and the pose matrix, includes: The body position parameterization of the object under test is converted into a three-degree-of-freedom vector in the base coordinate system; The human coordinate system is transformed into a reference task coordinate system using three degrees of freedom. Obtain representative poses from the representative task pose set, and obtain the matrix of the representative poses in the reference task coordinate system according to the reference task coordinate system; The pose of the ultrasonic robot's actuator in the base coordinate system is obtained based on the pose matrix and the transformation matrix.

7. The method according to claim 5, characterized in that, The step of obtaining the postural performance objective function based on the joint angle vector includes: The maneuverability of the ultrasonic robot's actuator is determined based on the joint angle vector; Based on the maneuverability, the average maneuverability of all representative task poses is obtained, and the average maneuverability is used as the objective function of the postural performance. The average maneuverability is calculated by the following formula. ； in, This represents the three-degree-of-freedom vector of the measured object's position in the base coordinate system. For the first The maneuverability of a representative pose, where N is the number of all representative task poses.

8. A carotid artery ultrasound scanning position optimization device, characterized in that, include: The data acquisition module is used to acquire historical task pose datasets in the human coordinate system. The representative task pose acquisition module is used to obtain a set of representative task poses based on the historical task dataset. The performance evaluation module is used to construct a body position performance evaluation model based on the representative task pose set. The body position optimization module is used to determine the body position parameters that meet the predetermined body position performance evaluation indicators according to the body position performance evaluation model, and to obtain the scanning position according to the body position parameters.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the method as described in any one of claims 1-7.

10. A carotid artery ultrasound scanning position optimization device, characterized in that, include: A processor; a memory for storing processor-executable commands; wherein the processor is configured to perform the method as described in any one of claims 1-7.

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