Human-machine collaborative telemedicine control method and medical robot based on dynamic medical needs

Abstract

The present application is applicable to the field of human-machine collaborative technology, and in particular relates to a human-machine collaborative remote medical control method and a medical robot based on dynamic medical needs. The method comprises: receiving a remote medical control request in real time; parsing the medical needs carried in the remote medical control request and obtaining multiple operating features of each robotic arm; determining an operating feature sequence according to the medical needs and each operating feature; performing operations according to the order of each operating feature in the operating feature sequence, and adjusting the operating parameters after the previous operation process based on the current operating feature during any operation. Therefore, the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiment of the present application can solve the problem of lack of dynamic adaptability to medical needs.

Description

Technical Field

[0001] The present application belongs to the field of human-machine collaborative technology, and in particular relates to a human-machine collaborative remote medical control method and a medical robot based on dynamic medical needs. Background Art

[0002] Human-machine collaborative telemedicine control technology based on dynamic medical needs is an innovative application that combines artificial intelligence, remote communication and medical technology. It aims to achieve efficient and accurate execution of telemedicine services through close cooperation between doctors and medical robots.

[0003] Traditional medical control technologies lack optimized designs tailored to the specific needs of medical scenarios. They fail to fully account for the complexity and diversity of medical operations, lack effective operation sequencing mechanisms, and are unable to adjust the order of operations in real time based on changing medical needs. This can lead to confusion in the execution order of operational instructions. During remote consultations, a patient's condition can change at any time. Failure to adjust the medical robot's operation sequence and operating parameters in real time to accommodate these changes can lead to decreased accuracy in consultation results. Therefore, current medical control methods lack dynamic adaptability to medical needs. Summary of the Invention

[0004] The embodiments of the present application provide a human-machine collaborative remote medical control method and a medical robot based on dynamic medical needs, which can solve the problem of lack of dynamic adaptability to medical needs.

[0005] In a first aspect, an embodiment of the present application provides a method for controlling human-machine collaborative telemedicine based on dynamic medical needs, including:

[0006] Receive telemedicine control requests in real time;

[0007] parsing the medical requirements carried in the remote medical control request and obtaining a plurality of operating characteristics of each robotic arm; wherein the medical requirements include different requirements for operating parameters of the plurality of robotic arms at multiple time periods;

[0008] Determining an operation feature sequence according to the medical need and each of the operation features; wherein the operation feature sequence is used to reflect the operation priority of the plurality of the operation features, and the operation features are used to reflect the control requirements for the operation parameters of the robotic arm;

[0009] The operation is performed according to the order of each operation feature in the operation feature sequence, and in any operation process, the operation parameters after the previous operation process are adjusted based on the current operation feature; wherein, one operation process corresponds to an operation feature in the operation feature sequence.

[0010] The above technical solutions in the embodiments of the present application have at least the following technical effects:

[0011] The embodiment of the present application provides a human-machine collaborative telemedicine control method based on dynamic medical needs, which receives a telemedicine control request in real time; parses the medical needs carried in the telemedicine control request and obtains multiple operation features of each robotic arm; determines an operation feature sequence based on the medical needs and each operation feature, reflecting the operation priority of multiple operation features, which helps doctors and medical robots to operate according to the priority order, thereby improving the orderliness and safety of medical operations; operates according to the order of each operation feature in the operation feature sequence, and during any operation, adjusts the operation parameters after the previous operation process based on the current operation feature. The operation feature provides a clear basis for adjusting the subsequent operation parameters, making the medical operation more accurate and in line with actual needs. Operating according to the order of the operation feature sequence is conducive to improving the efficiency of medical operations. Adjusting the operation parameters according to the current operation feature realizes dynamic optimization of the operation parameters, which is conducive to improving the adaptability and flexibility of medical operations. Therefore, the human-machine collaborative telemedicine control method based on dynamic medical needs provided by the embodiment of the present application, through human-machine collaboration, combines the doctor's experience and intelligent algorithms, enables medical operations to be carried out in an orderly manner, which is conducive to improving the dynamic adaptability of human-machine collaborative telemedicine control based on dynamic medical needs to medical needs.

[0012] In a possible implementation of the first aspect, parsing the medical requirement carried in the telemedicine control request and obtaining multiple operating characteristics of each robotic arm includes:

[0013] Acquiring real-time images and real-time tremor frequencies; wherein the real-time images include images of the patient's body tissue and each of the robotic arms, and the real-time tremor frequencies include the tremor frequencies of the distal ends of each of the robotic arms;

[0014] Obtaining the diaphragm movement trajectory, the positions of each robotic arm, and the human tissue boundary according to the real-time image;

[0015] A respiratory signal is obtained according to the prediction of the diaphragm movement trajectory;

[0016] determining a target area based on the medical need;

[0017] Obtaining a tissue displacement vector according to the respiratory signal and the target area;

[0018] obtaining a dynamic target position according to the medical need and the tissue displacement vector;

[0019] Determining a real-time trajectory of each of the robotic arms according to the real-time tremor frequency, the posture of each of the robotic arms, the human tissue boundary, and the dynamic target position;

[0020] The corresponding plurality of operation features are determined based on the real-time operation trajectory of each of the robotic arms.

[0021] In a possible implementation of the first aspect, determining the operation feature sequence according to the medical need and each of the operation features includes:

[0022] Determining the operation priority of each of the robotic arms based on the medical needs;

[0023] Based on the priority of each operation, sorting the operation features in chronological order to obtain the operation feature sequence;

[0024] In a possible implementation of the first aspect, sorting the operation features in chronological order based on the operation priorities to obtain the operation feature sequence includes:

[0025] Determining the real-time operation information of each of the robotic arms based on each of the real-time operation trajectories; wherein the real-time operation information includes posture, speed, and intention information;

[0026] When it is determined based on the real-time operation information that conflicting operations exist between the plurality of robotic arms, the operation features with lower operation priorities corresponding to the conflicting operations are split and delayed to obtain the operation feature sequence.

[0027] In a possible implementation of the first aspect, performing the operation according to the order of each operation feature in the operation feature sequence includes:

[0028] Establishing a flow chart based on the order of each operation feature in the operation feature sequence; wherein the flow chart includes a plurality of nodes, and each operation feature corresponds to at least one of the nodes;

[0029] Operations are performed according to the dependency relationships between the nodes in the flowchart.

[0030] In a possible implementation of the first aspect, establishing a flowchart based on the order of each operation feature in the operation feature sequence includes:

[0031] Obtaining an initial flowchart based on the operation feature sequence; wherein each operation feature corresponds to at least one initial node on the initial flowchart;

[0032] Acquiring adjustment information; wherein the adjustment information is used to reflect changes in the medical needs;

[0033] Adding and / or deleting initial nodes on the initial flow chart according to the adjustment information to obtain the flow chart.

[0034] In a possible implementation of the first aspect, the operating according to the dependency relationship between the nodes in the flowchart includes:

[0035] Generate a control instruction based on the node corresponding to the current operation feature; wherein the control instruction is used to change the operation parameters after the operation is performed on the parent node of the node;

[0036] An operation is performed based on the control instruction.

[0037] In a possible implementation of the first aspect, adjusting, during any operation process, the operation parameters after the previous operation process based on the current operation characteristics includes:

[0038] The operation parameters of the current operation feature in the operation parameters after the last operation process are modified based on the current operation feature, and the operation parameters after the last operation process except the operation parameters of the current operation feature are kept unchanged.

[0039] In a possible implementation of the first aspect, in any operation process, after adjusting the operation parameters after the previous operation process based on the current operation characteristics, the method further includes:

[0040] Verifying the operating parameters after the current operation to obtain operating parameters that meet a first preset condition; wherein the first preset condition is used to indicate whether the operating parameters of the current operation feature are within a reasonable range and meet the requirements for operating parameters in the next operation process in the medical need;

[0041] The operating parameters that meet the first preset conditions are determined as initial operating parameters for the next operation process.

[0042] In a possible implementation manner of the first aspect, before performing the operation according to the order of each operation feature in the operation feature sequence, the method further includes:

[0043] Initializing the operating parameters and performing verification processing to obtain the initial operating parameters that meet the second preset condition; wherein the second preset condition is used to indicate whether the initial operating parameters are consistent with the medical needs;

[0044] The initial operating parameters that meet the second preset conditions are used as the initial operating parameters for the first operation process.

[0045] In a second aspect, an embodiment of the present application provides a human-machine collaborative remote medical control device based on dynamic medical needs, comprising:

[0046] A receiving module, used for receiving remote medical control requests in real time;

[0047] a feature module, configured to parse the medical requirements carried in the remote medical control request and obtain a plurality of operating features of each robotic arm; wherein the medical requirements include different requirements for operating parameters of the plurality of robotic arms at multiple time periods;

[0048] a feature sequence module, configured to determine an operation feature sequence based on the medical need and each of the operation features; wherein the operation feature sequence is configured to reflect the operation priority of the plurality of operation features, and the operation features are configured to reflect the control requirements for the operation parameters of the robotic arm;

[0049] An operation module is used to operate according to the order of each operation feature in the operation feature sequence, and in any operation process, adjust the operation parameters after the previous operation process based on the current operation feature; wherein, an operation process corresponds to an operation feature in the operation feature sequence.

[0050] In a third aspect, an embodiment of the present application provides a medical robot comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method as described in any one of the first aspects above is implemented.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0052] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a medical robot, enables the medical robot to execute any of the methods described in the first aspect above.

[0053] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 This is a flowchart of a human-machine collaborative telemedicine control method based on dynamic medical needs provided by an embodiment of the present application;

[0056] Figure 2 2 is a schematic diagram of the implementation flow of step S200 in the human-machine collaborative telemedicine control method based on dynamic medical needs provided in one embodiment of the present application;

[0057] Figure 3 1 is a schematic diagram of the implementation flow of steps S300, S320, S400, S410 and S420 in the human-machine collaborative telemedicine control method based on dynamic medical needs provided in one embodiment of the present application;

[0058] Figure 4 4 is a schematic diagram of the implementation flow of steps S400 and S430 in the human-machine collaborative telemedicine control method based on dynamic medical needs provided in one embodiment of the present application;

[0059] Figure 5 This is a schematic diagram of the structure of a human-machine collaborative remote medical control device based on dynamic medical needs provided by an embodiment of the present application;

[0060] Figure 6 It is a structural diagram of the medical robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0062] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0063] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0064] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0065] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0066] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0067] Related technologies lack optimized designs tailored to the specific needs of medical scenarios, fail to fully consider the complexity and diversity of medical operations, and lack effective operation sequencing mechanisms. This makes it impossible to adjust the order of operations in real time based on changing medical needs, potentially leading to confusion in the execution order of operational instructions. During remote consultations, a patient's condition can change at any time. Failure to adjust the medical robot's operation sequence and operating parameters in real time to accommodate these changes could lead to a decrease in the accuracy of consultation results. Therefore, current medical control methods lack dynamic adaptability to medical needs.

[0068] To solve the above problems, the embodiment of the present application provides a human-machine collaborative remote medical control method and a medical robot based on dynamic medical needs. In this method, a remote medical control request is received in real time; the medical needs carried in the remote medical control request are parsed and multiple operation features of each robotic arm are obtained; an operation feature sequence is determined according to the medical needs and each operation feature, reflecting the operation priority of multiple operation features, which helps doctors and medical robots to operate according to the priority order, and is conducive to improving the orderliness and safety of medical operations; operations are performed according to the order of each operation feature in the operation feature sequence, and during any operation, the operation parameters after the previous operation process are adjusted based on the current operation feature. The operation feature provides a clear basis for the adjustment of subsequent operation parameters, making the medical operation more accurate and in line with actual needs. Operating in the order of the operation feature sequence is conducive to improving the efficiency of medical operations. Adjusting the operation parameters according to the current operation feature realizes dynamic optimization of the operation parameters, which is conducive to improving the adaptability and flexibility of medical operations. Therefore, the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiment of the present application uses human-machine collaboration, combined with the doctor's experience and intelligent algorithms, to enable medical operations to proceed in an orderly manner, which is conducive to improving the dynamic adaptability of human-machine collaborative remote medical control based on dynamic medical needs to medical needs.

[0069] The human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiment of the present application can be applied to a medical robot. In this case, the medical robot is the executor of the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the medical robot.

[0070] For example, the medical robot may include a control device and a robotic arm, and the control device and the robotic arm are communicatively connected. For example, the control device may be a mobile phone, a tablet computer, a wearable device, an in-vehicle device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a smart screen, a smart TV, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, an Internet of Vehicles terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, customer premise equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved public land mobile network (PLMN) network.

[0071] In order to better understand the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiment of the present application, the specific implementation process of the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiment of the present application is exemplarily introduced below.

[0072] Figure 1 A schematic flow chart of a human-machine collaborative telemedicine control method based on dynamic medical needs provided by an embodiment of the present application is shown. The human-machine collaborative telemedicine control method based on dynamic medical needs includes:

[0073] S100, receiving a remote medical control request in real time.

[0074] For example, a Python socket library can be used to listen to a specific port and receive remote medical control requests from the client. For example, when the client sends a JSON format request containing patient information and medical needs, the server-side program can capture and parse the request in real time.

[0075] S200: parsing the medical requirements carried in the remote medical control request and obtaining multiple operating characteristics of each robotic arm, wherein the medical requirements include different requirements for operating parameters of multiple robotic arms at multiple times.

[0076] It is understood that operating characteristics include position, time, speed, force and load. Operating parameters refer to the specific values ​​of operating characteristics.

[0077] For example, multiple operating features of each robotic arm can be extracted and allocated according to medical needs.

[0078] In one possible implementation, see Figure 2 , S200, parses the medical requirements carried in the remote medical control request and obtains multiple operating characteristics of each robotic arm, including:

[0079] S210: Acquire real-time images and real-time tremor frequencies, wherein the real-time images include images of the patient's body tissue and each robotic arm, and the real-time tremor frequencies include the tremor frequencies of the ends of each robotic arm.

[0080] For example, real-time images can be acquired through high-precision medical imaging equipment (such as ultrasound, CT, or MRI scanners). At the same time, sensors such as accelerometers and gyroscopes can be used to monitor tiny tremors at the ends of each robotic arm and record the real-time tremor frequency.

[0081] S220, obtaining the diaphragm movement trajectory, the positions of each robotic arm, and the human tissue boundary based on the real-time image.

[0082] As you can understand, the diaphragm is the primary driving muscle for respiratory movement, and its motion trajectory reflects the patient's respiratory status. Posture typically consists of two components: position and attitude. Position refers to the absolute coordinates of the robotic arm's end effector in space, while attitude describes the orientation of the end effector.

[0083] For example, when the patient does not need anesthesia, a deep learning model (such as convolutional neural network and U-Net, etc.) can be used for image segmentation and feature extraction to distinguish the diaphragm from other tissue structures, thereby extracting the diaphragm movement trajectory; a visual servo-based algorithm is used to use image information to estimate the 3D position of the end effector of the robotic arm to obtain the posture of each robotic arm; and an edge detection algorithm (such as Sobel operator, Laplace operator and Canny operator, etc.) is used to identify the edges of tissue structures in real-time images to obtain the boundaries of human tissue.

[0084] By accurately extracting the diaphragm's motion trajectory, the patient's respiratory signals can be accurately predicted, thereby optimizing the timing of medical operations. Accurately identifying the robotic arm's position and tissue boundaries helps avoid accidental damage to the patient's vital tissues.

[0085] S230: Obtain a respiratory signal based on the diaphragm movement trajectory prediction.

[0086] It can be understood that the respiratory signal includes respiratory frequency, respiratory amplitude and respiratory phase.

[0087] For example, a time series prediction algorithm, such as a long short-term memory network (LSTM) or support vector regression (SVR), can be used to capture the time-dependent features of the diaphragm motion trajectory (such as velocity, acceleration, and displacement). This can then be trained to generate a respiratory prediction model, which can then be used to predict the respiratory signal. The respiratory prediction model reflects the corresponding relationship between the diaphragm motion trajectory and the respiratory signal.

[0088] Obtaining respiratory signals through prediction can help improve the respiratory synchronization accuracy in the process of human-machine collaborative telemedicine control based on dynamic medical needs and reduce errors caused by the patient's respiratory movements.

[0089] S240, determining a target area based on medical needs.

[0090] For example, the specific location and range of the target area in the real-time image can be determined according to medical needs.

[0091] S250 , obtaining a tissue displacement vector according to the respiratory signal and the target area.

[0092] It can be understood that the tissue displacement vector is the moving direction and moving distance of the target area relative to the current position within a future preset time period, and is used to reflect the position change of the target area caused by respiratory movement.

[0093] For example, the tissue displacement vector can be predicted based on the respiratory signal and the target area using physical simulation or numerical calculation methods (such as the Euler method, the Runge-Kutta method, and support vector regression). For example, the diaphragm position and velocity of the target area in the real-time image are recorded, and the velocity of the current frame is added to the diaphragm position of the previous frame to simulate and predict the physical movement of the diaphragm. The starting and ending points of the prediction are determined by combining the respiratory signal, thereby obtaining the target area's movement direction and distance relative to the current position within a preset time period in the future, i.e., the tissue displacement vector.

[0094] S260, obtaining a dynamic target position according to medical needs and tissue displacement vectors.

[0095] For example, the dynamic target position can be updated in real time based on the medical needs and tissue displacement vector, and visual feedback of the dynamic target position can also be provided.

[0096] S270 , determining the real-time trajectory of each robotic arm according to the real-time vibration frequency, the posture of each robotic arm, the human tissue boundary, and the dynamic target position.

[0097] For example, a trajectory planning algorithm can be used to obtain an initial trajectory based on the position of each robotic arm, the human tissue boundary, and the dynamic target position. The initial trajectory can then be smoothed using methods such as polynomial fitting and spline interpolation. Based on changes in the human tissue boundary and the dynamic target position, the initial trajectory of each robotic arm can be adjusted in real time to obtain the real-time trajectory of each robotic arm. For example, when an increase in tremor frequency is detected, the smoothness of the trajectory can be adjusted to reduce the impact of tremor. When changes in the human tissue boundary are detected, an obstacle avoidance strategy based on the artificial potential field method can be used to adjust the robotic arm's trajectory in real time based on the changes in the human tissue boundary.

[0098] For example, a multi-objective optimization algorithm (such as the multi-objective grey wolf optimization algorithm, multi-objective particle swarm optimization, and non-dominated sorting genetic algorithm) can also be used to plan the real-time trajectory of the robotic arm. Furthermore, the real-time trajectory can be visualized using virtual reality or augmented reality technology.

[0099] By comprehensively considering the real-time tremor frequency, robotic arm posture, human tissue boundary and dynamic target position, precise planning and real-time adjustment of the robotic arm's motion trajectory are achieved. While pursuing target position tracking accuracy, tremor suppression and collision avoidance of the robotic arm are also considered. By continuously adjusting and optimizing the trajectory parameters, the real-time operation trajectory of the robotic arm is obtained, which is conducive to improving the stability and safety of telemedicine.

[0100] S280: Determine a plurality of corresponding operation features based on the real-time operation trajectory of each robotic arm.

[0101] For example, signal processing or data mining techniques may be used to extract operational features and extract information such as time, position, speed, force, and load from the real-time trajectory of the robotic arm.

[0102] Through steps S210 to S280, comprehensive monitoring and optimization of surgical procedures can be achieved. Real-time image and tremor frequency monitoring improves surgical accuracy and safety; the application of respiratory prediction models and tissue displacement vectors enhances surgical adaptability and robustness; and the planning of dynamic target positions and robotic arm trajectory enhances surgical flexibility and efficiency.

[0103] S300: Determine an operation feature sequence based on the medical requirement and each operation feature, wherein the operation feature sequence is used to reflect the operation priority of multiple operation features, and the operation features are used to reflect the control requirements for the operation parameters of the robotic arm.

[0104] It can be understood that the operation feature sequence reflects the operation requirements of each robotic arm at different time points.

[0105] For example, each operational feature in the medical need can be prioritized based on its urgency and importance to determine the operational feature sequence. Alternatively, each operational feature can be time-planned based on its timing requirements and order of precedence to determine the operational feature sequence. Alternatively, an intelligent algorithm (such as a genetic algorithm or particle swarm algorithm) can be used to optimize and solve the medical need to determine the operational feature sequence.

[0106] In one possible implementation, see Figure 3 S300 determines an operation feature sequence according to medical needs and various operation features, including:

[0107] S310: Determine the operation priority of each robotic arm based on medical needs.

[0108] For example, project management tools such as the critical path method or the four-quadrant approach can be used to determine the operational priority of each robotic arm based on the urgency of its corresponding operational characteristics in the medical need. For example, for operations that require immediate execution, the corresponding robotic arm should be given the highest operational priority. At the same time, the operational priority of each robotic arm can be dynamically adjusted based on the current usage status of the robotic arm (including factors such as load, time, and location) and changes in the medical need.

[0109] S320 , sorting the operation features in chronological order based on the priority of each operation to obtain an operation feature sequence.

[0110] For example, the operation features may be sorted in chronological order using a sorting algorithm (such as bubble sort, insertion sort, and heap sort), and an operation feature sequence may be obtained according to priority and time constraints.

[0111] Through steps S310 to S320, an ordered sequence of operational features is automatically generated based on the medical needs and the manipulator's operational characteristics, enabling rapid response to remote medical control requests, reducing manual intervention and decision-making time, and thus improving operational efficiency and accuracy. Resource allocation is dynamically adjusted based on the manipulator's priority and current status, ensuring that critical operations receive priority, avoiding resource waste and waiting time. By employing a universal parsing and sorting algorithm, the system can support a variety of medical needs and combinations of manipulator types, enhancing the flexibility and scalability of the medical robot.

[0112] Optionally, see Figure 3 S320: sort the operation features in chronological order based on the priority of each operation to obtain an operation feature sequence, including:

[0113] S321: Determine the real-time operation information of each robotic arm based on each real-time operation trajectory, wherein the real-time operation information includes position, speed, and intention information.

[0114] It can be understood that the intention information is the action or task that the robotic arm may perform next.

[0115] For example, various sensors installed on the robotic arm (such as position sensors, speed sensors, etc.) can be used to collect the motion data of the robotic arm (including the angles and angular velocities of each joint of the robotic arm) in real time, and the collected data can be processed by kinematic algorithms to calculate the real-time posture of the robotic arm. At the same time, the real-time operation trajectory of the robotic arm is combined with the current posture and speed of the robotic arm for analysis to obtain the intention information of the robotic arm.

[0116] S322 , when it is determined based on the real-time operation information that there are conflicting operations between the multiple robotic arms, the operation features with lower operation priorities corresponding to the conflicting operations are split and delayed to obtain an operation feature sequence.

[0117] For example, the priority of each operation can be used to determine which robot arm's operation should be executed first and which robot arm's operation needs to be split or delayed. For operations that need to be split or delayed, the conflicting operation can be split into multiple small steps, and the robot arm's motion trajectory can be replanned so that these small steps can be executed without causing conflicts. Alternatively, the conflicting operation can be simply delayed until the other robot arm completes its current task and releases resources. After splitting or delaying the operation, the robot arm's motion trajectory is replanned and verified so that the new motion trajectory does not cause new conflicts or safety issues.

[0118] Through steps S321 to S322, accurate real-time operational information from multiple robotic arms can be acquired and conflicting operations effectively handled. This facilitates collaborative operation between robotic arms and improves the overall efficiency and stability of the system. Furthermore, by optimizing the characteristic sequence of operations, the risk of collisions between robotic arms can be further reduced, ensuring system safety.

[0119] S400: Perform operations according to the order of each operation feature in the operation feature sequence, and adjust the operation parameters after the previous operation process based on the current operation feature during any operation process, wherein one operation process corresponds to one operation feature in the operation feature sequence.

[0120] For example, each operating feature can be operated sequentially according to the sequence of operating features. During operation, the operating parameters from the previous operation process are adjusted based on the requirements of the current operating feature. Alternatively, as each operating feature is executed, the operating results can be monitored in real time through sensors or feedback mechanisms, and the operating parameters can be adjusted in real time based on the monitoring results to ensure operational accuracy and stability. Alternatively, a predictive control algorithm (such as model predictive control (MPC)) can be used to predict the operating process and obtain a predicted result, and the operating parameters can be adjusted in advance based on the predicted result.

[0121] In one possible implementation, see Figure 3 , S400, performing operations according to the order of each operation feature in the operation feature sequence, including:

[0122] S410: Create a flow chart based on the order of each operation feature in the operation feature sequence, wherein the flow chart includes multiple nodes, and each operation feature corresponds to at least one node.

[0123] For example, the flowchart may be obtained based on the order of each operation feature in the operation feature sequence using methods such as an adjacency list, an adjacency matrix, or a Petri net.

[0124] Optionally, see Figure 3 S410: Based on the order of each operation feature in the operation feature sequence, a flow chart is established, including:

[0125] S411: Obtain an initial flow chart based on the operation feature sequence, wherein each operation feature corresponds to at least one initial node on the initial flow chart.

[0126] For example, each operation feature can be mapped to one or more initial nodes on the flowchart according to the order of the operation feature sequence, and the nodes can be connected according to the logical relationship between the operation features (such as sequence, condition, loop, etc.) to form an initial flowchart.

[0127] S412: Acquire adjustment information, wherein the adjustment information is used to reflect changes in medical needs.

[0128] For example, adjustment information can be obtained based on additions, deletions, and modifications to medical needs.

[0129] S413: Add and / or delete initial nodes on the initial flow chart according to the adjustment information to obtain a flow chart.

[0130] For example, according to the addition, deletion and modification of medical needs, a node can be added after the corresponding initial node and / or the corresponding initial node can be deleted to obtain a flow chart.

[0131] By creating a flowchart through steps S411 to S413, the order and dependencies between operational features can be clearly displayed, facilitating subsequent operational execution and process optimization. Dynamic adjustment of the flowchart based on the adjustment information can ensure that the flowchart remains synchronized with medical needs and technological developments.

[0132] S420: Perform operations according to the dependency relationship between the nodes in the flowchart.

[0133] For example, conditional statements and loop structures may be used to simulate decision points and repeated execution parts in a flowchart, thereby executing a characteristic sequence of operations according to the dependency relationship between nodes in the flowchart.

[0134] Through steps S410 to S420, a complex sequence of operational features can be presented in the form of a flowchart, and operations can be efficiently executed based on the dependencies in the flowchart. This facilitates process automation, standardization, and visualization, improving work efficiency and quality. Furthermore, the visualization of flowcharts also helps to better understand and track process execution, allowing for timely identification and resolution of issues.

[0135] Optionally, see Figure 3 , S420, performs operations according to the dependency relationship between the nodes in the flowchart, including:

[0136] S421: Generate a control instruction based on the node corresponding to the current operation feature, wherein the control instruction is used to change the operation parameters after the operation is performed on the parent node of the node.

[0137] For example, when a certain node is executed, a corresponding control instruction is generated according to the operation characteristics of the node.

[0138] S422, performing operations based on the control instruction.

[0139] For example, corresponding operations (including modifying operating parameters, calling specific functional modules, or executing specific algorithms, etc.) may be performed according to the generated control instructions.

[0140] Through the above steps S421 to S422, control instructions are generated and corresponding operations are executed, so that each operation feature is executed according to a predetermined order and dependency relationship, thereby improving the accuracy and efficiency of the operation.

[0141] In one possible implementation, see Figure 4 S400, during any operation, adjusting the operation parameters after the previous operation based on the current operation characteristics, including:

[0142] S430: modifying the operation parameters of the current operation feature among the operation parameters after the last operation process based on the current operation feature, and keeping the operation parameters after the last operation process except the operation parameters of the current operation feature unchanged.

[0143] It will be appreciated that when executing the current operating feature, the operating parameters after the previous operating process are first checked. Then, based on the requirements of the current operating feature, the operating parameters of the current operating feature in the operating parameters after the previous operating process are modified. Meanwhile, the operating parameters after the previous operating process, except for the operating parameters of the current operating feature, remain unchanged.

[0144] By adjusting the operation parameters after the last operation process through the above step S430, the execution effect of the current operation feature can be made to meet the predetermined requirements while avoiding unnecessary interference with other operation features.

[0145] Optionally, see Figure 4 S430: During any operation, after adjusting the operation parameters after the previous operation based on the current operation characteristics, the method further includes:

[0146] S431: Verify the operation parameters after the current operation to obtain operation parameters that meet a first preset condition. The first preset condition is used to indicate whether the operation parameters of the current operation feature are within a reasonable range and meet the requirements of the next operation process in the medical need.

[0147] For example, after executing the current operation feature, its operating parameters may be verified (including whether the operating parameters are within a reasonable range and whether they meet the operating parameter requirements of the next operation process in the medical need, etc.). For example, if the next operation process requires the robotic arm to move to position A within 120ms, with a force not exceeding 3N, then the first preset conditions are: position A within ±0.3mm, time within 120ms ±50ms, force not exceeding 3N, and load not exceeding 2kg.

[0148] S432: Determine the operating parameters that meet the first preset conditions as initial operating parameters for the next operation process.

[0149] It can be understood that if the operating parameters after the current operation meet the first preset conditions, they will be determined as the initial operating parameters for the next operation process.

[0150] Through the above steps S431 to S432, the verification process can promptly discover and correct errors or unreasonableness in the operating parameters, ensuring the accuracy and reliability of the operation. The operating parameters that meet the verification conditions are used as the initial operating parameters for the next operation process, which can ensure the continuity and stability of the entire operation process.

[0151] In one possible implementation, see Figure 4 S400, before performing an operation according to the order of each operation feature in the operation feature sequence, the method further includes:

[0152] S401: Initialize the operating parameters and perform verification processing to obtain the initial operating parameters that meet the second preset condition, wherein the second preset condition is used to indicate whether the initial operating parameters meet the medical needs.

[0153] For example, before starting the operation process, the operation parameters may be initialized and then verified to meet the second preset conditions. For example, the second preset conditions are: the position is at point B (±0.2 mm), the movement time does not exceed 10 ms, the speed does not exceed ±0.1 m / s, the force does not exceed 2 N, and the load does not exceed 2 kg.

[0154] S402: Using the initial operating parameters that meet the second preset conditions as the initial operating parameters for the first operation process.

[0155] It can be understood that if the initialized operating parameters meet the second preset conditions, they will be used as the initial operating parameters for the first operation process.

[0156] Through the above steps S401 to S402, by initializing the operating parameters and performing verification processing, it is possible to ensure that the initial state of the operating process is accurate and reliable. Using the initial operating parameters that meet the verification conditions as the initial operating parameters for the first operation process is conducive to improving the continuity and stability of the entire operating process.

[0157] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0158] Corresponding to the human-machine collaborative telemedicine control method based on dynamic medical needs described in the above embodiment, the embodiment of the present application also provides a human-machine collaborative telemedicine control device based on dynamic medical needs, and the various modules of the device can implement the various steps of the human-machine collaborative telemedicine control method based on dynamic medical needs. Figure 5A structural block diagram of a human-machine collaborative remote medical control device based on dynamic medical needs provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0159] Reference Figure 5 , the device comprises:

[0160] A receiving module, used for receiving remote medical control requests in real time;

[0161] a feature module, configured to parse the medical requirements carried in the remote medical control request and obtain a plurality of operating features of each robotic arm; wherein the medical requirements include different requirements for operating parameters of the plurality of robotic arms at multiple time periods;

[0162] a feature sequence module, configured to determine an operation feature sequence based on the medical need and each of the operation features; wherein the operation feature sequence is configured to reflect the operation priority of the plurality of operation features, and the operation features are configured to reflect the control requirements for the operation parameters of the robotic arm;

[0163] An operation module is used to operate according to the order of each operation feature in the operation feature sequence, and in any operation process, adjust the operation parameters after the previous operation process based on the current operation feature; wherein, an operation process corresponds to an operation feature in the operation feature sequence.

[0164] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0165] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0166] The present application also provides a medical robot. Figure 6This is a schematic diagram of the structure of a medical robot provided in one embodiment of the present application. Figure 6 As shown, the medical robot 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown), at least one memory 61 ( Figure 6 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the medical robot 6 implements any of the steps in the above-mentioned embodiments of the human-machine collaborative remote medical control method based on dynamic medical needs, or implements the functions of the modules / units in the above-mentioned device embodiments.

[0167] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the medical robot 6.

[0168] The medical robot 6 may include a manipulator and a control device. The manipulator and the control device are connected in communication (either by wired communication or wireless communication). The control device is used to control the manipulator. The control device of the medical robot may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The control device of the medical robot may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 6 This is merely an example of the medical robot 6 and does not constitute a limitation on the medical robot 6. The medical robot 6 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the medical robot 6 may also include input and output devices, network access devices, buses, etc.

[0169] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0170] In some embodiments, the memory 61 may be an internal storage unit of the medical robot 6, such as the medical robot 6's hard drive or memory. In other embodiments, the memory 61 may also be an external storage device of the medical robot 6, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 61 may include both the internal storage unit of the medical robot 6 and an external storage device. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is about to be output.

[0171] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0172] An embodiment of the present application provides a computer program product. When the computer program product runs on a medical robot, the medical robot implements the steps of any of the above method embodiments.

[0173] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the medical robot, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0174] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0175] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0176] In the embodiments provided herein, it should be understood that the disclosed medical robots and methods can be implemented in other ways. For example, the medical robot embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, other divisions may be used, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device, or unit, which may be electrical, mechanical, or other means.

[0177] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0178] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A human-machine collaborative telemedicine control method based on dynamic medical needs, characterized in that: include: Receive telemedicine control requests in real time; parsing the medical requirements carried in the remote medical control request and obtaining a plurality of operating characteristics of each robotic arm; wherein the medical requirements include different requirements for operating parameters of the plurality of robotic arms at multiple time periods; Determining an operation feature sequence based on the medical need and each of the operation features includes: adjusting the operation priority of each of the robotic arms based on the current use state of each robotic arm and the medical need; obtaining the operation feature sequence based on the operation priority of each of the robotic arms and the time constraint of each of the operation features; wherein the current use state includes load, time, and position, the operation feature sequence is used to reflect the operation priority of the plurality of operation features, and the operation features are used to reflect the control requirements for the operation parameters of the robotic arms; The operation is performed according to the order of each operation feature in the operation feature sequence, and in any operation process, the operation parameters after the previous operation process are adjusted based on the current operation feature; wherein, one operation process corresponds to an operation feature in the operation feature sequence.

2. The human-machine collaborative telemedicine control method based on dynamic medical needs according to claim 1, characterized in that: The medical requirement carried in the remote medical control request is parsed to obtain multiple operating characteristics of each robotic arm, including: Acquiring real-time images and real-time tremor frequencies; wherein the real-time images include images of the patient's body tissue and each of the robotic arms, and the real-time tremor frequencies include the tremor frequencies of the distal ends of each of the robotic arms; Obtaining the diaphragm movement trajectory, the positions of each robotic arm, and the human tissue boundary according to the real-time image; A respiratory signal is obtained according to the prediction of the diaphragm movement trajectory; determining a target area based on the medical need; Obtaining a tissue displacement vector according to the respiratory signal and the target area; obtaining a dynamic target position according to the medical need and the tissue displacement vector; Determining a real-time trajectory of each of the robotic arms according to the real-time tremor frequency, the posture of each of the robotic arms, the human tissue boundary, and the dynamic target position; The corresponding plurality of operation features are determined based on the real-time operation trajectory of each of the robotic arms.

3. The human-machine collaborative telemedicine control method based on dynamic medical needs according to claim 2, characterized in that: The determining of the operation feature sequence according to the medical need and each of the operation features includes: Determining the operation priority of each of the robotic arms based on the medical needs; Based on the priority of each operation, sorting the operation features in chronological order to obtain the operation feature sequence; And / or, sorting the operation features in chronological order based on the operation priorities to obtain the operation feature sequence includes: Determining the real-time operation information of each of the robotic arms based on each of the real-time operation trajectories; wherein the real-time operation information includes posture, speed, and intention information; When it is determined based on the real-time operation information that conflicting operations exist between the plurality of robotic arms, the operation features with lower operation priorities corresponding to the conflicting operations are split and delayed to obtain the operation feature sequence.

4. The human-machine collaborative telemedicine control method based on dynamic medical needs according to claim 1, characterized in that: The operating according to the order of each operation feature in the operation feature sequence includes: Establishing a flow chart based on the order of each operation feature in the operation feature sequence; wherein the flow chart includes a plurality of nodes, and each operation feature corresponds to at least one of the nodes; Operations are performed according to the dependency relationships between the nodes in the flowchart.

5. The human-machine collaborative telemedicine control method based on dynamic medical needs according to claim 4, characterized in that: The process of establishing a flow chart based on the order of each operation feature in the operation feature sequence comprises: Obtaining an initial flowchart based on the operation feature sequence; wherein each operation feature corresponds to at least one initial node on the initial flowchart; Acquiring adjustment information; wherein the adjustment information is used to reflect changes in the medical needs; Adding and / or deleting initial nodes on the initial flow chart according to the adjustment information to obtain the flow chart.

6. The human-machine collaborative telemedicine control method based on dynamic medical needs according to claim 4, characterized in that: The operation according to the dependency relationship between the nodes in the flowchart includes: Generate a control instruction based on the node corresponding to the current operation feature; wherein the control instruction is used to change the operation parameters after the operation is performed on the parent node of the node; An operation is performed based on the control instruction.

7. The human-machine collaborative telemedicine control method based on dynamic medical needs according to claim 1, characterized in that: The adjusting of the operation parameters after the previous operation process based on the current operation characteristics during any operation process includes: The operation parameters of the current operation feature in the operation parameters after the last operation process are modified based on the current operation feature, and the operation parameters after the last operation process except the operation parameters of the current operation feature are kept unchanged.

8. The human-machine collaborative remote medical control method based on dynamic medical needs according to claim 1, characterized in that: In any operation process, after adjusting the operation parameters after the previous operation process based on the current operation characteristics, the method further includes: Verifying the operating parameters after the current operation to obtain operating parameters that meet a first preset condition; wherein the first preset condition is used to indicate whether the operating parameters of the current operation feature are within a reasonable range and meet the requirements for operating parameters in the next operation process in the medical need; The operating parameters that meet the first preset conditions are determined as initial operating parameters for the next operation process.

9. The method according to claim 1, characterized in that Before performing the operation according to the order of each operation feature in the operation feature sequence, the method further includes: Initializing the operating parameters and performing verification processing to obtain the initial operating parameters that meet the second preset condition; wherein the second preset condition is used to indicate whether the initial operating parameters are consistent with the medical needs; The initial operating parameters that meet the second preset conditions are used as the initial operating parameters for the first operation process.

10. A medical robot comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

Images