Man-machine cooperation remote medical control method based on dynamic medical requirements and medical robot

Abstract

The invention is suitable for the technical field of man-machine collaboration, and particularly relates to a man-machine collaboration remote medical control method based on dynamic medical requirements and a medical robot, and the method comprises the steps: receiving a remote medical control request in real time; analyzing a medical demand carried by the remote medical control request, and obtaining a plurality of operation characteristics of each mechanical arm; determining an operation feature sequence according to the medical demand and each operation feature; and performing operation according to the sequence of each operation feature in the operation feature sequence, and in any operation process, adjusting the operation parameters after the previous operation process based on the current operation feature. Therefore, the man-machine cooperation remote medical treatment control method based on the dynamic medical treatment requirements can solve the problem that dynamic adaptability to the medical treatment requirements is lacked.

Description

Technical Field

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

[0002] The human-machine collaborative remote medical control technology based on dynamic medical needs is an innovative application that combines artificial intelligence, remote communication, and medical technology, aiming to achieve the efficient and precise execution of remote medical services through the close cooperation between doctors and medical robots.

[0003] Traditional medical control technologies lack optimized designs for specific requirements of medical scenarios, do not fully consider the complexity and diversity of medical operations, do not establish an effective operation sequencing mechanism, and cannot adjust the operation order in real time according to changes in medical needs, which may lead to chaos in the execution order of operation instructions. In remote consultations, the patient's condition may change at any time. If the operation order and operation parameters of the medical robot cannot be adjusted in real time to adapt to this change, the accuracy of the consultation results may decrease. Therefore, the current medical control methods have the problem of lacking dynamic adaptability to medical needs. Summary of the Invention

[0004] The embodiments of this 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 lacking dynamic adaptability to medical needs.

[0005] In a first aspect, the embodiments of this application provide a human-machine collaborative remote medical control method based on dynamic medical needs, including: Receiving a remote medical control request in real time; Analyzing the medical needs carried in the remote medical control request and obtaining multiple operation characteristics of each robotic arm; wherein, the medical needs include different requirements for the operation parameters of multiple robotic arms in multiple periods; Determining an operation characteristic sequence according to the medical needs and each operation characteristic; wherein, the operation characteristic sequence is used to reflect the operation priorities of multiple operation characteristics, and the operation characteristic is used to reflect the control requirements for the operation parameters of the robotic arm; Performing operations according to the order of each operation characteristic in the operation characteristic sequence, and during any operation process, adjusting the operation parameters after the previous operation process based on the current operation characteristic; wherein, one operation process corresponds to one operation characteristic in the operation characteristic sequence.

[0006] The above technical solutions in the embodiments of this application have at least the following technical effects: The human - machine collaborative remote medical control method based on dynamic medical needs provided by the embodiments of the present application receives remote medical control requests in real - time; analyzes the medical needs carried in the remote medical control requests and obtains multiple operation characteristics of each robotic arm; determines an operation characteristic sequence according to the medical needs and each operation characteristic, which reflects the operation priorities of multiple operation characteristics, helps doctors and medical robots to operate according to the priority order, and is beneficial to improving the orderliness and safety of medical operations; operates according to the order of each operation characteristic in the operation characteristic sequence, and during any operation process, adjusts the operation parameters after the previous operation process based on the current operation characteristic. The operation characteristic provides a clear basis for the adjustment of subsequent operation parameters, making the medical operation more accurate and in line with actual needs. Operating according to the order of the operation characteristic sequence is beneficial to improving the efficiency of medical operations. Adjusting the operation parameters according to the current operation characteristic realizes the dynamic optimization of operation parameters and is beneficial to improving the adaptability and flexibility of medical operations. Therefore, the human - machine collaborative remote medical control method based on dynamic medical needs provided by the embodiments of the present application, through the human - machine collaborative method, combines the experience of doctors and intelligent algorithms to make medical operations proceed orderly, which is beneficial to improving the dynamic adaptability of human - machine collaborative remote medical control based on dynamic medical needs to medical needs.

[0007] In a possible implementation manner of the first aspect, the analyzing the medical needs carried in the remote medical control request and obtaining multiple operation characteristics of each robotic arm includes: Obtaining real - time images and real - time tremor frequencies; wherein, the real - time images include the human tissues of the patient and the images of each robotic arm, and the real - time tremor frequencies include the end - point tremor frequencies of each robotic arm; Obtaining the diaphragm movement trajectory, the poses of each robotic arm, and the boundaries of human tissues according to the real - time images; Predicting a respiratory signal according to the diaphragm movement trajectory; Determining a target area based on the medical needs; Obtaining a tissue displacement vector according to the respiratory signal and the target area; Obtaining a dynamic target position according to the medical needs and the tissue displacement vector; Determining the real - time running trajectories of each robotic arm according to the real - time tremor frequencies, the poses of each robotic arm, the boundaries of human tissues, and the dynamic target position; Determining the corresponding multiple operation characteristics based on the real - time running trajectories of each robotic arm.

[0008] In a possible implementation manner of the first aspect, the determining an operation characteristic sequence according to the medical needs and each operation characteristic includes: Determine the operation priorities of the robotic arms based on the medical needs; Sort the operation features in chronological order based on the operation priorities to obtain the operation feature sequence; In a possible implementation of the first aspect, the sorting the operation features in chronological order based on the operation priorities to obtain the operation feature sequence includes: Determine the real-time operation information of the robotic arms based on the real-time operation trajectories; wherein, the real-time operation information includes pose, speed, and intention information; In the case where it is determined that there are conflicting operations among the robotic arms based on the real-time operation information, split and delay the operation features with lower operation priorities corresponding to the conflicting operations to obtain the operation feature sequence.

[0009] In a possible implementation of the first aspect, the operating according to the order of each operation feature in the operation feature sequence includes: Establish a flowchart based on the order of each operation feature in the operation feature sequence; wherein, the flowchart includes multiple nodes, and each operation feature corresponds to at least one of the nodes; Operate according to the dependency relationships between the nodes in the flowchart.

[0010] In a possible implementation of the first aspect, the establishing a flowchart based on the order of each operation feature in the operation feature sequence includes: Obtain an initial flowchart based on the operation feature sequence; wherein, each operation feature corresponds to at least one initial node on the initial flowchart; Obtain adjustment information; wherein, the adjustment information is used to reflect the changes in the medical needs; Perform addition and / or deletion processing on the initial nodes on the initial flowchart according to the adjustment information to obtain the flowchart.

[0011] In a possible implementation of the first aspect, the operating according to the dependency relationships 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 at the parent node of the node; Operate based on the control instruction.

[0012] In a possible implementation of the first aspect, the adjusting the operation parameters after the previous operation process based on the current operation feature during any operation process includes: Modify the operation parameters of the current operation feature in the operation parameters after the previous operation process based on the current operation feature, and keep the operation parameters other than the operation parameters of the current operation feature in the operation parameters after the previous operation process unchanged.

[0013] In a possible implementation manner of the first aspect, after adjusting the operation parameters after the previous operation process based on the current operation feature during any operation process, the method further includes: Perform a verification process on the operation parameters after the current operation to obtain operation parameters that meet the first preset condition; wherein, 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 operation parameters for the next operation process in the medical needs; Determine the operation parameters that meet the first preset condition as the initial operation parameters for the next operation process.

[0014] In a possible implementation manner of the first aspect, before operating according to the order of each operation feature in the operation feature sequence, the method further includes: Initialize the operation parameters and perform a verification process to obtain initial operation parameters that meet the second preset condition; wherein, the second preset condition is used to indicate whether the initial operation parameters conform to the medical needs; Use the initial operation parameters that meet the second preset condition as the initial operation parameters for the first operation process.

[0015] In a second aspect, an embodiment of the present application provides a human-machine collaborative remote medical control device based on dynamic medical needs, including: A receiving module, configured to receive a remote medical control request in real time; A feature module, configured to parse the medical needs carried in the remote medical control request and obtain multiple operation features of each robotic arm; wherein, the medical needs include different requirements for the operation parameters of multiple robotic arms in multiple periods; A feature sequence module, configured to determine an operation feature sequence according to the medical needs and each operation feature; wherein, the operation feature sequence is used to reflect the operation priorities of multiple operation features, and the operation feature is used to reflect the control requirements for the operation parameters of the robotic arm; An operation module, configured to operate according to the order of each operation feature in the operation feature sequence, and during any operation process, adjust the operation parameters after the previous operation process based on the current operation feature; wherein, one operation process corresponds to one operation feature in the operation feature sequence.

[0016] In a third aspect, an embodiment of the present application provides a medical robot, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above first aspects is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method described in any one of the above first aspects is implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a medical robot, the medical robot is caused to execute the method described in any one of the above first aspects.

[0019] It can be understood that the beneficial effects of the above second to fifth aspects can be referred to the relevant descriptions in the above first aspect, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 is a schematic flowchart of a human-machine collaborative remote medical control method based on dynamic medical needs provided by an embodiment of the present application; Figure 2 is a schematic flowchart of the implementation of step S200 in the human-machine collaborative remote medical control method based on dynamic medical needs provided by an embodiment of the present application; Figure 3 is a schematic flowchart of the implementation of steps S300, S320, S400, S410, and S420 in the human-machine collaborative remote medical control method based on dynamic medical needs provided by an embodiment of the present application; Figure 4 is a schematic flowchart of the implementation of steps S400 and S430 in the human-machine collaborative remote medical control method based on dynamic medical needs provided by an embodiment of the present application; Figure 5 is a schematic structural diagram of a human-machine collaborative remote medical control device based on dynamic medical needs provided by an embodiment of the present application; Figure 6 is a schematic structural diagram of a medical robot provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures, technologies, etc. are presented to provide a thorough understanding of the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also 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 unnecessary details from obscuring the description of the present application.

[0023] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0024] It should also be understood that the term "and / or" used in the specification and the appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] As used in the specification and the appended claims of the present application, the term "if" can be interpreted as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0026] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for differentiating descriptions and cannot be understood as indicating or implying relative importance.

[0027] The reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0028] In the related art, there is a lack of optimized design for the specific requirements of medical scenarios, the complexity and diversity of medical operations are not fully considered, an effective operation sequencing mechanism is not established, and the operation order cannot be adjusted in real time according to the changes in medical needs, which may lead to chaos in the execution order of operation instructions. In remote consultations, the patient's condition may change at any time. If the operation order and operation parameters of the medical robot cannot be adjusted in real time to adapt to this change, the accuracy of the consultation result may decrease. Therefore, the current medical control method has the problem of lacking dynamic adaptability to medical needs.

[0029] To solve the above problems, the embodiments of the present application provide a human-machine collaborative remote medical control method and a medical robot based on dynamic medical needs. In this method, by receiving a remote medical control request in real time; parsing the medical needs carried in the remote medical control request and obtaining multiple operation characteristics of each robotic arm; determining an operation characteristic sequence according to the medical needs and each operation characteristic, which reflects the operation priorities of multiple operation characteristics, helps doctors and medical robots to operate according to the priority order, and is beneficial to improving the orderliness and safety of medical operations; operating according to the order of each operation characteristic in the operation characteristic sequence, and during any operation process, adjusting the operation parameters after the previous operation process based on the current operation characteristic. The operation characteristic provides a clear basis for the adjustment of subsequent operation parameters, making the medical operation more accurate and in line with actual needs. Operating according to the order of the operation characteristic sequence is beneficial to improving the efficiency of medical operations. Adjusting the operation parameters according to the current operation characteristic realizes the dynamic optimization of operation parameters and is beneficial to improving the adaptability and flexibility of medical operations. Therefore, the human-machine collaborative remote medical control method based on dynamic medical needs provided by the embodiments of the present application enables medical operations to proceed in an orderly manner through a human-machine collaborative manner, combining doctors' experience and intelligent algorithms, and is beneficial to improving the dynamic adaptability of human-machine collaborative remote medical control based on dynamic medical needs to medical needs.

[0030] The human-machine collaborative remote medical control method based on dynamic medical needs provided by the embodiments of the present application can be applied to a medical robot. At this time, the medical robot is the execution subject of the human-machine collaborative remote medical control method based on dynamic medical needs provided by the embodiments of the present application. The embodiments of the present application do not impose any restrictions on the specific type of the medical robot.

[0031] For example, a medical robot may include a control device and a robotic arm. 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, a vehicle-mounted 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 large screen, a smart TV, a handheld device with wireless communication function, a computing device or other processing devices connected to a wireless modem, a vehicle-mounted device, a vehicle-to-everything (V2X) terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a customer premise equipment (CPE), and / or other devices for communicating on a wireless system, as well as a next-generation communication system. For example, a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0032] To better understand the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiments of the present application, the following provides an exemplary introduction to the specific implementation process of the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiments of the present application.

[0033] Figure 1 FIG. shows a schematic flowchart of the human-machine collaborative remote medical control method based on dynamic medical needs provided in the embodiments of the present application. The human-machine collaborative remote medical control method based on dynamic medical needs includes: S100, receiving a remote medical control request in real time.

[0034] Exemplarily, the socket library of Python can be used to listen on a specific port to receive a remote medical control request from a client. For example, when the client sends a request in JSON format containing patient information and medical needs, the server-side program can capture and parse the request in real time.

[0035] S200, parsing the medical needs carried in the remote medical control request and obtaining multiple operation characteristics of each robotic arm. Among them, the medical needs include different requirements for the operation parameters of multiple robotic arms in multiple periods.

[0036] It can be understood that the operation characteristics include position, time, speed, force, and load. The operation parameter refers to the specific value of the operation characteristic.

[0037] Exemplarily, according to medical needs, multiple operation characteristics of each robotic arm can be extracted and allocated.

[0038] In a possible implementation, refer to Figure 2 , S200, parse the medical needs carried in the remote medical control request, and obtain multiple operation characteristics of each robotic arm, including: S210, obtain real-time images and real-time tremor frequencies. Among them, the real-time images include images of the patient's human tissues and each robotic arm, and the real-time tremor frequencies include the end tremor frequencies of each robotic arm.

[0039] Exemplarily, real-time images can be obtained through high-precision medical imaging devices (such as ultrasonic, CT, or MRI scanners). At the same time, sensors such as accelerometers and gyroscopes are used to monitor the minute tremors at the ends of each robotic arm, and the real-time tremor frequencies are recorded.

[0040] S220, obtain the diaphragm movement trajectory, the poses of each robotic arm, and the boundaries of human tissues based on the real-time images.

[0041] It can be understood that the diaphragm is the main driving muscle of respiratory movement, and the diaphragm movement trajectory reflects the patient's respiratory state. The pose generally includes two parts: position and orientation. The position refers to the absolute coordinates of the end effector of the robotic arm in space, while the orientation describes the orientation of the end effector.

[0042] Exemplarily, in the case where the patient does not require anesthesia, deep learning models (such as convolutional neural networks 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; an algorithm based on visual servo is used to estimate the 3D position of the end effector of the robotic arm using image information to obtain the poses of each robotic arm; edge detection algorithms (such as Sobel operator, Laplace operator, and Canny operator, etc.) are used to identify the edges of tissue structures in the real-time images to obtain the boundaries of human tissues.

[0043] By accurately extracting the diaphragm movement trajectory, the patient's respiratory signal can be accurately predicted, thereby optimizing the timing of medical operations. Accurately identifying the poses of the robotic arm and the boundaries of human tissues helps to avoid accidentally injuring important human tissues of the patient.

[0044] S230, predict the respiratory signal based on the diaphragm movement trajectory.

[0045] It can be understood that the respiratory signal includes respiratory frequency, respiratory amplitude, and respiratory phase.

[0046] Exemplarily, a time series prediction algorithm such as a long short-term memory network (LSTM) or support vector regression (SVR) can be adopted to capture the time-dependent features (such as movement speed, acceleration, and displacement) in the diaphragmatic movement trajectory, and a respiration prediction model is trained to predict the respiration signal according to the respiration prediction model. Among them, the respiration prediction model is used to reflect the corresponding relationship between the diaphragmatic movement trajectory and the respiration signal.

[0047] Predicting the respiration signal helps to improve the respiration synchronization accuracy in the human-machine collaborative remote medical control process based on dynamic medical needs and reduce the errors caused by the patient's respiratory movement.

[0048] S240, determining the target area based on medical needs.

[0049] Exemplarily, the specific position and range of the target area in the real-time image can be determined according to medical needs.

[0050] S250, obtaining the tissue displacement vector according to the respiration signal and the target area.

[0051] 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 preset time period in the future, and is used to reflect the position change of the target area caused by respiratory movement.

[0052] Exemplarily, physical simulation or numerical calculation methods (such as Euler method, Runge-Kutta method, and support vector regression, etc.) can be adopted according to the respiration signal and the target area to predict and obtain the tissue displacement vector. For example, record the diaphragmatic position and movement speed of the target area in the real-time image, add the movement speed of the current frame to the diaphragmatic position of the previous frame, so as to simulate and predict the physical movement of the diaphragm, and at the same time combine the respiration signal to determine the starting point and ending point of the prediction, so as to obtain the moving direction and moving distance of the target area relative to the current position within a preset time period in the future, that is, the tissue displacement vector.

[0053] S260, obtaining the dynamic target position according to medical needs and the tissue displacement vector.

[0054] Exemplarily, the dynamic target position can be updated in real time according to medical needs and the tissue displacement vector and obtained. Visual feedback can also be performed on the dynamic target position.

[0055] S270, determining the real-time operating trajectories of the robotic arms according to the real-time tremor frequency, the poses of the robotic arms, the human tissue boundary, and the dynamic target position.

[0056] Exemplarily, an initial operation trajectory can be obtained by using a trajectory planning algorithm based on the poses of each robotic arm, the boundaries of human tissues, and the positions of dynamic targets. Methods such as polynomial fitting and spline interpolation can be used to smooth the initial operation trajectory, and the initial operation trajectories of each robotic arm can be adjusted in real time according to the changes in the boundaries of human tissues and the positions of dynamic targets to obtain the real-time operation trajectories of each robotic arm. For example, when the tremor frequency is detected to increase, the smoothness of the trajectory is adjusted to reduce the impact of tremors; when the boundaries of human tissues are detected to change, an obstacle avoidance strategy based on the artificial potential field method is used to adjust the motion trajectory of the robotic arm in real time according to the changes in the boundaries of human tissues.

[0057] Exemplarily, a multi-objective optimization algorithm (such as multi-objective grey wolf optimization algorithm, multi-objective particle swarm optimization, and non-dominated sorting genetic algorithm, etc.) can also be used to plan and obtain the real-time operation trajectory of the robotic arm. Moreover, the real-time operation trajectory can be visually displayed through virtual reality or augmented reality technology.

[0058] By comprehensively considering the real-time tremor frequency, the poses of the robotic arm, the boundaries of human tissues, and the positions of dynamic targets, the precise planning and real-time adjustment of the motion trajectory of the robotic arm are realized. While pursuing the tracking accuracy of the target position, the 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 beneficial to improving the stability and safety of telemedicine.

[0059] S280. Determine a corresponding plurality of operation features based on the real-time operation trajectories of each robotic arm.

[0060] Exemplarily, signal processing or data mining techniques can be used to extract operation features, and information such as time, position, speed, force, and load can be extracted from the real-time operation trajectories of the robotic arm.

[0061] Through the above steps S210 to S280, the comprehensive monitoring and optimization of surgical operations can be achieved. The monitoring of real-time images and tremor frequency is beneficial to improving the accuracy and safety of surgical operations; the application of the respiration prediction model and tissue displacement vector is beneficial to improving the adaptability and robustness of surgical operations; the planning of dynamic target positions and robotic arm operation trajectories is beneficial to improving the flexibility and efficiency of surgical operations.

[0062] S300. Determine an operation feature sequence according to medical needs and each operation feature. Among them, the operation feature sequence is used to reflect the operation priorities of multiple operation features, and the operation feature is used to reflect the control requirements for the operation parameters of the robotic arm.

[0063] It can be understood that the operation feature sequence reflects the operation requirements of each robotic arm at different time points.

[0064] Exemplarily, each operation feature can be prioritized according to the urgency and importance of each operation feature in the medical need, so as to determine the operation feature sequence. It is also possible to plan the time for each operation feature according to the time requirements and sequence of each operation feature in the medical need, so as to determine the operation feature sequence. It is also possible to use intelligent algorithms (such as genetic algorithms, particle swarm algorithms, etc.) to optimize and solve the medical need to obtain the operation feature sequence.

[0065] In a possible implementation, please refer to Figure 3 , S300, determining the operation feature sequence according to the medical need and each operation feature, including: S310, determining the operation priorities of each robotic arm based on the medical need.

[0066] Exemplarily, project management tools such as the critical path method or the four-quadrant method can be used to determine the operation priorities of each robotic arm according to the urgency of the operation features corresponding to each robotic arm in the medical need. For example, for operations that need to be performed immediately, the relevant robotic arm should have the highest operation priority. At the same time, the operation priorities of each robotic arm are dynamically adjusted according to the current usage status of the robotic arm (including factors such as load, time, and position) and changes in the medical need.

[0067] S320, based on each operation priority, sorting each operation feature in chronological order to obtain the operation feature sequence.

[0068] Exemplarily, each operation feature can be sorted in chronological order through sorting algorithms (such as bubble sort, insertion sort, and heap sort) to obtain the operation feature sequence according to the priority and time constraints.

[0069] Through the above steps S310 to S320, an ordered operation feature sequence can be automatically generated according to the medical need and the operation features of the robotic arm, quickly responding to the remote medical control request, reducing manual intervention and decision-making time, thereby improving the efficiency and accuracy of the operation. According to the priority and current status of the robotic arm, the resource allocation is dynamically adjusted to ensure that critical operations are given priority, avoiding resource waste and waiting time. By adopting general parsing and sorting algorithms, it is possible to support combinations of various medical needs and robotic arm types, thereby enhancing the flexibility and scalability of the medical robot.

[0070] Optionally, please refer to Figure 3 , S320, based on each operation priority, sorting each operation feature in chronological order to obtain the operation feature sequence, including: S321, determining the real-time operation information of each robotic arm based on each real-time operation trajectory. Among them, the real-time operation information includes pose, speed, and intention information.

[0071] It can be understood that the intention information is the possible actions or tasks that the robotic arm will perform next.

[0072] Exemplarily, 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 in real time (including the angles, angular velocities, etc. of the joints of the robotic arm). The collected data is processed through kinematic algorithms to calculate the real-time pose of the robotic arm. At the same time, by analyzing the real-time operation trajectory of the robotic arm in combination with the current pose and speed of the robotic arm, the intention information of the robotic arm is obtained.

[0073] S322. In the case where it is determined based on each real-time operation information that there are conflicting operations among multiple robotic arms, the operation features with a lower operation priority corresponding to the conflicting operations are split and delayed to obtain an operation feature sequence.

[0074] Exemplarily, it can be determined according to the operation priority which operation of which robotic arm should be executed first and which operation of which robotic arm needs to be split or delayed. For the operations that need to be split or delayed, the conflicting operations can be split into multiple small steps, and the motion trajectory of the robotic arm is re-planned so that these small steps can be executed without causing conflicts. It is also possible to simply delay the execution of the conflicting operations until other robotic arms complete their current tasks and release resources. After splitting or delaying the operations, the motion trajectory of the robotic arm is re-planned and verified to ensure that the new motion trajectory will not cause new conflicts or safety problems.

[0075] Through the above steps S321 to S322, the accurate acquisition of the real-time operation information of multiple robotic arms and the effective processing of conflicting operations can be achieved. This is beneficial to realizing the collaborative work among robotic arms, improving the overall efficiency and stability of the system. At the same time, by optimizing the operation feature sequence, the collision risk among robotic arms can be further reduced, ensuring the safety of the system.

[0076] S400. Operate according to the order of each operation feature in the operation feature sequence, and during any operation process, adjust the operation parameters after the previous operation process based on the current operation feature. Wherein, one operation process corresponds to one operation feature in the operation feature sequence.

[0077] Exemplarily, each operation feature can be operated in sequence according to the order of the operation feature sequence. During the operation process, the operation parameters after the previous operation process are adjusted according to the requirements of the current operation feature. It is also possible to monitor the operation results in real time through sensors or feedback mechanisms when each operation feature is executed, and adjust the operation parameters in real time according to the monitoring results to ensure the accuracy and stability of the operation. It is also possible to use a predictive control algorithm (such as model predictive control MPC) to predict the operation process to obtain a prediction result, and adjust the operation parameters in advance according to the prediction result.

[0078] In one possible implementation, please refer to Figure 3 , S400, operate according to the order of each operation feature in the operation feature sequence, including: S410, based on the order of each operation feature in the operation feature sequence, establish a flowchart. Wherein, the flowchart includes multiple nodes, and each operation feature corresponds to at least one node.

[0079] Exemplarily, a flowchart can be obtained by using methods such as an adjacency list, an adjacency matrix, or a Petri net based on the order of each operation feature in the operation feature sequence.

[0080] Optionally, please refer to Figure 3 , S410, based on the order of each operation feature in the operation feature sequence, establish a flowchart, including: S411, obtain an initial flowchart based on the operation feature sequence. Wherein, each operation feature corresponds to at least one initial node on the initial flowchart.

[0081] Exemplarily, according to the order of the operation feature sequence, each operation feature can be mapped to one or more initial nodes on the flowchart, and the nodes are connected according to the logical relationships (such as sequence, condition, loop, etc.) between the operation features to form an initial flowchart.

[0082] S412, obtain adjustment information. Wherein, the adjustment information is used to reflect changes in medical needs.

[0083] Exemplarily, the adjustment information can be obtained according to the situation of addition, deletion, or modification of medical needs.

[0084] S413, perform addition and / or deletion processing on the initial nodes on the initial flowchart according to the adjustment information to obtain a flowchart.

[0085] Exemplarily, according to the situation of addition, deletion, or modification of medical needs, nodes can be added after the corresponding initial nodes and / or the corresponding initial nodes can be deleted to obtain a flowchart.

[0086] Through the above steps S411 to S413, by establishing a flowchart, the sequence and dependency relationships between operation characteristics can be clearly shown, which helps with subsequent operation execution and process optimization. Dynamically adjusting the flowchart according to the adjustment information can ensure that the flowchart keeps pace with medical needs and technological development.

[0087] S420. Perform operations according to the dependency relationships between the various nodes in the flowchart.

[0088] Exemplarily, conditional statements and loop structures can be used to simulate the decision points and repetitive execution parts in the flowchart, so as to execute the sequence of operation characteristics according to the dependency relationships between the various nodes in the flowchart.

[0089] Through the above steps S410 to S420, a complex sequence of operation characteristics can be presented in the form of a flowchart, and operations can be efficiently executed according to the dependency relationships in the flowchart. This is conducive to realizing the automation, standardization, and visualization of the process, improving work efficiency and quality. At the same time, the visualization feature of the flowchart also helps to better understand and track the execution of the process, so as to discover and solve problems in a timely manner.

[0090] Optionally, please refer to Figure 3 , S420. Perform operations according to the dependency relationships between the various nodes in the flowchart, including: S421. Generate a control instruction based on the node corresponding to the current operation characteristic. Among them, the control instruction is used to change the operation parameters after the operation at the parent node of the node.

[0091] Exemplarily, it can be that when reaching a certain node, a corresponding control instruction is generated according to the operation characteristic of the node.

[0092] S422. Perform operations based on the control instruction.

[0093] Exemplarily, corresponding operations (including modifying operation parameters, calling specific function modules, or executing specific algorithms, etc.) can be performed according to the generated control instruction.

[0094] Through the above steps S421 to S422, a control instruction is generated and the corresponding operation is performed, so that each operation characteristic is executed according to the predetermined sequence and dependency relationships, thereby improving the accuracy and efficiency of the operation.

[0095] In a possible implementation manner, please refer to Figure 4 , S400. During any operation process, adjust the operation parameters after the previous operation process based on the current operation characteristic, including: S430. Modify the operation parameters of the current operation feature among the operation parameters after the previous operation process based on the current operation feature, and keep the operation parameters other than the operation parameters of the current operation feature in the operation parameters after the previous operation process unchanged.

[0096] It can be understood that when executing the current operation feature, first check the operation parameters after the previous operation process. Then, according to the requirements of the current operation feature, modify the operation parameters of the current operation feature among the operation parameters after the previous operation process. At the same time, keep the other operation parameters in the operation parameters after the previous operation process unchanged except for the operation parameters of the current operation feature.

[0097] By adjusting the operation parameters after the previous operation process through the above step S430, the execution effect of the current operation feature can meet the predetermined requirements, and at the same time, unnecessary interference to other operation features can be avoided.

[0098] Optionally, please refer to Figure 4 , S430. After adjusting the operation parameters after the previous operation process based on the current operation feature during any operation process, the method further includes: S431. Perform a verification process on the operation parameters after the current operation to obtain operation parameters that meet the first preset condition. Wherein, 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 operation parameters for the next operation process in the medical needs.

[0099] Exemplarily, it can be to perform a verification process on its operation parameters (including whether the operation parameters are within a reasonable range, whether they meet the requirements of the operation parameters for the next operation process in the medical needs, etc.) after executing the current operation feature. For example, the next operation process requires the robotic arm to move to position A and complete it within 120 ms, and at the same time the force does not exceed 3 N. Then the first preset condition is: the position is within A±0.3 mm, the time is within the range of 120 ms±50 ms, the force does not exceed 3 N, and the load does not exceed 2 kg.

[0100] S432. Determine the operation parameters that meet the first preset condition as the initial operation parameters for the next operation process.

[0101] It can be understood that if the operation parameters after the current operation meet the first preset condition, they are determined as the initial operation parameters for the next operation process.

[0102] Through the above steps S431 to S432, through the verification process, errors or unreasonable parts in the operation parameters can be found and corrected in time to ensure the accuracy and reliability of the operation. Taking the operation parameters that meet the verification conditions as the initial operation parameters for the next operation process can ensure the continuity and stability of the entire operation process.

[0103] In a possible implementation, refer to Figure 4 , S400, before operating according to the order of each operation feature in the operation feature sequence, the method further includes: S401, initialize operation parameters and perform verification processing to obtain initial operation parameters that meet the second preset condition. Wherein, the second preset condition is used to indicate whether the initial operation parameters match the medical needs.

[0104] Exemplarily, it may be that before starting to execute the operation process, the operation parameters are initialized, and the initialized operation parameters are verified to meet the second preset condition. For example, the second preset condition is: 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.

[0105] S402, use the initial operation parameters that meet the second preset condition as the initial operation parameters for the first operation process.

[0106] It can be understood that if the initialized operation parameters meet the second preset condition, they are used as the initial operation parameters for the first operation process.

[0107] Through the above steps S401 to S402, by initializing the operation parameters and performing verification processing, it can be ensured that the starting state of the operation process is accurate and reliable. Using the initial operation parameters that meet the verification conditions as the initial operation parameters for the first operation process is beneficial to improving the continuity and stability of the entire operation process.

[0108] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do 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 to the implementation process of the embodiments of the present application.

[0109] Corresponding to the human-machine collaborative remote medical control method based on dynamic medical needs described in the above embodiments, the embodiments of the present application also provide a human-machine collaborative remote medical control device based on dynamic medical needs. Each module of this device can implement each step of the human-machine collaborative remote medical control method based on dynamic medical needs. Figure 5 The structural block diagram of the human-machine collaborative remote medical control device provided by the embodiments of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0110] Refer to Figure 5 , this device includes: A receiving module, configured to receive a remote medical control request in real time; A feature module, configured to parse the medical needs carried in the remote medical control request and obtain multiple operation features of each robotic arm; wherein, the medical needs include different requirements for the operation parameters of the multiple robotic arms in multiple periods. A feature sequence module, configured to determine an operation feature sequence according to the medical needs and each of the operation features; wherein, the operation feature sequence is used to reflect the operation priorities of the multiple operation features, and the operation features are used to reflect the control requirements for the operation parameters of the robotic arm. An operation module, configured to perform operations according to the order of each operation feature in the operation feature sequence, and during any operation process, adjust the operation parameters after the previous operation process based on the current operation feature; wherein, one operation process corresponds to one operation feature in the operation feature sequence.

[0111] It should be noted that the information interaction, execution process, etc. between the above modules, due to being based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details are not elaborated here.

[0112] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used for illustration. In actual applications, the above functions can be allocated to different functional units and modules according to needs, 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. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above device can refer to the corresponding process in the foregoing method embodiment, and details are not elaborated here.

[0113] The embodiment of the present application further provides a medical robot. Figure 6 It is a schematic structural diagram of a medical robot provided by an embodiment of the present application. As Figure 6 shown, the medical robot 6 in this embodiment includes: at least one processor 60 ( Figure 6 only one is shown here), at least one memory 61 ( Figure 6only one is shown) 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 is caused to implement the steps in any of the above-described embodiments of the human-machine collaborative remote medical control method based on dynamic medical needs, or the medical robot 6 is caused to implement the functions of each module / unit in the above-described device embodiments.

[0114] Exemplarily, the computer program 62 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 61 and executed by the processor 60 to complete 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.

[0115] The medical robot 6 may include a robotic arm and a control device. The robotic arm and the control device are communicatively connected (which may be a wired communication connection or a wireless communication connection), and the control device is used to control the robotic arm. The control device of this medical robot may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The control device of this medical robot may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 6 merely examples of the medical robot 6, which do not constitute a limitation on the medical robot 6, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, buses, etc.

[0116] The processor 60 may be a central processing unit (CPU), and the processor 60 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0117] The memory 61 may be an internal storage unit of the medical robot 6 in some embodiments, such as the hard disk or memory of the medical robot 6. The memory 61 may also be an external storage device of the medical robot 6 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the medical robot 6. Further, the memory 61 may also include both the internal storage unit of the medical robot 6 and the external storage device. The memory 61 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store the data that has been output or will be output.

[0118] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0119] An embodiment of the present application provides a computer program product, and when the computer program product runs on a medical robot, the medical robot implements the steps in any of the above method embodiments.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of the present application, a computer program may be used to instruct relevant hardware to complete. The computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps in the above method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the medical robot, a recording medium, a computer memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium may not be an electrical carrier signal and a telecommunication signal.

[0121] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0122] Those of ordinary skill in the art will realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0123] In the embodiments provided in this application, 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 the modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A human-machine collaborative remote medical control method based on dynamic medical needs, characterized in that: include: Receive telemedicine control requests in real time; The medical needs carried in the remote medical control request are parsed to obtain a plurality of operating characteristics of each robotic arm; wherein the medical needs include different requirements for operating parameters of a plurality of the robotic arms in a plurality of periods; Determine an operation feature sequence according to the medical needs 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; 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 remote medical control method based on dynamic medical needs as claimed in claim 1, characterized in that: The medical requirement carried in the remote medical control request is parsed to obtain multiple operation characteristics of each robotic arm, including: Acquire real-time images and real-time tremor frequencies; wherein the real-time images include images of the patient's human tissue and each of the robotic arms, and the real-time tremor frequencies include the tremor frequencies of the ends of each of the robotic arms; According to the real-time image, the diaphragm movement trajectory, the positions of each robotic arm and the human tissue boundary are obtained; Predicting a respiratory signal according to 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 running 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 remote medical control method based on dynamic medical needs as claimed in claim 2, characterized in that: The determining of the operation feature sequence according to the medical need and each of the operation features comprises: Determining the operation priority of each of the robotic arms based on the medical needs; Based on the priority of each operation, sorting each operation feature in chronological order to obtain the operation feature sequence; And / or, based on the operation priorities, the operation features are sorted in chronological order to obtain the operation feature sequence, including: Determine 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 each of the real-time operation information that there are conflicting operations among the plurality of robot 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 remote medical control method based on dynamic medical needs as claimed in claim 1, characterized in that: The operating according to the order of each operation feature in the operation feature sequence comprises: Based on the order of each operation feature in the operation feature sequence, a flow chart is established; wherein the flow chart includes a plurality of nodes, and each operation feature corresponds to at least one of the nodes; The operation is performed according to the dependency relationship between the nodes in the flowchart.

5. The human-machine collaborative remote medical control method based on dynamic medical needs as claimed in claim 4, characterized in that: The step of establishing a flow chart based on the order of each operation feature in the operation feature sequence comprises: An initial flow chart is obtained based on the operation feature sequence; wherein each operation feature corresponds to at least one initial node on the initial flow chart; Acquiring adjustment information; wherein the adjustment information is used to reflect changes in the medical needs; The initial nodes on the initial flow chart are added and / or deleted according to the adjustment information to obtain the flow chart.

6. The human-machine collaborative remote medical control method based on dynamic medical needs as claimed in claim 4, characterized in that: The operating 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; The operation is performed based on the control instruction.

7. The human-machine collaborative remote medical control method based on dynamic medical needs as claimed in claim 1, characterized in that: The step of adjusting the operation parameters after the last operation process based on the current operation characteristics during any operation process includes: The operating parameters of the current operating feature in the operating parameters after the last operating process are modified based on the current operating feature, and the operating parameters other than the operating parameters of the current operating feature in the operating parameters after the last operating process are kept unchanged.

8. The human-machine collaborative remote medical control method based on dynamic medical needs as claimed in claim 1, characterized in that: In any operation process, after adjusting the operation parameters after the last operation process based on the current operation characteristics, the method further includes: Verifying the operation parameters after the current operation to obtain operation parameters that meet the first preset condition; wherein 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 for the operation parameters; The operating parameters meeting the first preset condition 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 comprises: 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 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, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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