A stable controlled operating table hydraulic control system and control method thereof

Abstract

The present invention discloses a stably controlled operating table hydraulic control system and a control method thereof, which belongs to the technical field of medical devices, and specifically comprises: automatically calculating and planning the optimal operating table posture by combining the operating task data and patient status information collected in real time by sensors through the input of the operating task by medical staff; after the control system accurately calculates the posture error, the posture adjustment of the operating table is realized by the hydraulic system actuator, and the posture and load changes are monitored in real time during the adjustment process, and the precise positioning of the operating table is ensured by using a fine-tuning algorithm; after the operation is completed, the system automatically returns the operating table, and allows the medical staff to confirm that the return is completed, thereby improving the efficiency and accuracy of the operation preparation, and ensuring the safety of the operation process and the comfort of the patient.

Description

Technical Field

[0001] The invention belongs to the technical field of medical equipment, and in particular to a stable controlled operating table hydraulic control system and a control method thereof. Background Art

[0002] At present, most of the domestically produced operating tables use gear transmission and foot-operated hydraulic control methods. Although they meet the basic operating requirements of operating tables to a certain extent, their reliability is poor and there are safety hazards that cannot be ignored. Specifically, the gear transmission method may cause a decrease in accuracy due to mechanical wear and increased clearance, while the foot-operated hydraulic control method may cause unexpected movements due to improper operation or hydraulic system failure, thereby affecting the smooth progress of the operation.

[0003] With the continuous development of medical industry, the operating table, as a key equipment in the operating room, its performance stability and control safety have become particularly important. In the complex and changing surgical environment, the operating table needs to be able to respond to the doctor's operating instructions accurately and quickly, while maintaining a high degree of stability and reliability to ensure the safety and success of the operation.

[0004] For example, the patent with publication number CN116672201A discloses a multifunctional adjustable operating bed using hydraulic control, which includes: a base, a head bed, a middle bed, a tail bed, an electrical control system and a hydraulic control system. The head bed includes a support seat for adjusting the elevation angle of the head bed and a head support arranged above the upper surface of the support seat; the head support includes a head support plate, a head support software, a stretchable support cloth and an adjustment mechanism. The head support plate is parallel to the support seat, the head support software is fixedly installed above the head support plate, the stretchable support cloth covers the head support software and the edge of the stretchable support cloth is fixedly connected to the support seat, and the adjustment mechanism is used to drive multiple head support plates to rise and fall on the support seat respectively. The multiple head support plates in this technical solution can rise and fall independently on the support seat under the action of the adjustment mechanism and form an arc transition that matches the shape of the human head, thereby effectively adapting to and stably supporting the patient's head, and effectively avoiding cervical spine injury or other adverse effects of the patient.

[0005] For example, the patent with the authorization announcement number CN214073950U discloses a control system of an electric hydraulic operating bed, including: a main control unit and a drive unit, characterized in that: the main control unit and the drive unit are respectively arranged on two circuit boards, the output end of the main control unit is respectively connected to the electromagnetic valve group and the translation motor, the hand controller is connected to the input end of the main control unit, and the hand controller is bidirectionally connected to the main control unit; the signal output end of the drive unit is connected to the oil pump motor of the hydraulic pump, the power supply unit is connected to the drive unit to provide working power for the drive unit, and the drive unit simultaneously sends the power signal of the output end of the power supply unit to the main control unit. In the control system of the electric hydraulic operating bed, by arranging the main control unit and the drive unit on two circuit boards respectively, the mutual interference between high-frequency signals and low-frequency signals is avoided, and the stability of the control system is improved. The MOS drive chip and the MOS drive circuit are used to drive the liquid pump motor to work, avoiding the disadvantage of burning the drive circuit in the prior art.

[0006] The above existing technologies all have the following problems: lack of comprehensive posture adjustment capabilities, lack of real-time feedback and adaptability; low levels of automation and intelligence, and lack of description of fault handling and safety mechanisms. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention proposes a stably controlled hydraulic control system for an operating table and a control method thereof. The medical staff inputs the surgical task, combines the surgical task data and patient status information collected in real time by the sensor, and automatically calculates and plans the optimal operating table posture. After the control system accurately calculates the posture error, the posture adjustment of the operating table is achieved through the hydraulic system actuator, and the posture and load changes are monitored in real time during the adjustment process, and a fine-tuning algorithm is used to ensure the precise positioning of the operating table. After the operation is completed, the system automatically returns the operating table and allows the medical staff to confirm that the return is complete, thereby improving the efficiency and accuracy of surgical preparation and ensuring the safety of the surgical process and the comfort of the patient.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A stable control method for operating table hydraulic pressure, comprising:

[0010] Step S1: The medical staff inputs the surgical task through the control panel;

[0011] Step S2: The sensor collects the current surgical task data and sends the collected surgical task data to the control system. The control system automatically calculates and plans the optimal operating table posture using a control strategy based on the input surgical task data and sensor data;

[0012] Step S3: The control system calculates the error between the optimal posture of the operating table and the current posture through the controller. According to the size and direction of the error, the control system sends an adjustment instruction to the hydraulic system to drive the actuator in the hydraulic system to adjust the posture of the operating table. At the same time, during the adjustment process, the sensor monitors the posture and load changes of the operating table in real time and feeds the data back to the control system.

[0013] Step S4: After the operating table is close to the optimal posture, the control system uses a fine-tuning algorithm to fine-tune the posture of the operating table based on the data fed back by the sensor and in combination with external interference factors;

[0014] Step S5: After the operation is completed, the control system returns the operating table to its initial state. At the same time, the medical staff confirms that the operating table has been returned to its initial state through the control panel and turns off the control system.

[0015] Specifically, the specific steps of step S2 include:

[0016] S2.1: The position, angle and load status information of the operating table are collected in real time through the position sensor, angle sensor and load sensor installed on the operating table, the surgical task data is obtained, and the surgical task data is converted into electrical signals;

[0017] S2.2: amplify and filter the converted electrical signal, and send the processed electrical signal to the control system wirelessly;

[0018] S2.3: The control system receives the electrical signal from the sensor, analyzes and verifies the received electrical signal, and compares and matches the verified surgical task data with the input surgical task data.

[0019] Specifically, the specific steps of step S2 also include:

[0020] S2.4: If the match is correct, then the posture planning algorithm is used according to the surgical task requirements and sensor data, combined with the kinematic characteristics, dynamic constraints, safety and efficiency factors of the operating table, to calculate the optimal path for a smooth transition from the current posture to the optimal surgical posture;

[0021] S2.5: Based on the output results of the attitude planning algorithm, the control system calculates the attitude control instructions to achieve the optimal path.

[0022] Specifically, the specific steps of S2.4 include:

[0023] S2.41: receiving sensor data, and extracting the current posture of the operating table and obstacle position data from the sensor data, and at the same time, obtaining the surgical task requirement data;

[0024] S2.42: Create an empty graph structure to store sampling points and connecting edges, and set the motion configuration space of the operating table according to the kinematic characteristics of the operating table;

[0025] S2.43: Set constraints and randomly sample n points in the configuration space based on the constraints;

[0026] S2.44: Establish a collision detection model between the operating table and obstacles, use the hierarchical bounding box algorithm to perform collision detection on each sampling point, determine whether the operating table collides with the obstacle in this posture, and evaluate the safety margin around the sampling point. The formula is:

[0027] ;

[0028] Where s represents the safety margin, represents the position vector of the kth operating table sampling point, represents the position vector of the nearest point on the j-th obstacle, represents the distance scaling factor between the operating table and the obstacle, represents the shape factor between the operating table and the obstacle, represents the directional factor between the operating table and the obstacle, represents the dynamic factor between the operating table and the obstacle, represents the Euclidean distance.

[0029] Specifically, the specific steps of S2.4 also include:

[0030] S2.45: Use the k-nearest neighbor search strategy to connect the collision-free sampling points, and add the sampling points and connecting edges to the graph to form a probabilistic roadmap;

[0031] S2.46: On the constructed probability roadmap, use the graph search algorithm to search for the path from the current posture to the optimal surgical posture, and smooth the searched path;

[0032] S2.47: Use dynamic simulation to verify the feasibility and safety of the path.

[0033] If feasibility and safety are met, the optimal path is output;

[0034] If the simulation results show that there are problems with the path, dynamic adjustments are made.

[0035] Specifically, the constraint formula in S2.43 is:

[0036] ;

[0037] in, represents the angle of the i-th joint of the operating table Y, and They represent the minimum and maximum angles allowed for the i-th joint of the operating table Y, represents the angle change of the i-th joint of the operating table Y between two sampling points, express The maximum change allowed in Represents the change in velocity between two sampling points, Indicates the maximum angular velocity allowed by the joint, represents the collision detection function, Indicates that the operating table Y is in posture without colliding with any obstacles.

[0038] Specifically, the specific steps of S2.44 include:

[0039] According to the actual size and shape of obstacles and operating tables, the 3D geometric model was constructed using SolidWorks 3D modeling software;

[0040] For the geometric models of the operating table and each obstacle, construct hierarchical bounding boxes respectively;

[0041] For n sampling points, determine whether the bounding box of each sampling point intersects with any bounding box of the obstacle. If the bounding boxes intersect, perform collision detection on the three-dimensional geometric model of the intersecting part.

[0042] After determining that there is no collision, the safety margin s around the sampling point is calculated.

[0043] Specifically, the specific formula of the fine-tuning algorithm in step S4 is:

[0044] ;

[0045] in, Indicates the amount of fine-tuning, that is, the angle or position that needs to be adjusted. represents the deviation between the current posture and the preset optimal posture, t represents the current time, represents a past time point in the integration process, represents the forgetting factor, represents the interference function, It represents the performance index of the control system. represents external interference factors, A, B, C represent control parameter coefficients, and D represents the gain coefficient of the adaptive term.

[0046] A stable controlled operating table hydraulic control system, comprising: a task processing module, a control planning module, and a feedback module;

[0047] The task processing module includes a task parsing unit, in which a pattern matching strategy is configured, and the pattern matching strategy is used to parse the input surgical task data into instructions that can be understood and executed within the control system;

[0048] The control planning module includes a posture planning unit, a control algorithm unit, and an instruction sending unit. The posture planning unit is configured with a control strategy, and the control strategy adopts a posture planning algorithm. The posture planning algorithm is used to calculate the optimal path and obtain the optimal posture of the operating table; the control algorithm unit is used to calculate the error between the current posture and the optimal posture; the instruction sending unit is used to drive the hydraulic system to complete the posture adjustment of the operating table;

[0049] The feedback module includes a fine-tuning unit, in which a fine-tuning algorithm is configured. The fine-tuning algorithm is used to adjust the posture of the operating table according to feedback data and external interference factors.

[0050] Specifically, an electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a stable control method for operating table hydraulic pressure when executing the computer program.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention proposes a stable control operating table hydraulic control system, and optimizes and improves the structure, operation steps and processes. The system has the advantages of simple process, low investment and operation cost, and low production cost.

[0053] 2. The present invention proposes a stable control method for the hydraulic control of an operating table. During the posture adjustment process of the operating table, the real-time monitoring system can timely feedback the posture and load changes of the operating table, and use a fine-tuning algorithm based on these data to automatically adjust the posture of the entire operating table in all directions and at multiple angles, ensuring that the operating table always remains in the best state, flexibly adapting to various surgical scenarios, and improving the safety and success rate of the operation; at the same time, by precisely controlling the posture of the operating table, it can better adapt to the patient's body curve, reduce the patient's discomfort during the operation, and avoid cervical spine injuries or adverse effects that may be caused by improper posture.

[0054] 3. The present invention proposes a stable control method for the hydraulic control of an operating bed, which achieves precise control and high stability of the operating bed's movements by optimizing the design and control strategy of the hydraulic system, thereby solving the problem of low automation and intelligence levels in the prior art and improving the overall performance of the operating bed. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1It is a schematic diagram of a hydraulic control method for a stable controlled operating table of the present invention;

[0056] Figure 2 This is a principle flow chart of a stable control method for operating table hydraulic pressure of the present invention;

[0057] Figure 3 It is a flow chart of optimal operating table posture planning of a stable controlled operating table hydraulic control method of the present invention;

[0058] Figure 4 This is an architecture diagram of a hydraulic control system for a stable controlled operating table according to the present invention. DETAILED DESCRIPTION

[0059] Example 1

[0060] See also Figure 1-Figure 3 , an embodiment of the present invention provides: a stable control method for operating table hydraulic pressure, comprising the following steps:

[0061] Step S1: The medical staff inputs the surgical task through the control panel;

[0062] Furthermore, in step S1, after the medical staff inputs the surgical task through the control panel, it is parsed into instructions or parameters that can be understood and executed within the control system. The specific steps include:

[0063] (1) Input reception: The task parsing unit receives input signals from the control panel. These signals may be in the form of keyboard tapping, touch screen operation, or button pressing. It also performs pre-processing on the received input signals, such as noise removal and format verification, to ensure the validity of the input.

[0064] (2) Parsing algorithm: Use regular expression matching or pattern matching algorithm to parse the preprocessed input and identify key information in the input, such as surgery type, patient position code, and adjustment parameters, according to the preset pattern or rule set;

[0065] (3) Mapping relationship: A set of mapping relationships between inputs and instructions is predefined in the control system. This mapping relationship can be a simple lookup table or a complex functional relationship.

[0066] (4) Analysis: Based on the analyzed key information and mapping relationships, the input is converted into instructions or parameters that can be recognized by the control system. These instructions or parameters include target position coordinates, adjustment speed, and adjustment angle;

[0067] (5) Instruction output: The converted instructions or parameters are sent to the control planning module of the control system for subsequent processing.

[0068] Specifically, suppose there is a button on the control panel for selecting the "lithotomy position" surgical position. After the user presses the button:

[0069] a. Input reception: The task parsing unit receives a signal that a button has been pressed;

[0070] b. Preprocessing: confirm that the signal is valid;

[0071] c. Analysis: According to the preset pattern or rule set, it is recognized that this is the operation of selecting the "lithotomy position";

[0072] d. Command conversion: Look up the mapping table to find the control system command or parameter corresponding to the "lithotomy position", such as the target position coordinates a, b, c and angle , , ;

[0073] e. Instruction output: The converted instructions or parameters are sent to the control planning module, which further plans and executes the posture adjustment of the operating table.

[0074] Step S2: The sensor collects the current surgical task data and sends the collected surgical task data to the control system. The control system automatically calculates and plans the optimal operating table posture using a control strategy based on the input surgical task data and sensor data;

[0075] Among them, surgical task data include: patient physiological data, such as heart rate, blood pressure, blood oxygen saturation, and respiratory rate; surgical type and site, including the name of the surgery, the expected surgical site, and the size of the specific site; surgical instrument and equipment requirements, such as the types and quantities of instruments and equipment required during the operation, and their specific requirements for the posture of the operating table; the current state of the operating table, such as the current posture, height, and angle parameters of the operating table; environmental parameters, such as the environmental conditions in the operating room, such as temperature, humidity, and light.

[0076] Step S3: The control system calculates the error between the optimal posture of the operating table and the current posture through the controller. According to the size and direction of the error, the control system sends an adjustment instruction to the hydraulic system to drive the actuator in the hydraulic system to adjust the posture of the operating table. At the same time, during the adjustment process, the sensor monitors the posture and load changes of the operating table in real time and feeds the data back to the control system.

[0077] Step S4: After the operating table is close to the optimal posture, the control system uses a fine-tuning algorithm to fine-tune the posture of the operating table based on the data fed back by the sensor and in combination with external interference factors;

[0078] Step S5: After the operation is completed, the control system returns the operating table to its initial state. At the same time, the medical staff confirms that the operating table has been returned to its initial state through the control panel and turns off the control system.

[0079] The specific steps of step S2 include:

[0080] S2.1: The position, angle and load status information of the operating table are collected in real time through the position sensor, angle sensor and load sensor installed on the operating table, the surgical task data is obtained, and the surgical task data is converted into electrical signals;

[0081] S2.2: amplify and filter the converted electrical signal, and send the processed electrical signal to the control system wirelessly;

[0082] S2.3: The control system receives the electrical signal from the sensor, analyzes and verifies the received electrical signal, and compares and matches the verified surgical task data with the input surgical task data;

[0083] It should be noted that the control system receives the radio signal from the sensor, and first decodes and verifies the signal to ensure the integrity and accuracy of the signal; then, the parsed data is verified, including checking the data format, range, and whether there are abnormal values; after the verification, the parsed operating table status data is compared and matched with the surgical task requirements preset or input in the system to confirm whether the current task is valid and whether the operating table status meets the task execution conditions.

[0084] Furthermore, the parsed surgical task data is compared with the surgical task data pre-input into the system to check whether the two match. The matching information includes: task type, parameters, and priority.

[0085] S2.4: If the match is correct, then the posture planning algorithm is used according to the surgical task requirements and sensor data, combined with the kinematic characteristics, dynamic constraints, safety and efficiency factors of the operating table, to calculate the optimal path for a smooth transition from the current posture to the optimal surgical posture;

[0086] S2.5: Based on the output results of the attitude planning algorithm, the control system calculates the attitude control instructions to achieve the optimal path.

[0087] The specific steps of S2.4 include:

[0088] S2.41: receiving sensor data, and extracting the current posture of the operating table and obstacle position data from the sensor data, and at the same time, obtaining the surgical task requirement data;

[0089] S2.42: Create an empty graph structure to store sampling points and connecting edges, and set the motion configuration space of the operating table according to the kinematic characteristics of the operating table;

[0090] S2.43: Set constraints and randomly sample n points in the configuration space based on the constraints;

[0091] S2.44: Establish a collision detection model between the operating table and obstacles, use the hierarchical bounding box algorithm to perform collision detection on each sampling point, determine whether the operating table collides with the obstacle in this posture, and evaluate the safety margin around the sampling point. The formula is:

[0092] ;

[0093] Where s represents the safety margin, represents the position vector of the kth operating table sampling point, represents the position vector of the nearest point on the j-th obstacle, The distance scaling factor between the operating table and the obstacle is shown in Table 1. The inventors of the present application have found that the distance scaling factor can be adjusted according to the relative size, shape or importance between the operating table and the obstacle. For example, for large or important obstacles, a smaller distance scaling factor can be set. value to increase the importance of safety margin, The shape factor between the operating table and the obstacle is considered, which takes into account the influence of the geometric shapes of the operating table and the obstacle on the safety margin. The inventor of this application found that different shapes may lead to different collision risks, so this factor can be used to adjust the safety margin. represents the direction factor between the operating table and the obstacle, because the relative direction of the operating table and the obstacle may also affect the possibility of collision. For example, if a sharp edge of the operating table is facing the obstacle, the risk of collision may increase. It represents the dynamic factor between the operating table and the obstacle. Considering the moving speed and acceleration of the operating table, this factor can reflect the impact of dynamic constraints on the safety margin. For example, when the operating table moves quickly or accelerates, a larger safety margin may be required to ensure safety. represents the Euclidean distance;

[0094] It should be noted that the inventors of the present application have discovered that during surgery, the operating table may produce tiny displacements or vibrations due to patient movement, the operation of surgical instruments, or the movement of other equipment. These dynamic changes may cause the distance that was originally safe under static conditions to become unsafe. Therefore, it is necessary to consider the impact of the dynamic changes of the operating table and its surrounding environment on collision detection. Therefore, a dynamic factor is creatively set up to measure the dynamic change data of the operating table and its surrounding environment under different operating conditions, and determine the appropriate dynamic factor through statistical analysis; based on the dynamic factor, the impact of the relative direction between the operating table and the obstacle on collision detection is considered, because even if the distance between the operating table and the obstacle is fixed, different relative directions may lead to significant collision risks. Therefore, the inventors of the present application creatively set a direction factor, which can measure the distance and collision situation between the operating table and the obstacle in different directions, and obtain the value of the direction factor through statistical analysis; the inventors of the present application further found that the shape and size characteristics of the operating table and the obstacle may affect the effective distance between them, and therefore further introduced a distance scaling factor for correction. By adjusting the distance scaling factor, the actual distance between the operating table and the obstacle can be more accurately calculated, and the acquisition method is: by analyzing the shape and size characteristics of the operating table and the obstacle, a mathematical model can be established to calculate the value of the distance scaling factor; finally, operating tables and obstacles of different shapes may have different collision risks at the same distance, so the shape factor is added.

[0095] For example, suppose the operating table is moving in a certain direction, and a sampling point on the operating table has been determined by the hierarchical bounding box algorithm. and the closest on the X-ray machine ,

[0096] First calculate and The Euclidean distance of

[0097] Then according to the settings , , , Calculate the safety margin s;

[0098] The calculated safety margin s is used to determine whether the operating table collides with the X-ray machine in this posture. If s is greater than the preset safety threshold, the operating table is considered safe in this posture; otherwise, the posture or path of the operating table needs to be adjusted to avoid collision.

[0099] Specifically, the setting methods include:

[0100] Assume that through experimental measurement and physical model analysis, it is found that the position deviation of the operating table is within the range of ±5mm due to the uneven ground and its own vibration during movement. To ensure safety, the dynamic factor can be set to a value slightly larger than this deviation range, such as ;

[0101] For example, when the direction angle is 90 degrees, the direction factor is the largest, such as , and when the direction angle is 0 degrees or 180 degrees, the direction factor is the smallest, such as ;

[0102] Assuming that the shapes of the operating table and the obstacle are relatively simple, but the edge of the operating table is sharp, while the edge of the obstacle is round. In order to reflect the impact of this shape difference on the effective distance, the distance scaling factor between the operating table and the obstacle is set to different values, for example, the distance scaling factor of the operating table is , and the distance scaling factor of the obstacle .

[0103] S2.45: Use a k-nearest neighbor search strategy to connect the collision-free sampling points, and add the sampling points and the connecting edges to the graph to form a probabilistic roadmap, wherein the k-nearest neighbor search strategy is a prior art content in this field, is not an inventive solution of the present application, and is not described in detail here;

[0104] S2.46: On the constructed probability roadmap, a graph search algorithm is used to search for a path from the current posture to the optimal surgical posture, and the searched path is smoothed. The graph search algorithm is a prior art in this field and is not an inventive solution of the present application, and is not described in detail here.

[0105] S2.47: Use dynamic simulation to verify the feasibility and safety of the path.

[0106] If feasibility and safety are met, the optimal path is output;

[0107] If the simulation results show that there are problems with the path, dynamic adjustments are made.

[0108] The constraint formula in S2.43 is:

[0109] ;

[0110] in, represents the angle of the i-th joint of the operating table Y, and They represent the minimum and maximum angles allowed for the i-th joint of the operating table Y, represents the angle change of the i-th joint of the operating table Y between two sampling points, express The maximum change allowed in Represents the change in velocity between two sampling points, Indicates the maximum angular velocity allowed by the joint, represents the collision detection function, Indicates that the operating table Y is in posture without colliding with any obstacles.

[0111] The specific steps of S2.44 include:

[0112] According to the actual size and shape of obstacles and operating tables, the 3D geometric model was constructed using SolidWorks 3D modeling software;

[0113] For the geometric models of the operating table and each obstacle, construct hierarchical bounding boxes respectively;

[0114] For n sampling points, determine whether the bounding box of each sampling point intersects with any bounding box of the obstacle. If the bounding boxes intersect, perform collision detection on the three-dimensional geometric model of the intersecting part.

[0115] After determining that there is no collision, the safety margin s around the sampling point is calculated.

[0116] The specific formula of the fine-tuning algorithm in step S4 is:

[0117] ;

[0118] in, Indicates the amount of fine-tuning, that is, the angle or position that needs to be adjusted. represents the deviation between the current posture and the preset optimal posture, t represents the current time, represents a past time point in the integration process, represents the forgetting factor, represents the interference function, It represents the performance index of the control system. represents external interference factors, A, B, C represent control parameter coefficients, and D represents the gain coefficient of the adaptive term.

[0119] It should be noted that the present invention considers adding the following elements during the control process:

[0120] 1. Forgetting Factor :A forgetting factor is introduced in the integral term , so that the contribution of earlier deviations to the integral term gradually decreases, which helps to reduce the system's dependence on past states, especially when the system's dynamics change greatly;

[0121] 2. Interference function :In the differential term, the deviation and the interference function multiplication, where the disturbance function is tailored to the nature and intensity of the disturbance in order to more accurately reflect the impact of the disturbance on the system dynamics;

[0122] 3. Adaptive Items : An adaptive term is introduced to dynamically adjust the parameters or output of the controller according to the operating state of the system or external conditions. The specific form of the adaptive term depends on the design goals of the controller and the characteristics of the system, and may include rule-based adjustments and the output of machine learning models.

[0123] Example 2

[0124] See also Figure 4 , an embodiment provided by the present invention: a stable controlled operating table hydraulic control system, comprising:

[0125] Task processing module, perception module, control planning module, feedback module;

[0126] The task processing module is used to receive and process the surgical task data input by the medical staff through the control panel, including parsing the task instructions, understanding the specific requirements of the operation on the operating table posture, and converting these requirements into a format or parameters that can be recognized within the control system. The task processing module is the starting point for the system to start and adjust the operating table posture;

[0127] The perception module is used to detect the current position, angle, and load status of the operating table in real time. The perception module obtains this data through a variety of sensors integrated on the operating table, such as position sensors, angle sensors, and pressure sensors, and converts this data into signals that the control system can understand and process. At the same time, this data is the basis for the control system to adjust and optimize the posture;

[0128] The control planning module is used to calculate and plan the optimal posture and adjustment path of the operating table according to the surgical task requirements provided by the task processing module and the current status information of the operating table provided by the perception module. The module uses advanced control algorithms to optimize the adjustment process to ensure that the operating table can reach the target posture smoothly and accurately. At the same time, the control planning module also considers the load changes of the operating table and external interference factors, such as uneven ground and equipment vibration, and formulates corresponding compensation strategies;

[0129] The feedback module is used to monitor the posture and load changes of the operating table in real time during the posture adjustment process of the operating table, and feed back these data to the control planning module. Through continuous feedback and adjustment, the feedback module helps the system to achieve closed-loop control to ensure that the operating table can be accurately maintained in the optimal posture. At the same time, the feedback module can also detect and handle abnormal situations that arise, such as mechanical failure or sensor failure during the adjustment process, and take safety measures to protect patients and medical staff.

[0130] The task processing module includes: a control panel unit and a task parsing unit;

[0131] A control panel unit, for providing a user interface to allow medical personnel to input surgical task parameters, such as surgical type and patient position requirements, wherein the medical personnel interact with the system through the unit to set the target state of the operating table;

[0132] The task analysis unit is used to analyze the surgical task data input by the control panel and convert it into instructions recognizable by the control system.

[0133] The perception module includes: a sensor unit and a data acquisition unit;

[0134] The sensor unit is used to obtain various status information of the operating table in real time through position sensors, angle sensors, and load sensors, and provide real-time data for the control system;

[0135] The data acquisition unit is responsible for collecting surgical task data from the sensor unit and performing preliminary processing and formatting to ensure the accuracy and consistency of the surgical task data.

[0136] The control planning module includes: attitude planning unit, control algorithm unit, instruction sending unit, execution and driving unit;

[0137] The posture planning unit is used to calculate the optimal posture of the operating table and its adjustment path according to the task requirements and sensor data;

[0138] The control algorithm unit is used to calculate the control instructions using the control algorithm to reduce the error between the current posture and the target posture. The control algorithm unit is the core part of the system to achieve precise control, which ensures that the operating table can reach the target posture smoothly and accurately.

[0139] The instruction sending unit is used to send the control instruction calculated by the control algorithm unit to the execution unit to drive the hydraulic system to complete the posture adjustment of the operating table, thereby ensuring the accurate transmission and execution of the control instruction;

[0140] The execution and drive unit is used to receive instructions from the control system and drive the actuator in the hydraulic system to complete the posture adjustment of the operating table, ensuring that the operating table can be adjusted according to the predetermined posture.

[0141] The feedback module includes: a feedback receiving unit, a fine-tuning unit, and a homing control unit;

[0142] A feedback receiving unit, used to receive real-time posture and load data from sensors during the posture adjustment process of the operating table;

[0143] The fine-tuning unit is used to make subtle adjustments to the posture of the operating table using a fine-tuning algorithm based on feedback data and external interference factors, ensuring that the operating table can maintain high precision and stability during the adjustment process;

[0144] The homing control unit is used to control the operating table to return to its initial state along the preset path after the operation. It is responsible for receiving the homing instructions from the medical staff and driving the actuator to complete the homing action. At the same time, it is also responsible for monitoring the state changes during the homing process to ensure the safety and accuracy of the homing action.

[0145] Example 3

[0146] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a stable control method for hydraulic control of an operating table are implemented. For details, please refer to the above method embodiment, which will not be repeated here.

[0147] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

[0148] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A stable control method for operating table hydraulic pressure, characterized in that: include: Step S1: The medical staff inputs the surgical task through the control panel; Step S2: The sensor collects the current surgical task data and sends the collected surgical task data to the control system. The control system automatically calculates and plans the optimal operating table posture using a control strategy based on the input surgical task data and sensor data; Step S3: The control system calculates the error between the optimal posture of the operating table and the current posture through the controller. According to the size and direction of the error, the control system sends an adjustment instruction to the hydraulic system to drive the actuator in the hydraulic system to adjust the posture of the operating table. At the same time, during the adjustment process, the sensor monitors the posture and load changes of the operating table in real time and feeds the data back to the control system. Step S4: After the operating table is close to the optimal posture, the control system uses a fine-tuning algorithm to fine-tune the posture of the operating table based on the data fed back by the sensor and in combination with external interference factors; Step S5: After the operation is completed, the control system returns the operating table to its initial state. At the same time, the medical staff confirms that the operating table has been returned to its initial state through the control panel and turns off the control system.

2. A stable control method for operating table hydraulic pressure according to claim 1, characterized in that: The specific steps of step S2 include: S2.1: The position, angle and load status information of the operating table are collected in real time through the position sensor, angle sensor and load sensor installed on the operating table, the surgical task data is obtained, and the surgical task data is converted into electrical signals; S2.2: amplify and filter the converted electrical signal, and send the processed electrical signal to the control system wirelessly; S2.3: The control system receives the electrical signal from the sensor, analyzes and verifies the received electrical signal, and compares and matches the verified surgical task data with the input surgical task data.

3. A stable control method for operating table hydraulic pressure according to claim 2, characterized in that: The specific steps of step S2 also include: S2.4: If the match is correct, then the posture planning algorithm is used according to the surgical task requirements and sensor data, combined with the kinematic characteristics, dynamic constraints, safety and efficiency factors of the operating table, to calculate the optimal path for a smooth transition from the current posture to the optimal surgical posture; S2.5: Based on the output results of the attitude planning algorithm, the control system calculates the attitude control instructions to achieve the optimal path.

4. A stable control method for operating table hydraulic pressure according to claim 3, characterized in that: The specific steps of S2.4 include: S2.41: receiving sensor data, and extracting the current posture of the operating table and obstacle position data from the sensor data, and at the same time, obtaining the surgical task requirement data; S2.42: Create an empty graph structure to store sampling points and connecting edges, and set the motion configuration space of the operating table according to the kinematic characteristics of the operating table; S2.43: Set constraints and randomly sample n points in the configuration space based on the constraints; S2.44: Establish a collision detection model between the operating table and obstacles, use the hierarchical bounding box algorithm to perform collision detection on each sampling point, determine whether the operating table collides with the obstacle in this posture, and evaluate the safety margin around the sampling point. The formula is: ； Where s represents the safety margin, represents the position vector of the kth operating table sampling point, represents the position vector of the nearest point on the j-th obstacle, represents the distance scaling factor between the operating table and the obstacle, represents the shape factor between the operating table and the obstacle, represents the directional factor between the operating table and the obstacle, represents the dynamic factor between the operating table and the obstacle, represents the Euclidean distance.

5. A stable control method for operating table hydraulic pressure according to claim 4, characterized in that: The specific steps of S2.4 also include: S2.45: Use the k-nearest neighbor search strategy to connect the collision-free sampling points, and add the sampling points and connecting edges to the graph to form a probabilistic roadmap; S2.46: On the constructed probability roadmap, use the graph search algorithm to search for the path from the current posture to the optimal surgical posture, and smooth the searched path; S2.47: Use dynamic simulation to verify the feasibility and safety of the path. If feasibility and safety are met, the optimal path is output; If the simulation results show that there are problems with the path, dynamic adjustments are made.

6. A stable control operating table hydraulic control method as claimed in claim 5, characterized in that: The constraint formula in S2.43 is: ； in, represents the angle of the i-th joint of the operating table Y, and They represent the minimum and maximum angles allowed for the i-th joint of the operating table Y, represents the angle change of the i-th joint of the operating table Y between two sampling points, express The maximum change allowed in Represents the change in velocity between two sampling points, Indicates the maximum angular velocity allowed by the joint, represents the collision detection function, Indicates that the operating table Y is in posture without colliding with any obstacles.

7. A stable control method for operating table hydraulic pressure according to claim 6, characterized in that: The specific steps of S2.44 include: According to the actual size and shape of obstacles and operating tables, the 3D geometric model was constructed using SolidWorks 3D modeling software; For the geometric models of the operating table and each obstacle, construct hierarchical bounding boxes respectively; For n sampling points, determine whether the bounding box of each sampling point intersects with any bounding box of the obstacle. If the bounding boxes intersect, perform collision detection on the three-dimensional geometric model of the intersecting part. After determining that there is no collision, the safety margin s around the sampling point is calculated.

8. A stable control method for operating table hydraulic pressure according to claim 7, characterized in that: The specific formula of the fine-tuning algorithm in step S4 is: ； in, Indicates the amount of fine-tuning, that is, the angle or position that needs to be adjusted. represents the deviation between the current posture and the preset optimal posture, t represents the current time, represents a past time point in the integration process, represents the forgetting factor, represents the interference function, It represents the performance index of the control system. represents external interference factors, A, B, C represent control parameter coefficients, and D represents the gain coefficient of the adaptive term.

9. A stable controlled operating table hydraulic control system, which is used to implement a stable controlled operating table hydraulic control method according to any one of claims 1 to 8, characterized in that: include: Task processing module, control planning module, feedback module; The task processing module includes a task parsing unit, in which a pattern matching strategy is configured, and the pattern matching strategy is used to parse the input surgical task data into instructions that can be understood and executed within the control system; The control planning module includes a posture planning unit, a control algorithm unit, and an instruction sending unit. The posture planning unit is configured with a control strategy, and the control strategy adopts a posture planning algorithm. The posture planning algorithm is used to calculate the optimal path and obtain the optimal posture of the operating table; the control algorithm unit is used to calculate the error between the current posture and the optimal posture; the instruction sending unit is used to drive the hydraulic system to complete the posture adjustment of the operating table; The feedback module includes a fine-tuning unit, in which a fine-tuning algorithm is configured. The fine-tuning algorithm is used to adjust the posture of the operating table according to feedback data and external interference factors.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a stable control operating table hydraulic control method as described in any one of claims 1-8 are implemented.

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