Control method for an anesthetic needle insertion positioning device

Abstract

The present invention discloses a control method for an anesthetic needle insertion positioning device. The method is based on an anesthetic needle insertion positioning device, which includes a platform mechanism, a pressure sensing mechanism, an encoder mechanism, a printing mechanism, and an intelligent processing mechanism. The pressure sensing mechanism, the encoder mechanism, and the printing mechanism are respectively arranged on the platform mechanism, and the pressure sensing mechanism, the encoder mechanism, and the printing mechanism are respectively connected to the intelligent processing mechanism. In the present invention, the pressure sensing component senses the force condition of the roller passing through the spinous process and the spinous process gap, realizes the feedback of the force of different tissues on the roller, the intelligent processing mechanism obtains real-time pressure data, the encoder converts the number of rotations of the recording side wheel into an electrical signal and transmits it to the intelligent processing mechanism, the intelligent processing mechanism fits the real-time pressure data and the real-time moving distance data, the intelligent processing mechanism automatically calculates the optimal needle insertion position according to the curve, and the intelligent processing mechanism controls the print head to move to the target position for marking.

Description

Technical Field

[0001] The present invention belongs to the field of medical devices and relates to a control method for an anesthetic needle insertion positioning device. Background Art

[0002] As a medical device for surgical anesthesia, the epidural anesthesia puncture device is widely used clinically. During epidural anesthesia simulation surgery, doctors need to select appropriate anesthetic needle insertion positions. The anesthetic needle insertion positions are generally selected at the interspace between lumbar vertebrae L2 and L3 or at the interspace between lumbar vertebrae L3 and L4. Currently, in simulation surgery, doctors often use two methods to locate the two interspaces. The first method is that the doctor first touches the sacrum of the human model, and then touches the spinous processes of the lumbar vertebrae upward along the sacrum, sequentially confirming each lumbar vertebra. After confirming the lumbar vertebrae, the doctor selects the position where the needle needs to be inserted and marks it with a pen. The second method is that the doctor stands behind the human model, spreads the thumb and index finger, presses the index finger on the upper limit of the iliac crest, and presses the thumb parallel to the spine. The doctor finds the vertebral interspace by pressing up and down and marks it with a pen.

[0003] Both of the above two methods are based on the needle insertion positions selected by the doctor's touch. The method of selecting the needle insertion position by relying on the doctor's touch sometimes makes the needle insertion position not centered enough, or fails to select the best puncture interspace, resulting in the problem of low puncture accuracy. In addition, during the puncture process, the bone may be punctured or the puncture direction may be deviated, ultimately leading to the failure of the epidural anesthesia simulation surgery. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a control method for an anesthetic needle insertion positioning device.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A control method for an anesthetic needle insertion positioning device, which is based on an anesthetic needle insertion positioning device. The anesthetic needle insertion positioning device includes a platform mechanism, a pressure sensing mechanism, an encoder mechanism, a printing mechanism, and an intelligent processing mechanism. The pressure sensing mechanism, the encoder mechanism, and the printing mechanism are respectively arranged on the platform mechanism, and the pressure sensing mechanism, the encoder mechanism, and the printing mechanism are respectively connected to the intelligent processing mechanism. The specific steps are as follows:

[0006] Step 1: Pretreatment of the anesthetic needle insertion positioning device, including confirming the operating states of the pressure sensing mechanism, the encoder mechanism, the printing mechanism, and the intelligent processing mechanism;

[0007] Step 2: The anesthetic needle insertion positioning device moves along the surface of the lumbar part of the human model spine, and the pressure sensing mechanism collects real-time pressure data and the encoder mechanism collects real-time moving distance data and transmits them to the intelligent processing mechanism;

[0008] Step 3: After the intelligent processing mechanism processes the data, the data is fitted to form a pressure fluctuation curve, and the target position is obtained according to the fitted pressure fluctuation curve;

[0009] Step 4: The intelligent processing mechanism controls the print head to move to the target position, and the print head prints a mark on the body surface at the target position, ending the step.

[0010] Further, the pressure sensing mechanism includes three groups of pressure sensing components. The three groups of pressure sensing components are respectively divided into Channel 1, Channel 2, and Channel 3. Channel 2 is located at the center of the lumbar vertebrae, and Channel 1 and Channel 3 are located on both sides of Channel 2.

[0011] Further, Step 3 includes the following steps:

[0012] Step 3.1: The intelligent processing mechanism receives the real-time pressure data output by the three groups of pressure detection modules in Step 2 to form corresponding pressure data sets F1 Ti , F2 Ti and F3 Ti ;

[0013] The real-time pressure data set collected by Channel 1 is set as F1 Ti , the real-time pressure data set collected by Channel 2 is set as F2 Ti , the real-time pressure data set collected by Channel 3 is set as F3 Ti , F1 Ti =(F1 i , T i ), F2 Ti =(F2 i , T i ) and F3 Ti =(F3 i , T i ), F represents the pressure value, T represents the sampling time, and i represents the set number;

[0014] Step 3.2: The pressure data sets F1 Ti , F2 Ti and F3 Ti are processed through a normalization function, and after processing, the pressure data sets and

[0015] Step 3.3: The intelligent processing mechanism receives the real-time moving distance data collected by the encoder mechanism in Step 2 and combines it with the pressure data processed in Step 3.2 to fit a 3D image;

[0016] Step 3.4: The Kalman filter filters the pressure data image and the 3D image;

[0017] Step 3.5: The intelligent processing mechanism samples the data filtered in Step 3.4, fits to form a pressure fluctuation curve, and obtains the target position according to the fitted pressure fluctuation curve.

[0018] Further, in step 3.2

[0019]

[0020]

[0021]

[0022] In equations (1) - (3), prF1Ti_list is the data list of pressure dataset F1 Ti prF2Ti_list is the data list of pressure dataset F2 Ti prF3Ti_list is the data list of pressure dataset F3 Ti prF1Ti_list.max() is the maximum value of pressure dataset F1 Ti prF2Ti_list.max() is the maximum value of pressure dataset F2 Ti prF3Ti_list.max() is the maximum value of pressure dataset F3 Ti α is the weight coefficient of the spring 1233 in the three groups of pressure sensing components 12.

[0023] Further, the calculation formula for fitting the 3D image in step 3.3 is:

[0024]

[0025] In equation (4), N represents Channel One, Channel Two, and Channel Three, Y is the position distance in the distribution directions of Channel One, Channel Two, and Channel Three, and D represents the pressing distance of the roller 126.

[0026] Further, the calculation formula for Kalman filter filtering in step 3.4 includes:

[0027] X(k, k - 1) = AX(k - 1) + BU(k)....................(5);

[0028] In equation (5), k represents the current moment, k - 1 represents the previous moment, X(k - 1) represents the optimal result of the previous state of the system, X(k, k - 1) represents the result of predicting the current state of the system using the optimal result of the previous state of the system, A and B are system parameters, for a multi - model system, A and B are set as matrices, U(k) represents the control quantity of the system at the current moment, A, B, and U(k) are set values, and U(k) can be set to 0, that is

[0029] There is no control quantity;

[0030] The covariance calculation formula corresponding to X(k, k - 1):

[0031] P(k, k - 1) = A * P(k - 1) * A T + Q.......................(6);

[0032] In equation (6), P(k, k - 1) is the covariance corresponding to X(k, k - 1), P(k - 1) is the covariance corresponding to X(k - 1), A T is the transpose matrix of A, Q is the covariance of the system process, and Q is a set value that does not change with the system state;

[0033] According to equations (5) and (6), equation (7) is obtained,

[0034] X(k) = X(k, k - 1) + K(k) * [Z(k) - H * X(k, k - 1)]....................(7)

[0035] In equation (7), X(k) is the optimal estimated value at time k, K(k) is the Kalman gain, In equation (4), H is a parameter of the measurement system. For a multi - measurement system, H is set as a matrix, H T is the transpose matrix of H, R is the covariance of the system measurement, and H and R are set values; Z(k) is the system measurement value, and Z(k) is a set value;

[0036] Covariance calculation formula for X(k):

[0037] P(k) = [I - K(k) * H] * P(k, k - 1)........................(9);

[0038] In equation (9), P(k) is the covariance corresponding to X(k), where I is set as a matrix, and I is a set value.

[0039] Furthermore, in step 3.2, α = 0.9.

[0040] Furthermore, the pressure sensing mechanism further includes a back plate and a transverse movement component. The pressure sensing component is arranged on the back plate through the transverse movement component, and the transverse movement component controls the synchronous inward or outward movement of the pressure sensing component.

[0041] Furthermore, the pressure sensing component includes a vertical plate, a pressure detection module, a pressure conduction module, and an elastic module. The pressure detection module and the pressure conduction module are arranged on the vertical plate. The pressure detection module includes a sensor. The vertical plate is provided with a sensor mounting seat, the sensor is arranged on the sensor mounting seat, the sensor faces the pressure conduction module, and the sensor is connected to the intelligent processing mechanism.

[0042] Further, the pressure detection module includes a sensor. A sensor mounting seat is provided on the vertical plate. The sensor is disposed on the sensor mounting seat, facing the pressure conduction module, and the sensor is connected to the intelligent processing mechanism.

[0043] In summary, the beneficial effects of the present invention are as follows:

[0044] 1) The pressure sensing component of the present invention senses the force conditions of the roller passing through the spinous process and the spinous process space, realizes the feedback of the force of different tissues on the roller. The intelligent processing mechanism obtains real-time pressure data. The encoder converts the number of rotations of the recording side wheel into an electrical signal and transmits it to the intelligent processing mechanism. The intelligent processing mechanism obtains the real-time moving distance of the device. The intelligent processing mechanism fits the real-time pressure data and the real-time moving distance data to form a pressure fluctuation curve that changes with distance. The intelligent processing mechanism automatically calculates the optimal needle insertion position according to the curve, and the intelligent processing mechanism controls the print head to move to the target position for marking. The present invention accurately locates the needle insertion position of epidural anesthesia during the simulation exercise, improves the accuracy of the anesthesia puncture needle insertion point, shortens the confirmation time of the needle insertion point. The present invention has a high degree of intelligence, and is used for simulating the exercise of anesthesia puncture needle insertion point positioning with fast response speed and high positioning accuracy.

[0045] 2) The present invention makes the pressure sensing component move synchronously inwards or outwards through the transverse movement component, thereby adjusting the distance between adjacent pressure sensing components and moving the pressure sensing component to a suitable position.

[0046] 3) The present invention is provided with an elastic module. Rotating the adjusting nut of the elastic module can compress or relax the spring, thereby increasing or decreasing the prestress of the roller.

[0047] 4) The present invention is provided with a limit block. The sliding range of the lower slider is limited by the limit block, thereby blocking the downward sliding of the roller support frame. When continuously compressing the spring downward, a large elastic stress is applied to the roller.

[0048] 5) The platform mechanism of the present invention is provided with a registration block. The spring force of the pressure sensing component is balanced with the self-weight of the device through the registration block. The platform mechanism is provided with multiple groups of side wheels. The side wheels provide a stable supporting force for the device and can reduce the resistance during the pushing process of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is the front view of the device of the present invention.

[0050] Figure 2 It is the rear view of the device of the present invention.

[0051] Figure 3 It is the front view of the pressure sensing mechanism of the present invention.

[0052] Figure 4Right view of the pressure sensing mechanism of the present invention.

[0053] Figure 5 Rear view of the pressure sensing mechanism of the present invention.

[0054] Figure 6 Front assembly view of the platform mechanism, encoder mechanism and printing mechanism of the present invention.

[0055] Figure 7 Side assembly view of the platform mechanism, encoder mechanism and printing mechanism of the present invention.

[0056] Figure 8 Anesthesia needle insertion positioning flowchart of the present invention.

[0057] Figure 9 Schematic diagram of the lumbar vertebra of the human body model of the present invention.

[0058] Figure 10 Pressure data images of Channel 1, Channel 2 and Channel 3 of the present invention.

[0059] Figure 11 3D image of the fitting of the moving distance data and pressure data of the present invention.

[0060] Figure 12 is Figure 10 Pressure data image filtered by Kalman filter.

[0061] Figure 13 is Figure 11 3D image filtered by Kalman filter.

[0062] Figure 14 Pressure fluctuation curve graph of the present invention.

[0063] Identifications in the figure: pressure sensing mechanism 1, platform mechanism 2, encoder mechanism 3, printing mechanism 4, back plate 10, transverse movement assembly 11, pressure sensing assembly 12, transverse slide rail 110, through cavity 101, transverse slider 111, bearing seat 112, knob 114, screw 113, adjustment plate 115, sensor mounting seat 120, sensor 121, conduction block 122, roller support frame 124, limit block 125, roller 126, vertical plate 127, vertical slide rail 128, upper slider 1221, lower slider 1241, adjusting nut 1231, screw 1232, spring 1233, registration block 21, handrail 22, side wheel frame 23, side wheel 24, encoder 31, encoder mounting plate 32, coupling 33, print head 41, print track 42. Detailed implementation manner

[0064] The following specific examples illustrate the implementation modes of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0065] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present invention. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0066] All directional indications (such as up, down, left, right, front, back, horizontal, vertical...) in the embodiments of the present invention are only used to explain the relative position relationship and movement conditions between components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0067] Due to reasons such as installation errors, the parallel relationship referred to in the embodiments of the present invention may actually be an approximate parallel relationship, and the vertical relationship may actually be an approximate vertical relationship.

[0068] Embodiment 1:

[0069] As Figures 1-7 shown, an anesthetic needle insertion positioning device includes a platform mechanism 2, a pressure sensing mechanism 1, an encoder mechanism 3, a printing mechanism 4, and an intelligent processing mechanism. The pressure sensing mechanism 1, the encoder mechanism 3, and the printing mechanism 4 are respectively arranged on the platform mechanism 2, and the pressure sensing mechanism 1, the encoder mechanism 3, and the printing mechanism 4 are respectively connected to the intelligent processing mechanism.

[0070] The pressure sensing mechanism 1 includes a back plate 10, a transverse movement component 11, and a pressure sensing component 12. The pressure sensing component 12 is arranged on the back plate 10 through the transverse movement component 11. The transverse movement component 11 includes a transverse slide rail 110, a transverse slide block 111, a bearing seat 112, a knob 114, a screw rod 113, and an adjustment plate 115. The transverse slide block 111 and the adjustment plate 115 are fixedly arranged on the pressure sensing component 12. The transverse slide rail 110 and the bearing seat 112 are fixedly arranged on both side surfaces of the back plate 10. The transverse slide rail 110 faces the pressure sensing component 12. The transverse slide block 111 is arranged on the transverse slide rail 110, and the transverse slide block 111 is slidably connected along the transverse slide rail 110. Preferably, two transverse slide rails 110 are provided, and the two transverse slide rails 110 are perpendicular to the length direction of the back plate and are distributed up and down along the length direction of the back plate. The length direction of the back plate is Figure 1In the x direction, so as to ensure the stability of the connection with the pressure sensing component 12 and the stability of the lateral movement of the pressure sensing component 12.

[0071] The backplane 10 is provided with a through cavity 101. The adjusting plate 115 penetrates through the through cavity 101. The screw 113 is rotatably connected to the bearing seat 112. Both ends of the screw 113 are fixedly connected to the knob 114. The screw 113 is threadedly connected to the adjusting plate 115. Preferably, two adjusting plates 115 are provided. The two adjusting plates 115 are symmetrically located on both sides of the bearing seat 112. The screw 113 is provided with threads with opposite helix directions. The adjusting plate 115 is arranged on the threads with opposite helix directions of the screw 113. By rotating any one of the knobs 114, the two adjusting plates 115 move inwards or outwards synchronously.

[0072] Multiple groups of pressure sensing components 12 are provided. Preferably, as Figure 1 shown, three groups of pressure sensing components 12 are provided. The three groups of pressure sensing components 12 are horizontally arrayed along the width direction of the backplane 10. The width direction of the backplane 10 is the Figure 1 y direction in. The pressure sensing component 12 located in the middle is fixedly arranged on the backplane 10. The pressure sensing components 12 located on both sides are fixedly connected to the transverse slider 111. The distance from the pressure sensing component 12 located in the middle is synchronously adjusted through the transverse movement component 11.

[0073] The pressure sensing component 12 includes a vertical plate 127, a pressure detection module, a pressure conduction module, and an elastic module. The pressure detection module and the pressure conduction module are arranged on the vertical plate 127. The pressure detection module, the elastic module, and the pressure conduction module are distributed along the length direction of the vertical plate 127. The length direction of the vertical plate 127 is the Figure 1 x direction in. The vertical plate 127 is provided with a vertical slide rail 128. The vertical slide rail 128 is distributed along the length direction of the vertical plate 127. The force received by the pressure conduction module is conducted to the pressure detection module through the elastic module.

[0074] The pressure detection module includes a sensor 121. The vertical plate 127 is provided with a sensor mounting seat 120. The sensor 121 is arranged on the sensor mounting seat 120. The sensor 121 faces the pressure conduction module. The sensor 121 is connected to the intelligent processing mechanism. The sensor 121 converts the received force data into an electrical signal and transmits it to the intelligent processing mechanism. The intelligent processing mechanism obtains real-time pressure data.

[0075] The pressure conduction module includes a conduction block 122 and a roller support frame 124. The conduction block 122 faces the sensor 121. The sensor 121 is used to detect the pressure conducted by the conduction block 122. The conduction block 122 and the roller support frame 124 are connected by an elastic module. The conduction block 122 is provided with an upper slider 1221. The upper slider 1221 is arranged on the vertical slide rail 128 and is slidably connected to the vertical slide rail 128. The roller support frame 124 is provided with a lower slider 1241. The lower slider 1241 is arranged on the vertical slide rail 128 and is slidably connected to the vertical slide rail 128. A roller 126 is provided at the lower end of the roller support frame 124. The upper end of the roller support frame 124 is connected to the conduction block 122 through an elastic module.

[0076] The elastic module includes an adjusting nut 1231, a screw rod 1232, and a spring 1233. The screw rod 1232 is connected to the conduction block 122 and the roller support frame 124. The spring 1233 is sleeved on the screw rod 1232. The adjusting nut 1231 is arranged on the screw rod 1232. In this embodiment, the spring 1233 is located between the adjusting nut 1231 and the roller support frame 124. By rotating the adjusting nut 1231, the spring 1233 can be compressed or relaxed, thereby increasing or decreasing the prestress of the roller 126.

[0077] A limit block 125 is provided at the lower end of the vertical plate 127. The limit block 125 is located at the lower end of the vertical slide rail 128 to limit the sliding range of the lower slider 1241, thereby blocking the downward sliding of the roller support frame 124. When continuously compressing the spring downward, a greater elastic stress is applied to the roller 126.

[0078] During the implementation of the pressure sensing assembly 12, the roller 126 contacts and rolls on the body surface. When the roller 126 rolls to the soft tissue position of the human body model, under the elastic force of the elastic module, the roller 126 sinks. At this time, the conduction block 122 moves downward, the force on the sensor 121 decreases, and the reading of the sensor also decreases. When the roller 126 rolls to the bone tissue position of the human body model, the roller 126 is pushed up by the bone tissue of the human body model, the spring 1233 is further compressed, the elastic force increases, the conduction block 122 moves upward, the force on the sensor 121 increases, and the reading of the sensor also increases.

[0079] The platform mechanism 2 is the carrier of the pressure sensing mechanism 1, the encoder mechanism 3, and the printing mechanism 4, including an upper end surface and a lower end surface. The lower end surface faces the body surface. The upper end surface of the platform mechanism 2 is provided with a registration block 21 and a handrail 22. According to Figure 1From a visual perspective, the registration blocks 21 are located at both ends of the platform mechanism 2 to ensure the balance of the platform mechanism 2. The registration blocks 21 are used to increase the counterweight so that the spring force of the pressure sensing component 12 is balanced with the self-weight of the device. The number of the registration blocks 21 can be increased or decreased according to actual needs. Two handrails 22 are provided, and the two handrails 22 are located on both sides of the platform mechanism 2 for convenient picking up and placing of the device. The lower end surface of the platform mechanism 2 is provided with side wheel frames 23 and side wheels 24. The side wheel frames 23 are arranged on both sides of the platform mechanism 2, and the side wheels 24 are arranged on the side wheel frames 23. Preferably, two sets of side wheels 24 are assembled on one set of side wheel frames 23. The side wheels 24 provide stable supporting force for the device and can reduce the resistance during the pushing process of the device.

[0080] The encoder mechanism 3 is arranged on the lower end surface of the platform mechanism 2. The encoder mechanism 3 includes an encoder 31, an encoder mounting plate 32 and a coupling 33. The encoder mounting plate 32 is arranged on the lower end surface of the platform mechanism 2, the encoder 31 is arranged on the encoder mounting plate 32, and the encoder 31 is connected to the rotating shaft of any one of the side wheels 24 through the coupling 33. When the side wheel 24 rotates, it drives the encoder 31 to rotate synchronously through the coupling 33. The encoder 31 records the number of rotations of the side wheel 24. The encoder 31 is connected to the intelligent processing mechanism, and the encoder 31 converts the recorded number of rotations of the side wheel 24 into an electrical signal and transmits it to the intelligent processing mechanism, and the intelligent processing mechanism obtains the real-time moving distance of the device.

[0081] The printing mechanism 4 is arranged on the lower end surface of the platform mechanism 2. The printing mechanism 4 includes a print head 41 and a printing track 42. The printing track 42 is arranged on the lower end surface of the platform mechanism 2 along the y direction. The print head 41 is arranged on the printing track 42 and can slide along the printing track 42. The print head 41 is connected to the intelligent processing mechanism. The intelligent processing mechanism controls the print head 41 to slide on the printing track 42 until it reaches the target position for printing. The print head 41 directly prints the needle insertion mark on the body surface, so as to realize the function of accurately positioning the needle insertion position of epidural anesthesia. The print head 41 is connected to the intelligent processing mechanism, and the intelligent processing mechanism controls the print head 41 to move to the target position and make a mark.

[0082] The intelligent processing mechanism combines the real-time pressure data and the real-time moving distance to obtain the distance between the spinous processes, and the maximum distance between the spinous processes is used as the needle insertion target position.

[0083] During the implementation of this embodiment, the operator holds the armrest 22, places the device on the back of the mannequin, adjusts the transverse movement assembly 11, separates the three rollers 26 of the three groups of pressure sensing assemblies 12 to an appropriate distance, pushes the device, and the three groups of pressure sensing assemblies 12 respectively record three groups of real-time pressure data. The encoder records the real-time rotation times of the side wheels to obtain the real-time movement distance data of the device. The intelligent processing mechanism fits the real-time pressure data and the real-time movement distance data to form a pressure fluctuation curve that changes with distance. The intelligent processing mechanism automatically calculates the optimal needle insertion position, that is, the target position, according to the curve. When the device is pushed on the human back again, the intelligent processing mechanism controls the printing assembly to move to the target position and print a mark at the target position.

[0084] As Figures 8-14 shown, the present application also provides a control method for an anesthetic needle insertion positioning device. This method is based on the above-mentioned anesthetic needle insertion positioning device. The anesthetic needle insertion positioning device includes a platform mechanism 2, a pressure sensing mechanism 1, an encoder mechanism 3, a printing mechanism 4, and an intelligent processing mechanism. The pressure sensing mechanism 1, the encoder mechanism 3, and the printing mechanism 4 are respectively arranged on the platform mechanism 2, and the pressure sensing mechanism 1, the encoder mechanism 3, and the printing mechanism 4 are respectively connected to the intelligent processing mechanism. The specific steps are as follows:

[0085] Step 1: Pretreat the anesthetic needle insertion positioning device, including confirming the operating states of the pressure sensing mechanism 1, the encoder mechanism 3, the printing mechanism 4, and the intelligent processing mechanism;

[0086] Step 2: Move the anesthetic needle insertion positioning device along the surface of the lumbar part of the object's spine, such as the surface of the lumbar part of the mannequin's spine (abbreviation: body surface). The pressure sensing mechanism 1 collects real-time pressure data, and the encoder mechanism 3 collects real-time movement distance data;

[0087] The anesthetic needle insertion positioning device is only used to identify the lumbar vertebrae of the mannequin. There are 5 lumbar vertebrae in the mannequin. As Figure 9 shown, from top to bottom, they are L1, L2, L3, L4, L5 respectively. Above L1 is the thoracic vertebra, and its structure is similar to that of the lumbar vertebra. After L5 is the sacrum, and the sacrum is a large triangular bone. In this embodiment, after the roller 126 passes through the spinous processes of the lumbar vertebrae L5-L1 in sequence, the pressure sensing mechanism 1 collects a data curve with at least 5 complete wave peaks.

[0088] Before using the anesthetic needle insertion positioning device, the waist of the mannequin is bent so that the lumbar part of the mannequin's spine protrudes backward, and the spinous processes of each lumbar vertebra can maintain a relatively large distance, so that the anesthetic needle insertion positioning device can better identify the interspinous space;

[0089] The pressure sensing mechanism 1 includes three groups of pressure sensing components 12. The contact positions of the rollers 126 of the three groups of pressure sensing components 12 with the body surface are different, and the waist of the human model bends when the anesthetic injection positioning device moves. Therefore, the deformation degrees of the springs 1233 of the elastic modules in the three groups of pressure sensing components 12 are different, and the real-time pressure data detected by the pressure detection modules of the three groups of pressure sensing components 12 are different. For the convenience of description and distinction, the three groups of pressure sensing components 12 are respectively divided into Channel 1, Channel 2, and Channel 3. Channel 2 is located at the center of the lumbar vertebrae, and Channel 1 and Channel 3 are located on both sides of Channel 2. The real-time pressure data collected by Channel 1, Channel 2, and Channel 3 and the real-time moving distance data collected by the encoder mechanism 3 are transmitted to the intelligent processing mechanism for data processing.

[0090] Step 3: After the intelligent processing mechanism processes the data, the data is fitted to form a pressure fluctuation curve, and the target position is obtained according to the fitted pressure fluctuation curve.

[0091] Step 4: The intelligent processing mechanism controls the print head to move to the target position, and the print head prints a mark on the body surface at the target position, ending the step.

[0092] The steps of data fitting in Step 3 include:

[0093] Step 3.1: The intelligent processing mechanism receives the real-time pressure data output by the three groups of pressure detection modules in Step 2 to form corresponding pressure data sets F1 Ti , F2 Ti and F3 Ti ;

[0094] The real-time pressure data set collected by Channel 1 is set as F1 Ti , the real-time pressure data set collected by Channel 2 is set as F2 Ti , the real-time pressure data set collected by Channel 3 is set as F3 Ti , F1 Ti =(F1 i , T i ), F2 Ti =(F2 i , T i ), and F3 Ti =(F3 i , T i ), F represents the pressure value, T represents the sampling time, and i represents the set number;

[0095] Step 3.2: The pressure data sets F1 Ti , F2 Ti and F3 Ti are processed by a normalization function, and after processing, the pressure data sets and

[0096]

[0097]

[0098]

[0099] In equations (1)-(3), prF1Ti_list is the data list of pressure dataset F1 Ti and prF2Ti_list is the data list of pressure dataset F2 Ti and prF3Ti_list is the data list of pressure dataset F3 Ti The maximum value of the data in prF1Ti_list is the maximum value of pressure dataset F1 Ti The maximum value of the data in prF2Ti_list is the maximum value of pressure dataset F2 Ti The maximum value of the data in prF3Ti_list is the maximum value of pressure dataset F3 Ti α is the weight coefficient of the spring 1233 in the three groups of pressure sensing components 12. In this application, α = 0.9;

[0100] According to the pressure dataset and obtain the pressure data images showing the variation of the pressures in Channel 1, Channel 2, and Channel 3 over time as shown in the appendix Figure 10 . In the appendix Figure 10 , the curve represented by sensor1 is the pressure data change curve of Channel 1, the curve represented by sensor2 is the pressure data change curve of Channel 2, and the curve represented by sensor3 is the pressure data change curve of Channel 3;

[0101] Step 3.3: The intelligent processing mechanism receives the real-time moving distance data collected by the encoder mechanism 3 in Step 2 and combines it with the pressure data processed in Step 3.2 to fit a 3D image and obtain the 3D image as shown in the appendix Figure 11 . In the 3D image, the X coordinate is the moving distance data collected by the encoder mechanism 3, the Y coordinate is the distance between the three groups of rollers 126, and the D coordinate is the pressing distance of the rollers 126

[0102] Calculation formula for fitting the 3D image:

[0103]

[0104] In equation (4), N represents Channel 1, Channel 2, and Channel 3, and Y is the position distance in the distribution direction of Channel 1, Channel 2, and Channel 3. For example, N = 1 represents Channel 1 Let \(P_1\) be the pressure data set for Channel 1, and \(Y_1\) be the position of Channel 1 in this direction. As shown in the figure, in this embodiment, the position of Channel 2 in this direction is set as the origin, that is, \(Y_2 = 0\). Channel 1 and Channel 3 are on both sides of Channel 2, and \(Y_1\) and \(Y_3\) are positive and negative values respectively. \(D\) represents the pressing distance of the roller 126;

[0105] Step 3.4: Filter the pressure data image and 3D image using Kalman filter;

[0106] Calculation formula for Kalman filter filtering:

[0107] \(X(k,k - 1)=AX(k - 1)+BU(k)\cdots\cdots(5)\)

[0108] In formula (5), \(k\) represents the current moment, \(k - 1\) represents the previous moment, \(X(k - 1)\) represents the optimal result of the system's previous state, \(X(k,k - 1)\) represents the result of predicting the current state of the system using the optimal result of the system's previous state, \(A\) and \(B\) are system parameters. For a multi - model system, \(A\) and \(B\) are set as matrices, \(U(k)\) represents the control quantity of the system at the current moment, \(A\), \(B\), and \(U(k)\) are set values, and \(U(k)\) can be set to 0, that is, there is no control quantity;

[0109] Covariance calculation formula corresponding to \(X(k,k - 1)\):

[0110] \(P(k,k - 1)=AP(k - 1)A^T+Q\cdots\cdots(6)\) T +Q\cdots\cdots(6)

[0111] In formula (6), \(P(k,k - 1)\) is the covariance corresponding to \(X(k,k - 1)\), \(P(k - 1)\) is the covariance corresponding to \(X(k - 1)\), \(A^T\) is the transpose matrix of \(A\), \(Q\) is the covariance of the system process, and \(Q\) is a set value that does not change with the system state; T For \(A^T\), \(A^T\) is the transpose matrix of \(A\), \(Q\) is the covariance of the system process, and \(Q\) is a set value that does not change with the system state;

[0112] According to formula (5) and formula (6), formula (7) is obtained,

[0113] \(X(k)=X(k,k - 1)+K(k)[Z(k)-HX(k,k - 1)]\cdots\cdots(7)\)

[0114] In formula (7), \(X(k)\) is the optimal estimated value at the \(k\) - th moment, and \(K(k)\) is the Kalman gain, In formula (4), \(H\) is the parameter of the measurement system. For a multi - measurement system, \(H\) is set as a matrix, \(H^T\) is the transpose matrix of \(H\), \(R\) is the covariance of the system measurement, \(H\) and \(R\) are set values; \(Z(k)\) is the system measurement value, and \(Z(k)\) is a set value; T For \(H^T\), \(H^T\) is the transpose matrix of \(H\), \(R\) is the covariance of the system measurement, \(H\) and \(R\) are set values; \(Z(k)\) is the system measurement value, and \(Z(k)\) is a set value;

[0115] Covariance calculation formula corresponding to X(k):

[0116] P(k)=[I - K(k)H]P(k,k - 1)........................(9)

[0117] In formula (9), P(k) is the covariance corresponding to X(k), where I is set as a matrix and I is a set value. For a single - model single - measurement system, I = 1. The autoregressive operation of Kalman filtering is realized through formula (9).

[0118] Filter the pressure data image and 3D image according to formulas (5)-(9) to obtain Figure 12 and Figure 13 , After Kalman filtering, the image appears smoother, the burrs are significantly reduced, and it is easier to distinguish whether it is the spinal spinous process or noise.

[0119] Step 3.5: The intelligent processing mechanism samples the data filtered in step 3.4, fits to form a pressure fluctuation curve, and obtains the target position according to the fitted pressure fluctuation curve;

[0120] The intelligent processing mechanism collects the pressure means of channel one, channel two, and channel three corresponding to the Y = 0 plane of the 3D image filtered in step 3.4 based on the same sampling time T, and obtains the data set (F i , d i ) corresponding to the pressure data and distance data, and forms a pressure fluctuation curve changing with distance as shown in Figure 14 ,

[0121] where, F i is the pressure mean of channel one, channel two, and channel three based on the same sampling time T, d i is the moving distance data collected by the encoder mechanism 3 based on the same sampling time T. The pressure peak of the pressure fluctuation curve is set as F pN , the distance corresponding to the pressure peak is set as d pN , Δd = d pN+1 -d pN , Δd represents the wave - peak spacing, and N represents the order number of the wave peak.

[0122] In this embodiment, after the roller 126 passes through the spinous processes of the lumbar vertebrae L5-L1 in sequence, the pressure sensing mechanism 1 collects a data curve with at least 5 complete wave peaks. Since the end of the spinal cord of the human body model is at the lower edge of L1 and the upper edge of L2. To reduce the risk of damage to the spinal cord of the model during the simulated puncture, when simulating, when performing an epidural puncture, avoid puncturing the spinous process space between L1-L2. Therefore, the number N in this embodiment is 4, corresponding to the lumbar vertebrae L5-L2 respectively. Δd is the spinous process space between L5-L2, and the spinous process space of the target position is set as D, D = Δd Max , Δd Max The corresponding d pN+1 And d pN Is the moving position of the printing mechanism 4

[0123] In step 4, the intelligent processing mechanism controls the print head to move to Δd in step 3.5 Max The corresponding d pN+1 And d pN Between them, the print head can print a mark on the body surface of the human body model at the target position

[0124] Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention

Claims

1. A control method for an anesthetic needle insertion positioning device, characterized in that: This method is based on an anesthetic needle insertion positioning device, which includes a platform mechanism, a pressure sensing mechanism, an encoder mechanism, a printing mechanism, and an intelligent processing mechanism. The pressure sensing mechanism, the encoder mechanism, and the printing mechanism are respectively arranged on the platform mechanism, and the pressure sensing mechanism, the encoder mechanism, and the printing mechanism are respectively connected to the intelligent processing mechanism. The pressure sensing mechanism includes three groups of pressure sensing components, which are respectively divided into Channel 1, Channel 2, and Channel 3. Channel 2 is located in the center of the lumbar vertebra, and Channel 1 and Channel 3 are located on both sides of Channel 2. The specific steps are as follows: Step 1: Pretreat the anesthetic needle insertion positioning device, including confirming the operating states of the pressure sensing mechanism, the encoder mechanism, the printing mechanism, and the intelligent processing mechanism; Step 2: Move the anesthetic needle insertion positioning device along the surface of the lumbar part of the human model spine. The pressure sensing mechanism collects real-time pressure data and the encoder mechanism collects real-time moving distance data and transmits them to the intelligent processing mechanism; Step 3: After the intelligent processing mechanism processes the data, the data is fitted to form a pressure fluctuation curve, and the target position is obtained according to the fitted pressure fluctuation curve; Step 3 includes the following steps: Step 3.1: The intelligent processing mechanism receives the real-time pressure data output by the three groups of pressure detection modules in Step 2 to form corresponding pressure data sets F1 Ti , F2 Ti and F3 Ti ; The real-time pressure data set collected by Channel 1 is set as F1 Ti 、The real-time pressure data set collected by Channel 2 is set as F2 Ti 、The real-time pressure data set collected by Channel 3 is set as F3 Ti , F1 Ti =(F1 i , T i ), F2 Ti =(F2 i , T i ) and F3 Ti =(F3 i , T i ), where F represents the pressure value, T represents the sampling time, and i represents the set number; Step 3.2: Pressure data sets F1 Ti , F2 Ti and F3 Ti are processed by a normalization function, and after processing, pressure data sets and Step 3.3: The intelligent processing mechanism receives the real-time moving distance data collected by the encoder mechanism in Step 2 and combines it with the pressure data processed in Step 3.2 to fit a 3D image; Step 3.4: Filter the pressure data image and the 3D image by Kalman filtering; Step 3.5: The intelligent processing mechanism samples the data filtered in Step 3.4, fits it to form a pressure fluctuation curve, and obtains the target position according to the fitted pressure fluctuation curve; Step 4: The intelligent processing mechanism controls the print head to move to the target position, and the print head prints a mark on the body surface at the target position, ending the step.

2. The control method of an anesthetic needle insertion positioning device according to claim 1, characterized in that: In Step 3.2 prF1 in Formula (1) - Formula (3) T The i_list is the data list of the pressure data set F1 Ti , and the prF2 Ti _list is the data list of the pressure data set F2 Ti , and the prF3 Ti _list is the data list of the pressure data set F3 Ti , and the prF1 Ti _list.max() is the maximum value of the pressure data set F1 Ti , and the prF2 Ti _list.max() is the maximum value of the pressure data set F2 Ti , and the prF3 Ti _list.max() is the maximum value of the pressure data set F3 Ti , and α is the weight coefficient of the spring in the three groups of pressure sensing components; the pressure sensing component includes a vertical plate, a pressure detection module, a pressure conduction module, and an elastic module. The pressure detection module and the pressure conduction module are arranged on the vertical plate. The pressure detection module, the elastic module, and the pressure conduction module are distributed along the length direction of the vertical plate. The force of the pressure conduction module is conducted to the pressure detection module through the elastic module. The pressure conduction module includes a conduction block and a roller support frame. The conduction block faces the sensor, and the sensor is used to detect the pressure conducted by the conduction block. A roller is arranged at the lower end of the roller support frame. The elastic module includes an adjusting nut, a screw rod, and a spring. The screw rod is connected to the conduction block and the roller support frame. The spring is sleeved on the screw rod, and the adjusting nut is arranged on the screw rod.

3. The control method of an anesthetic needle insertion positioning device according to claim 2, characterized in that: The calculation formula for fitting the 3D image in Step 3.3 is: In Equation (4), N represents Channel 1, Channel 2, and Channel 3, Y is the position distance in the distribution direction of Channel 1, Channel 2, and Channel 3, and D represents the pressing distance of the roller.

4. The control method of an anesthetic needle insertion positioning device according to claim 1, characterized in that: The calculation formula for Kalman filtering in Step 3.4 includes: X(k, k - 1) = AX(k - 1) + BU(k)....................(5); In Equation (5), k represents the current moment, k - 1 represents the previous moment, X(k - 1) represents the optimal result of the previous state of the system, X(k, k - 1) represents the result of predicting the current state of the system using the optimal result of the previous state of the system, A and B are system parameters. For a multi-model system, A and B are set as matrices, U(k) represents the control quantity of the system at the current moment, A, B, and U(k) are set values, and U(k) can be set to 0, that is, there is no control quantity; The covariance calculation formula corresponding to X(k, k - 1): P(k, k - 1) = AP(k - 1)A T + Q.......................(6); In Equation (6), P(k, k - 1) is the covariance corresponding to X(k, k - 1), P(k - 1) is the covariance corresponding to X(k - 1), A T is the transpose matrix of A, Q is the covariance of the system process, and Q is a set value that does not change with the change of the system state; According to Equation (5) and Equation (6), Equation (7) is obtained, X(k) = X(k, k - 1) + K(k)[Z(k) - HX(k, k - 1)]....................(7) In Equation (7), X(k) is the optimal estimated value at time k, and K(k) is the Kalman gain. In Equation (4), H is a parameter of the measurement system. For a multi-measurement system, H is set as a matrix, and H T is the transpose matrix of H, R is the covariance of the system measurement, and H and R are set values; Z(k) is the system measurement value, and Z(k) is a set value. The covariance calculation formula corresponding to X(k): P(k) = [I - K(k)H]P(k, k - 1)........................(9); In Equation (9), P(k) is the covariance corresponding to X(k), where I is set as a matrix and I is a set value.

5. The control method of an anesthetic needle insertion positioning device according to claim 2, characterized in that: In step 3.2, α = 0.

9.

6. The control method of an anesthetic needle insertion positioning device according to claim 1, characterized in that: The pressure sensing mechanism further includes a back plate and a transverse movement assembly. The pressure sensing assembly is arranged on the back plate through the transverse movement assembly, and the transverse movement assembly controls the pressure sensing assembly to move inwards or outwards synchronously.

7. The control method of an anesthetic needle insertion positioning device according to claim 1, characterized in that: The pressure sensing assembly includes a vertical plate, a pressure detection module, a pressure conduction module and an elastic module. The pressure detection module and the pressure conduction module are arranged on the vertical plate. The pressure detection module includes a sensor. The vertical plate is provided with a sensor mounting seat. The sensor is arranged on the sensor mounting seat. The sensor faces the pressure conduction module and is connected to the intelligent processing mechanism.

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