Skull traction operation auxiliary device and control method thereof

Abstract

The invention relates to the technical field of orthopedic medical instruments, in particular to a skull traction operation auxiliary device and a control method thereof. The traction bed body provides a lying position / prone position for a patient, the skull traction bow is fixed to the head of the patient and used for traction, and the flexible traction arm is arranged on one side of the traction bed body and used for being connected with the skull traction bow. The flexible traction arm is provided with a connecting rod used for installing the skull traction bow, and the connecting rod is provided with an intelligent control stopwatch used for being matched with the skull traction bow. The auxiliary device for the skull traction operation has the advantages that the situation that the cervical curvature is inconvenient to adjust in the operation is improved, and the operation convenience of the auxiliary device for the skull traction operation is improved.

Description

Technical Field

[0001] This application relates to the technical field of orthopedic medical devices, and in particular to a cranial traction surgical aid device and its control method. Background Technology

[0002] Surgical aids for cranial traction are primarily used for intraoperative traction treatment of cervical spine fractures, dislocations, basilar impression, and atlantoaxial dislocation. Through mechanical transmission and precise adjustment, they assist in stabilizing the patient's head and neck, maintaining the physiological curvature and stability of the cervical spine. Surgical aids for cranial traction provide a safe and controllable traction environment for the surgical procedure, while reducing intraoperative risks such as spinal cord injury.

[0003] In related technologies, commonly used auxiliary devices in cranial traction surgery mainly include cranial traction bows, traction frames, and sliding rail traction head frames. The cranial traction bow is fixed to both sides of the patient's skull, and cervical traction is achieved in conjunction with traction ropes and pulley systems. Alternatively, a three- or four-screw traction head frame combined with a sliding rail structure can achieve horizontal traction.

[0004] Regarding the aforementioned technologies, the devices have limited ability to adjust the angle, lack precise control over the traction force, and cannot quantify the angle adjustment. Doctors must rely on experience to make intraoperative adjustments to accommodate different cervical curvature requirements. However, simultaneously adjusting the device to change the cervical curvature during surgery is quite inconvenient. Summary of the Invention

[0005] To improve the convenience of operating the auxiliary device for cranial traction surgery, which is inconvenient for adjusting the cervical curvature during surgery and lacks precise control of traction force, this application provides an auxiliary device for cranial traction surgery and its control method.

[0006] In a first aspect, this application provides a cranial traction surgical aid device, which adopts the following technical solution:

[0007] A cranial traction surgical aid device includes a traction bed that provides a lying / prone position for the patient, a cranial traction bow that is fixed to the patient's head for traction, and a flexible traction arm located on one side of the traction bed for connecting to the cranial traction bow.

[0008] The flexible traction arm has a connecting rod for mounting the cranial traction bow, and the connecting rod is equipped with a smart control code for cooperating with the cranial traction bow.

[0009] By adopting the above technical solution and adjusting the rotating connection structure between the components and the connecting rod, the cranial traction bow can be flexibly adjusted in multiple dimensions (such as horizontal, vertical, and rotational), overcoming the limitation of traditional devices that only support traction in a single direction. The intelligent control code on the connecting rod can monitor and quantify the angle changes of the cranial traction bow in real time, converting the doctor's experience into digital parameters to ensure the accuracy of cervical curvature adjustment and reduce human error. The flexible traction arm is designed separately from the traction bed, avoiding the problem of frequent adjustments to the overall structure during surgery required by traditional devices. The doctor can control the traction bow angle independently through the intelligent control code, reducing interference with the main surgical procedure. The intelligent control code, in conjunction with the adjustment components, can preset the target angle based on preoperative imaging data or adjust to the optimal traction posture in real time during surgery, shortening operation time and improving surgical efficiency. The flexible traction arm and intelligent control code can be adapted to different models of cranial traction bows, and the multi-angle movement range of the adjustment components covers various cervical curvature requirements, making it suitable for complex cases such as basilar impression and atlantoaxial dislocation.

[0010] Furthermore, the cranial traction bow includes a first bow body, a second bow body, and a locking component disposed on the first bow body and used to cooperate with the second bow body;

[0011] The first bow body is rotatably connected to the end of the connecting rod. An adjusting screw for adjusting the first bow body is provided on the connecting rod. The first bow body has a sliding groove for inserting the second bow body. The second bow body is inserted into and slidably installed in the sliding groove.

[0012] By employing the above technical solution, the sliding groove and locking component work together to achieve the sliding installation of the second arch within the first arch. This allows for flexible adjustment of the arch spacing according to the patient's skull size, adapting to different skull shapes and improving the device's versatility. The rotational cooperation between the first arch and the connecting rod enables free adjustment of the angle of the cranial traction arch. Combined with the intelligent control code, the arch angle can be precisely controlled to meet the intraoperative needs for cervical curvature adjustment. The separate design of the first and second arches optimizes the mechanical transmission path, resulting in a more uniform distribution of traction force, avoiding local stress concentration, and improving device stability. The adjustment screw on the connecting rod allows for precise angle fine-tuning of the first arch. Combined with real-time feedback from the intelligent control code, this ensures a refined adjustment process and improves the convenience of surgical operation. The sliding installation design of the second arch provides additional traction displacement compensation, adapting to the repositioning needs of complex cervical spine lesions. The second arch is locked to the first arch by the locking component, ensuring quick and reliable assembly and preventing intraoperative arch displacement from affecting the traction effect.

[0013] Furthermore, the locking assembly includes a locking pin that passes through the first bow body and is used to lock the relative position of the second bow body, a locking tooth block that is movably connected to the end of the locking pin, and an elastic element that is sleeved outside the locking pin and is used to abut against the locking tooth block. The second bow body has a one-way ratchet on its outer wall for cooperating with the locking tooth block.

[0014] By adopting the above technical solution, the one-way ratchet on the outer wall of the second bow and the locking block form a one-way locking structure. The locking block can be quickly unlocked by pressing it, and the elastic element automatically resets to lock after release. This simple and efficient operation improves intraoperative adjustment efficiency. The locking pin passes through the first bow and connects to the locking block, ensuring stable transmission of locking force and preventing mis-locking due to external impact, thus improving the reliability of the device. The elastic element is sleeved outside the locking pin, always applying preload to the locking block, ensuring a tight fit between the locking block and the one-way ratchet, preventing the second bow from loosening due to vibration or fluctuations in traction force, and ensuring stability during the traction process.

[0015] Furthermore, the first bow body has a first adjusting part for connecting with the connecting rod and a first fixing part for fixing the skull. The first adjusting part and the first fixing part are integrally connected. The angle between the length direction of the first adjusting part and the length direction of the first fixing part is ≥90°. The sliding groove is opened along the length direction of the first adjusting part.

[0016] The first fixing part has at least one first fixing end for fixing the skull at the end away from the first adjusting part.

[0017] By adopting the above technical solution, the angle between the first adjusting part and the first fixing part is ≥90°, forming an approximately "L"-shaped structure. This allows the traction force transmitted from the first adjusting part to the first fixing part to be effectively distributed across the skull surface, avoiding stress concentration at a single connection point and improving the stability and safety of mechanical transmission. At least one first fixing end is provided at the end of the first fixing part furthest from the adjusting part, allowing for single-point or multi-point fixation depending on the patient's skull size and shape, enhancing the device's adaptability to different skull morphologies and improving fixation reliability. A sliding groove is formed along the length of the adjusting part, ensuring a reasonable mechanical fit between the sliding direction of the second arch and the adjusting direction of the first adjusting part, facilitating rapid adjustment of the traction angle and displacement during surgery.

[0018] Furthermore, the second bow body has a second adjusting part for cooperating with the first adjusting part and a second fixing part for fixing the skull. The second adjusting part and the second fixing part are integrally connected. The angle between the length direction of the second adjusting part and the length direction of the second fixing part is ≤90°. The one-way ratchet is provided on the outer wall of the second adjusting part.

[0019] The second fixing part has a second fixing end for fixing the skull threadedly connected at the end away from the second adjusting part.

[0020] By adopting the above technical solution, the second adjustment part is integrally connected with the first adjustment part, and the angle between the length direction of the second adjustment part and the length direction of the second fixation part is ≤90°, forming an approximately acute angle mechanical transmission structure. This allows the traction force to be efficiently transmitted to the second fixation part through the second adjustment part, while reducing force dispersion loss and improving traction efficiency. The design of the second adjustment part in conjunction with the unidirectional ratchet, combined with its sliding adjustment mechanism with the first adjustment part, enables flexible adjustment of the skull traction bow in multiple dimensions (horizontal, tilt), adapting to the reduction needs of different cervical curvatures and lesion types. The second fixation part is connected to the second fixation end via a thread, allowing for quick disassembly and replacement of the end. This facilitates the selection of appropriate fixation methods (such as threaded nails, hooks, etc.) based on the patient's skull morphology. At the same time, the threaded connection provides higher fixation strength, preventing loosening or displacement during surgery.

[0021] Furthermore, the traction bed body is detachably connected to a mounting base beam on one side for mounting the flexible traction arm. The flexible traction arm includes a fixed rod located at the middle position of the connecting base beam, a first flexible shaft rotatably connected to the fixed rod, a second flexible shaft rotatably connected to the first flexible shaft, and a connecting rod. The connecting rod is movably connected to the end of the second flexible shaft away from the first flexible shaft and is used to mount the first pole body. The adjusting screw passes vertically through the connecting rod and is threadedly connected to the first bow body.

[0022] By adopting the above technical solution, the flexible traction arm, through the combination of a fixed rod, a first flexible shaft, and a second flexible shaft, forms a multi-degree-of-freedom flexible adjustment structure. This allows for flexible adjustment of the cranial traction bow in three-dimensional space, adapting to different patients' cervical curvatures and intraoperative adjustment needs. The mounting base beam is detachably connected to the traction bed, facilitating quick replacement or adjustment of the traction arm layout according to the type of surgery, improving the device's versatility and adaptability. The adjustment screw passes vertically through the connecting rod and is threaded to the first bow body. Rotating the adjustment screw allows for precise angle fine-tuning of the first bow body, avoiding errors from traditional manual adjustments and improving the accuracy of traction angle control. The rotating connection design of the first and second flexible shafts provides a certain degree of elastic buffering capacity, absorbing vibrations caused by patient head movement or external impacts during surgery, ensuring the stability of the traction process.

[0023] Furthermore, the adjusting screw has an adjusting screw for cooperating with the first bow body and a cap at the end of the adjusting screw. The smart control code is embedded in the central axis of the cap, and the smart control code is electrically connected to the skull traction bow and the flexible traction arm.

[0024] By adopting the above technical solution, the intelligent control code meter is embedded in the central axis of the adjustment screw cap, allowing doctors to directly read real-time data (such as angle and traction force) through the cap while rotating the adjustment screw, eliminating the need for additional observation of external equipment and improving the convenience and efficiency of the operation. The intelligent control code meter is electrically connected to the cranial traction bow and flexible traction arm, collecting and displaying traction angle, displacement, and mechanical parameters in real time, supporting precise intraoperative control and postoperative data traceability, aligning with the development trend of intelligent surgery. The intelligent control code meter provides real-time quantitative data on angle and traction force, combined with the multi-dimensional adjustment capabilities of the flexible traction arm, helping doctors precisely control the adjustment of cervical curvature, improving the accuracy and safety of the surgery. The electrical connection between the intelligent control code meter and the cranial traction bow and flexible traction arm supports the collaborative work of multiple components, forming a closed-loop control system that can automatically calibrate traction parameters according to preoperative settings or intraoperative needs, reducing human intervention.

[0025] Secondly, this application provides a control method for a cranial traction surgical aid device, which adopts the following technical solution:

[0026] A method for controlling a cranial traction surgical aid device includes:

[0027] The intelligent control code meter is calibrated using a preset intelligent control code meter calibration method;

[0028] After the intelligent control code meter is calibrated, the intelligent control code meter is started to collect traction force, angle and displacement data in real time at a preset frequency. At the same time, the preset multi-parameter monitor is started to collect the patient's physiological data at the corresponding preset frequency. During the data collection process, the collected data is filtered in real time using a preset filtering algorithm. At the same time, a data backup mechanism is established to back up the collected data at preset time intervals.

[0029] Real-time imaging data of the cervical spine was acquired and compared with the preoperative reduction target. Image recognition technology and measurement algorithms were used to calculate the deviation of cervical curvature change and fracture reduction displacement.

[0030] The calculated deviations in cervical curvature and fracture reduction displacement are compared with their respective preset thresholds to obtain comparison results.

[0031] The comparison results and the patient's real-time physiological data are input into the optimal traction adjustment strategy analysis model, and traction adjustment suggestions are output, including the adjustment of traction force, adjustment direction, adjustment range, and adjustment time interval.

[0032] By adopting the above technical solution, this control method can accurately control the process of cranial traction surgery, adjust traction parameters in a timely manner, and improve surgical safety and cervical spine reduction treatment by calibrating the intelligent control code meter, collecting and processing multi-source data in real time, combining imaging comparison analysis, and outputting traction adjustment suggestions through the model.

[0033] Furthermore, the intelligent control code meter calibration method includes:

[0034] Collect relevant patient information, including age, weight, severity of illness, skull structural parameters, and past medical history;

[0035] Based on the disease type, a personalized cervical spine biomechanical response model is constructed by using a pre-defined disease-mechanical feature mapping model and combining it with patient information.

[0036] Based on the constructed personalized cervical spine biomechanical response model and referring to the historical case database, the range of initial traction force is estimated using a preset statistical analysis algorithm;

[0037] Within the estimated initial traction force range, multiple calibration points are uniformly selected according to preset rules. Using a preset standard force source, the standard force value corresponding to each calibration point is applied to the smart control code meter in sequence. The actual reading of the smart control code meter under each standard force value is accurately recorded. Using a preset curve fitting algorithm, a calibration curve is fitted based on the recorded standard force value and the corresponding reading. The curve parameters are adjusted to control the measurement error of the smart control code meter within the preset allowable range after calibration.

[0038] Select several verification force values ​​outside the calibration force range, apply the verification force values ​​using a standard force source, record the measurement results of the smart control code meter, compare the measurement results with the standard force values, calculate the measurement error, and if the error exceeds the preset allowable range, recalibrate the operation, adjust the calibration curve parameters, until the error meets the requirements.

[0039] By employing the above technical solution, a model is constructed based on multi-dimensional patient information to predict the force range. A calibration curve is fitted through multiple steps, including calibration points and force value verification. This ensures that the intelligent control code meter's measurements closely match the patient's actual condition, keeping measurement errors within a controllable range and providing reliable assurance for accurate measurements in cranial traction surgery.

[0040] Furthermore, based on the constructed personalized cervical spine biomechanical response model and referring to the historical case database, the initial traction force range is estimated using a pre-set statistical analysis algorithm, including:

[0041] Based on disease type, age stratification, severity grading, and skull structure parameter range, Boolean logic query statements are used to select similar subsets from the historical case database, clean and filter abnormal and missing values ​​in the cases, and standardize the units of initial traction force, force value adjustment, and treatment effect indicators through a preset standardized algorithm.

[0042] In the personalized cervical spine biomechanical response model, time series analysis is used to capture the dynamic changes of parameters and extract personalized model features;

[0043] The preprocessed historical case data and personalized model features are input into the trained initial traction force analysis model that integrates multiple preset statistical analysis algorithms to obtain the initial traction force prediction value, and the preliminary range is determined by combining the preset confidence interval algorithm.

[0044] The steps for constructing the initial traction force analysis model are as follows:

[0045] Cases were screened from the historical case database by disease, age, condition, and skull structure. The data was cleaned, and outliers and missing values ​​were removed. For key data, a pre-set standardized algorithm was used to unify the units. At the same time, pre-set core parameters were extracted from the personalized cervical spine biomechanical model to construct a complete dataset.

[0046] The data is divided into training and testing sets according to a preset ratio. First, a gradient boosting decision tree is trained on the training set. A preset number of decision trees are set, and each decision tree grows to a preset depth. Iterative training is performed with a preset learning rate to output feature vectors. Then, the output of the gradient boosting decision tree is used as input, and the true value of the initial traction force is used as the label to train support vector regression. A preset kernel function is selected, and the preset penalty parameter is adjusted so that the support vector regression can accurately predict the initial traction force based on the extracted features.

[0047] Cross-validation is used to divide the training set into subsets according to a preset number of times for training and validation. Based on the validation results, the preset number of decision trees for gradient boosting and the preset penalty parameters for support vector regression are dynamically adjusted to improve the model's prediction accuracy and complete the model construction.

[0048] By employing the above technical solutions, and leveraging multi-dimensional case screening, data preprocessing, and model building, combined with time series analysis and hybrid algorithm models, we can fully mine data, accurately predict the initial traction force range, improve model prediction accuracy, and provide a scientific and patient-specific initial traction plan for cranial traction surgery.

[0049] In summary, this application includes at least one of the following beneficial technical effects:

[0050] 1. The cranial traction surgical aid device, through a multi-module collaborative design, achieves precise and intelligent adjustment of the traction bow during surgery, improving surgical efficiency and safety. The flexible traction arm adopts a three-section rotating connection structure of a fixed rod, a first flexible shaft, and a second flexible shaft. Combined with the detachable design of the mounting base beam, it forms a multi-degree-of-freedom three-dimensional adjustment system, which can flexibly adapt to the cervical curvature of different patients and their intraoperative adjustment needs. The intelligent control code is embedded in the adjustment screw cap and electrically connected to the cranial traction bow and the flexible traction arm. It collects and displays the traction angle, displacement, and mechanical parameters in real time. Doctors can directly read the data when rotating the adjustment screw, improving the convenience and accuracy of operation.

[0051] 2. The first and second arch bodies of the cranial traction bow cooperate with the locking assembly via sliding grooves to achieve dual-dimensional adjustment of spacing and angle. Unidirectional ratchet teeth and elastic elements ensure stable transmission of traction force during surgery, avoiding misplacement. The first and second adjustment parts of the cranial traction bow form an efficient multi-angle mechanical transmission path. The angle between the first adjustment part and the first fixation part is ≥90°, dispersing traction force and avoiding stress concentration; the angle between the second adjustment part and the second fixation part is ≤90°, optimizing force transmission efficiency. The unidirectional ratchet teeth and elastic element design of the locking assembly, combined with the threaded connection of the second fixation end, enables quick assembly and disassembly and high-precision locking, adapting to different skull shapes. An intelligent control coder embedded in the rotating cap design allows doctors to directly read data and adjust parameters, simplifying the intraoperative process. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the overall structure of a cranial traction surgical aid device according to an embodiment of this application.

[0053] Figure 2 This is a schematic diagram of the overall structure of the skull traction bow and flexible traction arm according to an embodiment of this application.

[0054] Figure 3 This is an exploded view of the skull traction bow and fixation rod in an embodiment of this application.

[0055] Figure 4 This is an exploded view of the skull traction bow structure according to an embodiment of this application.

[0056] Figure 5 This is a cross-sectional schematic diagram of the skull traction bow according to an embodiment of this application.

[0057] Explanation of reference numerals in the attached drawings: 1. Traction bed body; 2. Skull traction bow; 21. First bow body; 211. First adjustment part; 2111. Sliding groove; 212. First fixing part; 2121. First fixing end; 22. Second bow body; 221. Second adjustment part; 2211. One-way ratchet; 222. Second fixing part; 2221. Second fixing end; 23. Locking assembly; 231. Locking pin; 232. Locking tooth block; 233. Elastic element; 3. Flexible traction arm; 31. Mounting base beam; 32. Fixing rod; 33. First flexible shaft; 34. Second flexible shaft; 35. Connecting rod; 36. Adjusting screw; 37. Traction bow adjusting screw; 371. Adjusting screw; 372. Screw cap; 38. Intelligent control code. Detailed Implementation

[0058] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-5 The present application will be further described in detail with reference to the embodiments.

[0059] This application discloses a surgical aid device for cranial traction. (Refer to...) Figure 1 The cranial traction surgical aid device includes a traction bed 1, a cranial traction bow 2, and a flexible traction arm 3. The traction bed 1 is used to provide the patient with a supine / prone position, the cranial traction bow 2 is fixed to the patient's head and used for traction, and the flexible traction arm 3 is located on one side of the traction bed 1 and is used to connect to the cranial traction bow 2.

[0060] Reference Figure 1 and Figure 2 The traction bed 1 has a detachable mounting base beam 31 on one side for mounting a flexible traction arm 3. The flexible traction arm 3 includes a fixed rod 32, a first flexible shaft 33, a second flexible shaft 34, and a connecting rod 35. The fixed rod 32 is vertically arranged and fixedly connected to the middle position of the connecting base beam. The first flexible shaft 33 is arranged along the extension line of the fixed rod 32 in the length direction and is rotatably connected to the end of the fixed rod 32 away from the connecting base beam. The second flexible shaft 34 is arranged along the extension line of the first flexible shaft 33 in the length direction and is rotatably connected to the end of the first flexible shaft 33 away from the fixed rod 32.

[0061] Reference Figure 2 and Figure 3 The connecting rod 35 is vertically positioned and rotatably connected to the end of the second flexible shaft 34 away from the first flexible shaft 33, and is used to install the skull traction bow 2. Adjusting screws 36 for adjustment / locking are provided at each rotatable connection position of the fixing rod 32, the first flexible shaft 33, the second flexible shaft 34, and the connecting rod 35.

[0062] Reference Figure 3 and Figure 4The cranial traction bow 2 includes a first bow body 21, a second bow body 22, and a locking assembly 23. The locking assembly 23 is disposed on the first bow body 21 and is used to lock the relative position of the second bow body 22 and the first bow body 21. The first bow body 21 is rotatably connected to the end of a connecting rod 35. An adjusting screw 36 for adjusting the first bow body 21 is threaded through and threaded onto the end of the connecting rod 35 away from the second flexible shaft 34.

[0063] In this embodiment, the power source for the flexible traction arm 3 is preferably cable-driven. The cable-driven flexible arm bends by pulling the internal cable, giving it high strength and rigidity, enabling it to withstand significant traction forces and ensuring stable application of traction force during cranial traction surgery. Simultaneously, the cable-driven method allows for precise motion control, meeting the accuracy requirements of surgery. Furthermore, its relatively compact structure and small footprint facilitate flexible placement within the surgical environment.

[0064] The adjusting screw 36 located at the end of the connecting rod 35 furthest from the second flexible shaft 34 is defined as the traction bow adjusting screw 3736. The traction bow adjusting screw 3736 includes an adjusting screw 371 for engaging with the first bow body 21 and a cap 372 fixedly connected to the end of the adjusting screw 371. A smart control code 38 is embedded in the cap 372 at its central axis position. The smart control code 38 is electrically connected to both the skull traction bow 2 and the flexible traction arm 3. The code is installed in an easily observable and operable position to ensure that the surgeon can conveniently read and adjust the traction force value during surgery. The code is calibrated before surgery to ensure that the displayed traction force value is accurate and reliable.

[0065] The first bow body 21 has a first adjusting part 211 for connecting to the connecting rod 35 and a first fixing part 212 for fixing the skull, and the first adjusting part 211 and the first fixing part 212 are integrally connected. The angle between the length direction of the first adjusting part 211 and the length direction of the first fixing part 212 is ≥90°. The interior of the first adjusting part 211 has a sliding groove 2111 along the length direction for the second bow body 22 to be inserted and slide. The first fixing part 212 has two first fixing ends 2121 for fixing the skull at the end away from the first adjusting part 211.

[0066] The second bow body 22 has a second adjustment part 221 for insertion into the sliding groove 2111 and a second fixation part 222 for fixing the skull. The second adjustment part 221 and the second fixation part 222 are integrally connected. The angle between the length direction of the second adjustment part 221 and the length direction of the second fixation part 222 is ≤90°. The second fixation part 222 has a second fixation end 2221 for fixing the skull threadedly connected to the end away from the second adjustment part 221. The threaded connection of the second fixation end 2221 allows for quick disassembly and replacement of the end, facilitating the selection of appropriate fixation methods such as threaded nails or hooks according to the patient's skull shape. At the same time, the threaded connection provides higher fixation strength, preventing loosening or displacement during surgery.

[0067] Reference Figure 4 and Figure 5 The second adjustment part 221 has a one-way ratchet 2211 on its outer wall for engaging with the locking assembly 23. The locking assembly 23 includes a locking pin 231, a locking tooth block 232, and an elastic element 233. The locking pin 231 passes through the first bow body 21 and is used to lock the relative position of the second bow body 22. The locking tooth block 232 is slidably mounted on the end of the locking pin 231, and the teeth of the locking tooth block 232 are used to engage with the one-way ratchet 2211. In this embodiment, the elastic element 233 is a spring, which is sleeved on the outside of the locking pin 231 and abuts against the locking tooth block 232. The one-way ratchet 2211 on the outer wall of the second bow body 22 and the locking tooth block 232 form a one-way locking structure. The locking can be quickly unlocked by pressing the locking tooth block 232, and the elastic element 233 automatically resets after being released to achieve locking. The operation is simple and efficient, improving the efficiency of intraoperative adjustment.

[0068] The implementation principle of the cranial traction surgical auxiliary device in this application embodiment is as follows: The traction bed 1 provides the patient with a lying / prone position. A base beam 31 is detachably connected to one side of the traction bed 1 for mounting a flexible traction arm 3. The flexible traction arm 3 consists of a fixed rod 32, a first flexible shaft 33, a second flexible shaft 34, and a connecting rod 35. The components are rotatably connected to form a flexibly adjustable structure. Each rotatable connection is equipped with an adjusting screw 36 for adjusting or locking the position. The flexible traction arm 3 is cable-driven, and the traction arm bends by pulling the internal cable, giving the traction arm high strength and rigidity, enabling it to withstand large traction forces to stably apply traction force. At the same time, it can achieve precise motion control to meet the requirements of surgical precision, and its compact structure occupies little space, facilitating the arrangement of the surgical environment.

[0069] The cranial traction bow 2 includes a first bow body 21, a second bow body 22, and a locking assembly 23. The first bow body 21 is rotatably connected to the end of a connecting rod 35. The second bow body 22 cooperates with the first bow body 21 through a sliding groove 2111 and the locking assembly 23, allowing the entire device to adapt to different head and cervical spine shapes of patients. The first adjustment part 211 of the first bow body 21 cooperates with an adjustment screw 36 perpendicularly inserted into the connecting rod 35, which can adjust the angle of the first bow body 21 relative to the connecting rod 35. The angle between the first adjustment part 211 and the first fixing part 212 is ≥90°, which helps to disperse the traction force. The sliding groove 2111 in the first adjustment part 211 cooperates with the second adjustment part 221 of the second bow body 22, realizing the sliding of the second bow body 22 within the first bow body 21 to adapt to different cranial spacing requirements. The angle between the second adjusting part 221 and the second fixing part 222 of the second bow body 22 is ≤90°. The one-way ratchet 2211 on the outer wall cooperates with the locking tooth block 232 of the locking assembly 23 to achieve one-way locking. Unlocking is achieved by pressing the locking tooth block 232, and the elastic element 233 (spring) resets and locks after release, making operation simple and efficient. The threaded connection of the second fixing part 222 facilitates the replacement of different fixing ends (such as threaded nails or hooks) and provides high fixing strength to prevent loosening and displacement. The intelligent control code 38 is embedded in the central axis position of the cap 372 of the traction bow adjusting screw 3736 and is electrically connected to the skull traction bow 2 and the flexible traction arm 3. After preoperative calibration, the traction force value can be easily read and adjusted during surgery, converting the doctor's experience judgment into digital parameters, ensuring the accuracy of cervical curvature adjustment and reducing human error.

[0070] This application also provides a control method for a cranial traction surgical aid device, including:

[0071] Step S100: The intelligent control code meter 38 is calibrated using a preset intelligent control code meter 38 calibration method.

[0072] Among them, the intelligent control code meter 38 is a device installed at the central axis position of the traction bow adjustment screw 3736 and the cap 372. It is electrically connected to the skull traction bow 2 and the flexible traction arm 3 and is used to display and adjust data such as traction force values ​​in real time.

[0073] Calibration: Adjust the intelligent control code meter 38 to ensure that the displayed traction force and other data are accurate and reliable, and conform to the actual physical values.

[0074] Step S200: After completing the calibration of the intelligent control code meter 38, start the intelligent control code meter 38 to collect traction force, angle and displacement data in real time at a preset frequency, and simultaneously start the preset multi-parameter monitor to collect patient physiological data at the corresponding preset frequency. During the data collection process, the collected data is filtered in real time using a preset filtering algorithm, and a data backup mechanism is established at the same time to back up the collected data at preset time intervals.

[0075] Multi-parameter monitor: A medical device used to collect patients' physiological data in real time. It can monitor a variety of physiological parameters such as heart rate, blood pressure, and blood oxygen saturation, and comprehensively reflect the patient's physical condition.

[0076] Preset frequency: Before surgery, the data acquisition frequency is preset based on clinical experience and surgical needs. For example, the Smart Control Code Table 38 may be set to collect data once per second, and the multi-parameter monitor may collect data once per minute to ensure that key data can be obtained in a timely and accurate manner.

[0077] Filtering algorithm: A mathematical method for processing collected data. This algorithm can remove noise interference from the data, making the data more accurate and reliable. For example, the common mean filtering algorithm can effectively smooth data fluctuations.

[0078] Data backup mechanism: To prevent data loss, a pre-established plan for regularly saving data is in place to ensure that all data during the surgical procedure is safely stored for subsequent analysis and retrieval.

[0079] The specific operation process is as follows: 1. Equipment Startup and Data Acquisition: After completing the calibration of the intelligent control code meter 38, turn it on. It will collect traction force, angle, and displacement data in real time at a preset frequency (e.g., once per second). At the same time, start the multi-parameter monitor and collect the patient's physiological data, such as heart rate and blood pressure, at a corresponding preset frequency (e.g., once per minute). 2. Data Filtering: During the data acquisition process, the system automatically uses preset filtering algorithms, such as the mean filtering algorithm, to process the data collected by the intelligent control code meter 38 and the multi-parameter monitor in real time. Taking the traction force data collected by the intelligent control code meter 38 as an example, the algorithm will average the data collected at multiple consecutive time points to remove abnormal fluctuations caused by interference, allowing the data to more accurately reflect the actual situation. 3. Data Backup: According to the pre-established data backup mechanism, every preset time interval (e.g., every 5 minutes), the system automatically backs up the data collected by the intelligent control code meter 38 and the multi-parameter monitor. The backed-up data will be stored in a dedicated storage device, such as the hospital's internal server, for convenient subsequent surgical analysis and review.

[0080] Step S300: Obtain real-time imaging data of the cervical spine and compare the real-time imaging data of the cervical spine with the preoperative planned reduction target. Using image recognition technology and measurement algorithms, calculate the deviation of cervical curvature change and fracture reduction displacement.

[0081] Real-time cervical spine imaging data: During cranial traction surgery, specific medical imaging equipment is used to acquire real-time images that clearly show the current shape, structure, and relationship with surrounding tissues of the patient's cervical spine, such as X-ray, CT, or MRI images of the cervical spine. This data can visually demonstrate the dynamic changes of the cervical spine during traction.

[0082] Preoperative reduction goals: Before the operation, the doctor uses professional knowledge and relevant medical software to pre-set the ideal reduction state parameters that the patient's cervical spine should achieve, including specific values ​​such as cervical curvature and fracture location, based on the patient's condition, cervical spine injury and expected treatment effect.

[0083] Image recognition technology: Using computer algorithms and programs, real-time cervical spine imaging images are analyzed and processed to identify key features such as cervical spine contours, skeletal structures, and fracture sites, thereby extracting relevant data for subsequent calculations.

[0084] Measurement Algorithm: A mathematical algorithm specifically designed to accurately calculate key data such as changes in cervical curvature and fracture reduction displacement based on feature information extracted using image recognition technology. For example, by measuring the distances and angles between specific bone points in an image, and combining this with a mathematical model, the specific values ​​of cervical curvature and fracture reduction displacement are derived.

[0085] The operation process is as follows: 1. Acquiring real-time imaging data: During cranial traction surgery, appropriate imaging equipment is activated as needed based on the surgical progress and the doctor's requirements. For example, when using a C-arm X-ray machine, the equipment is moved to a suitable position to ensure that the X-rays can clearly irradiate the patient's cervical spine. Following the equipment's operating procedures, real-time X-ray images of the cervical spine are acquired and transmitted to a connected image processing workstation. 2. Data comparison and calculation: The acquired real-time cervical spine imaging data is input into a computer equipped with specific medical image processing software. The software first uses image recognition technology to identify features of the cervical spine in the images, marking key structures such as each vertebra and fracture site. Then, using measurement algorithms, based on the marked feature points in the image, the actual value of the current cervical curvature and the displacement of the fracture site relative to the preoperative state are calculated. Then, the calculated actual value of cervical curvature is compared with the ideal value of cervical curvature in the preoperative reduction target to obtain the deviation of cervical curvature change; similarly, the calculated value of fracture reduction displacement is compared with the preoperative fracture reduction displacement target value to obtain the fracture reduction displacement deviation.

[0086] Step S400: Compare the calculated deviations in cervical curvature and fracture reduction displacement with their respective preset thresholds to obtain comparison results.

[0087] Preset thresholds: Before surgery, doctors pre-set numerical standards based on the patient's specific condition, physical state, and clinical experience to measure whether deviations in cervical curvature and fracture reduction displacement are within reasonable ranges. For example, the allowable range for cervical curvature deviation might be ±2°, and the allowable range for fracture reduction displacement deviation might be ±3mm.

[0088] The operation process is as follows: 1. Data Reading: During the surgery, after step S300 completes the calculation of the deviation in cervical curvature and the deviation in fracture reduction displacement, the surgical assistance system automatically reads these two sets of data from the computer storage location. 2. Comparison Calculation: The system compares the read value of the deviation in cervical curvature with a preset threshold for the deviation in cervical curvature. For example, if the calculated deviation in cervical curvature is +3°, while the preset threshold is ±2°, then the deviation is determined to exceed the threshold range. Similarly, the value of the deviation in fracture reduction displacement is compared with the corresponding preset threshold. 3. Result Output: Based on the comparison calculation results, the system generates comparison result information. If both the deviation in cervical curvature and the deviation in fracture reduction displacement are within the preset threshold range, the result information is displayed as "normal"; if any one or both exceed the threshold range, the result information will clearly indicate the deviation item and its specific value, such as "Cervical curvature deviation exceeds the threshold, current deviation is +3°", and this comparison result information will be transmitted to the optimal traction adjustment strategy analysis model required in the next step S500.

[0089] Step S500: Input the comparison results information and the patient's real-time physiological data into the optimal traction adjustment strategy analysis model, and output traction adjustment suggestions, including adjusting traction force, adjustment direction, adjustment range, and adjustment time interval.

[0090] Comparison Result Information: This is the result data generated in step S400 after comparing the deviation of cervical curvature change and fracture reduction displacement with their respective preset thresholds. The data includes whether the deviation is within a reasonable range; if it exceeds the range, it will clearly indicate the deviation item and its specific value, providing a basis for subsequent adjustments.

[0091] Real-time patient physiological data: During the surgery, a multi-parameter monitor collects patient physiological indicators such as heart rate, blood pressure, and blood oxygen saturation in real time at a preset frequency. This data can directly reflect the patient's physical function during the surgery.

[0092] Optimal Traction Adjustment Strategy Analysis Model: This is a computer algorithm model built upon extensive clinical data and medical expertise. It comprehensively analyzes input comparison results and real-time patient physiological data to calculate and output the most suitable traction adjustment plan, ensuring the safe and effective progress of the surgery.

[0093] Traction Adjustment Recommendations: Specific operational recommendations output by the optimal traction adjustment strategy analysis model, covering the magnitude of the traction force, the direction of adjustment (such as increasing or decreasing the traction force), the adjustment range (such as how many Newtons of traction force to increase or decrease), and the adjustment time interval (such as how often to adjust), providing doctors with precise surgical operation guidance.

[0094] The operation process is as follows: 1. Data Input: The surgical assistance system automatically inputs the comparison result information obtained in step S400 and the patient's real-time physiological data read from the storage location of the multi-parameter monitor into the optimal traction adjustment strategy analysis model embedded in the surgical assistance system. 2. Model Calculation: After receiving the input data, the optimal traction adjustment strategy analysis model performs calculations according to its internal preset algorithms and rules. The model analyzes the deviations in the comparison result information, determines whether the cervical curvature and fracture reduction deviate from the normal range and the degree of deviation, and combines the patient's real-time physiological data to assess the patient's tolerance to the current traction state. For example, if the patient's heart rate suddenly increases and the deviation of the cervical curvature exceeds the threshold, the model will comprehensively consider these factors to formulate an adjustment plan. 3. Output Suggestions: After the model calculation, traction adjustment suggestions are generated for the current surgical situation, including the specific value of the traction force, the direction of adjustment (whether to increase or decrease the traction force), the adjustment range (e.g., increase the traction force by 5N), and the adjustment time interval (e.g., it is recommended to adjust every 15 minutes). The surgical assistance system displays these traction adjustment suggestions in an intuitive way on the operating room screen, making it easy for doctors to view and adjust the skull traction surgical assistance device accordingly to ensure the smooth progress of the surgery and achieve better treatment results.

[0095] The calibration method for the Smart Control Code Reader 38 includes:

[0096] Step S110: Collect relevant patient information.

[0097] Patient-related information: A series of data closely related to the individual patient's condition, crucial for subsequent calibration of the intelligent control code table 38 and the development of personalized surgical plans. Among them, age reflects the patient's life stage, and physical function and recovery ability vary at different ages; weight affects the body's biomechanical distribution and its ability to withstand traction force; the severity of the condition is used to assess the progression of the patient's cervical spine injury or disease; skull structural parameters involve the shape and size of the skull, which are related to the fit of the skull traction bow 2 and the location of traction force application; past medical history includes the patient's previous diseases and treatments, which may affect the current surgery.

[0098] The procedure is as follows: After the patient is admitted to the hospital for cranial traction surgery, the medical staff responsible for medical record management will record the patient's age, weight, severity of illness, skull structural parameters, and past medical history in the "Intelligent Control Code Table 38 Calibration Information" section specifically set up in the patient's medical record. During the recording process, information obtained from different sources must be cross-checked to ensure the accuracy and completeness of the information. For example, the past medical history obtained from interviewing the patient will be compared with the records in the medical record file. If there are any discrepancies, further verification will be conducted. After the information collection is completed, the medical record will serve as an important reference for subsequent calibration of the Intelligent Control Code Table 38 and surgical planning.

[0099] Step S120: Based on the disease type, a personalized cervical spine biomechanical response model is constructed using a preset disease-mechanical feature mapping model and patient information.

[0100] Disease type: refers to the specific type of cervical spine-related disease the patient suffers from, such as cervical fracture, cervical disc herniation, cervical spondylosis, etc. Different types of diseases have different biomechanical characteristics and treatment methods.

[0101] The pre-defined disease-mechanical characteristic mapping model is a mathematical model pre-constructed by researchers based on extensive clinical case studies and biomechanical experimental data. This model can link different disease types with corresponding cervical spine mechanical characteristics, such as the stress distribution and deformation characteristics of the cervical spine under different fracture types.

[0102] Personalized cervical spine biomechanical response model: Based on the patient's specific information and disease-mechanical feature mapping model, a model is specially constructed for the patient to simulate the biomechanical response of the cervical spine under different traction forces, such as simulating the displacement, deformation and internal stress changes of the cervical spine.

[0103] The operation process is as follows: 1. During the surgical preparation phase, the medical data analyst in the surgical team opens the computer terminal in the operating room, logs into the hospital's electronic medical record system, retrieves the patient's medical record, and obtains the patient information recorded in step S110. Simultaneously, the analyst accesses the hospital's medical data analysis server through the terminal and calls the preset disease-mechanical feature mapping model. 2. Using professional medical modeling software, the disease type from the patient information is input into the disease-mechanical feature mapping model. The model outputs cervical spine mechanical feature data corresponding to the disease type based on the input information. 3. Next, the data analyst inputs this mechanical feature data, along with other patient information (such as age, weight, skull structure parameters, etc.), into the modeling software and uses the software's built-in algorithm to construct a personalized cervical spine mechanical response model for the patient. After construction, the model is stored in a specific folder on the operating room computer for use in subsequent step S130.

[0104] Step S130: Based on the constructed personalized cervical spine biomechanical response model and referring to the historical case database, the initial traction force range is estimated using a preset statistical analysis algorithm.

[0105] For details, please refer to steps S131 to S133, which will not be elaborated here.

[0106] Step S140: Within the estimated initial traction force range, multiple calibration points are uniformly selected according to preset rules. Using a preset standard force source, the standard force value corresponding to each calibration point is applied to the intelligent control code meter 38 in sequence. The actual reading of the intelligent control code meter 38 under each standard force value is accurately recorded. Using a preset curve fitting algorithm, a calibration curve is fitted based on the recorded standard force value and the corresponding reading. The curve parameters are adjusted so that the measurement error of the intelligent control code meter 38 after calibration is controlled within the preset allowable range.

[0107] The initial traction force range is the range of traction force values ​​that should be applied to the patient's cervical spine at the beginning of the cranial traction surgery, estimated by a statistical analysis algorithm in step S130 based on the patient's personalized cervical spine biomechanical response model and historical case database, such as 5N-15N.

[0108] Calibration points: Specific traction force values ​​selected uniformly according to certain rules within the initial traction force range for calibrating the intelligent control code meter 38. For example, if the initial traction force range is 5N-15N, and calibration points are selected at 2N intervals, then the calibration points are 5N, 7N, 9N, 11N, 13N, and 15N.

[0109] Preset standard force source: A device that can accurately output a known standard force value. The accuracy of its output force value meets the relevant standards for medical device calibration. It is used to apply a precise force to the smart control code meter 38 to calibrate the accuracy of the smart control code meter 38's readings. Common examples include high-precision weight sets or professional force calibrators.

[0110] Standard force value: The magnitude of the force output by a preset standard force source that is accurately known. For example, when using a 5N weight as the standard force source, 5N is the standard force value.

[0111] Actual reading: The force value displayed by the intelligent control code meter 38 after being subjected to the standard force value applied by the standard force source. Due to possible errors in the intelligent control code meter 38, the actual reading may not be completely consistent with the standard force value.

[0112] The preset curve fitting algorithm is a pre-defined mathematical algorithm that processes a series of known data points (in this case, the standard force value and the corresponding actual reading of the smart control code meter 38) to find a curve equation that best reflects the relationship between these data points. This is used to calibrate the reading of the smart control code meter 38 and make subsequent measurements more accurate.

[0113] Calibration curve: The curve is obtained by fitting the standard force value and the actual reading of the smart control code meter 38 using a preset curve fitting algorithm. This curve reflects the relationship between the reading of the smart control code meter 38 and the actual standard force value, and is used to calibrate the subsequent measurement data of the smart control code meter 38.

[0114] Curve parameters: Adjustable coefficients and other parameters in the calibration curve equation. By adjusting these parameters, the shape and position of the calibration curve can be changed, keeping the measurement error of the intelligent control code meter 38 within a preset allowable range. For example, in a linear function curve y = kx + b, k and b are the curve parameters.

[0115] Preset allowable range: In medical device calibration standards, the pre-defined acceptable range of measurement error for the Smart Control Code 38 is such as ±0.5N. When the measurement error of the Smart Control Code 38 is within this range, its measurement results are considered accurate and reliable.

[0116] Step S150: Select several verification force values ​​outside the calibration force value range, apply the verification force values ​​using a standard force source, record the measurement results of the intelligent control code meter 38, compare the measurement results with the standard force values, calculate the measurement error, and if the error exceeds the preset allowable range, recalibrate the operation, adjust the calibration curve parameters until the error meets the requirements.

[0117] Verification force value: A force value specifically selected outside the calibration force value range to verify the calibration effect of the Smart Control Code Meter 38. For example, if the calibration force value range is 5N-15N, then 2N, 18N, etc. can be selected as verification force values.

[0118] Standard force source: A device that can accurately output known standard force values, such as a high-precision weight set or a professional force calibrator, used to apply precise force to the smart control code 38 to ensure the accuracy of calibration and verification.

[0119] Measurement results: The value of the force displayed by the intelligent control code meter 38 after being subjected to the verification force applied by the standard force source.

[0120] Measurement error: The difference between the measurement result displayed by the smart control code meter 38 and the actual verification force value applied by the standard force source. For example, if the standard force source applies a verification force of 18N, and the smart control code meter 38 displays 17.5N, the measurement error is 18-17.5=0.5N.

[0121] Preset allowable range: The range of acceptable measurement error of the smart control code meter 38, which is specified in advance in the medical device calibration standard, for example, set to ±0.5N.

[0122] Based on the constructed personalized cervical spine biomechanical response model and referring to the historical case database, the initial traction force range is estimated using a preset statistical analysis algorithm, including:

[0123] Step S131: Based on disease type, age stratification, severity level, and skull structure parameter range, Boolean logic query statement is used to select similar subsets from the historical case database, clean and filter abnormal and missing values ​​in the cases, and unify the dimensions of initial traction force, force value adjustment, and treatment effect indicators through a preset standardized algorithm.

[0124] Boolean query statements: A type of logical expression used for database queries. It combines conditions using logical operators such as "AND", "OR", and "NOT" to accurately filter cases that meet specific criteria from a historical case database. For example, "Disease type = cervical fracture AND age stratification = 31-40 years old".

[0125] Historical Case Database: A large database of past medical records accumulated and stored by the hospital over a long period of time, containing detailed medical records of patients with various cervical spine diseases, which is the basic data source for screening similar cases.

[0126] Similar subset: A set of cases that are similar to the current patient in terms of disease type, age stratification, severity of illness, and range of skull structural parameters, selected from the historical case database through Boolean logic queries.

[0127] The automated operation process is as follows: The system automatically writes Boolean logic query statements, such as "Disease type = cervical fracture AND age stratification = 41-50 years old...", and inputs them into the database query interface. After the server filters out similar cases and returns results, the system uses software cleaning functions to automatically mark outliers and use mean imputation to handle missing values ​​based on data characteristics. Finally, the system inputs data such as initial traction force into the standardization algorithm module, automatically converting units to a unified dimension for subsequent analysis.

[0128] Step S132: In the personalized cervical spine biomechanical response model, time series analysis is used to capture the dynamic change characteristics of parameters and extract personalized model features.

[0129] Time series analysis: a mathematical analysis method that studies the patterns of data changes over time to discover trends, cycles, and other characteristics. In this context, it's used to analyze the changes in parameters over time in a personalized cervical spine biomechanical response model.

[0130] Dynamic change characteristics of parameters: refers to the characteristics of changes in parameters such as cervical spine displacement and force magnitude over time in a personalized cervical spine biomechanical response model, such as whether the displacement gradually increases or increases first and then decreases.

[0131] Personalized model features: These are characteristics in the model that reflect the unique biomechanical properties of a patient's cervical spine, such as the correlation between certain cervical spine parameters or the special curve shape exhibited by the model. These features vary from person to person. Extracting personalized model features: After completing the time series analysis, the software then runs a preset feature extraction algorithm. This algorithm searches the model and identifies personalized model features that represent the individual characteristics of the patient's cervical spine, such as identifying specific combinations of cervical spine parameters.

[0132] Step S133: Input the preprocessed historical case data and personalized model features into the trained initial traction force analysis model that integrates multiple preset statistical analysis algorithms to obtain the initial traction force prediction value, and combine it with the preset confidence interval algorithm to determine the preliminary range.

[0133] Among them, the initial traction force analysis model is a model that integrates multiple preset statistical analysis algorithms and can predict the initial traction force value based on the input data.

[0134] Initial traction force prediction: The initial traction force prediction value for the current patient is calculated by the initial traction force analysis model based on the input preprocessed historical case data and personalized model features.

[0135] Preset confidence interval algorithm: A pre-set mathematical algorithm used to determine the confidence range of the predicted value. This algorithm can combine the initial traction force prediction value to determine a reasonable numerical range as the initial traction force range.

[0136] Determining the initial range: After the model calculates the initial traction force prediction value, the software automatically calls the preset confidence interval algorithm. Based on the predicted value and the set confidence level (e.g., 95% confidence level), this algorithm calculates a reasonable numerical range, which is the initially determined initial traction force range for this patient. For example, if the initial traction force prediction value is 10N, the algorithm calculates a preliminary range of 8N-12N. The final initial traction force prediction value and preliminary range will be displayed on the data analysis software's interface.

[0137] The steps for constructing the initial traction force analysis model are as follows:

[0138] Step Sa00 involves filtering cases from the historical case database by disease, age, condition, and skull structure, cleaning the data, removing outliers and missing values, standardizing the units of key data using a pre-defined algorithm, and extracting pre-defined core parameters from a personalized cervical spine biomechanical model to construct a complete dataset.

[0139] Dataset construction: The filtered, cleaned, and dimensionally standardized historical case data are integrated with the core parameters extracted from the personalized cervical spine biomechanical model to form a complete dataset, which is stored in a designated location on the operating room computer for subsequent model construction steps.

[0140] Step Sb00: Divide the data into training and testing sets according to a preset ratio. First, train a gradient boosting decision tree on the training set, setting a preset number of decision trees. Each decision tree grows to a preset depth and is iteratively trained with a preset learning rate, outputting feature vectors. Then, using the output of the gradient boosting decision tree as input and the true value of the initial traction force as the label, train support vector regression, selecting a preset kernel function and adjusting the preset penalty parameters so that support vector regression can accurately predict the initial traction force based on the extracted features.

[0141] Gradient Boosting Decision Tree: An ensemble learning algorithm that enhances a model's predictive ability by combining multiple decision trees. Preset Number of Decision Trees: The number of decision trees participating in the gradient boosting decision tree model is predetermined, affecting model complexity and accuracy. Preset Depth: The maximum number of layers each decision tree grows is predefined to avoid overfitting. Preset Learning Rate: Controls the step size of model updates in each iteration, affecting model convergence speed and accuracy. Feature Vector: The vector output after gradient boosting decision tree training that represents the features of the input data and contains key data information. Support Vector Regression: A regression algorithm based on support vector machines used to predict continuous numerical values; here, it's used to predict initial traction force. Preset Kernel Function: The function used in support vector regression that maps low-dimensional data to a high-dimensional space, helping the model better fit the data. Preset Penalty Parameter: A parameter that balances model complexity and fitting error, preventing overfitting or underfitting.

[0142] Training Gradient Boosting Decision Trees: In the model training interface of the data analysis software, select the gradient boosting decision tree model for training. First, in the parameter settings, the analyst determines and inputs a preset number of decision trees based on experience and previous experiments, for example, 50. Next, set the preset depth of each decision tree, for example, 6 layers, to prevent the decision trees from growing too complex and causing overfitting. Simultaneously, determine the preset learning rate, such as 0.1, which affects the step size of the model in each iteration. After setting the parameters, select the training set data as input and click the "Start Training" button. The software begins running the gradient boosting decision tree algorithm. During training, each decision tree is built sequentially based on the residuals of the previous decision tree, continuously fitting the complex relationships in the data. After multiple rounds of iterative training, the final output is a feature vector that represents the characteristics of the training set data. These feature vectors will serve as input to the subsequent support vector regression model.

[0143] Training Support Vector Regression: After completing the gradient boosting decision tree training, switch to the support vector regression model training interface in the same data analysis software. Import the feature vectors output by the gradient boosting decision tree into the input port of the support vector regression model, and simultaneously import the corresponding initial traction force values ​​from the training set as label data. In the model parameter setting area, select a pre-defined kernel function, such as the commonly used radial basis function (RBF). This function can map low-dimensional data to a high-dimensional space, enhancing the model's ability to fit complex data relationships. Then, adjust the preset penalty parameter, for example, starting with an initial value of 1. By observing the model's performance during training, gradually fine-tune this parameter to balance model complexity and fitting error. After setting the parameters, click the training button, and the software will begin training the support vector regression model. During training, the model continuously adjusts its internal parameters to predict the initial traction force as accurately as possible based on the input feature vectors. The trained model will be used for subsequent cross-validation and prediction of the initial traction force for actual patients.

[0144] Step Sc00: Use cross-validation to divide the training set into subsets according to a preset number of times for training and validation. Based on the validation results, dynamically adjust the preset number of decision trees for gradient boosting and the preset penalty parameters for support vector regression to improve the model's prediction accuracy and complete the model construction.

[0145] Cross-validation is a method for evaluating and optimizing a model. It involves dividing the training set into multiple subsets, which are used alternately for training and validation to comprehensively evaluate model performance. Preset number of iterations: This is the pre-defined number of times the training set will be divided for cross-validation, determining the level of detail in the evaluation and optimization; for example, 5 iterations. Subset: A portion of the training set partitioned according to cross-validation rules; each partitioned subset is used to train or validate the model. Dynamic adjustment: Based on the results of cross-validation, model parameters (such as the number of decision trees and penalty parameters) are adaptively changed to improve model performance.

[0146] The operation process is as follows: 1. Start cross-validation: The medical data analyst opens the cross-validation function of the data analysis software on the computer in the operating room. The software will automatically divide the training set into a corresponding number of subsets according to the preset number of times, such as 5 times. 2. Subset training and validation: In each loop, the software selects one subset as the validation set, and the remaining subsets are combined as the new training set. First, the initial traction analysis model (including gradient boosting decision trees and support vector regression) is trained using the new training set, and then the model's accuracy in predicting the initial traction is evaluated using the validation set. 3. Parameter adjustment: Based on the validation results, if the model's prediction error is large, the software automatically or under the analyst's operation dynamically adjusts the preset number of decision trees in the gradient boosting decision tree (such as increasing or decreasing the number of decision trees), and the preset penalty parameters of the support vector regression (fine-tuning parameter values). 4. Complete the construction: Repeat the training, validation, and parameter adjustment process until the model's prediction accuracy in multiple cross-validations reaches a satisfactory level. At this point, the initial traction analysis model is completed and can be used for actual prediction.

[0147] In addition, the control method for the cranial traction surgical aid device also includes steps following the output traction adjustment recommendations, as follows:

[0148] Step SA00: Analyze whether the doctor's voice message regarding traction direction adjustment has been received.

[0149] Voice messages: These are the voice commands given by the doctor during surgery to adjust the direction of traction in cranial traction surgery, such as "slightly adjust the traction direction to the left."

[0150] Voice recognition system: Installed in the operating room, it is used to collect, process and recognize voice signals, converting the doctor's voice into data information that can be recognized and processed by the equipment.

[0151] The general process is described as follows: During the surgery, the voice recognition system runs continuously. After the doctor issues a voice command related to traction direction adjustment, the microphone quickly captures the voice signal and converts it into a digital signal, transmitting it to the voice recognition system. The system uses a specific voice recognition algorithm to compare and analyze the received voice signal with a preset command model to determine whether it belongs to the voice information for traction direction adjustment. If the relevant information is recognized, it outputs the information and proceeds to the next step; if not, it continues to monitor.

[0152] If step SB00 is yes, then the preset instruction parsing method is used to perform in-depth analysis of the voice information, determine the specific traction direction and adjustment amplitude parameters, and integrate the specific traction direction and adjustment amplitude parameters to form a complete adjustment instruction set.

[0153] The preset instruction parsing method is a set of rules and algorithms pre-written and embedded in the system to interpret the voice instructions output by the speech recognition system and extract key information. It includes logic for recognizing and analyzing specific words and sentence structures, accurately determining the traction direction and adjustment range in the instructions. Traction direction: In cranial traction surgery, this refers to the direction in which the traction device applies traction force to the patient's skull, commonly such as left, right, up, or down. Adjustment range parameters: These represent the specific degree of traction direction adjustment, such as the degree of adjustment angle or the distance of displacement, used to precisely control the adjustment actions of the traction device. Adjustment instruction set: This is a set of instructions integrating the traction direction and adjustment range parameters. It is the core data for controlling the movement of the flexible traction arm 3, converting the doctor's voice instructions into machine-executable commands.

[0154] The general process is as follows: When step SA00 receives the doctor's voice information regarding traction direction adjustment, the system immediately invokes a preset instruction parsing method. This method first segments the voice information, identifying keywords such as "left" and "increase by 5 degrees." Then, based on preset grammatical rules and semantic logic, it determines the traction direction and adjustment range parameters. For example, recognizing "turn 3 degrees to the right" determines the traction direction as "right" and the adjustment range parameter as "3 degrees." Finally, these parameters are integrated according to a predetermined format to form a complete adjustment instruction set containing the traction direction and adjustment range, which is then transmitted to the next step for further processing.

[0155] Step SC00: Based on the generated adjustment instruction set and combined with the mechanical structure characteristics of the flexible traction arm 3, the motion path and rotation angle of each joint of the flexible traction arm 3 are planned using a kinematic algorithm.

[0156] The flexible traction arm 3 consists of a fixed rod 32, a rotatable first flexible shaft 33, a second flexible shaft 34, and a connecting rod 35, and can achieve traction adjustment in multiple directions and angles.

[0157] Mechanical structure characteristics: It involves the size, shape, connection relationship of each component of the flexible traction arm, as well as the rotation range and constraint conditions of each shaft.

[0158] Kinematic algorithm: Using mathematical models and mechanical principles, and based on the adjustment instruction set and mechanical structure characteristics, an algorithm is used to calculate the rotation angles and motion paths of each joint of the flexible traction arm.

[0159] The general process is as follows: 1. Establish a coordinate system: Taking the base of the fixed rod 32 of the flexible traction arm 3 as the origin, the direction of the fixed rod 32 is set as the Z-axis. The X-axis and Y-axis are determined on the horizontal plane to construct a Cartesian coordinate system, providing a unified reference for describing the position and attitude of each component. 2. Determine link parameters: Based on the mechanical structure of the flexible traction arm 3, determine the length, offset, and other parameters of the fixed rod 32, the first flexible shaft 33, the second flexible shaft 34, and the connecting rod 35. Using the DH parameter method, define four parameters for each pair of adjacent links: link length, link torsion angle, link offset, and joint angle, to describe their relative position and attitude relationship. 3. Construct a transformation matrix: Based on the DH parameters, construct a homogeneous transformation matrix for adjacent links. This matrix can represent the transformation relationship from one link coordinate system to the next link coordinate system. 4.

[0160] Forward kinematics solution: Starting from fixed link 32, the transformation matrices of each link are multiplied sequentially to obtain the transformation matrix of the end effector (connected to the skull traction bow 2) relative to the base coordinate system. This matrix contains the position and attitude information of the end effector. 5. Inverse kinematics solution: Based on the traction direction and amplitude in the adjustment command, the target position and attitude of the end effector are determined. Numerical methods such as the Newton-Raphson method are used to solve the nonlinear equation system, and the joint angles of the first flexible axis 33 and the second flexible axis 34 are iteratively adjusted to make the actual position and attitude of the end effector close to the target value. 6. Path planning: Combining the calculated joint rotation angles and motion constraints such as the maximum rotation speed and acceleration of each axis, the motion path of the flexible traction arm 3 from the current position to the target position is planned using methods such as polynomial interpolation to ensure smooth and stable motion. The sequence of joint rotation angles and motion path data are obtained for subsequent simulation and actual adjustment.

[0161] Step SD00: Simulate the movement of the flexible traction arm 3 to check for any motion interference or exceeding the safe movement range.

[0162] Motion simulation of flexible traction arm 3: Using computer software, the flexible traction arm 3 is virtually moved according to the planned motion path and the rotation angle of each joint to check for any problems in the motion process.

[0163] Motion interference: When the flexible traction arm 3 moves, its components (such as the first flexible shaft 33, the second flexible shaft 34, the connecting rod 35, etc.) may collide or squeeze with each other or with surrounding surgical equipment, the patient's body, etc., causing mutual interference.

[0164] Safe range of motion: The range of motion parameters that the flexible traction arm 3 can operate safely, determined based on the mechanical structural strength of the flexible traction arm 3, the maximum rotation angle limit of each joint, the motor drive capability, and the safety requirements of the surgical environment, including the range of rotation angles of each joint, the range of motion speed, and the range of motion acceleration.

[0165] The general process is described as follows: After acquiring the planned motion path and joint rotation angle data of the flexible traction arm 3, as well as the mechanical structure characteristics and surgical environment information of the flexible traction arm 3, the system constructs a virtual model of the flexible traction arm 3 in a computer simulation environment. Following the planned motion path and joint rotation angles, the virtual flexible traction arm 3 is driven to perform simulated motion. During the simulation, the relative positional relationships between the components of the flexible traction arm 3 are monitored in real time to check for any distances between components that are less than the safe distance, thus determining whether motion interference has occurred. Simultaneously, parameters such as the rotation angle, motion speed, and acceleration of each joint are monitored to determine if they exceed the pre-set safe motion range. If motion interference or exceeding the safe motion range is detected during the simulation, the system records the location, time, and specific parameter information of the problem and generates an alarm. If the simulation is completed successfully without any abnormalities, it indicates that the planned motion scheme is feasible, and the process proceeds to the next step.

[0166] In step SE00, the planned motion scheme is displayed in a visual form on the surgical console screen for the doctor to confirm. Based on the adjustment scheme confirmed by the doctor, the control system sends control commands to the drive device of the flexible traction arm 3. The drive device drives the motor according to the commands, which drives the first flexible shaft 33 and the second flexible shaft 34 to rotate, thereby realizing the adjustment of the traction direction.

[0167] Motion Plan: This refers to the detailed plan developed in step SC00 for the flexible traction arm 3 to move from its current position to the target position in a specific sequence and manner, including information such as the rotation angles of each joint and the motion path. Visualization: Through intuitive visual presentation methods such as graphics and images, the abstract data in the motion plan is transformed into dynamic or static displays that are easy for doctors to understand, such as simulating the movement of the flexible traction arm 3 using a 3D model on a screen. Surgical Control Console Screen: Located in the surgical operation area, this device is specifically used to display relevant surgical information (such as patient vital signs, equipment operating status, operation prompts, etc.) to the doctor and to receive the doctor's operating instructions. Drive Unit: Connected to the flexible traction arm 3, this mechanical control component drives the motor to rotate according to instructions issued by the control system, thereby rotating the first flexible shaft 33 and the second flexible shaft 34 of the flexible traction arm 3 to adjust the traction direction.

[0168] The system transforms the motion plan designed in step SC00 into visualized data, displaying the simulated motion of the flexible traction arm 3 on the surgical console screen as a 3D model or dynamic diagram. The surgeon can visually observe how the flexible traction arm 3 rotates according to the planned path and angle, including the rotation of the first flexible shaft 33 and the second flexible shaft 34, as well as the displacement of the connecting rod 35. The surgeon confirms the display based on the patient's actual condition and surgical needs. If the surgeon confirms the adjustment plan is feasible, the control system sends precise control commands to the drive unit based on the confirmation information. Upon receiving the commands, the drive motor precisely controls the rotation angle and speed of the first flexible shaft 33 and the second flexible shaft 34 according to the command requirements, driving the flexible traction arm 3 to adjust the actual traction direction according to the planned motion plan, ensuring that the adjustment process meets the surgical expectations.

[0169] In addition, step SC00 can also consider the case where the patient is under anesthesia. In this case, the following steps will be taken:

[0170] Step SC10: After receiving the traction direction adjustment command and parsing and confirming the adjustment command set, the patient's real-time anesthesia depth data and vital sign data are simultaneously obtained from the anesthesia equipment.

[0171] The data includes: Traction Direction Adjustment Command: Commands issued by the doctor to change the traction direction during cranial traction surgery, such as "slightly adjust the traction direction to the left." Adjustment Command Set: A set of commands determined after parsing the traction direction adjustment command, containing specific traction directions (left, right, etc.) and adjustment range parameters (such as adjustment degrees). Anesthesia Depth Data: Data reflecting the degree of inhibition of the cerebral cortex under anesthesia, generally obtained through monitoring by anesthesia equipment, and expressed in different quantitative ways (such as the bispectral index (BIS) value). Vital Signs Data: Includes basic physiological indicators such as the patient's heart rate, blood pressure, and blood oxygen saturation, which directly reflect the patient's physical condition and are collected in real time by the anesthesia equipment.

[0172] The general process is as follows: When the voice recognition system receives the traction direction adjustment command from the doctor, and after parsing and confirming the adjustment command set, the control system immediately initiates a data request to the anesthesia equipment. The anesthesia equipment transmits the patient's anesthesia depth data and vital sign data, which have been collected in real time, to the control system through an interface according to a pre-set data transmission protocol. The control system receives and stores this data for subsequent risk assessment and other operations, providing a basis for comprehensively judging whether traction direction adjustment can be safely performed under the current anesthesia state.

[0173] Step SC20 uses a pre-set anesthesia-traction risk assessment model, combined with individual patient differences, to assess the risk of adjusting the traction direction under the current anesthesia state.

[0174] Anesthesia-Traction Risk Assessment Model: This algorithmic model, constructed using statistical methods and medical knowledge, comprehensively considers individual patient characteristics, anesthetic status, and factors related to traction procedures. It quantifies the risk of adjusting the traction direction under the current anesthetic state. Individual Patient Differences: This includes patient age, gender, weight, underlying diseases (such as heart disease, hypertension, diabetes, etc.), and allergy history, all of which influence patient responses during anesthesia and traction. Current Anesthesia Status: This reflects the patient's current anesthetic condition based on data obtained from anesthesia equipment, such as depth of anesthesia (e.g., bispectral index BIS) and vital signs (e.g., heart rate, blood pressure, blood oxygen saturation).

[0175] The general process is described below:

[0176] 1. Data Preprocessing: Data Cleaning: Patient individual difference information obtained from the EMR system and current anesthesia status data obtained from the anesthesia equipment are checked to remove missing values, outliers, and duplicate data. For example, if the heart rate data at a certain moment is significantly outside the normal range (e.g., heart rate of 0 or 500 beats / minute), it is considered an outlier and processed accordingly. Data Standardization: Different types of data are standardized to make them comparable. For continuous data such as age and weight, Z-score standardization is used to convert them into data with a mean of 0 and a standard deviation of 1; for categorical data such as underlying diseases and allergy history, one-hot encoding is used to convert them into binary vectors.

[0177] 2. Feature Extraction and Weighting: Feature Extraction: Extracting key features related to anesthesia-traction risk from the preprocessed data. Examples include patient age, severity of underlying diseases, depth of anesthesia, and heart rate fluctuation range. Feature Weighting: Assigning appropriate weights to each feature based on medical knowledge and clinical experience. For example, the severity of underlying diseases may have a higher weight because it significantly impacts the patient's risk during anesthesia and traction; while the patient's gender may have a lower weight.

[0178] 3. Risk Assessment Calculation: Model Selection: Various machine learning algorithms can be used for anesthesia-traction risk assessment models, such as logistic regression, decision trees, and neural networks. Logistic regression will be used as an example here. Risk Calculation: The logistic regression model calculates the risk probability P using the following formula:

[0179]

[0180] Where b0 is the intercept term, b1, b2, ..., b n Let x1, x2, ..., x be the regression coefficients for each feature. nThese are the extracted feature values. The regression coefficients of the model were obtained by training on a large amount of historical surgical data.

[0181] 3. Risk Assessment and Output: Risk Assessment: The calculated risk probability P is compared with a preset risk threshold. If P is less than or equal to the threshold, the risk is considered to be within an acceptable range; if P is greater than the threshold, the risk is considered to be higher than an acceptable range. Output: The risk assessment results are output to the control system in an intuitive way for subsequent decision-making. For example, information such as "low risk," "medium risk," and "high risk" may be output.

[0182] In step SC30, if the assessed risk is within the preset acceptable risk range, then based on the generated adjustment instruction set and combined with the mechanical structural characteristics of the flexible traction arm 3 (the rotation range of each axis, the length of the connecting rod 35, etc., all have preset ranges), the motion path of the flexible traction arm 3 and the rotation angle of each joint are planned using a kinematic algorithm. The motion path planning must ensure that the flexible traction arm 3 moves within the preset safe movement range, and the rotation angle of each joint must also be within its respective allowed preset range.

[0183] The acceptable risk range is defined as the risk limit that, under the current anesthesia state, adjusting the traction direction is considered to have no significant adverse effects on the patient, based on medical standards, past surgical experience, and equipment safety parameters.

[0184] In step SC40, if the assessed risk is higher than the preset acceptable risk range, a voice warning will be issued to the doctor, clearly indicating that adjusting the traction direction under the current anesthesia state carries a high risk, prompting caution in adjusting the traction direction, and waiting for the doctor's confirmation before continuing the procedure.

[0185] Voice alerts: These alerts are delivered to doctors via voice broadcast, informing them of the risks associated with the current surgical procedure so that doctors can make informed decisions.

[0186] In step SC50, if the doctor confirms the need to continue adjusting the traction direction, the system executes the adjustment operation of the flexible traction arm 3 according to the planned motion path and joint rotation angle. During the adjustment process, the system continuously monitors the patient data from the anesthesia equipment and the traction device data fed back by the intelligent control code table 38. If the patient's vital signs or depth of anesthesia exceed the preset safety range, or if the traction device malfunctions (such as the actual motion angle deviating from the preset angle by more than the preset deviation range), the adjustment operation is immediately paused.

[0187] The general process is described as follows: After the control system receives the doctor's confirmation to continue adjusting the traction direction, it retrieves the motion path and joint rotation angle data planned in step SC30 and sends a control signal to the drive device of the flexible traction arm 3, driving it to move according to the predetermined plan. During the adjustment process, the control system continuously acquires patient data from the anesthesia equipment through the data interface and traction device data from the intelligent control code table 38 through the communication connection. The acquired patient vital signs and anesthesia depth data are compared with the preset safety range, and the actual motion angle of the traction device is compared with the preset angle and preset deviation range. Once it is found that the patient's vital signs or anesthesia depth exceed the preset safety range, or the actual motion angle of the traction device deviates from the preset angle by more than the preset deviation range, the control system immediately sends a stop signal to the drive device to suspend the adjustment operation and ensure surgical safety.

[0188] In step SC60, if the doctor chooses not to continue adjusting, the system maintains the current traction state and waits for the doctor to reassess the patient's condition or issue new instructions.

[0189] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A cranial traction surgical aid device, characterized in that: It includes a traction bed (1) that provides a lying / prone position for the patient, a cranial traction bow (2) fixed to the patient's head for traction, and a flexible traction arm (3) located on one side of the traction bed (1) for connecting the cranial traction bow (2). The flexible traction arm (3) has a connecting rod (35) for mounting the skull traction bow (2), and the connecting rod (35) is provided with a smart control code (38) for cooperating with the skull traction bow (2); The cranial traction bow (2) includes a first bow body (21), a second bow body (22), and a locking component (23) disposed on the first bow body (21) and used to cooperate with the second bow body (22); The first bow body (21) is rotatably connected to the end of the connecting rod (35). The connecting rod (35) is provided with an adjusting screw (36) for adjusting the first bow body (21). The first bow body (21) is provided with a sliding groove (2111) for the second bow body (22) to be inserted. The second bow body (22) is inserted and slidably installed in the sliding groove (2111).

2. The cranial traction surgical aid device according to claim 1, characterized in that: The locking assembly (23) includes a locking pin (231) that passes through the first bow body (21) and is used to lock the relative position of the second bow body (22), a locking tooth block (232) that is movably connected to the end of the locking pin (231), and an elastic member (233) that is sleeved on the outside of the locking pin (231) and is used to abut against the locking tooth block (232). The second bow body (22) has a one-way ratchet (2211) on its outer wall for cooperating with the locking tooth block (232).

3. The cranial traction surgical aid device according to claim 2, characterized in that: The first bow body (21) has a first adjustment part (211) for connecting with the connecting rod (35) and a first fixing part (212) for fixing the skull. The first adjustment part (211) and the first fixing part (212) are integrally connected. The angle between the length direction of the first adjustment part (211) and the length direction of the first fixing part (212) is ≥90°. The sliding groove (2111) is opened along the length direction of the first adjustment part (211). The first fixing part (212) has at least one first fixing end (2121) for fixing the skull at one end away from the first adjusting part (211).

4. The cranial traction surgical aid device according to claim 3, characterized in that: The second bow body (22) has a second adjustment part (221) for cooperating with the first adjustment part (211) and a second fixation part (222) for fixing the skull. The second adjustment part (221) and the second fixation part (222) are integrally connected. The angle between the length direction of the second adjustment part (221) and the length direction of the second fixation part (222) is ≤90°. The one-way ratchet (2211) is provided on the outer wall of the second adjustment part (221). The second fixing part (222) has a second fixing end (2221) for fixing the skull threadedly connected at the end away from the second adjusting part (221).

5. The cranial traction surgical aid device according to claim 1, characterized in that: The traction bed (1) is detachably connected to a mounting base beam (31) for mounting the flexible traction arm (3) on one side. The flexible traction arm (3) includes a fixed rod (32) located at the middle position of the connecting base beam, a first flexible shaft (33) rotatably connected to the fixed rod (32), a second flexible shaft (34) rotatably connected to the first flexible shaft (33), and a connecting rod (35). The connecting rod (35) is movably connected to the end of the second flexible shaft (34) away from the first flexible shaft (33) and is used to mount the first bow body (21). The adjusting screw (36) passes vertically through the connecting rod (35) and is threadedly connected to the first bow body (21).

6. The cranial traction surgical aid device according to claim 5, characterized in that: The adjusting screw (36) has an adjusting screw (371) for cooperating with the first bow body (21) and a cap (372) at the end of the adjusting screw (371). The smart control code (38) is embedded in the central axis of the cap (372). The smart control code (38) is electrically connected to the skull traction bow (2) and the flexible traction arm (3).

7. A control method for a cranial traction surgical aid device, characterized in that, Applied to the apparatus as described in any one of claims 1 to 6, comprising: The intelligent control code meter (38) is calibrated using a preset intelligent control code meter (38) calibration method; After the intelligent control code meter (38) is calibrated, the intelligent control code meter (38) is started to collect traction force, angle and displacement data in real time at a preset frequency. At the same time, the preset multi-parameter monitor is started to collect the patient's physiological data at the corresponding preset frequency. During the data collection process, the preset filtering algorithm is used to filter the collected data in real time. At the same time, a data backup mechanism is established to back up the collected data every preset time interval. Real-time imaging data of the cervical spine was acquired and compared with the preoperative reduction target. Image recognition technology and measurement algorithms were used to calculate the deviation of cervical curvature change and fracture reduction displacement. The calculated deviations in cervical curvature and fracture reduction displacement are compared with their respective preset thresholds to obtain comparison results. The comparison results and the patient's real-time physiological data are input into the optimal traction adjustment strategy analysis model, and traction adjustment suggestions are output, including the adjustment of traction force, adjustment direction, adjustment range, and adjustment time interval.

8. The control method for a cranial traction surgical aid device according to claim 7, characterized in that, The calibration methods for the intelligent control code meter (38) include: Collect relevant patient information, including age, weight, severity of illness, skull structural parameters, and past medical history; Based on the disease type, a personalized cervical spine biomechanical response model is constructed by using a pre-defined disease-mechanical feature mapping model and combining it with patient information. Based on the constructed personalized cervical spine biomechanical response model and referring to the historical case database, the range of initial traction force is estimated using a preset statistical analysis algorithm; Within the estimated initial traction force range, multiple calibration points are uniformly selected according to preset rules. Using a preset standard force source, the standard force value corresponding to each calibration point is applied to the smart control code meter (38) in sequence. The actual reading of the smart control code meter (38) under each standard force value is accurately recorded. Using a preset curve fitting algorithm, a calibration curve is fitted according to the recorded standard force value and the corresponding reading. The curve parameters are adjusted so that the measurement error of the smart control code meter (38) after calibration is controlled within the preset allowable range. Select several verification force values ​​outside the calibration force value range, apply the verification force values ​​using a standard force source, record the measurement results of the intelligent control code table (38), compare the measurement results with the standard force value, calculate the measurement error, and if the error exceeds the preset allowable range, recalibrate the operation, adjust the calibration curve parameters until the error meets the requirements.

9. The control method for a cranial traction surgical aid device according to claim 8, characterized in that, Based on the constructed personalized cervical spine biomechanical response model and referring to the historical case database, the initial traction force range is estimated using a preset statistical analysis algorithm, including: Based on disease type, age stratification, severity grading, and skull structure parameter range, Boolean logic query statements are used to select similar subsets from the historical case database, clean and filter abnormal and missing values ​​in the cases, and standardize the units of initial traction force, force value adjustment, and treatment effect indicators through a preset standardized algorithm. In the personalized cervical spine biomechanical response model, time series analysis is used to capture the dynamic changes of parameters and extract personalized model features; The preprocessed historical case data and personalized model features are input into the trained initial traction force analysis model that integrates multiple preset statistical analysis algorithms to obtain the initial traction force prediction value, and the preliminary range is determined by combining the preset confidence interval algorithm. The steps for constructing the initial traction force analysis model are as follows: Cases were screened from the historical case database by disease, age, condition, and skull structure. The data was cleaned, and outliers and missing values ​​were removed. For key data, a pre-set standardized algorithm was used to unify the units. At the same time, pre-set core parameters were extracted from the personalized cervical spine biomechanical model to construct a complete dataset. The data is divided into training and testing sets according to a preset ratio. First, a gradient boosting decision tree is trained on the training set. A preset number of decision trees are set, and each decision tree grows to a preset depth. Iterative training is performed with a preset learning rate to output feature vectors. Then, the output of the gradient boosting decision tree is used as input, and the true value of the initial traction force is used as the label to train support vector regression. A preset kernel function is selected, and the preset penalty parameter is adjusted so that the support vector regression can accurately predict the initial traction force based on the extracted features. Cross-validation is used to divide the training set into subsets according to a preset number of times for training and validation. Based on the validation results, the preset number of decision trees for gradient boosting and the preset penalty parameters for support vector regression are dynamically adjusted to improve the model's prediction accuracy and complete the model construction.

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