Posture correction system for neurosurgery postoperative posture intervention based on data processing

Abstract

The present invention provides a posture correction system for postoperative neurosurgery posture intervention based on data processing, which relates to the technical field of data processing. The present invention can comprehensively collect the patient's body physical characteristic data and biometric characteristic data, as well as comfort data in different body postures by introducing a data acquisition module. By designing three independent coefficient generation modules, the system can comprehensively evaluate the three dimensions of body physical characteristics, biometric characteristics and comfort, and generate a more personalized posture adjustment strategy. This method not only optimizes the accuracy of posture adjustment, but also greatly improves the patient's comfort and safety. Finally, the posture adjustment construction module automatically adjusts the bed to the recommended posture by integrating the evaluation coefficients of the three dimensions, thereby achieving optimal management in the postoperative recovery process.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a posture correction system for postoperative neurosurgery posture intervention based on data processing. Background Art

[0002] With the rapid development of modern medical technology, the precision and complexity of neurosurgery have been continuously improved, and the management of postoperative recovery has also received widespread attention. Among them, postoperative body position management, as an important factor affecting the speed and quality of patient recovery, has gradually been valued by medical researchers and clinicians; traditional body position adjustment mainly relies on the experience of medical staff for manual operation. This method not only consumes human resources, but also makes it difficult to achieve the optimal personalized adjustment. In recent years, body position adjustment systems based on data processing have begun to emerge, especially systems that use sensors and data analysis technology to monitor and adjust patient position; these systems can be dynamically adjusted according to the patient's biofeedback, thereby improving the accuracy and efficiency of the adjustment.

[0003] In the prior art, the publication number CN116823792A discloses a posture correction system for postoperative neurosurgery posture intervention based on data processing, which relates to the field of data processing technology. The image processing module detects the first posture image according to the edge detection algorithm to obtain the second posture image, and the second posture image is the patient's human body contour image; the posture information adjustment module determines the patient's target posture image according to the operation type information and the body state information; and calculates the similarity between the target posture image and the second posture image to obtain the similarity; and adjusts the patient's posture according to the similarity and the preset value. In this way, the patient's current posture image and the target posture image are calculated for similarity, and the target posture image is determined according to the operation type information and the body state information, and then the patient's posture is adjusted according to the similarity and the preset value, thereby achieving the accuracy of the posture adjustment indicated to the patient.

[0004] However, the existing technology still has some shortcomings, such as insufficient real-time data processing, limited universality and accuracy of the model, and weak adaptability to specific patient groups. Especially in the recovery period after complex neurosurgery, how to comprehensively collect the patient's physical characteristics and biometric data, as well as the comfort data in different postures, in order to build a more accurate posture adjustment model;

[0005] How to comprehensively evaluate the three dimensions of body physical characteristics, biological characteristics and comfort, generate a more personalized posture adjustment strategy, and automatically adjust the bed to the recommended posture by integrating the evaluation coefficients of the three dimensions to achieve optimal management during postoperative recovery is a direction that needs to be improved at present;

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a posture correction system for postoperative neurosurgery posture intervention based on data processing to solve the problems raised in the above-mentioned background technology.

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

[0009] Posture correction system for neurosurgery postoperative posture intervention based on data processing, specifically including:

[0010] Data acquisition module: used to collect the patient's physical characteristics and biometric data, as well as the patient's comfort data in different postures, and finally transmit the collected data to the central data processing unit by wireless means;

[0011] Model training module: used to obtain the medical records of different patients and the corresponding optimal postoperative posture data, use neural network to train the deep learning model, use the medical records as model input, and output the optimal postoperative posture data. The medical records information includes physical feature data, biometric data and comfort data;

[0012] The first coefficient generation module is used to obtain body physical characteristic data, which includes weight value, height value, limb size value, body surface area and joint range of motion value, and analyze and process it to generate a first posture evaluation coefficient, which is used to evaluate the best postoperative posture data based on the current patient's body physical characteristic data;

[0013] The second coefficient generation module is used to obtain biometric data, which includes heart rate value, blood pressure value, and respiratory rate, and analyze and process them to generate a second posture evaluation coefficient, which is used to evaluate the best posture data after surgery based on the current patient's biometric data;

[0014] The third coefficient generation module is used to obtain comfort data, which includes pain score and comfort score, and analyze and process them to generate a third posture evaluation coefficient, which is used to evaluate the best posture data after surgery based on the current patient's comfort data;

[0015] Posture adjustment construction module: used to obtain the first posture evaluation coefficient, the second posture evaluation coefficient and the third posture evaluation coefficient, and perform analysis and processing to construct a posture adjustment model. The model is used to analyze the relationship between body physical feature data, biometric data and comfort data and the patient's postoperative recovery posture, generate a posture correction strategy, and automatically adjust the bed to the recommended position through the posture correction strategy.

[0016] Furthermore, the posture adjustment coefficient of the posture adjustment model is defined as , the formula is as follows:

[0017]

[0018] Among them, PAI, PAI2, and PAI3 are three posture evaluation coefficients; d1, d2, and d3 are non-negative weights of each posture coefficient; Hf, Xl, and Xy are respiratory rate, heart rate, and blood pressure values, respectively; e is the base of the natural logarithm, and k and m are parameters for adjusting the influence of biometric features;

[0019] d1, d2, and d3 are the weights corresponding to PAI, PAI2, and PAI3, respectively, and are adjusted according to different clinical needs;

[0020] k and m control the sensitivity and offset of the function based on the biometric data Hf, Xl, Xy. Their increase or decrease will affect sensitivity to biological data;

[0021] set up The value range of is (0,1), The closer it is to 1, the closer the current posture is to the ideal posture, and the less adjustment is needed; The closer it is to 0, the more position adjustment is needed to improve the patient's comfort and recovery.

[0022] Will The value range (0,1) is divided into the following intervals:

[0023] The interval (0.00, 0.25) indicates the need for height adjustment. Patients in this interval need a large adjustment of their body position, specifically 85% to 100% of the current body position, to quickly improve their physiological state and comfort. The bed needs to be adjusted to a full recumbent position, and the most comfortable tilt angle needs to be adjusted according to the patient's comfort requirements.

[0024] The interval [0.25, 0.50) indicates moderate adjustment needs, requiring a large position adjustment of 50% to 84% to promote blood circulation and reduce muscle pain; the bed is automatically adjusted to a semi-recumbent position, and the current mattress hardness is adjusted by 20% to support the patient's waist and legs;

[0025] The interval [0.50, 0.75) indicates a slight adjustment requirement, where the current body position reaches 70% to 90% of the ideal posture, but the current body position needs to be adjusted by 20% to 35%, mainly to increase the patient's comfort; the slight adjustment of the bed angle is within 10%, including tilting or adjusting the height of the pillow to help the patient breathe or rest better;

[0026] The interval [0.75, 1.00) indicates the need for micro-adjustment. The patient's current body position has reached more than 90% of the ideal posture, and only 0% to 19% of the current body position amplitude needs to be slightly adjusted; specifically, the pillow angle is fine-tuned or the bed surface is slightly adjusted.

[0027] Compared with the prior art, the present invention has the following beneficial effects: by introducing a data acquisition module, the patient's physical and biological characteristics data, as well as comfort data in different body positions and postures can be fully collected, which provides a basis for building a more accurate body position adjustment model; secondly, the system adopts advanced neural network training technology, and analyzes the relationship between medical record information and the optimal body position and posture after surgery through a deep learning model, which greatly improves the adaptability and prediction accuracy of the model;

[0028] By designing three independent coefficient generation modules, the system can comprehensively evaluate the three dimensions of body physical characteristics, biological characteristics and comfort to generate a more personalized position adjustment strategy. This method not only optimizes the accuracy of position adjustment, but also greatly improves the comfort and safety of patients. Finally, the position adjustment construction module automatically adjusts the bed to the recommended position by integrating the evaluation coefficients of the three dimensions, thereby achieving optimal management of the postoperative recovery process. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the overall system flow of the present invention. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0031] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0032] Embodiment 1:

[0033] See also Figure 1 , the present invention provides a technical solution:

[0034] Posture correction system for neurosurgery postoperative posture intervention based on data processing, specifically including:

[0035] Data acquisition module: used to collect the patient's physical characteristics and biometric data, as well as the patient's comfort data in different postures, and finally transmit the collected data to the central data processing unit by wireless means;

[0036] Model training module: used to obtain the medical records of different patients and the corresponding optimal postoperative posture data, use neural network to train the deep learning model, use the medical records as model input, and output the optimal postoperative posture data. The medical records information includes physical feature data, biometric data and comfort data;

[0037] The model training module specifically includes the following contents:

[0038] 1.1) Data preparation: Prepare a patient medical record data set including physical feature data, biometric data, and comfort data. Physical feature data include height, weight, and body shape; biometric data include heart rate, blood pressure, respiratory rate, etc.; comfort data is obtained through the patient's subjective feedback or physiological parameters to evaluate the patient's comfort level in different postures;

[0039] 1.2) Neural network architecture selection: Choose a neural network architecture that is suitable for processing multiple input data and outputting continuous value results. Considering the diversity and complexity of the input data, choose a deep neural network, such as a multi-layer perceptron (MLP), a convolutional neural network (CNN), or a recurrent neural network (RNN);

[0040] 1.3) Model design: Design the structure of the neural network, including the input layer, hidden layer and output layer. The input layer can accept multiple features such as physical feature data, biometric data and comfort data. The number of hidden layers and the number of neurons in each hidden layer can be adjusted according to the complexity of the data and the performance requirements of the model. The output layer should output the best posture data after surgery.

[0041] 1.4) Loss function selection: Select an appropriate loss function to measure the difference between the model output and the actual postoperative optimal posture data; use mean square error (MSE) or other loss functions commonly used in other regression tasks;

[0042] 1.5) Model training: Use the prepared data set to train the designed neural network model. During the training process, the model parameters are continuously adjusted through the back propagation algorithm and the gradient descent method of the optimizer, so that the model can more accurately predict the optimal postoperative posture data;

[0043] 1.6) Model evaluation: Use a test data set independent of the training data to evaluate the trained model and evaluate the model's performance in predicting the optimal postoperative posture data; use the root mean square error (RMSE) and mean absolute error (MAE) indicators for evaluation.

[0044] The first coefficient generation module is used to obtain body physical characteristic data, which includes weight value, height value, limb size value, body surface area and joint range of motion value, and analyze and process it to generate a first posture evaluation coefficient, which is used to evaluate the best postoperative posture data based on the current patient's body physical characteristic data;

[0045] The second coefficient generation module is used to obtain biometric data, which includes heart rate value, blood pressure value, and respiratory rate, and analyze and process them to generate a second posture evaluation coefficient, which is used to evaluate the best posture data after surgery based on the current patient's biometric data;

[0046] The third coefficient generation module is used to obtain comfort data, which includes pain score and comfort score, and analyze and process them to generate a third posture evaluation coefficient, which is used to evaluate the best posture data after surgery based on the current patient's comfort data;

[0047] Posture adjustment construction module: used to obtain the first posture evaluation coefficient, the second posture evaluation coefficient and the third posture evaluation coefficient, and perform analysis and processing to construct a posture adjustment model. The model is used to analyze the relationship between body physical feature data, biometric data and comfort data and the patient's postoperative recovery posture, generate a posture correction strategy, and automatically adjust the bed to the recommended position through the posture correction strategy.

[0048] Embodiment 2:

[0049] Further explanation based on Example 1, the weight value is marked as Tz, the height value is marked as Sg, the limb size value is marked as ZTc, the body surface area is marked as BSA, and the joint range of motion value is marked as GJd;

[0050] Among them, the weight value Tz is a measure of human body mass, measured in kilograms (kg), using a weight scale or weighing instrument.

[0051] The height value Sg is the vertical distance from the top of the human body to the sole of the foot, measured in centimeters (cm), using equipment such as a stadiometer, a height measuring rod or anthropometer.

[0052] Limb size values ​​ZTc include arm length and leg length, which refer to the length of the limbs in centimeters (cm). The relevant limbs are measured by measuring instruments, such as measuring rulers and Epson multi-function measuring instruments;

[0053] Let AB represent the arm length and CD represent the leg length. Use the following formula to integrate these two measurements:

[0054] ZTc=(AB×w1+CD×w2) / (w1+w2)

[0055] Among them, w1 and w2 are non-negative weight coefficients, which are used to adjust the relative importance of arm length and leg length in the integrated limb size. When w1>w2, it means that the arm length is more important, and when w1<w2, it means that the leg length is more important.

[0056] Body surface area (BSA) is the total area of ​​the body surface, measured in square meters (㎡). The body surface area calculation formulas include the Dubois formula, the Mosteller formula, and the Gehan and George formula, which take weight and height parameters into account.

[0057] Specifically, the Dubois formula is as follows:

[0058] Male: body surface area (BSA) = 0.007184 × height (cm) ^ 0.725 × weight (kg) ^ 0.425;

[0059] Female: body surface area (BSA) = 0.006355 × height (cm) ^ 0.725 × weight (kg) ^ 0.425;

[0060] Let's further calculate through specific examples:

[0061] Assuming there is an adult male with a height of 180 cm and a weight of 75 kg, use the Dubois formula to calculate the body surface area;

[0062] BSA=0.007184×180^0.725×75^0.425;

[0063] First calculate the exponent: 180^0.725≈147.12975^0.425≈32.792;

[0064] Then substitute the index into the formula for calculation: BSA ≈ 0.007184 × 147.129 × 32.792;

[0065] Final result: BSA ≈ 3.13 m2.

[0066] The joint range of motion value GJd is the joint angle, the maximum range of motion of the joint in a specific plane, which is evaluated by a handheld angle meter, a motion meter or a motion analysis system device. The joint range of motion is expressed in degrees;

[0067] Several joints of the patient are marked to form a joint category set {1, 2, ..., i, ..., n}, where GJdi represents the joint angle of the i-th joint category, n represents the total number of joints, and the joint angle is the range of motion angle of each joint.

[0068] The Min-Max normalization method is used to normalize the weight value Tz, height value Sg, limb size value ZTc, body surface area BSA, and joint range of motion value GJd to the same value range in sequence, and map the data in any range to a specified value interval. The value interval in this embodiment is (0,1);

[0069] The formula for Min-Max normalization is as follows:

[0070]

[0071] Among them, X is the original data; It is the normalized data, and GY means normalized; is the minimum value of the original data; is the maximum value of the original data;

[0072] Apply this formula to each parameter and normalize them to the value range of (0,1). Specifically, for each parameter Tz, Sg, ZTc, BSA, GJd, calculate the normalized value;

[0073] For the GJd parameter, you need to first calculate the minimum joint range of motion and maximum range of motion , and then substitute these two values ​​into the Min-Max normalization formula.

[0074] The first posture evaluation coefficient is defined as PAI, and the calculation formula is as follows:

[0075]

[0076] Among them, PAI represents the first posture evaluation coefficient, Tz, Sg, ZTc, BSA, GJdi represent weight, height, limb size, body surface area and range of motion of the i-th joint, respectively, and n represents the total number of joints; , , They represent the minimum value of weight, the maximum value of height, and the maximum value of body surface area, respectively, and are used for data normalization;

[0077] a1, a2, a3, and a4 are non-negative weight parameters obtained through data fitting to ensure that each factor affects the final PAI according to its actual importance, a1+a2+a3+a4=1, and the specific values ​​of a1, a2, a3, and a4 are determined by the expert group through experimental data.

[0078] 1.1. The distribution ratio of a1 weight:

[0079] When body weight Tz is crucial to the first posture evaluation coefficient PAI and has a greater impact than other factors, the weight ratio range is 5% to 15%; specifically, assuming that the body weight range is between 50kg and 100kg, and based on actual data analysis, the specific weight ratio is determined to be 5% to 15%;

[0080] When the weight has a small effect on the posture adjustment index, but still has a certain influence, in this case, the weight ratio range is 1% to 5%. Specifically, assuming that the weight range is between 100kg and 150kg, and based on actual data analysis, the specific weight ratio is determined to be 1% to 5%;

[0081] In both scenarios, the specific allocation ratios are determined based on actual data analysis and professional judgment on the degree of impact.

[0082] 1.2. The distribution ratio of a2 weight:

[0083] When the height Sg is crucial to the first posture evaluation coefficient PAI of the posture adjustment index and has a greater impact than other factors, the weight ratio range is 10% to 20%. Specifically, assuming that the height range is between 150cm and 200cm, and based on actual data analysis, the specific weight ratio is determined to be 10% to 20%;

[0084] When height has a small effect on the posture adjustment index, but still has a certain influence, in this case, the weight ratio range is 5% to 10%. Specifically, assuming that the height range is between 120cm and 150cm, and based on actual data analysis, the specific weight ratio is determined to be 5% to 10%;

[0085] In both scenarios, the specific allocation ratios are determined based on actual data analysis and professional judgment on the degree of impact.

[0086] 1.3. The distribution ratio of a3 weight:

[0087] When the limb size ZTc is crucial to the first posture evaluation coefficient PAI and has a greater impact than other factors, the weight ratio ranges from 6% to 16%. Specifically, the weight ratio is determined to be 6% to 16% based on the specific limb size range and data analysis;

[0088] When the influence of limb size on the posture adjustment index is small, but still has a certain influence, in this case, the weight ratio range is 2% to 4%. Specifically, the specific weight ratio is determined to be 2% to 4% based on the specific limb size range and data analysis;

[0089] In both scenarios, the specific allocation ratios are determined based on actual data analysis and professional judgment on the degree of impact.

[0090] 1.4. The distribution ratio of a4 weight:

[0091] When the body surface area BSA is crucial to the first posture evaluation coefficient PAI and has a greater impact than other factors, the weight ratio range is 10% to 20%. Specifically, the specific weight ratio is determined to be 10% to 20% based on the specific body surface area range and data analysis;

[0092] When the body surface area has a smaller effect on the posture adjustment index, but still has a certain influence, in this case, the weight ratio range is 7% to 13%. Specifically, the specific weight ratio is determined to be 7% to 13% based on the specific body surface area range and data analysis;

[0093] In both scenarios, the specific allocation ratios are determined based on actual data analysis and professional judgment on the degree of impact.

[0094] The output value of the first posture evaluation coefficient PAI formula is set in the range of (0,1); the closer PAI is to 1, the closer the current posture is to the ideal posture, and the closer it is to 0, the more adjustments are needed.

[0095] The PAI range is divided into the following four intervals, each of which corresponds to specific posture adjustment suggestions and technical effects:

[0096] Interval (0.0, 0.25), this interval indicates that the patient's current posture is far from the ideal posture and requires major adjustments; in this interval, some physical parameters of the body seriously deviate from the healthy range, such as an unbalanced ratio of weight or height to body surface area, or a too small range of joint motion; it is recommended to use a comprehensive physical assessment and consider more support equipment or postoperative rehabilitation training;

[0097] The adjustment goal is to increase the range of joint motion by at least 20% and the body position comfort by at least 30%. For every 0.05 increase in the PAI value, the range of joint motion is expected to increase by 5% and the patient's body position comfort will increase by 10%.

[0098] In the interval [0.25, 0.5), the patient's posture requires moderate adjustment. Patients in this interval show slight physical parameter imbalance or joint movement limitation. Targeted physical therapy and adjustment of specific joints or postures are recommended.

[0099] The goal is to increase the range of motion of the joints by 10% and the comfort of the patient's position by 15%. For every 0.05 increase in the PAI value, the range of motion of the joints is expected to increase by 3% and the comfort of the patient's position by 5%.

[0100] In the interval [0.5, 0.75), the patient's posture is close to the ideal posture, but there is still room for improvement. In this interval, the patient's physical parameters are relatively balanced, but there is a slight need for adjustment to achieve the best postoperative recovery effect. Posture adjustment and light physical intervention are considered;

[0101] The adjustment goal is to increase the range of joint motion by 5% and the body position comfort by 7%. For every 0.05 increase in the PAI value, the range of joint motion can be increased by 2% and the patient's body position comfort can be increased by 3%.

[0102] The PAI value is [0.75, 1.0], which indicates that the patient's posture is very close to or has reached the ideal posture. In this range, all physical parameters are within the ideal range, the range of joint motion is good, no additional adjustment is required, the current state is maintained, and regular monitoring is performed to ensure continued comfort and recovery; for every 0.01 increase in the PAI value, the range of joint motion and patient posture comfort can be fine-tuned and increased by 1%.

[0103] Embodiment three:

[0104] Further explanation based on the second embodiment, the heart rate value is marked as Xl, the blood pressure value is marked as Xy, and the respiratory frequency is marked as Hf;

[0105] Heart rate refers to the number of times the heart beats per minute, usually measured in beats per minute. It is measured by heart rate monitors, electrocardiograms (ECGs), pulse detectors and other equipment that can monitor the patient's heart rate in real time and record the data for subsequent analysis and processing.

[0106] Blood pressure values ​​represent the pressure of blood against the blood vessel wall, measured in millimeters of mercury (mmHg) using a sphygmomanometer.

[0107] Respiratory rate refers to the number of breaths per minute, that is, the respiratory rate, which is measured in breaths per minute. The respiratory rate is measured by observing the rise and fall of the patient's chest and is quantitatively measured using special respiratory monitoring instruments. These devices can record the patient's respiratory rate and present it in digital form.

[0108] The Min-Max normalization method was used to process the heart rate, blood pressure and respiratory rate data in turn.

[0109] The heart rate value, blood pressure value, and respiratory rate after data processing are obtained, and analyzed and processed to generate a second posture evaluation coefficient. The calculation formula of the second posture evaluation coefficient is as follows;

[0110]

[0111] Among them, PAI2 is the second posture evaluation coefficient, Xy is the blood pressure value, is the average heart rate value; is the maximum measurable respiratory rate; b1, b2, and b3 are non-negative parameters for adjusting the weights of each biological feature; Xl is the heart rate value, the number of heart beats per minute; Hf is the respiratory rate, that is, the number of breaths per minute.

[0112] b1+b2+b3=1, and the distribution ratio analysis of b1, b2, and b3 is as follows:

[0113] For heart rate weight b1:

[0114] Scenarios with large heart rate fluctuations: When heart rate fluctuations are significant, such as unstable heart rate during postoperative recovery, the weight of b1 needs to be increased to reflect its importance in the overall assessment. In this case, the weight range of b1 is set to 0.45 to 0.55;

[0115] In this scenario, if b1=0.50, the influence of heart rate fluctuation on the evaluation coefficient is significant and will dominate. If the heart rate increases compared to the average heart rate, Increase, resulting in an increase in PAI2, reflecting that the patient's recovery is not ideal and the body position needs to be adjusted;

[0116] Relatively stable heart rate scenario: If the patient's heart rate is relatively stable, it indicates that the heart function is not significantly affected. At this time, the weight of b1 should be reduced, and the weight range is 0.10 to 0.20;

[0117] In this scenario, if b1 = 0.15, small fluctuations in heart rate have less impact on PAI2, allowing the model to pay more attention to changes in blood pressure and respiratory rate, thereby more comprehensively assessing the patient's condition.

[0118] For blood pressure weight b2:

[0119] Scenario of significant blood pressure fluctuation: Fluctuation of blood pressure has a great impact on the overall health of patients, especially in the postoperative recovery period. In the case of significant blood pressure fluctuation, the weight of b2 should be relatively increased and set to 0.40 to 0.50;

[0120] When b2=0.45 is selected, the effect of blood pressure on PAI2 is enhanced. If blood pressure deviates from the normal value, it will significantly affect the evaluation coefficient, suggesting that medical personnel need to pay attention to blood pressure management;

[0121] Scenario of relatively stable blood pressure: When blood pressure is stable, it has little effect on the patient's overall condition, and the weight of b2 should be appropriately reduced, ranging from 0.20 to 0.30;

[0122] If b2=0.25 is set, then when blood pressure is stable, its contribution to PAI2 is reduced, making the model more focused on the fluctuations of other variables;

[0123] For the respiratory rate weight b3:

[0124] Abnormal respiratory rate: Abnormal changes in respiratory rate indicate that the patient is in immediate life danger. At this time, the weight of b3 should be increased, ranging from 0.40 to 0.50;

[0125] Under the setting of b3=0.45, large fluctuations in respiratory rate will significantly affect PAI2, prompting medical personnel to immediately check and adjust the patient's condition.

[0126] The value range of PAI2 is set to (0,1), where the closer PAI2 is to 1, the closer the current biometric data corresponds to the ideal posture, and the closer it is to 0, the more posture adjustments are needed. Through comprehensive analysis of heart rate, blood pressure and respiratory rate, the model can accurately evaluate the patient's optimal posture after surgery and assist medical staff in more effective intervention.

[0127] The PAI2 value range (0,1) is divided into the following specific intervals. Each interval is related to a specific position adjustment guide, and the specific value range and corresponding medical action of each interval are precisely defined. The following is the specific division based on PAI2 and its interactive effect:

[0128] Interval A (0.00, 0.20) indicates that the patient's posture is far from the ideal posture and requires urgent posture adjustment; by increasing the Xl value or decreasing value, while reducing Hf or increasing , so that PAI2 increases to the ideal posture, and the adjustment range is 80% of the corresponding value of the current posture. When PAI2 is in this range, it indicates a major risk and the patient's posture needs to be adjusted immediately to a completely different posture. If PAI2=0.15, the weight of the heart rate b1 needs to be increased to 0.55, and the abnormality of the respiratory rate needs to be checked at the same time;

[0129] The operating instructions are that the patient needs to be turned over or the lower limbs raised to improve blood circulation and respiratory function;

[0130] Interval B [0.20, 0.50), this interval indicates that the patient's posture needs to be adjusted to approach the ideal posture; adjust the values ​​of Xy and Hf to increase PAI2 to the standard level, and the adjustment range is 50% of the corresponding value of the current posture. In this range, the PAI2 value shows that the patient's condition has improved but is still not ideal. If PAI2 = 0.35, it is recommended to reduce the respiratory rate weight b3 to 0.25 and adjust the posture;

[0131] The operating instructions are to use slight position adjustments, such as adjusting the height of the pillow or slightly adjusting the patient's lying position;

[0132] Interval C [0.50, 0.80), this interval indicates that the patient's posture is close to the ideal posture and needs slight adjustment. The adjustment range is 30% of the corresponding value of the current posture. When PAI2 is in this range, it indicates that the patient's biometric data is relatively ideal, but still needs fine-tuning. If PAI2=0.65, the blood pressure weight b2 can be slightly increased to 0.35 to fine-tune the patient's posture.

[0133] The operating instructions are to slightly adjust the patient's position, such as slightly adjusting the lying angle, to ensure that no part of the body is compressed;

[0134] Interval D [0.80, 1.00), this interval indicates that the patient's posture is very close to the ideal posture, and posture adjustment is rarely or not required; the adjustment range is 0% to 5% of the corresponding value of the current posture. PAI2 close to 1 indicates that the patient's condition is close to perfect and almost no further posture adjustment is required. If PAI2 = 0.90, it is recommended to maintain the current posture and only monitor minor changes in biometric data;

[0135] The operating instructions are to maintain the current body position and check regularly to ensure that the biometric data remains stable.

[0136] Embodiment 4:

[0137] Further explanation based on Example 3, the pain score is marked as TTp and the comfort score is marked as SSd;

[0138] The following information is obtained through questionnaire surveys or patient-initiated scoring:

[0139] For pain score TTp;

[0140] Pain score 1: mild discomfort or no obvious pain, the patient feels slight discomfort, but it does not affect daily activities;

[0141] Pain score 2: mild pain, but tolerable, the patient feels slight discomfort, but it does not affect daily life and activities;

[0142] Pain score 3: Mild pain, with noticeable discomfort, but still able to carry out daily activities without causing significant distress;

[0143] Pain score 4: moderate pain, the patient feels obvious pain, which affects daily activities, but is still tolerable;

[0144] Pain score 5: moderate pain, which is more obvious and affects daily life and activities, but is still tolerable;

[0145] Pain score 6: moderate or above pain, the patient feels quite obvious pain and needs to take some measures to relieve the pain, such as medication or rest;

[0146] Pain score 7: severe pain, the patient feels obvious pain and needs to take active measures to relieve the pain, such as medication;

[0147] Pain score 8: severe pain, the patient feels very strong pain, which affects daily life and activities and requires active treatment and management;

[0148] Pain score 9: Very severe pain, the patient can hardly tolerate the pain and requires urgent medical intervention to relieve the pain;

[0149] Pain score 10: Extremely severe pain, the patient is in extreme pain and requires immediate emergency treatment to relieve the pain.

[0150] The following is a rating and description of the comfort level for each integer value of the comfort score SSd from 1 to 10:

[0151] Comfort score 10: Extremely uncomfortable, the patient feels very uncomfortable and cannot tolerate the current position or posture, and needs to be adjusted immediately to improve comfort;

[0152] Comfort score 9: Very uncomfortable, the patient feels very uncomfortable and needs adjustments to relieve the discomfort;

[0153] Comfort score 8: not very comfortable, the patient feels some discomfort and needs adjustments to improve comfort;

[0154] Comfort score 7: slightly uncomfortable, the patient feels some discomfort but can tolerate it, and a 25% adjustment is required to improve comfort;

[0155] Comfort score 6: Generally comfortable, the patient feels generally comfortable, but still needs 16% adjustment to improve comfort;

[0156] Comfort score 5: relatively comfortable, the patient feels relatively comfortable, but needs 10% adjustment to improve the comfort experience;

[0157] Comfort score 4: quite comfortable, the patient feels quite comfortable, and 8% fine-tuning is needed to further improve the comfort;

[0158] Comfort score 3: Very comfortable, the patient feels very comfortable but still needs less than 6% adjustment to achieve optimal comfort;

[0159] Comfort score 2: Extremely comfortable, the patient feels extremely comfortable and only needs to be adjusted within 3% to achieve optimal comfort;

[0160] Comfort rating 1: Extremely comfortable, the patient felt extremely comfortable, no adjustments were required, and the experience was very pleasant.

[0161] The third posture evaluation coefficient is defined as PAI3, and the calculation formula is as follows:

[0162]

[0163] in, To express the influence of pain score, exponential decay is used to simulate the negative influence of pain on comfort, where c1 is the sensitivity parameter for adjusting the influence of pain, and the increase of c1 indicates that the negative influence of pain score on posture evaluation is enhanced;

[0164] To express the influence of comfort score, a logarithmic function is used to smooth the gain effect of high comfort score, where c2 is used to adjust the influence of comfort score. When c2 increases, the positive influence of high comfort score on posture evaluation is enhanced; c1 and c2 are both positive numbers, and c1+c2=1.

[0165] The weight distribution ratio of c1 is as follows:

[0166] In the scenario with a large impact, when the negative impact of pain score on posture evaluation is significantly higher than other factors, c1 should account for 60%-80%;

[0167] Assume TTp=2, SSd=3, =0.5, =0.2, T=10; if c1=0.5, exp(-0.5×2)=0.3679; if c1=1.0, exp(-1.0×2)=0.1353; it can be seen that the increase of c1 will lead to the enhancement of exponential decay, thereby increasing the negative impact of pain score on posture evaluation;

[0168] In the scenario with less impact: when other factors have a relatively prominent impact on posture evaluation, c1 drops to 20%-40%;

[0169] If c1=0.5, exp(-0.5×2)=0.3679; if c1=1.0, exp(-1.0×2)=0.1353; at this time, the increase of c1 has a relatively small impact on the comprehensive evaluation because the impact of other factors is more significant;

[0170] The weight distribution ratio of c2 is as follows:

[0171] In the scenario with a large impact: when the positive impact of high comfort score on posture evaluation is dominant, c2 should account for 60%-80%;

[0172] When SSd=4, c2=0.5, ; When c2=1.0, ; As c2 increases, the positive impact of comfort score on posture evaluation increases, thereby improving the overall evaluation coefficient;

[0173] In the case of less impact: when other factors have a significant impact on posture evaluation, adjust the proportion of c2 to 20%-40%;

[0174] Continuing with the above parameters, when SSd=4, c2=0.5, ; c2 = 1.0, ; At this time, the increase of c2 will make the positive impact of comfort score on posture evaluation more obvious, but when other factors have a greater impact, its impact is relatively small;

[0175] By explaining in detail the weight distribution ratio of c1 and c2 in different scenarios, we can better understand their role in the evaluation coefficient and adjust the weight ratio according to the specific situation to achieve the optimal evaluation result.

[0176] In the denominator is a time integral with a decay factor, which is used to simulate the effect of the passage of time on the evaluation coefficient. is a regularization parameter, An increase will lead to a decrease in the overall evaluation coefficient, thus maintaining the stability of the evaluation. is the decay rate, An increase in will lead to a faster decay of the time effect, indicating that over time, the impact of past ratings on the current evaluation decreases. T is the evaluation time window, which represents the time span covered by the evaluation coefficient.

[0177] The value range of PAI3 is set to (0,1). When PAI3 is closer to 1, it means that the patient is closer to the ideal postoperative posture and the comfort is better. On the contrary, when PAI3 is closer to 0, it means that more posture adjustments are needed and the comfort is worse.

[0178] Divide the PAI3 value range (0,1) into the following intervals;

[0179] The interval [0.90, 1.00) indicates that the patient is very comfortable and has almost no pain. At this time, the patient's pain score TTp is 1 or 2, and the comfort score SSd is 1 or 2. This interval indicates that the patient's current posture is very close to the ideal posture, and the current posture reaches more than 90% of the ideal posture;

[0180] The interval [0.75, 0.90) indicates that the patient is relatively comfortable and the pain is controllable, indicating that the patient's pain score TTp is 2 or 3, and the comfort score SSd is 2 or 3. This interval indicates that the patient's posture is good but can still be optimized, and the current posture reaches 70% to 90% of the ideal posture;

[0181] The interval [0.50, 0.75) indicates that the patient feels moderate comfort and has obvious pain, which means that the patient's pain score TTp is 4 or 5, and the comfort score SSd is 4 or 5. This interval indicates that the patient needs moderate posture adjustment, and the current posture reaches between 60% and 70% of the ideal posture;

[0182] The interval [0.25, 0.50) indicates that the patient is not very comfortable and the pain is severe, indicating that the patient's pain score TTp is 6 or 7, and the comfort score SSd is 6 or 7. This interval requires a greater posture adjustment, and the current posture reaches between 45% and 60% of the ideal posture;

[0183] The interval (0.00, 0.25) indicates that the patient is extremely uncomfortable and the pain is very severe, which means that the patient's pain score TTp is 8, 9 or 10. This interval means that the current posture is far from the ideal posture and needs urgent adjustment. The current posture is less than 45% of the ideal posture.

[0184] Embodiment five:

[0185] Further explanation based on the fourth embodiment, the posture adjustment coefficient of the posture adjustment model is defined as , the formula is as follows:

[0186]

[0187] Among them, PAI, PAI2, and PAI3 are three posture evaluation coefficients; d1, d2, and d3 are non-negative weights of each posture coefficient; Hf, Xl, and Xy are respiratory rate, heart rate, and blood pressure values, respectively; e is the base of the natural logarithm, and k and m are parameters for adjusting the influence of biometric features;

[0188] d1, d2, and d3 are the weights corresponding to PAI, PAI2, and PAI3, respectively, and are adjusted according to different clinical needs.

[0189] The weight distribution ratio of d1 is:

[0190] In the case of a larger impact, when the posture assessment coefficient PAI has a significant and crucial impact on the posture adjustment model, the proportion of d1 ranges from 40% to 60%, corresponding to 0.4-0.6;

[0191] Assuming that the influence of PAI on the posture adjustment model is dominant and other factors are relatively small, if d1=0.4, the contribution of PAI to the total influence of the posture adjustment model is 40%; if d1=0.6, the contribution of PAI is 60%. In this case, increasing the value of d1 will significantly enhance the influence of PAI on the posture adjustment model, thereby improving the posture evaluation of patients;

[0192] In the case of smaller impact: when other factors are relatively important in affecting the body position adjustment model, the weight of d1 is reduced to 20%-40%; if PAI2 and PAI3 have a more significant impact on the body position adjustment model, then if d1=0.2, the contribution of PAI to the body position adjustment model is 20%; if d1=0.4, the contribution of PAI is 40%. In this case, increasing the value of d1 will still enhance the impact of PAI, but its relative impact will decrease.

[0193] The weight distribution ratio of d2 is:

[0194] In the case of a large impact: When the influence of PAI2 on the posture adjustment model is critical, the proportion of d2 ranges from 30% to 50%; assuming that the influence of PAI2 on the posture adjustment model is large and indispensable, if d2=0.3, the contribution of PAI2 to the model is 30%; if d2=0.5, the contribution of PAI2 is 50%. Increasing the value of d2 will enhance the influence of PAI2, thereby improving the posture evaluation of the patient;

[0195] In the case of smaller impact: when other factors are relatively important, the weight of d2 is reduced to 20%-40%. If PAI and PAI3 have a greater impact on the body position adjustment model, then if d2=0.2, the contribution of PAI2 to the body position adjustment model is 20%; if d2=0.4, the contribution of PAI2 is 40%. In this case, increasing the value of d2 will still enhance the impact of PAI2, but its relative impact will decrease.

[0196] The weight distribution ratio of d3 is:

[0197] In the case of a large impact: When the influence of PAI3 on ​​the posture adjustment model is crucial, the proportion of d3 ranges from 20% to 40%; assuming that PAI3 has a large impact on the posture adjustment model but less than PAI and PAI2, if d3=0.2, then PAI3 contributes 20% to the model; if d3=0.4, then PAI3 contributes 40%. Increasing the value of d3 will enhance the influence of PAI3, thereby improving the posture evaluation of patients;

[0198] In the case of smaller impact: when other factors are relatively important, reduce the weight of d3 to 10%-30%; if PAI and PAI2 have a greater impact on the body position adjustment model, then if d3=0.1, the contribution of PAI3 to the body position adjustment model is 10%; if d3=0.3, the contribution of PAI3 is 30%. In this case, increasing the value of d3 will still enhance the impact of PAI3, but its relative impact will decrease.

[0199] k and m control the sensitivity and offset of the function based on the biometric data Hf, Xl, Xy. Their increase or decrease will affect sensitivity to biological data;

[0200] The value range of k is:

[0201] In the case of high importance: When the biometric adjustment parameter k has a very important impact on the posture adjustment model, the value range of k is 0.1-0.3; assuming k=0.1, The attenuation effect is not significant; if k=0.3, the attenuation effect is more obvious. Increasing the value of k will increase the attenuation rate. A larger k value means that the biometrics have a greater adjustment effect on the model.

[0202] In the case of low importance: when other factors have a greater impact on the model, the value range of k is adjusted to 0.0-0.2; if the weights of other factors such as d1, d2, and d3 have a more significant impact on the model, then if k=0.0, the impact of the biological feature on the model is small; if k=0.2, the impact is relatively enhanced, but still inferior to other factors. Increasing the value of k will accelerate the impact of the biological feature.

[0203] The value range of m: In the case of high importance: the value range of the parameter m for adjusting the overall characteristics of the model is 5-10. Assuming m=5, the adjustment effect of the overall characteristics of the model is relatively gentle; if m=10, the adjustment effect is more significant. Increasing the value of m will lead to a more significant adjustment effect of the overall characteristics;

[0204] In the case of low importance: when other factors have a greater impact on the model, m can be adjusted to 3-8. When other factors such as k are more important to the model, the effect of adjusting m is relatively reduced. If m=3, the overall feature adjustment effect is small; if m=8, the adjustment effect is slightly prominent. Increasing the value of m will enhance the overall feature adjustment effect of the model.

[0205] set up The value range of is (0,1), which enables the evaluation coefficient output by the model to intuitively indicate the suitability of the patient's current position; The closer it is to 1, the closer the current posture is to the ideal posture, and the less adjustment is needed; The closer it is to 0, the more position adjustments are needed to improve the patient's comfort and recovery.

[0206] Will The value range (0,1) is divided into the following intervals:

[0207] The interval (0.00, 0.25) indicates a high degree of adjustment demand. Patients in this interval need a large adjustment of their body position, specifically 85% to 100% of the current body position range, to quickly improve the patient's physiological state and comfort;

[0208] Automatically adjust the bed to a full recumbent position or a specific tilt angle, adding automatic adjustment of the mattress to relieve pressure points, suitable for patients with high discomfort, the specific tilt angle is the tilt angle when the patient feels most comfortable;

[0209] The interval [0.25, 0.50) indicates moderate adjustment needs, requiring a large position adjustment of 50% to 84% to promote blood circulation and reduce muscle pain; the bed is automatically adjusted to a semi-recumbent position, and the current mattress hardness is adjusted by 20% to support the patient's waist and legs;

[0210] The range is [0.50, 0.75), slight adjustment needs, the current body position reaches 70% to 90% of the ideal posture, but slight adjustment is needed, but 20% to 35% of the current body position needs to be adjusted, mainly to increase the patient's comfort; slight adjustment of the bed angle, including slight tilt or adjustment of the pillow height, to help the patient breathe or rest better;

[0211] The range is [0.75, 1.00), indicating fine-tuning or maintenance: the patient's current position reaches more than 90% of the ideal posture, and only fine-tuning or no adjustment is needed, with an adjustment range of 0% to 19%; maintain the current bed position or make very slight adjustments, such as fine-tuning the pillow angle or slightly adjusting the hardness of the bed.

[0212] Embodiment six:

[0213] Further explanation based on Example 5, this example aims to verify the posture adjustment model Practicality and effectiveness;

[0214] First, the experimental group was set up, and 50 patients who were bedridden after neurosurgery were selected. Different posture evaluation coefficients PAI, PAI2, and PAI3 were set for these patients, where PAI represents the first posture evaluation coefficient, PAI2 represents the second posture evaluation coefficient, and PAI3 represents the third posture evaluation coefficient. These coefficients were obtained based on the actual feedback of patients and the observation of medical staff.

[0215] Before the experiment, each patient was initially evaluated and their baseline biometric data, including respiratory rate Hf, heart rate Xl, and blood pressure Xy, were recorded. Then, based on these data, the formula was used to calculate the blood pressure of each patient. , used to assess the suitability of the patient’s current position;

[0216] The setting of weights d1, d2, d3 and adjustment parameters k and m is based on the experience of clinicians and previous small-scale experiments; in this experiment, k is set to enhance the model's sensitivity to changes in heart rate and blood pressure, and m is adjusted to adapt to the average level of different biological characteristics; the weight factor will be adjusted according to the importance of different posture evaluation coefficients. For example, if PAI attaches more importance to comfort, d1 will be set to the highest.

[0217] Next, according to The bed angle and hardness are automatically adjusted according to the value of the bed position, from full recumbent position to slight adjustment of various angles. The patient's posture evaluation coefficient is re-evaluated after each adjustment, and the biometric data before and after the adjustment are recorded. This process continues until the optimal Values, i.e., values ​​close to 1, indicate that the patient's position is close to the ideal position posture;

[0218] In addition, the patient's comfort feedback, changes in physiological parameters, and the frequency and amplitude of adjustments required will be recorded during the experiment to evaluate the actual utility of the model and the actual effect of the adjustments. These data will be used to further verify The accuracy and usefulness of the model and its potential to improve patient comfort and recovery in the clinic;

[0219] To show in more detail how to use the model Analyze and adjust the patient's posture, and automatically adjust the bed to the recommended position. The following is a specific data table containing the relevant data of five patients, including basic physiological parameters, posture evaluation coefficients, model weights, adjustment parameters, and final , as shown in the following example Table 1, shows how the experimental data is recorded:

[0220] Table 1

[0221] Patient number Respiratory rate (Hf) Heart rate (Xl) Blood pressure (Xy) PAI PAI2 PAI3 d1 d2 d3 k m Body position adjustment factor Patient 1 25bpm 95bpm 150 / 95 mmHg 0.6 0.4 0.5 0.5 0.3 0.2 1.1 15 0.2 Patient 2 20bpm 85bpm 135 / 85 mmHg 0.8 0.7 0.8 0.4 0.3 0.3 1.2 14 0.78 Patient 3 22bpm 90bpm 140 / 90 mmHg 0.5 0.5 0.4 0.6 0.2 0.2 1 16 0.48 Patient 4 18bpm 80bpm 120 / 80 mmHg 0.9 0.8 0.85 0.3 0.4 0.3 0.9 13 0.85 Patient 5 30bpm 100bpm 160 / 100 mmHg 0.3 0.2 0.3 0.7 0.2 0.1 1.3 17 0.1

[0222] Data Analysis:

[0223] Patient 1 The value is 0.20, which falls in the interval [0.00, 0.25). The physiological indicators of patient 1 show a relatively unstable state, such as high blood pressure and a faster heart rate, indicating that he has a high need for adjustment. Therefore, the bed is automatically adjusted to a full-recumbent position and the automatic adjustment of the mattress is increased to relieve pressure points and quickly improve the patient's physiological state and comfort.

[0224] Patient 2 The value is 0.78, which falls in the range of [0.75, 1.00). The patient's heart and respiratory rates are relatively stable, and the blood pressure is normal, indicating that the patient's posture is close to the ideal posture and only requires minor adjustments, such as slightly adjusting the angle of the pillow or the hardness of the bed.

[0225] Patient 3 The value is 0.48, which falls in the range of [0.25, 0.50), which is a moderate physiological indicator and shows moderate adjustment needs; the bed position is automatically adjusted to a semi-recumbent position, and the mattress hardness is appropriately adjusted to support the patient's waist and legs, promote blood circulation and reduce muscle pain;

[0226] Patient 4 The value is 0.85, which is the same as patient 2 and also falls in the interval of [0.75, 1.00). The physiological parameters are relatively ideal, indicating that the body position is very close to the ideal and the adjustment range is very small;

[0227] Patient 5 The value is 0.10, the same as patient 1, falling in the interval [0.00,0.25). Some physiological parameters such as heart rate and blood pressure are higher than normal, indicating discomfort and severe adjustment needs. The bed position is significantly adjusted to a full recumbent position, and the automatic adjustment of the mattress is increased to significantly reduce body pressure and improve physiological status.

[0228] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0229] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0230] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0231] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A posture correction system for postoperative neurosurgery posture intervention based on data processing, characterized in that: Specifically include: Data acquisition module: used to collect the patient's physical characteristics and biometric data, as well as the patient's comfort data in different postures, and finally transmit the collected data to the central data processing unit by wireless means; Model training module: used to obtain the medical records of different patients and the corresponding optimal postoperative posture data, use neural network to train the deep learning model, use the medical records as model input, and output the optimal postoperative posture data. The medical records information includes physical feature data, biometric data and comfort data; The first coefficient generation module is used to obtain body physical characteristic data, which includes weight value, height value, limb size value, body surface area and joint range of motion value, and analyze and process it to generate a first posture evaluation coefficient, which is used to evaluate the best postoperative posture data based on the current patient's body physical characteristic data; The second coefficient generation module is used to obtain biometric data, which includes heart rate value, blood pressure value, and respiratory rate, and analyze and process them to generate a second posture evaluation coefficient, which is used to evaluate the best posture data after surgery based on the current patient's biometric data; The third coefficient generation module is used to obtain comfort data, which includes pain score and comfort score, and analyze and process them to generate a third posture evaluation coefficient, which is used to evaluate the best posture data after surgery based on the current patient's comfort data; Posture adjustment construction module: used to obtain the first posture evaluation coefficient, the second posture evaluation coefficient and the third posture evaluation coefficient, and perform analysis and processing to construct a posture adjustment model, which is used to analyze the relationship between the body physical feature data, the biometric feature data and the comfort data and the patient's postoperative recovery posture, generate a posture correction strategy, and automatically adjust the bed to the recommended position through the posture correction strategy; The posture adjustment coefficient of the posture adjustment model is defined as PAItotal, and its formula is as follows: Among them, PAI, PAI 2, and PAI 3 are three posture evaluation coefficients; d1, d2, and d3 are non-negative weights of each posture coefficient; Hf, Xl, and Xy are respiratory rate, heart rate, and blood pressure values, respectively; e is the base of the natural logarithm, and k and m are parameters for adjusting the influence of biometric features; d1, d2, and d3 are the weights corresponding to PAI, PAI2, and PAI3, respectively, and are adjusted according to different clinical needs; k and m control the sensitivity and offset of the function based on the biometric data Hf, Xl, Xy. Their increase or decrease will affect the sensitivity of PAItotal to the biometric data. The value range of PAItotal is set to (0,1). The closer the PAItotal value is to 1, the closer the current posture is to the ideal posture and the smaller the adjustment required. The closer the PAItotal value is to 0, the more posture adjustments are required to improve the patient's comfort and recovery. Divide the PAItotal value range (0,1) into the following intervals: The interval (0.00, 0.25) indicates the need for height adjustment. Patients in this interval need a large adjustment of their body position, specifically 85% to 100% of the current body position, to quickly improve their physiological state and comfort. The bed needs to be adjusted to a full recumbent position, and the most comfortable tilt angle needs to be adjusted according to the patient's comfort requirements. The interval [0.25, 0.50) indicates moderate adjustment needs, requiring a large position adjustment of 50% to 84% to promote blood circulation and reduce muscle pain; automatically adjust the bed to a semi-recumbent position, adjusting the current mattress hardness by 20% to support the patient's waist and legs; The interval [0.50, 0.75) indicates a slight adjustment requirement, where the current body position is between 70% and 90% of the ideal posture, but the current body position needs to be adjusted by 20% to 35% to increase the patient's comfort; the slight adjustment of the bed angle is within 10%, including tilting or adjusting the height of the pillow to help the patient breathe or rest better; The interval [0.75, 1.00) indicates the need for micro-adjustment. The patient's current body position reaches more than 90% of the ideal posture, and only 0% to 19% of the current body position amplitude needs to be micro-adjusted; specifically, the pillow angle is fine-tuned or the hardness of the bed is slightly adjusted.

2. The posture correction system for postoperative neurosurgery posture intervention based on data processing according to claim 1 is characterized in that: The weight value is marked as Tz, the height value is marked as Sg, the limb size value is marked as ZTc, the body surface area is marked as BSA, and the range of motion of the joints is marked as GJd; The first posture evaluation coefficient is defined as PAI, and the calculation formula is as follows: Among them, PAI represents the first posture evaluation coefficient, Tz, Sg, ZTc, BSA, and GJdi represent weight, height, limb size, body surface area, and range of motion of the i-th joint, respectively, and n represents the total number of joints; Tzmin, Sgmax, and BSAmax represent the minimum value of weight, the maximum value of height, and the maximum value of body surface area, respectively, and are used for data normalization; a1, a2, a3, and a4 are non-negative weight parameters; the output value of the first posture evaluation coefficient PAI formula is set to be in the range of (0,1); the closer the PAI is to 1, the closer the current posture is to the ideal posture, and the closer it is to 0, the more posture adjustments are made; The PAI range is divided into the following four intervals, each of which corresponds to specific posture adjustment suggestions: The interval (0.0, 0.25) indicates that the patient’s current posture is far from the ideal posture. The adjustment suggestion is to increase the range of joint motion by 20% to improve the posture comfort by 30%; In the interval [0.25, 0.5), the patient's posture requires moderate adjustment, and the adjustment recommendation is to increase the range of motion of the joints by 10% to improve the posture comfort by 15%; The patient’s posture is close to the ideal posture in the interval [0.5, 0.75). The adjustment suggestion is to increase the range of joint motion by 5% to improve the posture comfort by 7%. The interval [0.75, 1.0) indicates that the patient's posture has reached the ideal posture, no additional adjustments are needed, the current state is maintained, and regular monitoring is performed to ensure continued comfort and recovery.

3. The posture correction system for postoperative neurosurgery posture intervention based on data processing according to claim 2 is characterized in that: The heart rate value is marked as Xl, the blood pressure value is marked as Xy, and the respiratory rate is marked as Hf; The heart rate value, blood pressure value, and respiratory rate after data processing are obtained, and analyzed and processed to generate a second posture evaluation coefficient. The calculation formula of the second posture evaluation coefficient is as follows; Among them, PAI2 is the second posture evaluation coefficient, Xy is the blood pressure value, Xlavg is the average heart rate value; Hfmax is the maximum respiratory rate; b1, b2, and b3 are non-negative parameters for adjusting the influence weights of each biological feature; Xl is the heart rate value, the number of heart beats per minute; Hf is the respiratory rate, that is, the number of breaths per minute; The value range of PAI2 is set to (0,1), where the closer PAI2 is to 1, the closer the current biometric data corresponds to the ideal posture, and the closer it is to 0, the more posture adjustments are needed; The value range (0,1) of PAI2 is divided into the following specific intervals, and the specific value range and corresponding medical actions of each interval are defined; Interval A (0.00, 0.20), this interval indicates that the patient's posture is far from the ideal posture and requires urgent posture adjustment. The patient needs to be turned over or the lower limbs raised to improve blood circulation and respiratory function; Interval B [0.20, 0.50), this interval indicates that the patient's posture needs to be adjusted by adjusting the pillow height or the patient's lying position to make PAI2 an ideal posture close to 1; Interval C [0.50, 0.80), this interval indicates that the patient's posture needs to be fine-tuned by 0% to 5% of the current lying angle to ensure that no part of the body is compressed, further making PAI2 an ideal posture close to 1; The interval D[0.80,1.00) indicates that the patient’s posture is in an ideal position and that the patient only needs to maintain the current posture and perform regular checks to ensure that the biometric data remains stable.

4. The posture correction system for postoperative neurosurgery posture intervention based on data processing according to claim 3 is characterized in that: The pain score is marked as TTp, and the comfort score is marked as SSd; Among them, the pain score TTp uses an integer ranging from 1 to 10 to score, and the larger the value, the greater the pain; The comfort score SSd is scored using integers ranging from 1 to 10, with the larger the value, the worse the comfort; The third posture evaluation coefficient is defined as PAI3, and the calculation formula is as follows: in, It expresses the effect of pain score, c1 is the sensitivity parameter that regulates the effect of pain, and the increase of c1 indicates that the negative effect of pain score on posture evaluation is enhanced; log(c2·SSd+1) expresses the influence of comfort score, where c2 is used to adjust the influence of comfort score. When c2 increases, the positive influence of high comfort score on posture evaluation increases. In the denominator is a time integral with a decay factor, which is used to simulate the effect of the passage of time on the evaluation coefficient, where γ is a regularization parameter, λ is the decay rate, T is the evaluation time window, which represents the time span covered by the evaluation coefficient, and t ranges from 0 to T; The value range of PAI3 is set to (0,1). When PAI3 is closer to 1, it means that the patient is closer to the ideal postoperative posture and the comfort is better. On the contrary, when PAI3 is closer to 0, it means that more posture adjustments are needed and the comfort is worse. The PAI3 value range (0,1) is divided into the following intervals; The interval is [0.90, 1.00), at which point the patient's pain score TTp is 1 or 2, the comfort score SSd is 1 or 2, and the current body position reaches more than 90% of the ideal posture; The interval [0.75, 0.90) indicates that the patient's pain score TTp is 2 or 3, the comfort score SSd is 2 or 3, and the current body position reaches between 70% and 90% of the ideal posture; The interval [0.50, 0.75) indicates that the patient’s pain score TTp is 4 or 5, the comfort score SSd is 4 or 5, and the current body position reaches between 60% and 70% of the ideal posture; The interval [0.25, 0.50) indicates that the patient’s pain score TTp is 6 or 7, the comfort score SSd is 6 or 7, and the current body position reaches between 45% and 60% of the ideal posture; The interval (0.00, 0.25) indicates that the patient’s pain score TTp is 8 or 9 or 10, the comfort score SSd is 8 or 9 or 10, and the current body position is less than 45% of the ideal posture.

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