Angle adjustment method and system of three-motor diagnostic bed based on intelligent body sensing

Abstract

The present invention relates to the field of intelligent control technology and discloses a method and system for adjusting the angle of a three-motor medical bed based on intelligent somatosensory sensing. The method comprises obtaining pressure distribution data of various parts of a patient's body on the bed surface, data on the patient's body posture, and data on medical treatment requirements; constructing a first equation based on the pressure distribution data and the posture data to obtain a first somatosensory feature and a second equation to obtain a second somatosensory feature; fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature; matching the first, second, and third somatosensory features with the medical treatment requirements data using a preset intelligent mapping model, converting them into a mapping relationship between the somatosensory features and the medical treatment requirements, and obtaining mapping relationship data; obtaining an angle adjustment instruction based on the mapping relationship data, and controlling the drive motor to adjust the bed's angle according to the angle adjustment instruction. This method avoids secondary injuries caused by moving the patient during surgery.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a method and system for adjusting the angle of a three-motor diagnostic and treatment bed based on intelligent body sensing. Background Art

[0002] At present, with the continuous advancement of medical technology, the precise adjustment of the patient's position during surgery has become increasingly important. During surgery, patients are usually under anesthesia and completely lose the ability to move independently. Due to their relatively weak physical condition, any improper movement may cause secondary harm to the patient. At the same time, the selection and adjustment of the surgical position is not only related to the exposure of the surgical field and the convenience of surgical operation, but also directly affects the patient's physiological function, postoperative recovery and even the safety of the operation. With the diversification of surgical types, the demand for surgical positions has become increasingly complex. For example, the prone position in neurosurgery and the lateral position in cardiac surgery require medical staff to have superb posture management skills and rich clinical experience. Therefore, ensuring the comfort and safety of patients during surgery has become one of the focuses of medical workers.

[0003] In one existing technology, accurate adjustment of the patient's position is particularly important to ensure the smooth progress of the operation and improve the safety and efficiency of the operation. After the patient completes anesthesia, an experienced medical team first safely moves them to the operating table and places them in a roughly appropriate starting position based on the type of surgery to be performed. Next, to further accurately adjust the patient's position, the operating room is equipped with manipulators with multiple degrees of freedom joints. The design of these manipulators allows them to move flexibly in three-dimensional space, allowing them to carefully adjust the patient's position to meet the specific needs of the surgical operation.

[0004] However, in the existing technology, it is difficult for the robot arm to accurately detect pressure when moving the body, which leads to excessive pressure during operation and the risk of secondary injury. Summary of the Invention

[0005] The present invention provides a method and system for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing, so as to avoid secondary injuries caused by moving the patient during surgery.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing, comprising:

[0007] Obtain the pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data and diagnosis and treatment demand data;

[0008] Obtaining a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data;

[0009] Obtaining a second somatosensory feature through a second equation based on the pressure distribution data and the posture data;

[0010] fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature;

[0011] Matching the first somatosensory feature, the second somatosensory feature, and the third somatosensory feature with the diagnosis and treatment requirement data through a preset intelligent mapping model, converting them into a mapping relationship between somatosensory features and diagnosis and treatment requirements, and obtaining mapping relationship data, wherein an initial deep learning model is trained based on pre-stored historical somatosensory feature data and historical diagnosis and treatment requirement data, and the trained initial deep learning model is used as the intelligent mapping model;

[0012] An angle adjustment instruction is obtained according to the conversion of the mapping relationship data, and a drive motor is controlled according to the angle adjustment instruction to adjust the angle of the bed.

[0013] In an optional embodiment, obtaining the pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data, and the diagnosis and treatment demand data includes:

[0014] Obtain the pressure distribution data of various parts of the patient's body on the bed surface through the pressure sensor;

[0015] Acquire the patient's body posture data through a depth camera;

[0016] Obtain diagnosis and treatment demand data through user input operations.

[0017] In an optional embodiment, obtaining the first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data includes:

[0018] constructing a contact area distribution matrix and a pressure value matrix according to the pressure distribution data;

[0019] Extracting head position coordinates according to the posture data;

[0020] Constructing a pressure coefficient matrix according to the contact area distribution matrix and the pressure value matrix;

[0021] Constructing a pressure threshold matrix according to the posture data and the pressure value matrix;

[0022] The head position coordinates, the pressure coefficient matrix, and the pressure threshold matrix are calculated by constructing a first equation to obtain a first somatosensory feature;

[0023] The elements in the pressure threshold matrix are calculated using the following formula:

[0024]

[0025] The first somatosensory feature is calculated using the following formula:

[0026]

[0027] Among them, x1 represents the first somatosensory feature, F1 represents the first somatosensory feature, C ij Represents the elements in the pressure coefficient matrix, P ij Represents the elements in the pressure value matrix, A ij represents an element in the contact area distribution matrix, H represents the head position coordinate, and w1, w2, and w3 represent preset weight coefficients.

[0028] In an optional embodiment, obtaining a second somatosensory feature through a second equation based on the pressure distribution data and the posture data includes:

[0029] The second somatosensory feature is obtained according to the following formula:

[0030]

[0031] Where x2 represents the second somatosensory feature, n represents the total amount of pressure distribution data and posture data; u i represents the i-th pressure distribution data, v i Represents the i-th posture data, represents the average value of the pressure distribution data, represents the average value of posture data; α represents the preset weight, and s represents the interval time for obtaining data of various parts of the patient's body.

[0032] In an optional embodiment, fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature includes:

[0033] Normalizing the first somatosensory feature and the second somatosensory feature to obtain standardized data;

[0034] Perform weighted fusion based on the standardized data to obtain a third somatosensory feature;

[0035] Among them, normalization is performed according to the following formula:

[0036]

[0037]

[0038] Among them, weighted fusion is performed according to the following formula:

[0039]

[0040] Among them, x′1 represents the standardized first feature, x′2 represents the standardized second feature, x1 represents the first somatosensory feature, x2 represents the second somatosensory feature, μ1 and μ2 are the means of the first somatosensory feature and the second somatosensory feature respectively, σ1 and σ2 are the standard deviations of the first somatosensory feature and the second somatosensory feature respectively, ω1 represents the preset first feature weight, ω2 represents the preset second feature weight, and x3 represents the third feature.

[0041] In an optional embodiment, the training process of the initial deep learning model includes:

[0042] Inputting pre-stored historical feature data and historical sample data into the input layer of the initial deep learning model for training, and obtaining predicted value data output by the output layer of the deep learning model;

[0043] Substituting the historical somatosensory feature data and the historical diagnosis and treatment demand data into a loss function to calculate a loss value to obtain loss value data;

[0044] Calculate the gradient of the output layer output of the deep learning model based on the loss value data, and pass the gradient forward layer by layer through the chain rule to calculate the gradient of the parameters of each layer to obtain gradient data;

[0045] Update the parameters of each layer of the deep learning model based on the gradient data and the preset learning rate;

[0046] The parameters of each layer are updated repeatedly until the training is completed when the number of training times of the deep learning model is greater than the preset number of times, or when the loss value data of the deep learning model is less than the preset loss threshold.

[0047] In an optional implementation, converting the mapping relationship data to obtain the angle adjustment instruction includes:

[0048] Performing data analysis on the mapping relationship data to extract angle-affecting parameters;

[0049] Based on the angle influencing parameters, the bed adjustment angle is obtained through inverse kinematics calculation;

[0050] According to the bed adjustment angle, an angle adjustment instruction is obtained by performing calculations using a PID control algorithm;

[0051] The angle adjustment instruction includes controlling the first drive to adjust the inclination angle of the bed, controlling the second drive to adjust the pitch angle of the bed, or controlling the third drive to adjust the rotation angle of the bed.

[0052] In a second aspect, the present invention provides a three-motor diagnostic bed angle adjustment system based on intelligent body sensing, comprising:

[0053] A data acquisition module is used to obtain the pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data and diagnosis and treatment demand data;

[0054] A first feature acquisition module, configured to obtain a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data;

[0055] A second feature acquisition module, configured to acquire a second somatosensory feature using a second equation based on the pressure distribution data and the posture data;

[0056] a third feature acquisition module, configured to fuse the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature;

[0057] a data mapping module, configured to match the first, second, and third somatosensory features with the diagnosis and treatment requirement data using a preset intelligent mapping model, convert the matching results into a mapping relationship between the somatosensory features and the diagnosis and treatment requirements, and obtain mapping relationship data, wherein an initial deep learning model is trained based on pre-stored historical somatosensory feature data and historical diagnosis and treatment requirement data, and the trained initial deep learning model is used as the intelligent mapping model;

[0058] The bed adjustment module is used to obtain an angle adjustment instruction according to the mapping relationship data, and control the drive motor to adjust the angle of the bed according to the angle adjustment instruction.

[0059] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing.

[0060] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned three-motor diagnostic bed angle adjustment methods based on intelligent body sensing.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention relates to the field of intelligent control technology, and discloses a method and system for adjusting the angle of a three-motor medical bed based on intelligent somatosensory. The method includes obtaining pressure distribution data of various parts of a patient's body on the bed surface, posture data of the patient's body, and medical treatment demand data; based on the pressure distribution data and the posture data, obtaining a first somatosensory feature by constructing a first equation; based on the pressure distribution data and the posture data, obtaining a second somatosensory feature by a second equation; fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature; matching the first somatosensory feature, the second somatosensory feature, and the third somatosensory feature with the medical treatment demand data through a preset intelligent mapping model, and converting them into a mapping relationship between somatosensory features and medical treatment demands to obtain mapping relationship data, wherein an initial deep learning model is trained according to pre-stored historical somatosensory feature data and historical medical treatment demand data, and the trained initial deep learning model is used as an intelligent mapping model; an angle adjustment instruction is obtained according to the conversion of the mapping relationship data, and a drive motor is controlled to adjust the angle of the bed according to the angle adjustment instruction. This method obtains the patient's pressure distribution, posture and treatment demand data, constructs and integrates somatosensory features, uses a trained intelligent mapping model to match somatosensory features with treatment needs, and finally converts them into angle adjustment instructions, thereby avoiding secondary injuries caused by moving the patient during the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing provided by the first embodiment of the present invention;

[0064] Figure 2 This is a structural diagram of the three-motor diagnostic bed angle adjustment system based on intelligent body sensing provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] Reference Figure 1 The first embodiment of the present invention provides a method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing, comprising the following steps:

[0067] S11, obtaining pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data, and diagnosis and treatment demand data;

[0068] S12, obtaining a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data;

[0069] S13, obtaining a second somatosensory feature through a second equation based on the pressure distribution data and the posture data;

[0070] S14, fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature;

[0071] S15, matching the first somatosensory feature, the second somatosensory feature, and the third somatosensory feature with the diagnosis and treatment requirement data through a preset intelligent mapping model, converting the matching into a mapping relationship between the somatosensory feature and the diagnosis and treatment requirement, and obtaining mapping relationship data, wherein an initial deep learning model is trained based on pre-stored historical somatosensory feature data and historical diagnosis and treatment requirement data, and the trained initial deep learning model is used as the intelligent mapping model;

[0072] S16, obtaining an angle adjustment instruction according to the mapping relationship data conversion, and controlling the drive motor to adjust the angle of the bed according to the angle adjustment instruction.

[0073] In step S11 , the pressure distribution data of each part of the patient's body on the bed surface, the patient's body posture data and the diagnosis and treatment demand data are obtained.

[0074] In a specific embodiment, the obtaining of pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data, and the diagnosis and treatment demand data includes:

[0075] Obtain the pressure distribution data of various parts of the patient's body on the bed surface through the pressure sensor;

[0076] Acquire the patient's body posture data through a depth camera;

[0077] Obtain diagnosis and treatment demand data through user input operations.

[0078] Specifically, first of all, through the pressure sensor array laid inside the mattress, such as smart skin-electric sensing gloves, smart skin-electric sensing vests, and smart pressure insoles, these sensors can accurately obtain the pressure distribution data of various parts of the patient's body on the bed surface, and sensitively respond to pressure changes in different parts of the human body. Whether it is slight pressure or greater pressure, it can be accurately captured and recorded.

[0079] Next, to obtain the patient's posture data, a depth camera is used to capture the patient's three-dimensional form. Using image processing algorithms, key skeletal points such as the head, shoulders, elbows, hips, knees, and ankles are precisely located. This data is used for subsequent posture analysis and stress assessment, helping the system accurately determine the patient's sleeping position and whether it is causing excessive pressure on certain body parts.

[0080] Finally, to better meet the patient's personalized diagnosis and treatment needs, the system also provides a user-friendly interface that allows users to input and obtain diagnosis and treatment data. This data includes the patient's medical history, current health status, and previous treatment experience.

[0081] In step S12, a first somatosensory feature is obtained by constructing a first equation based on the pressure distribution data and the posture data.

[0082] In a specific embodiment, obtaining the first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data includes:

[0083] constructing a contact area distribution matrix and a pressure value matrix according to the pressure distribution data;

[0084] Extracting head position coordinates according to the posture data;

[0085] Constructing a pressure coefficient matrix according to the contact area distribution matrix and the pressure value matrix;

[0086] Constructing a pressure threshold matrix according to the posture data and the pressure value matrix;

[0087] The head position coordinates, the pressure coefficient matrix, and the pressure threshold matrix are calculated by constructing a first equation to obtain a first somatosensory feature;

[0088] The elements in the pressure threshold matrix are calculated using the following formula:

[0089]

[0090] The first somatosensory feature is calculated using the following formula:

[0091]

[0092] Among them, x1 represents the first somatosensory feature, C ij Represents the elements in the pressure coefficient matrix, P ij Represents the elements in the pressure value matrix, A ij represents an element in the contact area distribution matrix, H represents the head position coordinate, and w1, w2, and w3 represent preset weight coefficients.

[0093] Specifically, first, based on the pressure distribution data of various parts of the patient's body on the bed surface, two key matrices are constructed: the contact area distribution matrix A and the pressure value matrix P. These two matrices record the contact area size of each part of the patient's body with the bed surface and the corresponding pressure value. Each element A in the contact area distribution matrix A is ij Represents the area of contact between a specific area of the patient's body and the bed surface; and each element P in the pressure value matrix P ij The two matrices are the basis for subsequent calculations and can help analyze the stress conditions of various parts of the patient's body.

[0094] Assuming that m×n pressure sensors are arranged on the mattress, the contact area distribution matrix A can be expressed as:

[0095]

[0096] Among them, A ij represents the contact area detected by the sensor at the i-th row and j-th column. These contact area values are obtained by calibrating and processing the output signals of the sensors.

[0097] Assuming that i×j pressure sensors are arranged on the mattress, the pressure value matrix P can be expressed as:

[0098]

[0099] Among them, P ij represents the pressure value detected by the sensor at the i-th row and j-th column. These pressure values are usually obtained by calibrating and processing the output signal of the sensor.

[0100] Next, the head position coordinates H are extracted based on the patient's body posture data obtained by the depth camera. Determining the head position is crucial to understanding the patient's overall posture and possible discomfort, because the relative position of the head can directly affect the pressure distribution on the neck and shoulders, thereby affecting the patient's comfort.

[0101] After having the above basic data, the pressure coefficient matrix C is further constructed according to the contact area distribution matrix A and the pressure value matrix P. Each element C in the pressure coefficient matrix ij Calculated using the following formula:

[0102]

[0103] Normalize pressure values to more accurately reflect the pressure per unit area.

[0104] Then, the pressure threshold matrix T is constructed based on the patient's body posture data and the pressure value matrix P. Each element T in the pressure threshold matrix ij This reflects the pressure threshold at which a part of the patient's body reaches discomfort. This step requires setting a reasonable threshold based on the body part where the sensor is located to ensure that potential risk areas can be accurately identified.

[0105] Assuming that i×j pressure sensors are arranged on the mattress, the pressure threshold matrix T can be expressed as:

[0106]

[0107] Among them, T ij represents the pressure threshold detected by the sensor at the i-th row and j-th column position. ij Set it based on the body part where the sensor is located. For example, if sensor T 11 Located in the head area, then T 11 It can be set to 10 units of pressure; if the sensor T 22 Located in the hip area, then T 22 The pressure can be set to 60 units. These values are examples only and can be adjusted according to individual differences in actual application.

[0108] Finally, the head position coordinates H, pressure coefficient matrix C, and pressure threshold matrix T are used as input and substituted into the preset first formula for comprehensive calculation to obtain the first somatosensory feature x1. This is achieved through the following formula:

[0109]

[0110] Among them, x1 represents the first somatosensory feature, C ij Represents the elements in the pressure coefficient matrix, P ij Represents the elements in the pressure value matrix, A ij represents the elements in the contact area distribution matrix, H represents the head position coordinates, w1, w2 and w3 represent the preset weight coefficients, which are used to adjust the relative importance of each factor in the final somatosensory characteristics.

[0111] The inclusion of head position coordinates H accounts for the impact of head position on overall somatosensory perception; the sum of the pressure coefficient matrix C reflects the total pressure per unit area across the patient's body; and the sum of the pressure threshold matrix T reflects the cumulative effect of potential discomfort areas. In this way, the system can comprehensively assess the patient's somatosensory state.

[0112] In step S13, a second somatosensory feature is obtained through a second equation based on the pressure distribution data and the posture data.

[0113] In a specific embodiment, obtaining the second somatosensory feature through a second equation based on the pressure distribution data and the posture data includes:

[0114] The second somatosensory feature is obtained according to the following formula:

[0115]

[0116] Where x2 represents the second somatosensory feature, n represents the total amount of pressure distribution data and posture data; u i represents the i-th pressure distribution data, v i Represents the i-th posture data, represents the average value of the pressure distribution data, represents the average value of posture data; α represents the preset weight, and s represents the interval time for obtaining data of various parts of the patient's body.

[0117] Specifically, the average value of the pressure distribution data is calculated by the following formula:

[0118]

[0119] The average value of the posture data is calculated using the following formula:

[0120]

[0121] Next, calculate the correlation between the pressure distribution data and the posture data. This step is achieved by calculating the covariance between the two. The specific formula is as follows:

[0122]

[0123] This formula calculates the difference between the pressure distribution value of each data point and the average pressure distribution value, multiplies it by the difference between the posture value of each data point and the average posture value, and then sums the results for all data points. This result reflects the linear relationship between the pressure distribution data and the posture data.

[0124] In order to standardize the time scale and ensure the comparability of the calculation results, it is necessary to introduce the interval time for obtaining data.

[0125] Finally, substitute the above calculation results into the following second equation to calculate the second somatosensory feature:

[0126]

[0127] Where x2 represents the second somatosensory feature, n represents the total amount of pressure distribution data and posture data; u i represents the i-th pressure distribution data, v i Represents the i-th posture data, represents the average value of the pressure distribution data, represents the average value of posture data; α represents the preset weight, and s represents the interval time for obtaining data of various parts of the patient's body.

[0128] Through the above steps, the second somatosensory feature is obtained. This feature value reflects the correlation between the pressure distribution data and the posture data, and can be used for further analysis and diagnosis.

[0129] In step S14, the first somatosensory feature and the second somatosensory feature are fused to obtain a third somatosensory feature.

[0130] In a specific embodiment, fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature includes:

[0131] Normalizing the first somatosensory feature and the second somatosensory feature to obtain standardized data;

[0132] Perform weighted fusion based on the standardized data to obtain a third somatosensory feature;

[0133] Among them, normalization is performed according to the following formula:

[0134]

[0135] Among them, weighted fusion is performed according to the following formula:

[0136]

[0137] Among them, x′1 represents the standardized first feature, x′2 represents the standardized second feature, x1 represents the first somatosensory feature, x2 represents the second somatosensory feature, μ1 and μ2 are the means of the first somatosensory feature and the second somatosensory feature respectively, σ1 and σ2 are the standard deviations of the first somatosensory feature and the second somatosensory feature respectively, ω1 represents the preset first feature weight, ω2 represents the preset second feature weight, and x3 represents the third feature.

[0138] Specifically, first, the first somatosensory feature and the second somatosensory feature are normalized to obtain normalized data. The purpose of normalization is to eliminate the dimensional differences between different features so that they can be compared and integrated on the same scale. The normalization formula is as follows:

[0139]

[0140]

[0141] Among them, x′1 represents the standardized first feature, x′2 represents the standardized second feature, x1 represents the first somatosensory feature, x2 represents the second somatosensory feature, v1 and μ2 are the means of the first somatosensory feature and the second somatosensory feature, respectively, and σ1 and σ2 are the standard deviations of the first somatosensory feature and the second somatosensory feature, respectively.

[0142] Next, weighted fusion is performed on the standardized data to obtain the third somatosensory feature. The purpose of weighted fusion is to assign different weights to different features according to different application scenarios to highlight the importance of certain features. The weighted fusion formula is as follows:

[0143]

[0144] Here, x′1 represents the normalized first feature, x′2 represents the normalized second feature, ω1 represents the preset weight of the first feature, ω2 represents the preset weight of the second feature, and x3 represents the third feature. The weights ω1 and ω2 can be adjusted based on specific application requirements to ensure that the final third somatosensory feature better reflects the actual somatosensory state.

[0145] Through the above steps, a third somatosensory feature is obtained. This feature value combines the information of the first somatosensory feature and the second somatosensory feature, can more comprehensively reflect the patient's somatosensory state, and can more accurately evaluate the patient's body position changes and pressure distribution.

[0146] In step S15, the first somatosensory feature, the second somatosensory feature and the third somatosensory feature are matched with the diagnosis and treatment requirement data through a preset intelligent mapping model, and converted into a mapping relationship between the somatosensory feature and the diagnosis and treatment requirement to obtain mapping relationship data, wherein the initial deep learning model is trained according to the pre-stored historical somatosensory feature data and the historical diagnosis and treatment requirement data, and the trained initial deep learning model is used as the intelligent mapping model.

[0147] In a specific embodiment, the training process of the initial deep learning model includes:

[0148] Inputting pre-stored historical feature data and historical sample data into the input layer of the initial deep learning model for training, and obtaining predicted value data output by the output layer of the deep learning model;

[0149] Substituting the historical somatosensory feature data and the historical diagnosis and treatment demand data into a loss function to calculate a loss value to obtain loss value data;

[0150] Calculate the gradient of the output layer output of the deep learning model based on the loss value data, and pass the gradient forward layer by layer through the chain rule to calculate the gradient of the parameters of each layer to obtain gradient data;

[0151] Update the parameters of each layer of the deep learning model based on the gradient data and the preset learning rate;

[0152] The parameters of each layer are updated repeatedly until the training is completed when the number of training times of the deep learning model is greater than the preset number of times, or when the loss value data of the deep learning model is less than the preset loss threshold.

[0153] Specifically, the new somatosensory feature data (first, second, and third somatosensory features) is input into the trained intelligent mapping model. The intelligent mapping model performs forward propagation calculations and outputs the diagnosis and treatment requirement data corresponding to the new somatosensory feature data. The resulting output data is the mapping relationship data between the somatosensory features and the diagnosis and treatment requirements.

[0154] Through the above steps, the deep learning model can be effectively used to establish a mapping relationship between somatosensory characteristics and diagnosis and treatment needs, thereby improving the accuracy and efficiency of adjusting the angle.

[0155] In step S16, an angle adjustment instruction is obtained according to the conversion of the mapping relationship data, and the driving motor is controlled to adjust the angle of the bed according to the angle adjustment instruction.

[0156] In a specific embodiment, converting the mapping relationship data to obtain the angle adjustment instruction includes:

[0157] Performing data analysis on the mapping relationship data to extract angle-affecting parameters;

[0158] Based on the angle influencing parameters, the bed adjustment angle is obtained through inverse kinematics calculation;

[0159] According to the bed adjustment angle, an angle adjustment instruction is obtained by performing calculations using a PID control algorithm;

[0160] The angle adjustment instruction includes controlling the first drive to adjust the inclination angle of the bed, controlling the second drive to adjust the pitch angle of the bed, or controlling the third drive to adjust the rotation angle of the bed.

[0161] Specifically, the mapping relationship data is analyzed to extract the angle influencing parameters. The mapping relationship data contains the correspondence between the somatosensory characteristics and the diagnosis and treatment needs. These data include multiple parameters, such as pressure distribution, posture, and heart rate. By analyzing these data, parameters related to the bed angle adjustment can be extracted, such as the unevenness of the pressure distribution and the pressure value of a specific part. Assume that the extracted angle influencing parameters are a1, a2, ..., a m , where m represents the number of parameters.

[0162] Based on the angle-affecting parameters, the bed adjustment angle is calculated through inverse kinematics. Inverse kinematics is an important concept in robotics, which is used to calculate the angles that each joint needs to rotate to achieve a specific position and posture. The inverse kinematics formula can be expressed as:

[0163] θ=f -1 (a1, a2, ..., a m )

[0164] Among them, θ represents the bed adjustment angle, f- 1 Represents the inverse kinematics function. The specific form of the inverse kinematics function depends on the mechanical structure and kinematic model of the bed.

[0165] According to the bed adjustment angle, the angle adjustment instruction is obtained by calculating the PID control algorithm. The PID control algorithm is a commonly used feedback control algorithm that adjusts the behavior of the system through three parts: proportional, integral and differential. The formula of the PID control algorithm can be expressed as:

[0166]

[0167] Among them, u(t) represents the control signal, that is, the angle adjustment instruction; K p , K i and K d are the gain coefficients of proportional, integral and differential respectively; e(τ) represents the error at the current moment, that is, the difference between the target angle and the current angle. For example, assuming the target adjustment angle is θ t , the current angle is θ c , then the error e(t) can be expressed as:

[0168] e(t)=θ t -θ c

[0169] The angle adjustment instructions calculated by the PID control algorithm generate specific control commands for controlling the drive motors to adjust the bed's angle. These angle adjustment instructions can include controlling the first drive to adjust the bed's inclination, the second drive to adjust the bed's pitch, or the third drive to adjust the bed's rotation.

[0170] The following describes the working process of the present invention using a common scenario as an example. Figure 1 , a method for adjusting the angle of a three-motor diagnosis and treatment bed based on intelligent body sensing, comprising the following steps:

[0171] First, the pressure sensor array within the mattress begins operating. These sensors precisely measure the pressure distribution of the patient's body parts on the bed surface. Simultaneously, a depth camera installed in a corner of the room activates, using advanced image processing technology to capture the patient's three-dimensional posture and precisely locate key anatomy points such as the head, shoulders, and elbows. Furthermore, medical staff input the patient's diagnosis and treatment requirements, including medical history and current health status, through an intuitive user interface.

[0172] The system then constructs a contact area distribution matrix and a pressure value matrix based on the collected pressure distribution and posture data. Next, it extracts the head position coordinates from the data acquired by the depth camera. Using this information, the system performs a series of mathematical operations to construct a pressure coefficient matrix and a pressure threshold matrix. Based on these matrices, the system calculates the first somatosensory feature, which reflects the force applied to various parts of the patient's body.

[0173] The system then uses the pressure distribution data and posture data again to calculate the covariance between the two to generate a second somatosensory signature. This step further reveals the relationship between pressure distribution and the patient's posture. The calculation of the second somatosensory signature takes into account the time interval, ensuring the accuracy of the results.

[0174] To comprehensively assess the patient's physical state, the system normalizes the primary and secondary somatosensory features and performs a weighted fusion based on preset weights, ultimately generating a third somatosensory feature. This feature value not only takes into account the pressure distribution across the patient's body but also changes in posture, providing a scientific basis for subsequent bed adjustments.

[0175] Finally, the system inputs the first, second, and third somatosensory characteristics, along with the patient's diagnosis and treatment needs data, into a pre-trained intelligent mapping model. Using this model, the system accurately predicts the optimal bed adjustment plan. Based on the model's output, the system uses inverse kinematics calculations and PID control algorithms to generate specific bed adjustment instructions. These instructions are sent to the bed's three drive motors, which are responsible for adjusting the bed's inclination, pitch, and rotation angles to achieve the most comfortable position adjustment.

[0176] As the bed was slowly adjusted to the optimal position, the patient suffered no secondary injury and the medical staff also achieved a more accurate surgical position.

[0177] In summary, the intelligent somatosensory-based three-motor diagnostic bed angle adjustment system uses a pressure sensor array and depth camera installed inside the mattress to accurately capture pressure distribution and posture data across the patient's body. Simultaneously, it collects the patient's personalized treatment needs data through a user-friendly interface. Based on this data, the system constructs a contact area distribution matrix, a pressure value matrix, a pressure coefficient matrix, and a pressure threshold matrix, and calculates the primary and secondary somatosensory features. These two features are normalized and weighted to generate a third somatosensory feature that comprehensively reflects the patient's somatosensory state. Using a pre-trained intelligent mapping model, the system matches the primary, secondary, and third somatosensory features with the patient's treatment needs data, converting them into a mapping relationship between somatosensory features and treatment needs. This mapping relationship enables the system to accurately predict the optimal bed adjustment plan. Finally, using inverse kinematics and a PID control algorithm, the system generates specific bed adjustment instructions, controlling the drive motors to adjust the bed angle for the most comfortable position.

[0178] The entire process not only reduces the operational burden on medical staff, but also avoids secondary injuries that may be caused by manual adjustment of the bed, significantly improving the safety and efficiency of surgery and treatment.

[0179] Reference Figure 2 The second embodiment of the present invention provides a three-motor diagnostic bed angle adjustment system based on intelligent body sensing, comprising:

[0180] A data acquisition module is used to obtain the pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data and diagnosis and treatment demand data;

[0181] A first feature acquisition module, configured to obtain a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data;

[0182] A second feature acquisition module, configured to acquire a second somatosensory feature using a second equation based on the pressure distribution data and the posture data;

[0183] a third feature acquisition module, configured to fuse the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature;

[0184] a data mapping module, configured to match the first, second, and third somatosensory features with the diagnosis and treatment requirement data using a preset intelligent mapping model, convert the matching results into a mapping relationship between the somatosensory features and the diagnosis and treatment requirements, and obtain mapping relationship data, wherein an initial deep learning model is trained based on pre-stored historical somatosensory feature data and historical diagnosis and treatment requirement data, and the trained initial deep learning model is used as the intelligent mapping model;

[0185] The bed adjustment module is used to obtain an angle adjustment instruction according to the mapping relationship data, and control the drive motor to adjust the angle of the bed according to the angle adjustment instruction.

[0186] It should be noted that the three-motor medical bed angle adjustment device based on intelligent body sensing provided in an embodiment of the present invention is used to execute all the process steps of the three-motor medical bed angle adjustment method based on intelligent body sensing in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0187] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing. When the processor executes the computer program, the steps in each of the above-mentioned embodiments of the method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the three-motor diagnosis and treatment bed angle adjustment module based on intelligent body sensing.

[0188] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0189] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0190] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, connecting various parts of the entire electronic device using various interfaces and lines.

[0191] The memory can be used to store the computer programs and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0192] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0193] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0194] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing, characterized in that: Executed by the controller, including: Obtain the pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data and diagnosis and treatment demand data; Obtaining a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data; Obtaining a second somatosensory feature through a second equation based on the pressure distribution data and the posture data; fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature; Matching the first somatosensory feature, the second somatosensory feature, and the third somatosensory feature with the diagnosis and treatment requirement data through a preset intelligent mapping model, converting them into a mapping relationship between somatosensory features and diagnosis and treatment requirements, and obtaining mapping relationship data, wherein an initial deep learning model is trained based on pre-stored historical somatosensory feature data and historical diagnosis and treatment requirement data, and the trained initial deep learning model is used as the intelligent mapping model; Obtaining an angle adjustment instruction according to the mapping relationship data conversion, and controlling a drive motor to adjust the angle of the bed according to the angle adjustment instruction; The obtaining of a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data includes: constructing a contact area distribution matrix and a pressure value matrix according to the pressure distribution data; Extracting head position coordinates according to the posture data; Constructing a pressure coefficient matrix according to the contact area distribution matrix and the pressure value matrix; Constructing a pressure threshold matrix according to the posture data and the pressure value matrix; The head position coordinates, the pressure coefficient matrix, and the pressure threshold matrix are calculated by constructing a first equation to obtain a first somatosensory feature; The elements in the pressure threshold matrix are calculated using the following formula: The first somatosensory feature is calculated using the following formula: in, Indicates the first somatosensory characteristic, represents the elements in the pressure coefficient matrix, represents the elements in the pressure value matrix, represents the elements in the contact area distribution matrix, Represents the head position coordinates, 、 and Represents the preset weight coefficient, Indicates the Rank the pressure threshold detected by the sensor at the column position; The obtaining a second somatosensory feature by using a second equation based on the pressure distribution data and the posture data includes: The second somatosensory feature is obtained according to the following formula:   in, Indicates the second somatosensory characteristic, The total amount of data representing pressure distribution data and posture data; Indicates the Pressure distribution data, Indicates the Posture data, represents the average value of the pressure distribution data, represents the average value of posture data; Represents the preset weight, Indicates the interval time for obtaining data of each part of the patient's body; The fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature includes: Normalizing the first somatosensory feature and the second somatosensory feature to obtain standardized data; Perform weighted fusion based on the standardized data to obtain a third somatosensory feature; Among them, normalization is performed according to the following formula: Among them, weighted fusion is performed according to the following formula: in, represents the normalized first feature, represents the normalized second feature, Indicates the first somatosensory characteristic, Indicates the second somatosensory characteristic, and are the means of the first and second somatosensory features, respectively. and are the standard deviations of the first and second somatosensory features, represents the preset first feature weight, represents the preset second feature weight, Indicates the third feature.

2. The method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing according to claim 1, characterized in that: The obtaining of pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data and diagnosis and treatment demand data includes: Obtain the pressure distribution data of various parts of the patient's body on the bed surface through the pressure sensor; Acquire the patient's body posture data through a depth camera; Obtain diagnosis and treatment demand data through user input operations.

3. The method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing according to claim 1, characterized in that: The training process of the initial deep learning model includes: Inputting pre-stored historical feature data and historical sample data into the input layer of the initial deep learning model for training, and obtaining predicted value data output by the output layer of the deep learning model; Substituting the historical somatosensory feature data and the historical diagnosis and treatment demand data into a loss function to calculate a loss value to obtain loss value data; Calculate the gradient of the output layer output of the deep learning model based on the loss value data, and pass the gradient forward layer by layer through the chain rule to calculate the gradient of the parameters of each layer to obtain gradient data; Update the parameters of each layer of the deep learning model based on the gradient data and the preset learning rate; The parameters of each layer are updated repeatedly until the training is completed when the number of training times of the deep learning model is greater than the preset number of times, or when the loss value data of the deep learning model is less than the preset loss threshold.

4. The method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing according to claim 1, characterized in that: The step of converting the mapping relationship data into an angle adjustment instruction includes: Performing data analysis on the mapping relationship data to extract angle-affecting parameters; Based on the angle influencing parameters, the bed adjustment angle is obtained through inverse kinematics calculation; According to the bed adjustment angle, an angle adjustment instruction is obtained by performing calculations using a PID control algorithm; The angle adjustment instruction includes controlling the first drive to adjust the inclination angle of the bed, controlling the second drive to adjust the pitch angle of the bed, or controlling the third drive to adjust the rotation angle of the bed.

5. A three-motor diagnostic bed angle adjustment system based on intelligent body sensing, characterized in that: include: A data acquisition module is used to obtain the pressure distribution data of various parts of the patient's body on the bed surface, the patient's body posture data and diagnosis and treatment demand data; A first feature acquisition module, configured to obtain a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data; A second feature acquisition module, configured to acquire a second somatosensory feature using a second equation based on the pressure distribution data and the posture data; a third feature acquisition module, configured to fuse the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature; a data mapping module, configured to match the first, second, and third somatosensory features with the diagnosis and treatment requirement data using a preset intelligent mapping model, convert the matching results into a mapping relationship between the somatosensory features and the diagnosis and treatment requirements, and obtain mapping relationship data, wherein an initial deep learning model is trained based on pre-stored historical somatosensory feature data and historical diagnosis and treatment requirement data, and the trained initial deep learning model is used as the intelligent mapping model; a bed adjustment module, configured to convert the mapping relationship data into an angle adjustment instruction, and control the drive motor to adjust the angle of the bed according to the angle adjustment instruction; The obtaining of a first somatosensory feature by constructing a first equation based on the pressure distribution data and the posture data includes: constructing a contact area distribution matrix and a pressure value matrix according to the pressure distribution data; Extracting head position coordinates according to the posture data; Constructing a pressure coefficient matrix according to the contact area distribution matrix and the pressure value matrix; Constructing a pressure threshold matrix according to the posture data and the pressure value matrix; The head position coordinates, the pressure coefficient matrix, and the pressure threshold matrix are calculated by constructing a first equation to obtain a first somatosensory feature; The elements in the pressure threshold matrix are calculated using the following formula: The first somatosensory feature is calculated using the following formula: in, Indicates the first somatosensory characteristic, represents the elements in the pressure coefficient matrix, represents the elements in the pressure value matrix, represents the elements in the contact area distribution matrix, Represents the head position coordinates, 、 and Represents the preset weight coefficient, Indicates the Rank the pressure threshold detected by the sensor at the column position; The obtaining a second somatosensory feature by using a second equation based on the pressure distribution data and the posture data includes: The second somatosensory feature is obtained according to the following formula:   in, Indicates the second somatosensory characteristic, The total amount of data representing pressure distribution data and posture data; Indicates the Pressure distribution data, Indicates the Posture data, represents the average value of the pressure distribution data, represents the average value of posture data; Represents the preset weight, Indicates the interval time for obtaining data of each part of the patient's body; The fusing the first somatosensory feature and the second somatosensory feature to obtain a third somatosensory feature includes: Normalizing the first somatosensory feature and the second somatosensory feature to obtain standardized data; Perform weighted fusion based on the standardized data to obtain a third somatosensory feature; Among them, normalization is performed according to the following formula: Among them, weighted fusion is performed according to the following formula: in, represents the normalized first feature, represents the normalized second feature, Indicates the first somatosensory characteristic, Indicates the second somatosensory characteristic, and are the means of the first and second somatosensory features, respectively. and are the standard deviations of the first and second somatosensory features, represents the preset first feature weight, represents the preset second feature weight, Indicates the third feature.

6. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for adjusting the angle of a three-motor diagnostic bed based on intelligent body sensing as described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the three-motor diagnostic bed angle adjustment method based on intelligent body sensing as described in any one of claims 1 to 4.

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