Nursing information sharing system and method based on augmented reality

Abstract

The invention discloses a nursing information sharing system and method based on augmented reality, and belongs to the field of medical care science. A nursing process prediction model is constructed according to nursing actions of historical nursing positions, nursing strength data and body change data in the historical nursing process; the real-time body abnormity analysis result of the patient is imported into the constructed nursing process prediction model, the nursing action and nursing strength data of the most appropriate nursing position are output, and nursing personnel conduct nursing operation according to the output nursing action and nursing strength data of the most appropriate nursing position. The nursing process prediction model is constructed according to the historical nursing process data and the nursing effect data, the nursing action and the nursing strength of the most appropriate nursing position are provided for patients with different symptoms, and the safety and the nursing effect in the nursing process are improved.

Description

Technical Field

[0001] The present invention belongs to the field of medical care, and in particular relates to a nursing information sharing system and method based on augmented reality. Background Art

[0002] In the field of healthcare, cloud computing technology can be used to share and manage patients' rehabilitation information, which can improve nursing efficiency and quality. During the rehabilitation treatment of patients, various medical devices and sensors can be used to collect patients' health data, and the collected data can be uploaded to the cloud server for analysis and sharing, which can optimize resource allocation and promote the rapid recovery of patients.

[0003] However, in the process of sharing nursing information, the existing technology cannot build a nursing process prediction model based on historical nursing process data and nursing effect data, so it cannot provide the most suitable nursing actions and nursing intensity for patients with different symptoms. Inappropriate nursing actions and nursing intensity can easily cause secondary injuries to patients, thereby reducing the safety and nursing effect of the nursing process. Most of the existing technologies have the above problems.

[0004] For example, a Chinese patent with application publication number CN118197521A discloses a rehabilitation information cloud sharing method for clinical nursing, which relates to the field of information sharing technology. The rehabilitation information of the patient is divided into different information types and then stored in a distributed cloud, anchoring standards and anchoring gaps of rehabilitation information of different information types are obtained, and the rehabilitation information is anonymized using the anchoring gaps, and the information similarity between patients is obtained and a reference rehabilitation group of the patient is constructed. The reference rehabilitation information of the patient under different information types and the rehabilitation information gap thereof are obtained in the reference rehabilitation group, and the obtained rehabilitation information gap is transmitted, and the reference rehabilitation information is obtained according to the received rehabilitation information gap and the current rehabilitation information. Through the technical solution of the invention, the privacy of the patient can be effectively protected, the security of the patient information can be improved, and the rehabilitation information of other patients with similar information can be used to form an effective reference for the rehabilitation process of the current patient, thereby reducing the risk of leakage of the rehabilitation information during the sharing process.

[0005] The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs a nursing information sharing system and method based on augmented reality. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention proposes a nursing information sharing system and method based on augmented reality.

[0007] To achieve the above object, the present invention provides the following technical solution: a nursing information sharing method based on augmented reality, which comprises the following specific steps:

[0008] Obtain the nursing action and nursing intensity data of the historical nursing position, and obtain the body change data during the historical nursing process;

[0009] Obtain real-time patient body change data and import it into the body abnormality value analysis strategy to conduct real-time body abnormality analysis;

[0010] Construct a nursing process prediction model based on nursing actions, nursing intensity data of historical nursing positions, and body change data during historical nursing processes;

[0011] Import the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the nursing action and nursing intensity data of the most appropriate nursing position;

[0012] The nursing staff performs nursing operations according to the output nursing action and nursing intensity data of the most appropriate nursing position.

[0013] As a preferred technical solution of the nursing information sharing method based on augmented reality, the specific steps of obtaining the nursing action and nursing intensity data of the historical nursing position and obtaining the body change data during the historical nursing process are:

[0014] S11, obtaining nursing position data, nursing action data corresponding to each nursing position, and nursing intensity data of each nursing position in the historical nursing process, and storing them in the first storage component;

[0015] S12. Obtain the patient's physical data before historical nursing care, and at the same time obtain the patient's physical data after nursing care, and store them in the second storage component, wherein the patient's physical data includes the patient's nursing position, patient's body temperature, patient's blood pressure, patient's heart rate and other data that can reflect the patient's physical constitution.

[0016] As a preferred technical solution of the nursing information sharing method based on augmented reality, the method of obtaining real-time patient body change data and importing it into the body abnormality value analysis strategy for real-time body abnormality analysis includes the following specific steps:

[0017] S21, obtaining the patient's physical data at the start time and the real-time physical data during the nursing action, and importing the patient's physical data into the physical abnormality value calculation formula to calculate the physical abnormality value, wherein the physical abnormality value calculation formula is: T is the duration required for monitoring, n is the type of physical data, dt is the time integral, ai is the proportion coefficient of the i-th physical data type, xit is the data of the i-th physical data type at time t, xitm is the median of the safety range of the i-th physical data type, xitmax is the maximum value of the i-th physical data type, and xitmin is the minimum value of the i-th physical data type;

[0018] S22, substituting the calculated starting time and the real-time physical abnormality value during the nursing action into the physical abnormality analysis value calculation formula corresponding to the nursing action to calculate the physical abnormality analysis value corresponding to the nursing action, wherein the physical abnormality analysis value calculation formula corresponding to the nursing action is: Among them, Stu is the real-time physical abnormality value during the nursing action, and Stm is the physical abnormality value of the patient at the starting moment.

[0019] As a preferred technical solution of the nursing information sharing method based on augmented reality, the nursing process prediction model is constructed based on the nursing actions, nursing intensity data of the historical nursing positions and the body change data during the historical nursing process, including the following specific steps:

[0020] S31, obtaining nursing action and nursing intensity data of historical nursing positions, and obtaining the physical abnormality analysis value corresponding to the nursing action and the physical abnormality value at the starting time, and constructing a deep learning neural network model with the physical abnormality value at the starting time as input and the nursing action and nursing intensity data corresponding to the minimum physical abnormality analysis value as output;

[0021] S32, the extracted nursing actions of the nursing positions, nursing intensity data, physical abnormality analysis values ​​corresponding to the nursing actions, and physical abnormality values ​​at the starting time are divided into a 70% parameter training set and a 30% parameter test set; the 70% parameter training set is input into the deep learning neural network model for training to obtain an initial deep learning neural network model; the 30% parameter test set is used to test the initial deep learning neural network model, and the initial deep learning neural network model that meets the maximum judgment accuracy of the physical abnormality analysis value is output as the deep learning neural network model, wherein the output strategy formula of the specific neurons in the deep learning neural network model is: in is the output of the s-term neuron in the z layer, is the connection weight between the z-1th layer neuron j and the zth layer neuron s, represents the output of neuron j in the z-1th layer, represents the bias of the linear relationship between the jth neuron in the z-1th layer and the sth neuron in the zth layer, σ() Represents the Sigmoid activation function, and m is the number of neurons in the z-1 layer.

[0022] As a preferred technical solution of the nursing information sharing method based on augmented reality, the method of importing the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the nursing action and nursing intensity data of the most appropriate nursing position includes the following specific contents:

[0023] The patient's physical data at the starting moment and the physical abnormality analysis value corresponding to the nursing action are obtained and substituted into the constructed deep learning neural network model, and the nursing action and nursing intensity data corresponding to the minimum physical abnormality analysis value are output as the nursing action and nursing intensity data for the most appropriate nursing position.

[0024] The nursing information sharing system based on augmented reality is implemented based on the above-mentioned nursing information sharing method based on augmented reality, and specifically includes:

[0025] The data acquisition module is used to obtain the nursing action and nursing intensity data of the historical nursing position, and also obtain the body change data during the historical nursing process;

[0026] The physical abnormality analysis module is used to obtain real-time patient physical change data and import it into the physical abnormality value analysis strategy to perform real-time physical abnormality analysis;

[0027] A nursing process prediction model building module is used to build a nursing process prediction model based on nursing actions, nursing intensity data of historical nursing positions, and body change data during historical nursing processes;

[0028] Model output module, used to import the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the most appropriate nursing action and nursing intensity data of the nursing position;

[0029] Nursing model module, used for nursing staff to perform nursing operations according to the output nursing actions and nursing intensity data of the most appropriate nursing position;

[0030] The control module is used for controlling the operation of the data acquisition module, the physical abnormality analysis module, the nursing process prediction model building module, the model output module and the nursing model module.

[0031] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0032] The processor executes the above-mentioned augmented reality-based nursing information sharing method by calling the computer program stored in the memory.

[0033] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned augmented reality-based nursing information sharing method.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention obtains nursing action and nursing intensity data of historical nursing positions, and simultaneously obtains body change data during historical nursing processes, obtains real-time body change data of patients and imports it into a body abnormality value analysis strategy to perform real-time body abnormality analysis, builds a nursing process prediction model according to the nursing action and nursing intensity data of historical nursing positions and the body change data during historical nursing processes, imports the real-time body abnormality analysis results of patients into the constructed nursing process prediction model to output the nursing action and nursing intensity data of the most appropriate nursing position, and nursing staff performs nursing operations according to the output nursing action and nursing intensity data of the most appropriate nursing position, builds a nursing process prediction model according to the historical nursing process data and nursing effect data, provides the most appropriate nursing action and nursing intensity of nursing positions for patients with different symptoms, and improves the safety and nursing effect of the nursing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the overall process of the nursing information sharing method based on augmented reality of the present invention;

[0037] Figure 2 A schematic diagram of the steps of the physical abnormality analysis strategy of the nursing information sharing method based on augmented reality of the present invention;

[0038] Figure 3 It is a schematic diagram of the overall framework of the nursing information sharing system based on augmented reality of the present invention;

[0039] Figure 4 It is a schematic diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0041] Example 1

[0042] To solve the technical problem raised in the background technology: in the process of sharing nursing information, the prior art cannot build a nursing process prediction model based on historical nursing process data and nursing effect data, so it cannot provide the most suitable nursing position and nursing intensity for patients with different symptoms. Inappropriate nursing actions and nursing intensity are likely to cause secondary injuries to patients, thereby reducing the safety and nursing effect of the nursing process. The present invention provides a preferred embodiment: Figure 1-Figure 2 As shown, the nursing information sharing method based on augmented reality includes the following specific steps:

[0043] S1. Obtain the nursing action and nursing intensity data of the historical nursing position, and obtain the body change data during the historical nursing process;

[0044] In this embodiment, the specific steps of obtaining the nursing action and nursing intensity data of the historical nursing position and obtaining the body change data during the historical nursing process are:

[0045] S11, obtaining nursing position data, nursing action data corresponding to each nursing position, and nursing intensity data of each nursing position in the historical nursing process, and storing them in the first storage component;

[0046] S12, obtaining the patient's physical data before the historical nursing care, and obtaining the patient's physical data after the nursing care, and storing them in the second storage component, wherein the patient's physical data includes the patient's nursing position, the patient's body temperature, the patient's blood pressure, the patient's heart rate, and other data that can reflect the patient's physical constitution;

[0047] The patient's physical constitution can be reflected by a variety of data. The following are some commonly used indicators and data:

[0048] 1. Weight and height: Body mass index (BMI) can be calculated from weight and height, which is an important indicator for assessing an individual's weight status and health status;

[0049] 2. Blood pressure: Blood pressure data can reflect the health of the cardiovascular system. High or low blood pressure may indicate physical problems;

[0050] 3. Heart rate: Heart rate can reflect heart function and overall health. Irregular heart rate may indicate cardiovascular problems.

[0051] 4. Hemoglobin level: Hemoglobin level measurement can be used to determine anemia in an individual. Low hemoglobin level usually indicates physical weakness.

[0052] 5. Immune indicators: such as immunoglobulin (IgG, IgA, IgM) levels, which can reflect the function of the immune system;

[0053] 6. Blood lipid levels: including total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and triglycerides, etc. These data can assess cardiovascular health risks;

[0054] 7. Blood sugar level: Blood sugar level can reflect an individual's sugar metabolism status. Abnormal blood sugar level may be an indicator of diabetes.

[0055] 8. Pulmonary function tests: such as forced expiratory volume (FVC) and forced expiratory volume in one second (FEV1), which can assess the health of the respiratory system;

[0056] 9. Creatinine and urea nitrogen levels: These data can reflect the function of the kidneys;

[0057] 10. C-reactive protein (CRP): Increased levels of CRP usually indicate the presence of inflammation in the body;

[0058] 11. Albumin levels: Low albumin levels may indicate malnutrition or liver disease;

[0059] 12. Physical fitness test: including grip strength, walking speed, balance ability, etc. These tests can assess the individual's muscle strength and endurance;

[0060] 13. Psychological assessment: Through psychological scales and questionnaires, the patient's psychological state and stress level can be assessed;

[0061] 14. Biomarkers: such as inflammatory markers, oxidative stress markers, etc., which can reflect the body's chronic inflammatory state and oxidative damage;

[0062] S2, obtaining real-time patient's physical change data and importing it into physical abnormality value analysis strategy to perform real-time physical abnormality analysis;

[0063] In this embodiment, obtaining real-time patient body change data and importing it into the body abnormality value analysis strategy for real-time body abnormality analysis includes the following specific steps:

[0064] S21, obtaining the patient's physical data at the start time and the real-time physical data during the nursing action, and importing the patient's physical data into the physical abnormality value calculation formula to calculate the physical abnormality value, wherein the physical abnormality value calculation formula is: T is the duration required for monitoring, n is the type of physical data, dt is the time integral, ai is the proportion coefficient of the i-th physical data type, xit is the data of the i-th physical data type at time t, xitm is the median of the safety range of the i-th physical data type, xitmax is the maximum value of the i-th physical data type, and xitmin is the minimum value of the i-th physical data type;

[0065] S22, substituting the calculated starting time and the real-time physical abnormality value during the nursing action into the physical abnormality analysis value calculation formula corresponding to the nursing action to calculate the physical abnormality analysis value corresponding to the nursing action, wherein the physical abnormality analysis value calculation formula corresponding to the nursing action is: Among them, Stu is the real-time physical abnormality value during the nursing action, and Stm is the physical abnormality value of the patient at the starting moment;

[0066] S3, building a nursing process prediction model based on the nursing actions of historical nursing positions, nursing intensity data, and body change data during historical nursing processes;

[0067] In this embodiment, constructing a nursing process prediction model based on nursing actions, nursing intensity data of historical nursing positions and body change data during historical nursing processes includes the following specific steps:

[0068] S31, obtain the nursing action and nursing intensity data of the historical nursing position, and at the same time obtain the physical abnormality analysis value corresponding to the nursing action and the physical abnormality value at the starting time, and construct a deep learning neural network model with the physical abnormality value at the starting time as input and the nursing action and nursing intensity data corresponding to the minimum physical abnormality analysis value as output; the code is as follows:

[0069]

[0070]

[0071] S32, the extracted nursing actions of the nursing positions, nursing intensity data, physical abnormality analysis values ​​corresponding to the nursing actions, and physical abnormality values ​​at the starting time are divided into a 70% parameter training set and a 30% parameter test set; the 70% parameter training set is input into the deep learning neural network model for training to obtain an initial deep learning neural network model; the 30% parameter test set is used to test the initial deep learning neural network model, and the initial deep learning neural network model that meets the maximum judgment accuracy of the physical abnormality analysis value is output as the deep learning neural network model, wherein the output strategy formula of the specific neurons in the deep learning neural network model is: in is the output of the s-term neuron in the z layer, is the connection weight between the z-1th layer neuron j and the zth layer neuron s, represents the output of neuron j in the z-1th layer, represents the bias of the linear relationship between the jth neuron in the z-1th layer and the sth neuron in the zth layer, σ() represents the Sigmoid activation function, and m is the number of neurons in the z-1 layer;

[0072] S4, importing the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the nursing action and nursing intensity data of the most appropriate nursing position;

[0073] In this embodiment, the real-time abnormality analysis results of the patient's body are imported into the constructed nursing process prediction model to output the nursing action and nursing intensity data of the most appropriate nursing position, including the following specific contents:

[0074] Obtain the patient's physical data at the start time and the physical abnormality analysis value corresponding to the nursing action, substitute it into the constructed deep learning neural network model, and output the nursing action and nursing intensity data corresponding to the minimum physical abnormality analysis value as the nursing action and nursing intensity data of the most appropriate nursing position;

[0075] S5. The nursing staff performs nursing operations according to the output nursing actions and nursing intensity data of the most appropriate nursing position;

[0076] Reduce errors in nursing operations, improve nursing quality, reduce the risk of patient harm caused by improper operations, and provide customized nursing plans based on the specific conditions of the patients. Through this method, nursing staff can perform nursing operations more scientifically and accurately, thereby improving the overall level of nursing services;

[0077] The beneficial effects of this embodiment compared to the prior art are as follows: obtaining nursing action and nursing intensity data of historical nursing positions, and simultaneously obtaining body change data during historical nursing processes, obtaining real-time body change data of patients and importing it into the body abnormality value analysis strategy to perform real-time body abnormality analysis, constructing a nursing process prediction model based on nursing action and nursing intensity data of historical nursing positions and body change data during historical nursing processes, importing the patient's real-time body abnormality analysis results into the constructed nursing process prediction model to output the most appropriate nursing action and nursing intensity data of nursing positions, nursing staff performing nursing operations based on the output most appropriate nursing action and nursing intensity data of nursing positions, constructing a nursing process prediction model based on historical nursing process data and nursing effect data, providing the most appropriate nursing action and nursing intensity of nursing positions for patients with different symptoms, thereby improving the safety and nursing effect of the nursing process.

[0078] Example 2

[0079] like Figure 3 As shown, the augmented reality-based nursing information sharing system is implemented based on the augmented reality-based nursing information sharing method, and the data acquisition module is used to obtain the nursing action and nursing intensity data of the historical nursing position, and at the same time obtain the body change data during the historical nursing process;

[0080] The physical abnormality analysis module is used to obtain real-time patient physical change data and import it into the physical abnormality value analysis strategy to perform real-time physical abnormality analysis;

[0081] A nursing process prediction model building module is used to build a nursing process prediction model based on nursing actions, nursing intensity data of historical nursing positions, and body change data during historical nursing processes;

[0082] Model output module, used to import the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the most appropriate nursing action and nursing intensity data of the nursing position;

[0083] Nursing model module, used for nursing staff to perform nursing operations according to the output nursing actions and nursing intensity data of the most appropriate nursing position;

[0084] The control module is used for controlling the operation of the data acquisition module, the physical abnormality analysis module, the nursing process prediction model building module, the model output module and the nursing model module.

[0085] Example 3

[0086] This embodiment provides an electronic device, such as Figure 4 As shown, it includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0087] The processor executes the above-mentioned augmented reality-based nursing information sharing method by calling the computer program stored in the memory.

[0088] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the augmented reality-based nursing information sharing method provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface to input and output data. This embodiment will not be described in detail here.

[0089] Example 4

[0090] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0091] When the computer program runs on a computer device, the computer device is enabled to execute the above-mentioned nursing information sharing method based on augmented reality.

[0092] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0093] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. A nursing information sharing method based on augmented reality, characterized in that: It includes the following specific steps: Obtain the nursing action and nursing intensity data of the historical nursing position, and obtain the body change data during the historical nursing process; Obtain real-time patient body change data and import it into the body abnormality value analysis strategy to conduct real-time body abnormality analysis; Construct a nursing process prediction model based on nursing actions, nursing intensity data of historical nursing positions, and body change data during historical nursing processes; Import the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the nursing action and nursing intensity data of the most appropriate nursing position; The nursing staff performs nursing operations according to the output nursing action and nursing intensity data of the most appropriate nursing position.

2. The nursing information sharing method based on augmented reality according to claim 1, characterized in that: The specific steps of obtaining the nursing action and nursing intensity data of the historical nursing position and obtaining the body change data during the historical nursing process are: S11, obtaining nursing position data, nursing action data corresponding to each nursing position, and nursing intensity data of each nursing position in the historical nursing process, and storing them in the first storage component; S12. Obtain the patient's physical data before historical nursing care, and at the same time obtain the patient's physical data after historical nursing care, and store them in the second storage component, wherein the patient's physical data includes the patient's nursing position, patient's body temperature, patient's blood pressure and patient's heart rate, which can reflect the patient's physical constitution.

3. The nursing information sharing method based on augmented reality according to claim 2, characterized in that: The method of obtaining real-time patient's physical change data and importing it into the physical abnormality value analysis strategy to perform real-time physical abnormality analysis includes the following specific steps: S21, obtaining the patient's physical data at the start time and the real-time physical data during the nursing action, and importing the patient's physical data into the physical abnormality value calculation formula to calculate the physical abnormality value, wherein the physical abnormality value calculation formula is: T is the duration required for monitoring, n is the type of physical data, dt is the time integral, ai is the proportion coefficient of the i-th physical data type, xit is the data of the i-th physical data type at time t, xitm is the median of the safety range of the i-th physical data type, xitmax is the maximum value of the i-th physical data type, and xitmin is the minimum value of the i-th physical data type; S22, substituting the calculated starting time and the real-time physical abnormality value during the nursing action into the physical abnormality analysis value calculation formula corresponding to the nursing action to calculate the physical abnormality analysis value corresponding to the nursing action, wherein the physical abnormality analysis value calculation formula corresponding to the nursing action is: Among them, Stu is the real-time physical abnormality value during the nursing action, and Stm is the physical abnormality value of the patient at the starting moment.

4. The nursing information sharing method based on augmented reality according to claim 3, characterized in that: The construction of a nursing process prediction model based on nursing actions, nursing intensity data of historical nursing positions and body change data during historical nursing processes includes the following specific steps: S31, obtaining nursing action and nursing intensity data of historical nursing positions, and obtaining the physical abnormality analysis value corresponding to the nursing action and the physical abnormality value at the starting time, and constructing a deep learning neural network model with the physical abnormality value at the starting time as input and the nursing action and nursing intensity data corresponding to the minimum physical abnormality analysis value as output; S32, the extracted nursing actions of the nursing positions, nursing intensity data, physical abnormality analysis values ​​corresponding to the nursing actions, and physical abnormality values ​​at the starting time are divided into a 70% parameter training set and a 30% parameter test set; the 70% parameter training set is input into the deep learning neural network model for training to obtain an initial deep learning neural network model; the 30% parameter test set is used to test the initial deep learning neural network model, and the initial deep learning neural network model that meets the maximum judgment accuracy of the physical abnormality analysis value is output as the deep learning neural network model, wherein the output strategy formula of the specific neurons in the deep learning neural network model is: in is the output of the s-term neuron in the z layer, is the connection weight between the z-1th layer neuron j and the zth layer neuron s, represents the output of neuron j in the z-1th layer, represents the bias of the linear relationship between the jth neuron in the z-1th layer and the sth neuron in the zth layer, σ() Represents the Sigmoid activation function, and m is the number of neurons in the z-1 layer.

5. The nursing information sharing method based on augmented reality according to claim 4, characterized in that: The method of importing the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the most appropriate nursing position nursing action and nursing intensity data includes the following specific contents: The patient's physical data at the starting moment and the physical abnormality analysis value corresponding to the nursing action are obtained and substituted into the constructed deep learning neural network model, and the nursing action and nursing intensity data corresponding to the minimum physical abnormality analysis value are output as the nursing action and nursing intensity data for the most appropriate nursing position.

6. A nursing information sharing system based on augmented reality, which is implemented based on the nursing information sharing method based on augmented reality as claimed in any one of claims 1 to 5, characterized in that: Specifically include: Specifically include: The data acquisition module is used to obtain the nursing action and nursing intensity data of the historical nursing position, and also obtain the body change data during the historical nursing process; The physical abnormality analysis module is used to obtain real-time patient physical change data and import it into the physical abnormality value analysis strategy to perform real-time physical abnormality analysis; A nursing process prediction model building module is used to build a nursing process prediction model based on nursing actions, nursing intensity data of historical nursing positions, and body change data during historical nursing processes; Model output module, used to import the patient's real-time physical abnormality analysis results into the constructed nursing process prediction model to output the most appropriate nursing action and nursing intensity data of the nursing position; Nursing model module, used for nursing staff to perform nursing operations according to the output nursing actions and nursing intensity data of the most appropriate nursing position; The control module is used for controlling the operation of the data acquisition module, the physical abnormality analysis module, the nursing process prediction model building module, the model output module and the nursing model module.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the augmented reality-based nursing information sharing method as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the augmented reality-based nursing information sharing method as described in any one of claims 1 to 5.

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