Surgical intelligent nursing system and method based on big data monitoring

Abstract

The application discloses a surgical intelligent nursing system and method based on big data monitoring, and relates to the technical field of surgical nursing.The application not only formulates a first surgical nursing plan according to the result of a prediction model, but also further optimizes the first surgical nursing plan through detailed data analysis to form a final surgical nursing plan.The double verification mechanism makes the final nursing plan not only reflect the quantitative result of the prediction model, but also combine actual data analysis, so that the nursing plan is more flexible and accurate.Meanwhile, the application introduces a dynamic adjustment mechanism through the design of a weight coefficient, so that the second surgical nursing plan can be updated in real time according to the actual recovery condition of a patient.The method overcomes the problem of static invariance of the existing model, so that the second surgical nursing plan can be dynamically adjusted according to the specific recovery condition of the patient, thereby effectively enhancing the flexibility and adaptability of the final surgical nursing plan.

Description

Technical Field

[0001] The present invention relates to the field of surgical nursing technology, and in particular to a surgical intelligent nursing system and method based on big data monitoring. Background Art

[0002] First, various sensors, wearable devices, and hospital information systems are used to collect data on patients' physiological parameters such as heart rate and blood pressure, lifestyle information such as diet and exercise, medical history and treatment records, etc. The collected data is then preprocessed to ensure the removal of duplicate or inaccurate data information and to ensure data quality and consistency. Subsequently, a machine learning algorithm is applied to build a prediction model, and the constructed prediction model is trained using the preprocessed data to obtain a trained prediction model. At the same time, new patient data is input into the trained prediction model to obtain prediction results. Finally, a surgical care plan is formulated based on the prediction results.

[0003] Although the existing big data monitoring and analysis methods can provide a scientific basis to support clinical decision-making and improve the accuracy of care plans, considering that the existing methods mainly rely on the results of predictive models to formulate care plans, these models are usually trained based on quantitative data such as physiological parameters and living habits. However, once the predictive model training is completed, its parameters are usually fixed, resulting in a static model, which cannot be dynamically adjusted according to the patient's actual recovery situation. This means that if the patient's health status changes or new situations arise, the model cannot adapt to these changes in time, resulting in the care plan being inflexible and inaccurate.

[0004] Therefore, the existing technology urgently needs technical solutions for surgical intelligent nursing systems and methods based on big data monitoring. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a surgical intelligent nursing system based on big data monitoring, which specifically includes the following modules:

[0006] Data collection and preprocessing module: used to collect historical data of surgical patients and perform preprocessing;

[0007] Prediction model acquisition module: connected to the data collection and preprocessing module, used to build a prediction model and train the constructed prediction model using the preprocessed historical data to obtain a trained prediction model. The patient data to be predicted is input into the trained prediction model to obtain the prediction results;

[0008] First surgical nursing plan formulation module: connected to the prediction model acquisition module, used to formulate the first surgical nursing plan based on the prediction results;

[0009] The second surgical nursing plan acquisition module is connected with the prediction model acquisition module, and is configured to analyze the patient data to be predicted, and obtain a second surgical nursing plan based on an analysis result;

[0010] The data classification unit is configured to classify the patient data to be predicted according to data characteristics, and obtain physiological parameter data, medical history information data and rehabilitation ability data respectively according to a classification result;

[0011] The standardization unit is configured to perform standardization processing on the physiological parameter data, the medical history information data and the rehabilitation ability data respectively;

[0012] The surgical patient comprehensive health status index calculation unit is configured to calculate a surgical patient comprehensive health status index according to the physiological parameter data, the medical history information data and the rehabilitation ability data after the standardization processing;

[0013] The surgical patient health index calculation sub-unit is configured to calculate a surgical patient health index according to the physiological parameter data after the standardization processing; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation and body mass index;

[0014] The first parameter acquisition interface is configured to acquire an ideal value and a fluctuation range of each physiological parameter data;

[0015] The physiological parameter data adjustment factor calculation interface is configured to calculate an adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and the fluctuation range of each physiological parameter data;

[0016] The surgical patient health index calculation interface is configured to calculate a surgical patient health index by comprehensively considering the adjustment factors of each physiological parameter data;

[0017] The surgical patient risk index calculation sub-unit is configured to calculate a surgical patient risk index according to the medical history information data after the standardization processing; the medical history information data includes white blood cell count, body temperature value and actual days of wound healing;

[0018] The second parameter acquisition interface is configured to acquire a critical value of white blood cell count, an ideal value and a fluctuation range of body temperature value, and an expected number of days of actual days of wound healing;

[0019] The white blood cell count adjustment factor calculation interface is configured to calculate a white blood cell count adjustment factor according to the white blood cell count and the critical value of the white blood cell count;

[0020] The body temperature adjustment factor calculation interface is configured to calculate a body temperature value adjustment factor according to the body temperature value, the ideal value and the fluctuation range of the body temperature value;

[0021] Wound healing adjustment factor calculation interface: used for calculating a wound healing adjustment factor according to an actual number of days of wound healing and an expected number of days of the actual number of days of wound healing;

[0022] Surgical patient risk index calculation interface: used for calculating a surgical patient risk index by comprehensively considering a white blood cell count adjustment factor, a body temperature value adjustment factor and a wound healing adjustment factor;

[0023] Surgical patient rehabilitation index calculation subunit: used for calculating a surgical patient rehabilitation index according to rehabilitation capability data after standardization processing; the rehabilitation capability data includes a pain score, an actual activity distance and an actual energy level;

[0024] Third parameter acquisition interface: used for acquiring a threshold value of the pain score, a target distance of the actual activity distance and a target energy level of the actual energy level;

[0025] Pain score adjustment factor calculation interface: used for calculating a pain score adjustment factor according to the pain score and the threshold value of the pain score;

[0026] Actual activity distance adjustment factor calculation interface: used for calculating an actual activity distance adjustment factor according to the actual activity distance and the target distance of the actual activity distance;

[0027] Actual energy level adjustment factor calculation interface: used for calculating an actual energy level adjustment factor according to the actual energy level and the target energy level of the actual energy level;

[0028] Surgical patient rehabilitation index calculation interface: used for calculating a surgical patient rehabilitation index by comprehensively considering the pain score adjustment factor, the actual activity distance adjustment factor and the actual energy level adjustment factor;

[0029] Weight coefficient calculation subunit: used for calculating a sum of a surgical patient health index, a surgical patient risk index and a surgical patient rehabilitation index, and respectively calculating a ratio of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index to the sum of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index, to obtain a weight coefficient of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index;

[0030] Surgical patient comprehensive health status index calculation subunit: used for calculating a surgical patient comprehensive health status index by comprehensively considering the surgical patient health index, the risk index and the rehabilitation index and the weight coefficient of the surgical patient health index, the risk index and the rehabilitation index;

[0031] Second surgical nursing plan making unit: used for making a second surgical nursing plan according to the surgical patient comprehensive health status index;

[0032] Final surgical nursing plan acquisition module: connected with the first surgical nursing plan making module and the second surgical nursing plan acquisition module, used for integrating the second surgical nursing plan with the first surgical nursing plan, obtaining the final surgical nursing plan and executing.

[0033] The surgical intelligent nursing method based on big data monitoring executes the surgical intelligent nursing system based on big data monitoring as described above, and includes the following steps:

[0034] Step S1, collect historical data of surgical patients and perform preprocessing;

[0035] Step S2, construct a prediction model, train the constructed prediction model using the preprocessed historical data, obtain a trained prediction model, input the patient data to be predicted into the trained prediction model, and obtain a prediction result;

[0036] Step S3, based on the prediction result, a first surgical nursing plan is developed;

[0037] Step S4, analyze the patient data to be predicted, and obtain a second surgical nursing plan based on the analysis result;

[0038] Step S4a, classify the patient data to be predicted according to data characteristics, and obtain physiological parameter data, medical history information data and rehabilitation ability data according to the classification result;

[0039] Step S4b, standardize the physiological parameter data, medical history information data and rehabilitation ability data respectively;

[0040] Step S4c, according to the physiological parameter data, medical history information data and rehabilitation ability data after standardization, the comprehensive health status index of the surgical patient is calculated;

[0041] Step S4c1, according to the physiological parameter data after standardization, the health index of the surgical patient is calculated; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation and body mass index;

[0042] Step S4c11, the ideal value and fluctuation range of each physiological parameter data are obtained;

[0043] Step S4c12, according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data, the adjustment factor of each physiological parameter data is calculated;

[0044] Step S4c13, the adjustment factor of each physiological parameter data is integrated to calculate the health index of the surgical patient;

[0045] Step S4c2, calculating a surgical patient risk index according to the history information data after standardization; the history information data includes white blood cell count, body temperature value and actual days of wound healing;

[0046] Step S4c21, obtaining a critical value of white blood cell count, an ideal value and fluctuation range of body temperature value, and an expected number of days of actual days of wound healing;

[0047] Step S4c22, calculating a white blood cell count adjustment factor according to the white blood cell count and the critical value of white blood cell count;

[0048] Step S4c23, calculating a body temperature value adjustment factor according to the body temperature value, the ideal value and fluctuation range of body temperature value;

[0049] Step S4c24, calculating a wound healing adjustment factor according to the actual days of wound healing and the expected number of days of actual days of wound healing;

[0050] Step S4c25, calculating a surgical patient risk index by comprehensively considering the white blood cell count adjustment factor, the body temperature value adjustment factor and the wound healing adjustment factor;

[0051] Step S4c3, calculating a surgical patient rehabilitation index according to the rehabilitation ability data after standardization; the rehabilitation ability data includes pain score, actual activity distance and actual energy level;

[0052] Step S4c31, obtaining a threshold value of pain score, a target distance of actual activity distance and a target energy level of actual energy level;

[0053] Step S4c32, calculating a pain score adjustment factor according to the pain score and the threshold value of pain score;

[0054] Step S4c33, calculating an actual activity distance adjustment factor according to the actual activity distance and the target distance of actual activity distance;

[0055] Step S4c34, calculating an actual energy level adjustment factor according to the actual energy level and the target energy level of actual energy level;

[0056] Step S4c35, calculating a surgical patient rehabilitation index by comprehensively considering the pain score adjustment factor, the actual activity distance adjustment factor and the actual energy level adjustment factor;

[0057] Step S4c4, calculate the sum of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index, and calculate the ratio of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index to the sum of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index respectively to obtain the weight coefficient of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index;

[0058] Step S4c5, integrate the surgical patient health index, the surgical patient risk index and the surgical patient recovery index with the weight coefficient of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index to obtain the comprehensive health status index of the surgical patient;

[0059] Step S4d, formulating a second surgical nursing plan according to the comprehensive health status index of the surgical patient;

[0060] Step S5, integrating the second surgical nursing plan with the first surgical nursing plan to obtain a final surgical nursing plan and executing.

[0061] The embodiment of the present application has the following technical effects:

[0062] The present application not only relies on the results of the prediction model to formulate the first surgical nursing plan, but also further optimizes the first surgical nursing plan through detailed data analysis to form the final surgical nursing plan. This double verification mechanism makes the final nursing plan not only reflect the quantitative results of the prediction model, but also combine the actual data analysis, so as to ensure that the nursing plan is more flexible and accurate. At the same time, the present application introduces a dynamic adjustment mechanism through the design of the weight coefficient, so as to allow the second surgical nursing plan to be updated in real time according to the actual recovery of the patient. This method overcomes the problem of static invariance of the existing model, so that the second surgical nursing plan can be dynamically adjusted according to the specific recovery of the patient, thereby effectively enhancing the flexibility and adaptability of the final surgical nursing plan. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor.

[0064] Figure 1 is the framework diagram of the surgical intelligent nursing system based on big data monitoring provided by the embodiment of the present application;

[0065] Figure 2 is the flowchart of the surgical intelligent nursing method based on big data monitoring provided by the embodiment of the present application. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0067] Example 1: Figure 1 As shown, the present invention provides a surgical intelligent nursing system based on big data monitoring, which includes the following modules:

[0068] Data collection and preprocessing module: used to collect historical data of surgical patients and perform preprocessing;

[0069] It is worth noting that data collection can be achieved through sensors and wearable devices, such as smart watches and electrocardiographs. These devices can continuously collect data and provide high-resolution time series data. They can also be used to obtain medical history and treatment records through hospital information systems. These systems store a large amount of structured and unstructured medical data, which is convenient for historical data analysis. They even use questionnaires and interviews to collect lifestyle information and some rehabilitation ability data. The collected historical data include physiological parameters: including blood pressure, heart rate, blood oxygen saturation, body mass index, etc. These data are key indicators for real-time monitoring and can reflect the patient's immediate health status; lifestyle information: such as eating habits, exercise frequency, sleep quality, etc. This type of data helps to understand the impact of the patient's lifestyle on health; medical history and treatment records: including previous medical history, surgical records, drug allergy history, white blood cell count, body temperature, wound healing status, etc. These data provide background information on the patient's long-term health status; rehabilitation ability data: including pain score, actual activity distance, actual energy level, etc. These data reflect the patient's ability to recover after surgery;

[0070] It's worth further explaining that the application of big data monitoring primarily involves devices like smartwatches and electrocardiographs, which can continuously collect patient physiological parameters such as blood pressure, heart rate, blood oxygen saturation, and body mass index. The high-resolution time series data provided by these devices constitutes a component of big data. For example, a smartwatch can capture heart rate data every minute, accumulating over time to form a massive data set. Hospital information systems also store large amounts of structured and unstructured medical data, including medical and treatment records, such as past medical history, surgical records, drug allergies, white blood cell counts, body temperature, and wound healing status. This data is not only vast in quantity but also rich in content, providing a solid foundation for subsequent analysis and, therefore, also a component of big data. Regular questionnaires and patient interviews can dynamically track patient lifestyle information, such as dietary habits, exercise frequency, and sleep quality, as well as some rehabilitation data, such as pain scores, actual distance covered, and actual energy levels. While relatively sparse, these data are also crucial components of big data.

[0071] It is worth further explaining that the preprocessing job is mainly aimed at the collected data, with the purpose of removing duplicate data, ensuring that each record is unique, avoiding redundancy, and filling missing values, such as using mean filling, interpolation or other statistical methods to fill missing values.

[0072] Prediction model acquisition module: connected to the data collection and preprocessing module, used to build a prediction model and train the constructed prediction model using the preprocessed historical data to obtain a trained prediction model. The patient data to be predicted is input into the trained prediction model to obtain the prediction results;

[0073] It's worth noting that the training process for the aforementioned predictive model begins with label definition—that is, defining the target labels for the training model. Common labels might be "good recovery," "complications developed," or "additional care required," and are typically based on the physician's professional judgment and the patient's specific circumstances. A neural network model, particularly a deep learning model, is then selected, as it excels on large datasets and is generally preferred. The preprocessed data is then combined into a dataset, which is then divided into training, validation, and test sets. The general ratio is 70% training set, 15% validation set, and 15% test set. Then 70% of the training set is used to train the selected model. During the training process, it is necessary to calculate the loss function and update the model parameters. The goal is to minimize the loss function value. Then, 15% of the validation set is used for hyperparameter optimization. Common methods include grid search, random search, and Bayesian optimization. By adjusting the hyperparameters of the model, such as learning rate, regularization coefficient, etc., until the optimal model configuration is found, the trained prediction model is obtained; among them, the selected indicators for model evaluation are usually accuracy, precision, recall rate, and F1 score, etc. Finally, 15% of the test set is used to verify the trained model to ensure that the model has strong generalization ability and will not overfit the training data. Finally, the trained model is deployed to the actual application scenario to receive new patient data in real time and make predictions.

[0074] First surgical nursing plan formulation module: connected to the prediction model acquisition module, used to formulate the first surgical nursing plan based on the prediction results;

[0075] It is worth noting that the above-mentioned prediction results, including the risk of postoperative complications, estimated recovery time, and required special care measures, provide an important reference for formulating a nursing plan. Based on the prediction results, a preliminary nursing plan is formulated, such as whether to strengthen monitoring, adjust medication regimens, arrange physical therapy, etc. For example, if the prediction results show that the patient has a higher risk of infection, the frequency of antibiotic use can be increased in the nursing plan.

[0076] Second surgical nursing plan acquisition module: connected to the prediction model acquisition module, used to analyze the patient data to be predicted and obtain the second surgical nursing plan based on the analysis results;

[0077] Data classification unit: used to classify the patient data to be predicted according to data characteristics, and obtain physiological parameter data, medical history information data and rehabilitation ability data according to the classification results;

[0078] It is worth noting that the data characteristics of physiological parameter data are continuity and periodicity, while medical history information data is mostly discrete data, and rehabilitation ability data has both quantitative and qualitative components. Therefore, based on these data characteristics, the patient data to be predicted can be quickly and reasonably classified.

[0079] It is worth further explaining that through classification, more accurate nursing strategies can be formulated for different types of data to improve nursing effects. For example, physiological parameter data can be used to monitor the patient's immediate health status, while medical history information data can be used to assess long-term health risks. Moreover, classified data is easier to conduct targeted analysis, reducing unnecessary computational complexity. For example, when calculating the health index, only the relevant physiological parameter data needs to be considered, without having to process all the raw data, that is, all the patient data to be predicted.

[0080] Standardization unit: used to standardize physiological parameter data, medical history information data and rehabilitation ability data respectively;

[0081] It is worth noting that the reason why physiological parameter data, medical history information data and rehabilitation ability data need to be standardized separately is that different types of physiological parameters, medical history information and rehabilitation ability data often have different dimensions. After standardization, they can be compared and analyzed on the same scale. For example, blood pressure and body mass index have different units. After standardization, they can be easily calculated while avoiding calculation errors.

[0082] Surgical patient comprehensive health status index calculation unit: used to calculate the surgical patient comprehensive health status index based on standardized physiological parameter data, medical history information data and rehabilitation ability data;

[0083] It is worth noting that the comprehensive health status index can comprehensively assess the patient's health status from multiple dimensions and provide a more scientific basis for decision-making. For example, the comprehensive health status index can simultaneously reflect the patient's physical health, risk level and rehabilitation ability, helping medical staff to develop a comprehensive care plan.

[0084] Surgical patient health index calculation subunit: used to calculate the surgical patient health index based on the standardized physiological parameter data; the physiological parameter data includes blood pressure , heart rate , blood oxygen saturation and body mass index ;

[0085] It's worth noting that calculating the health index allows for refined management of a patient's physiological state and timely identification of potential issues. For example, the health index can serve as a reference for daily health monitoring, helping medical staff adjust treatment plans in a timely manner. Furthermore, based on the health index, personalized health management recommendations can be provided to each patient. For example, patients with a low health index might be advised to increase rest time or adjust medication dosages; those with a high health index might be encouraged to increase their activity levels.

[0086] The first parameter acquisition interface is used to obtain the ideal value and fluctuation range of each physiological parameter data;

[0087] Physiological parameter data adjustment factor calculation interface: used to calculate the adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data;

[0088] The calculation formula group of the adjustment factor of each physiological parameter data is:

[0089]

[0090]

[0091]

[0092]

[0093] Where, 、 、 and Represents blood pressure , heart rate , blood oxygen saturation and body mass index The adjustment factor of 、 、 and Represents blood pressure , heart rate , blood oxygen saturation and body mass index The ideal value of 、 、 and Represents blood pressure , heart rate , blood oxygen saturation and body mass index The fluctuation range;

[0094] It is worth noting that the above calculations are 、 、 and , mainly used to reflect various parameters such as blood pressure , heart rate , blood oxygen saturation and body mass index The gap between the actual value and the ideal value is taken into account, and its fluctuation range is taken into account. When the adjustment factor is closer to 1, it means that the parameter is closer to the ideal state; when it is closer to 0, it means that the parameter deviates further from the ideal state. At the same time, the parameters such as blood pressure , heart rate , blood oxygen saturation and body mass index It is included in the calculation after being standardized. Similarly, the ideal value and fluctuation range of each parameter also need to be included in the calculation after being standardized. Therefore, the calculated 、 、 and It is a dimensionless proportionality coefficient that indicates the degree to which the actual value deviates from the ideal value relative to its allowed fluctuation range.

[0095] Surgical patient health index calculation interface: used to integrate the adjustment factors of each physiological parameter data to calculate the surgical patient health index;

[0096] The calculation formula for the surgical patient health index is:

[0097]

[0098] Where, stands for Surgical Patient Health Index; 、 、 and Represents blood pressure , heart rate , blood oxygen saturation and body mass index The adjustment factor of

[0099] The above calculation formula for the surgical patient health index is mainly used to comprehensively reflect the patient's overall health status. The higher the health index, the better the patient's overall health status. 、 、 and is a dimensionless proportionality coefficient, so It is also a dimensionless value, usually between 0 and 1, and the larger the value, the better the health status.

[0100] Surgical patient risk index calculation subunit: used to calculate the surgical patient risk index based on the standardized medical history information data; the medical history information data includes white blood cell count (WBC), body temperature (Temp) and the actual number of days for wound healing;

[0101] It's worth noting that calculating a risk index can help identify high-risk patients in advance and implement preventative measures. For example, the risk index can serve as a key indicator in preoperative assessments, helping physicians determine whether additional preoperative preparation is necessary. Furthermore, the risk index can be used to rationally allocate medical resources and improve overall care efficiency. For example, patients with a higher risk index can be assigned additional monitoring staff and equipment to ensure safety.

[0102] Second parameter acquisition interface: used to obtain the critical value of white blood cell count, the ideal value and fluctuation range of body temperature, and the expected number of days for wound healing;

[0103] White blood cell count adjustment factor calculation interface: used to calculate the white blood cell count adjustment factor based on the white blood cell count and the critical value of the white blood cell count;

[0104] The calculation formula for the white blood cell count adjustment factor is:

[0105]

[0106] Where, stands for white blood cell count adjustment factor; stands for white blood cell count; represents the cut-off value of white blood cell count;

[0107] It is worth noting that the calculation formula of the white blood cell count adjustment factor mainly reflects the gap between the actual value of the white blood cell count and the critical value; the smaller the adjustment factor, the closer the white blood cell count is to the critical value, indicating that there may be infection or other inflammatory reactions.

[0108] Body temperature adjustment factor calculation interface: used to calculate the body temperature adjustment factor based on the body temperature value, the ideal value of the body temperature value and the fluctuation range;

[0109] The calculation formula of the body temperature adjustment factor is:

[0110]

[0111] Where, represents the body temperature adjustment factor; represents the ideal value of body temperature; Represents the fluctuation range of body temperature;

[0112] It is worth noting that the calculation formula of the temperature adjustment factor reflects the gap between the actual and ideal body temperature values ​​and takes into account its fluctuation range. The smaller the adjustment factor, the closer the body temperature is to the ideal value, indicating that the patient's body temperature is normal or near normal. The body temperature value represents the actual value of the surgical patient's body temperature.

[0113] Wound healing adjustment factor calculation interface: used to calculate the wound healing adjustment factor based on the actual number of days for wound healing and the expected number of days for wound healing;

[0114] The calculation formula of wound healing adjustment factor is:

[0115]

[0116] Where, stands for wound healing adjustment factor; Represents the actual number of days for wound healing; expected days representing the actual number of days for wound healing;

[0117] It is worth noting that the calculation formula of the wound healing adjustment factor mainly reflects the gap between the actual healing days and the expected healing days. When the adjustment factor is smaller, it means that the wound healing time is closer to the expected time, indicating that the healing situation is good.

[0118] At the same time, in the above, since the white blood cell count WBC, body temperature Temp and the actual number of days for wound healing are included in the calculation after being standardized, the following parameters are also included: 、 、 and It also needs to be standardized before it can be used in calculations, so the above calculations 、 and is a dimensionless proportionality coefficient.

[0119] Surgical patient risk index calculation interface: used to integrate the white blood cell count adjustment factor, body temperature adjustment factor and wound healing adjustment factor to calculate the surgical patient risk index;

[0120] The calculation formula for the surgical patient risk index is:

[0121]

[0122] Where, represents the surgical patient risk index; stands for white blood cell count adjustment factor; represents the body temperature adjustment factor; stands for wound healing adjustment factor;

[0123] It is worth noting that the calculation formula for surgical patient risk index is mainly used to comprehensively reflect the patient's postoperative risk level. The higher the risk index, the greater the risk faced by the patient. At the same time, due to the various adjustment factors involved, such as 、 and is a dimensionless proportional coefficient, so the calculated It is also a dimensionless number, usually between 0 and 1, with larger values ​​indicating higher risks.

[0124] Surgical patient rehabilitation index calculation subunit: used to calculate the surgical patient rehabilitation index based on the standardized rehabilitation ability data; the rehabilitation ability data includes pain score Pain, actual activity distance Mobility and actual energy level Energy;

[0125] It's worth noting that calculating the rehabilitation index can help develop targeted rehabilitation plans and accelerate a patient's recovery. For example, the rehabilitation index can serve as a basis for adjusting the intensity of rehabilitation training, helping patients gradually return to their optimal state.

[0126] The third parameter acquisition interface is used to obtain the threshold of the pain score, the target distance of the actual activity distance, and the target energy level of the actual energy level;

[0127] Pain score adjustment factor calculation interface: used to calculate the pain score adjustment factor based on the pain score and the pain score threshold;

[0128] The calculation formula of the pain score adjustment factor is:

[0129]

[0130] Where, represents the pain score adjustment factor; represents the threshold value of pain score;

[0131] It is worth noting that the calculation formula for the pain score adjustment factor reflects the gap between the actual value of the pain score and the threshold. The smaller the adjustment factor, the closer the pain score is to the threshold, indicating a lower degree of pain.

[0132] Actual activity distance adjustment factor calculation interface: used to calculate the actual activity distance adjustment factor based on the actual activity distance and the target distance of the actual activity distance;

[0133] The calculation formula of the actual activity distance adjustment factor is:

[0134]

[0135] Where, Represents the actual activity distance adjustment factor; Target distance representing the actual activity distance;

[0136] It is worth noting that the calculation formula for the actual activity distance adjustment factor reflects the gap between the actual activity distance and the target distance. The smaller the adjustment factor, the closer the actual activity distance is to the target distance, indicating that the patient's rehabilitation progress is good.

[0137] Actual energy level adjustment factor calculation interface: used to calculate the actual energy level adjustment factor based on the actual energy level and the target energy level of the actual energy level;

[0138] The calculation formula of the actual energy level adjustment factor is:

[0139]

[0140] Where, represents the actual energy level adjustment factor; a target energy level representing the actual energy level;

[0141] It is worth noting that the calculation formula for the actual energy level adjustment factor reflects the gap between the actual energy level and the target energy level. The smaller the adjustment factor, the closer the actual energy level is to the target energy level, indicating that the patient's energy recovery is good.

[0142] At the same time, the pain score, actual activity distance, and actual energy level are included in the calculation after being standardized. 、 、 It also needs to be standardized before it can be used in calculations. Therefore, the above calculations 、 and is a dimensionless proportionality coefficient.

[0143] Surgical patient rehabilitation index calculation interface: used to calculate the surgical patient rehabilitation index by integrating the pain score adjustment factor, actual activity distance adjustment factor, and actual energy level adjustment factor;

[0144] The calculation formula for the surgical patient recovery index is:

[0145]

[0146] Where, stands for Surgical Patient Recovery Index; represents the pain score adjustment factor; Represents the actual activity distance adjustment factor; represents the actual energy level adjustment factor;

[0147] It is worth noting that the calculation formula for the surgical patient rehabilitation index is mainly used to comprehensively reflect the patient's postoperative recovery ability. The higher the rehabilitation index, the stronger the patient's recovery ability. At the same time, due to the 、 and is a dimensionless proportionality coefficient, so It is also a dimensionless value, usually between 0 and 1, and the larger the value, the stronger the recovery ability.

[0148] A weight coefficient calculation subunit is used to calculate the sum of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index, and respectively calculate the ratio of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index to the sum of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index, to obtain the weight coefficients of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index;

[0149] Among them, the calculation formula group of the weight coefficients of the surgical patient health index, surgical patient risk index and surgical patient recovery index is:

[0150]

[0151]

[0152]

[0153] Where, The weight coefficient representing the health index of surgical patients; represents the weight coefficient of the surgical patient risk index; represents the weight coefficient of the surgical patient recovery index; 、 and represent the surgical patient health index, surgical patient risk index, and surgical patient recovery index, respectively;

[0154] The calculation formula group of the above weight coefficients is mainly used to calculate the weight coefficients of the health index, risk index and rehabilitation index, so as to reasonably and scientifically allocate their importance in the comprehensive health status index, and the weight coefficient reflects the relative importance of each indicator in the overall evaluation; at the same time, They are all dimensionless values, so the calculated weight coefficients are also dimensionless proportional coefficients.

[0155] It's worth noting that by dynamically adjusting the weight coefficients, care plans can be flexibly adjusted based on the patient's actual health status, improving the accuracy and effectiveness of care. For example, when a patient's physiological parameter changes significantly, the system automatically adjusts the weight of that parameter in the comprehensive health status index, thereby updating the care plan. At the same time, the comprehensive health status index based on the weight coefficients provides a scientific basis for decision-making, helping subsequent medical staff develop the optimal care plan. For example, the comprehensive health status index can be used as a criterion for prioritizing care, ensuring that high-risk patients receive timely attention.

[0156] A surgical patient comprehensive health status index calculation subunit is used to combine the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index with the weight coefficients of the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index to obtain the surgical patient comprehensive health status index;

[0157] The calculation formula for the comprehensive health status index of surgical patients is:

[0158]

[0159] Where, Represents the comprehensive health status index of surgical patients; The weight coefficient representing the health index of surgical patients; represents the weight coefficient of the surgical patient risk index; represents the weight coefficient of the surgical patient recovery index; 、 and represent the surgical patient health index, surgical patient risk index, and surgical patient recovery index, respectively;

[0160] It is worth noting that the comprehensive health status index can fully reflect the patient's health status and provide a reliable quantitative basis for formulating the final care plan. For example, the comprehensive health status index can simultaneously reflect the patient's physical health, risk level and rehabilitation ability, helping subsequent medical staff to formulate a comprehensive care plan. At the same time, according to the comprehensive health status index of different patients, personalized care plans can be formulated to improve the care effect. For example, for patients with a low comprehensive health status index, it will be recommended to increase the frequency of monitoring and adjust the drug dosage; for patients with strong rehabilitation ability, nursing intervention can be appropriately reduced to encourage independent recovery;

[0161] At the same time, it is worth further explaining that the calculation formula for the comprehensive health status index of surgical patients is mainly used to calculate the comprehensive health status index of surgical patients. It is calculated based on the health index, risk index and rehabilitation index and their weight coefficients, and comprehensively reflects the overall health status of the patient. The higher the comprehensive health status index, the better the overall health status of the patient. At the same time, due to are all dimensionless values, and each weight coefficient is also a dimensionless proportional coefficient, so, It is also a dimensionless value, usually between 0 and 1, and the larger the value, the better the overall health status.

[0162] Second surgical nursing plan formulation unit: used to formulate the second surgical nursing plan based on the comprehensive health status index of surgical patients;

[0163] It is worth noting that the development of a nursing plan for the Second Surgery Department involves analyzing the patient's current health status, risk level, and rehabilitation ability based on a comprehensive health status index. For example, by analyzing the changing trends of the health index, risk index, and rehabilitation index, the patient's overall health status can be understood. Specific nursing measures can then be developed based on the analysis results, such as adjusting medication regimens and increasing the intensity of rehabilitation training. For example, for patients with a low comprehensive health status index, increased monitoring frequency and medication dosage adjustments may be recommended; for patients with strong rehabilitation abilities, nursing interventions can be appropriately reduced, and autonomous rehabilitation can be encouraged.

[0164] At the same time, it is worth further explaining that the Second Surgery Nursing Plan is a comprehensive, scientific and personalized nursing plan that covers multiple aspects such as short-term risk control, mid-term health management and long-term rehabilitation guidance to ensure the effectiveness and sustainability of nursing. For example, for patients with a low comprehensive health status index, it may be recommended to increase the frequency of monitoring and adjust the drug dosage; for patients with strong rehabilitation ability, nursing intervention can be appropriately reduced to encourage independent recovery.

[0165] Final surgical nursing plan acquisition module: connected to the first surgical nursing plan formulation module and the second surgical nursing plan acquisition module, used to integrate the second surgical nursing plan with the first surgical nursing plan, obtain the final surgical nursing plan and execute it;

[0166] It is worth noting how to integrate the second surgical care plan with the first to arrive at the final surgical care plan. The specific integration process is to first compare the first and second surgical care plans to identify differences and similarities. For example, if the first plan focuses on short-term risk control, while the second plan emphasizes long-term recovery, then through comparison, find the balance between the two. Then, combine the advantages of both plans to develop the final care plan. For example, if the first plan focuses on short-term risk control, while the second plan emphasizes long-term recovery, then the key measures of both plans will be included in the final plan.

[0167] Example 2: Figure 2 As shown, the present invention also proposes a surgical intelligent nursing method based on big data monitoring. Executing the above-mentioned surgical intelligent nursing system based on big data monitoring includes the following steps:

[0168] Step S1: collecting historical data of surgical patients and performing preprocessing;

[0169] Step S2: construct a prediction model, and use the preprocessed data to train the constructed prediction model to obtain a trained prediction model, input the patient data to be predicted into the trained prediction model to obtain a prediction result;

[0170] Step S3: formulating a first surgical care plan based on the prediction result;

[0171] Step S4: analyzing the patient data to be predicted, and obtaining a second surgical care plan based on the analysis results;

[0172] Step S4a: classify the patient data to be predicted according to data characteristics, and obtain physiological parameter data, medical history information data, and rehabilitation ability data according to the classification results;

[0173] Step S4b, standardizing the physiological parameter data, medical history information data, and rehabilitation ability data respectively;

[0174] Step S4c: Calculate the comprehensive health status index of the surgical patient based on the standardized physiological parameter data, medical history information data, and rehabilitation ability data;

[0175] Step S4c1: Calculating the surgical patient's health index based on the standardized physiological parameter data; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation, and body mass index;

[0176] Step S4c11: obtaining the ideal value and fluctuation range of each physiological parameter data;

[0177] Step S4c12, calculating an adjustment factor of each physiological parameter data according to each physiological parameter data and ideal value and fluctuation range of each physiological parameter data;

[0178] Step S4c13, calculating a surgical patient health index by integrating adjustment factors of each physiological parameter data;

[0179] Step S4c2, calculating a surgical patient risk index according to the history information data after standardization; the history information data includes white blood cell count, body temperature value and actual days of wound healing;

[0180] Step S4c21, obtaining a critical value of white blood cell count, ideal value and fluctuation range of body temperature value, and expected days of actual days of wound healing;

[0181] Step S4c22, calculating a white blood cell count adjustment factor according to white blood cell count and the critical value of white blood cell count;

[0182] Step S4c23, calculating a body temperature value adjustment factor according to body temperature value, ideal value and fluctuation range of body temperature value;

[0183] Step S4c24, calculating a wound healing adjustment factor according to actual days of wound healing and expected days of actual days of wound healing;

[0184] Step S4c25, calculating a surgical patient risk index by integrating the white blood cell count adjustment factor, the body temperature value adjustment factor and the wound healing adjustment factor;

[0185] Step S4c3, calculating a surgical patient rehabilitation index according to the rehabilitation ability data after standardization; the rehabilitation ability data includes pain score, actual activity distance and actual energy level;

[0186] Step S4c31, obtaining a threshold value of pain score, target distance of actual activity distance and target energy level of actual energy level;

[0187] Step S4c32, calculating a pain score adjustment factor according to pain score and the threshold value of pain score;

[0188] Step S4c33, calculating an actual activity distance adjustment factor according to actual activity distance and target distance of actual activity distance;

[0189] Step S4c34, calculating an actual energy level adjustment factor according to actual energy level and target energy level of actual energy level;

[0190] Step S4c35, calculating the surgical patient rehabilitation index by combining the pain score adjustment factor, the actual activity distance adjustment factor, and the actual energy level adjustment factor;

[0191] Step S4c4, calculating the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index, and calculating the ratios of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index to the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index, respectively, to obtain weight coefficients of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index;

[0192] Step S4c5, synthesizing the surgical patient health index, surgical patient risk index, and surgical patient recovery index with the weight coefficients of the surgical patient health index, surgical patient risk index, and surgical patient recovery index to obtain a comprehensive health status index of the surgical patient;

[0193] Step S4d, formulating a second surgical nursing plan based on the surgical patient's comprehensive health status index;

[0194] Step S5: Integrate the second surgical care plan with the first surgical care plan to obtain a final surgical care plan and execute it.

[0195] It should be noted that the terms used in the present invention are only for describing specific embodiments and are not intended to limit the scope of this application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular and may also include the plural. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the presence of other identical elements in the process, method or device comprising the elements.

[0196] It should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like, indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are used only to facilitate the description of the present application and simplify the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise specifically defined and limited, the terms "mount", "connect", "connect" and the like should be broadly understood, for example, can be fixedly connected, can also be detachably connected, or integrally connected; can be mechanically connected, can also be electrically connected; can be directly connected, can also be indirectly connected through an intermediate medium; can be internal communication of two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0197] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. The surgical intelligent nursing system based on big data monitoring is characterized by: Includes the following modules: Data collection and preprocessing module: used to collect historical data of surgical patients and perform preprocessing; Prediction model acquisition module: connected to the data collection and preprocessing module, used to build a prediction model and train the constructed prediction model using the preprocessed historical data to obtain a trained prediction model. The patient data to be predicted is input into the trained prediction model to obtain the prediction results; First surgical nursing plan formulation module: connected to the prediction model acquisition module, used to formulate the first surgical nursing plan based on the prediction results; Second surgical nursing plan acquisition module: connected to the prediction model acquisition module, used to analyze the patient data to be predicted and obtain the second surgical nursing plan based on the analysis results; Final surgical nursing plan acquisition module: connected to the first surgical nursing plan formulation module and the second surgical nursing plan acquisition module, used to integrate the second surgical nursing plan with the first surgical nursing plan, obtain the final surgical nursing plan and execute it; The step of analyzing the patient data to be predicted and obtaining a second surgical care plan based on the analysis results includes: Data classification unit: used to classify the patient data to be predicted according to data characteristics, and obtain physiological parameter data, medical history information data and rehabilitation ability data according to the classification results; Standardization unit: used to standardize physiological parameter data, medical history information data and rehabilitation ability data respectively; Surgical patient comprehensive health status index calculation unit: used to calculate the surgical patient comprehensive health status index based on standardized physiological parameter data, medical history information data and rehabilitation ability data; Second surgical nursing plan formulation unit: used to formulate the second surgical nursing plan based on the comprehensive health status index of surgical patients; The comprehensive health status index of surgical patients is calculated based on the standardized physiological parameter data, medical history information data and rehabilitation ability data, including: The surgical patient health index calculation subunit is used to calculate the surgical patient health index based on the standardized physiological parameter data; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation and body mass index; Surgical patient risk index calculation subunit: used to calculate the surgical patient risk index based on the standardized medical history information data; the medical history information data includes white blood cell count, body temperature and the actual number of days for wound healing; Surgical patient rehabilitation index calculation subunit: used to calculate the surgical patient rehabilitation index based on the standardized rehabilitation ability data; the rehabilitation ability data includes pain score, actual activity distance and actual energy level; A weight coefficient calculation subunit is used to calculate the sum of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index, and respectively calculate the ratio of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index to the sum of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index, to obtain the weight coefficients of the surgical patient health index, the surgical patient risk index and the surgical patient recovery index; A surgical patient comprehensive health status index calculation subunit is used to combine the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index with the weight coefficients of the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index to obtain the surgical patient comprehensive health status index; The actual energy level adjustment factor is calculated as follows: ； Where, represents the actual energy level adjustment factor; a target energy level representing the actual energy level; The calculation formula of surgical patient recovery index is: ； Where, stands for Surgical Patient Recovery Index; represents the pain score adjustment factor; Represents the actual activity distance adjustment factor; represents the actual energy level adjustment factor; The calculation formulas for the weight coefficients of the surgical patient health index, surgical patient risk index, and surgical patient recovery index are as follows: ； ； ； Where, The weight coefficient representing the health index of surgical patients; represents the weight coefficient of the surgical patient risk index; represents the weight coefficient of the surgical patient recovery index; 、 and represent the surgical patient health index, surgical patient risk index, and surgical patient recovery index, respectively; The calculation formula for the comprehensive health status index of surgical patients is: ； Where, Represents the comprehensive health status index of surgical patients; The weight coefficient representing the health index of surgical patients; represents the weight coefficient of the surgical patient risk index; represents the weight coefficient of the surgical patient recovery index; 、 and represent the surgical patient health index, surgical patient risk index, and surgical patient recovery index, respectively; The system introduces a dynamic adjustment mechanism by designing weight coefficients, and updates the second surgical care plan in real time according to the patient's actual recovery status.

2. The surgical intelligent nursing system based on big data monitoring according to claim 1 is characterized in that: The health index of surgical patients is calculated based on the physiological parameter data after normalization; The physiological parameter data includes blood pressure, heart rate, blood oxygen saturation and body mass index, including: The first parameter acquisition interface is used to obtain the ideal value and fluctuation range of each physiological parameter data; Physiological parameter data adjustment factor calculation interface: used to calculate the adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data; Surgical patient health index calculation interface: used to integrate the adjustment factors of each physiological parameter data to calculate the surgical patient health index.

3. The surgical intelligent nursing system based on big data monitoring according to claim 2 is characterized in that: The surgical patient risk index is calculated based on the standardized medical history information data; The medical history information data includes white blood cell count, body temperature and actual number of days for wound healing; including: Second parameter acquisition interface: used to obtain the critical value of white blood cell count, the ideal value and fluctuation range of body temperature, and the expected number of days for wound healing; White blood cell count adjustment factor calculation interface: used to calculate the white blood cell count adjustment factor based on the white blood cell count and the critical value of the white blood cell count; Body temperature adjustment factor calculation interface: used to calculate the body temperature adjustment factor based on the body temperature value, the ideal value of the body temperature value and the fluctuation range; Wound healing adjustment factor calculation interface: used to calculate the wound healing adjustment factor based on the actual number of days for wound healing and the expected number of days for wound healing; Surgical patient risk index calculation interface: used to integrate the white blood cell count adjustment factor, body temperature adjustment factor and wound healing adjustment factor to calculate the surgical patient risk index.

4. The surgical intelligent nursing system based on big data monitoring according to claim 3 is characterized in that: The surgical patient rehabilitation index is calculated based on the standardized rehabilitation ability data; The rehabilitation ability data includes pain score, actual activity distance and actual energy level, including: The third parameter acquisition interface is used to obtain the threshold of the pain score, the target distance of the actual activity distance, and the target energy level of the actual energy level; Pain score adjustment factor calculation interface: used to calculate the pain score adjustment factor based on the pain score and the pain score threshold; Actual activity distance adjustment factor calculation interface: used to calculate the actual activity distance adjustment factor based on the actual activity distance and the target distance of the actual activity distance; Actual energy level adjustment factor calculation interface: used to calculate the actual energy level adjustment factor based on the actual energy level and the target energy level of the actual energy level; Surgical patient rehabilitation index calculation interface: used to calculate the surgical patient rehabilitation index by integrating the pain score adjustment factor, actual activity distance adjustment factor and actual energy level adjustment factor.

5. The surgical intelligent nursing system based on big data monitoring according to claim 4 is characterized in that: The following steps are involved: Step S1: collecting historical data of surgical patients and performing preprocessing; Step S2: construct a prediction model, and use the preprocessed historical data to train the constructed prediction model to obtain a trained prediction model, input the patient data to be predicted into the trained prediction model to obtain a prediction result; Step S3: formulating a first surgical care plan based on the prediction result; Step S4: analyzing the patient data to be predicted, and obtaining a second surgical care plan based on the analysis results; Step S4a: classify the patient data to be predicted according to data characteristics, and obtain physiological parameter data, medical history information data, and rehabilitation ability data according to the classification results; Step S4b, standardizing the physiological parameter data, medical history information data, and rehabilitation ability data respectively; Step S4c: Calculate the comprehensive health status index of the surgical patient based on the standardized physiological parameter data, medical history information data, and rehabilitation ability data; Step S4c1: Calculating the surgical patient's health index based on the standardized physiological parameter data; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation, and body mass index; Step S4c11: obtaining the ideal value and fluctuation range of each physiological parameter data; Step S4c12: Calculate the adjustment factor of each physiological parameter data according to each physiological parameter data, the ideal value of each physiological parameter data, and the fluctuation range of each physiological parameter data; Step S4c13: Calculate the surgical patient health index by combining the adjustment factors of each physiological parameter data; Step S4c2: Calculate the surgical patient risk index based on the standardized medical history data, wherein the medical history data includes white blood cell count, body temperature, and actual number of days for wound healing; Step S4c21: Obtaining the critical value of white blood cell count, the ideal value and fluctuation range of body temperature, and the expected number of days for actual wound healing; Step S4c22: Calculate a white blood cell count adjustment factor based on the white blood cell count and the white blood cell count critical value; Step S4c23: Calculate the body temperature adjustment factor based on the body temperature value, the ideal value of the body temperature value, and the fluctuation range; Step S4c24: Calculate a wound healing adjustment factor based on the actual number of days for wound healing and the expected number of days for wound healing; Step S4c25, calculating the surgical patient risk index by combining the white blood cell count adjustment factor, the body temperature adjustment factor, and the wound healing adjustment factor; Step S4c3: Calculating the surgical patient rehabilitation index based on the standardized rehabilitation ability data; the rehabilitation ability data includes pain score, actual activity distance, and actual energy level; Step S4c31, obtaining a threshold value of a pain score, a target distance of an actual activity distance, and a target energy level of an actual energy level; Step S4c32: Calculate a pain score adjustment factor based on the pain score and the pain score threshold; Step S4c33: Calculate an actual activity distance adjustment factor based on the actual activity distance and the target distance of the actual activity distance; Step S4c34: Calculate an actual energy level adjustment factor based on the actual energy level and the target energy level of the actual energy level; Step S4c35, calculating the surgical patient rehabilitation index by combining the pain score adjustment factor, the actual activity distance adjustment factor, and the actual energy level adjustment factor; Step S4c4, calculating the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index, and calculating the ratios of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index to the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index, respectively, to obtain weight coefficients of the surgical patient health index, the surgical patient risk index, and the surgical patient recovery index; Step S4c5, combining the surgical patient health index, surgical patient risk index, and surgical patient recovery index with the weight coefficients of the surgical patient health index, surgical patient risk index, and surgical patient recovery index to obtain a comprehensive health status index of the surgical patient; Step S4d, formulating a second surgical nursing plan based on the surgical patient's comprehensive health status index; Step S5: Integrate the second surgical care plan with the first surgical care plan to obtain a final surgical care plan.

Images