Neurosurgery nursing management system based on cloud computing

Abstract

The invention relates to the technical field of cloud computing, in particular to a neurosurgery nursing management system based on cloud computing. The method comprises the following steps: acquiring nursing process data of a neurosurgery patient, wherein the nursing process data comprises an operation type, a postoperative recovery monitoring index, a nursing intervention measure and nervous system medication information; performing data cleaning and standardization processing on the nursing process data, and serializing nursing behaviors to generate a nursing behavior framework diagram with a time node and feature relationship; performing time sequence simulation of postoperative recovery indexes and intervention measures based on a neural network model according to the nursing behavior framework diagram, and establishing a mapping relation between recovery states and nursing intervention nodes according to preset historical nursing path data to obtain nursing path prediction data. Through the nursing execution dynamic iteration module, a nursing scheme is tracked and optimized in real time, and it is ensured that intervention measures are synchronous with the actual recovery state of a patient.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular, to a neurosurgical nursing management system based on cloud computing. Background Art

[0002] Cloud Computing is a technology and service model that provides computing resources (such as servers, storage, databases, networks, software, etc.) in the form of on-demand resource allocation through the Internet. Cloud computing enables users to obtain computing resources and services flexibly on demand without directly managing and maintaining hardware facilities. Neurosurgical nursing management refers to providing professional nursing services to patients during the diagnosis and treatment process of neurosurgery, and scientifically managing nursing resources, processes, and personnel to ensure the quality of preoperative, intraoperative, and postoperative care of patients. Neurosurgical nursing needs to combine the particularity of nervous system diseases, pay attention to the observation of the changes in the condition, the monitoring of vital signs, and the guidance of postoperative rehabilitation.

[0003] However, traditional cloud computing-based neurosurgical nursing management systems often have the following problems: Most of the existing technologies are presented in a static manner, unable to provide serialized nursing path analysis, and it is difficult to identify the key nodes and resource requirements of nursing behaviors, resulting in the lack of flexibility in adjusting nursing plans. Due to the lack of dynamic performance modeling and data tracking, it is difficult to effectively correlate resource allocation with differences in nursing quality, and it is difficult to provide accurate monitoring feedback, increasing the uncertainty of postoperative care for patients. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a neurosurgical nursing management system based on cloud computing to solve at least one of the above technical problems.

[0005] To achieve the above object, a neurosurgical nursing management system based on cloud computing includes the following modules:

[0006] A neurosurgical nursing data acquisition module, which is used to obtain the nursing process data of neurosurgical patients, where the nursing process data includes the type of surgery, postoperative recovery monitoring indicators, nursing intervention measures, and nervous system medication information; perform data cleaning and standardization processing on the nursing process data, and sequence the nursing behaviors to generate a nursing behavior framework diagram with time nodes and feature relationships;

[0007] A nursing path intelligent prediction module, which is used to perform time series simulation of postoperative recovery indicators and intervention measures based on a neural network model according to the nursing behavior framework diagram, and establish a mapping relationship between the recovery status and nursing intervention nodes according to the preset historical nursing path data to obtain nursing path prediction data; perform optimization analysis based on resource requirements on the nursing path prediction data to obtain nursing path optimization data;

[0008] The nursing goal parameterized evaluation module is used to obtain nursing rehabilitation index data; associate the nursing path optimization data with the nursing rehabilitation index data for the correlation between the nursing goal and the patient's recovery status, and conduct a quantitative analysis of resource allocation and index differences through parametric modeling, so as to obtain the nursing goal evaluation model;

[0009] The nursing execution dynamic iteration module is used to dynamically track the allocation and execution of neurosurgical nursing resources according to the nursing goal evaluation model, compare the actual nursing path with the nursing rehabilitation index data, and generate nursing execution deviation data; generate a feedback optimized nursing plan based on the nursing execution deviation data, and dynamically adjust the nursing intervention measures and update the nursing behavior framework diagram according to the optimized nursing plan, so as to obtain the neurosurgical nursing data and the updated nursing behavior framework diagram;

[0010] The cloud nursing data security management module is used to hierarchically manage the neurosurgical nursing data and the updated nursing behavior framework diagram through a cloud computing-based storage architecture, and construct a multi-level data security and permission control mechanism to achieve access control of the system.

[0011] The present invention provides a comprehensive, accurate, and efficient nursing data management and intelligent prediction solution by integrating multiple modules, which can significantly improve the quality and efficiency of the neurosurgical nursing process. The core functions of the system include multiple modules such as nursing data collection, intelligent prediction of nursing paths, parametric evaluation of nursing goals, dynamic iteration of nursing execution, and data security management. Through the organic cooperation of each module, the whole process of intelligent management from nursing data collection to path optimization and then to execution feedback is realized. First, the neurosurgical nursing data collection module ensures the comprehensiveness and diversity of nursing data by collecting multi-dimensional data such as the patient's surgical type, postoperative recovery monitoring indicators, nursing intervention measures, and neurological medication information. After the data collection is completed, the system strictly cleans and standardizes the data, eliminating possible outliers, missing values, or duplicate data to ensure the accuracy and consistency of the data. At the same time, this module also sequences nursing behaviors to generate a nursing behavior framework diagram with time nodes and characteristic relationships, which not only clearly shows each link in the nursing process but also provides the necessary basic data support for subsequent nursing path prediction and optimization. Through this process, the system can ensure that the collected data has high reliability and systematicness, providing a solid foundation for subsequent intelligent analysis and decision-making. Next, the intelligent prediction module of the nursing path uses the generated nursing behavior framework diagram to simulate the time series of postoperative recovery indicators and intervention measures through a neural network model. The key to this process is to establish a mapping relationship between the recovery state and nursing intervention nodes through the establishment of historical nursing path data, and based on this, obtain nursing path prediction data. This module can not only accurately simulate the patient's postoperative recovery process but also make personalized predictions according to the patient's specific situation, helping medical staff better understand the patient's recovery trend, so as to adjust the nursing plan in a timely manner. In addition, the system will also optimize and analyze the nursing path based on resource requirements, provide a nursing plan that better conforms to the actual resource situation, effectively improve resource utilization, avoid resource waste, and optimize the nursing effect. After the nursing path is optimized, the parametric evaluation module of nursing goals plays a crucial role. This module first obtains the patient's nursing rehabilitation index data, and then comprehensively analyzes the nursing path optimization data and the rehabilitation index data to establish the association between nursing goals and the patient's recovery state. Through parametric modeling, the system can quantitatively analyze the effect of resource allocation and index differences, so as to obtain a scientific and reasonable nursing goal evaluation model. This evaluation model can not only accurately reflect the patient's recovery state but also help medical staff quantitatively manage future nursing goals, provide data support for the formulation of personalized nursing plans, and further improve the nursing quality and rehabilitation effect. The dynamic iteration module of nursing execution focuses on the dynamic tracking of the allocation and execution of nursing resources. The system generates nursing execution deviation data by real-time tracking the execution of the nursing path and comparing the difference between the actual execution result and the nursing goal.These deviation data provide the basis for subsequent optimization, helping medical staff to dynamically adjust the nursing plan. During this process, the system can optimize the nursing plan according to the feedback, and update the nursing behavior framework diagram in a timely manner to achieve continuous improvement and optimization. The core advantage of this module is its ability to quickly respond to any deviations that occur during the nursing process, ensuring the precise execution of nursing activities and maximizing the rehabilitation effect. Finally, the cloud-based nursing data security management module hierarchically manages neurosurgical nursing data through a cloud computing architecture, ensuring the security of data during storage, transmission, and processing. The system constructs a multi-level data security and permission control mechanism to ensure that personnel with different roles can only access the data they are authorized to, effectively preventing data leakage and abuse. Through this module, the nursing data of patients is strictly protected, safeguarding the privacy and data security of patients and avoiding the occurrence of information security incidents. Generally speaking, this neurosurgical nursing management system comprehensively improves the intelligence and personalization level of nursing work through efficient data collection, accurate nursing path prediction, scientific nursing goal assessment, dynamic nursing execution optimization, and strict data security management. The system can not only improve the quality of nursing, ensure the optimal allocation of resources, but also provide customized and continuously optimized nursing plans for patients, ultimately achieving the best rehabilitation effect for patients and maximizing the value of nursing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0013] Figure 1 It is a schematic diagram of the step flow of the cloud-based neurosurgical nursing management system of the present invention;

[0014] Figure 2 is Figure 1 a detailed step flow schematic diagram of step S1 in

[0015] Figure 3 is Figure 1 a detailed step flow schematic diagram of step S2 in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0018] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a cloud computing-based neurosurgical nursing management system, and the system includes the following modules:

[0020] S1: Neurosurgical nursing data acquisition module, which is used to obtain the nursing process data of neurosurgical patients, where the nursing process data includes the type of surgery, postoperative recovery monitoring indicators, nursing intervention measures, and neurological medication information; perform data cleaning and standardization processing on the nursing process data, and serialize the nursing behaviors to generate a nursing behavior framework diagram with time nodes and feature relationships;

[0021] S2: Nursing path intelligent prediction module, which is used to perform time series simulation of postoperative recovery indicators and intervention measures based on a neural network model according to the nursing behavior framework diagram, and establish a mapping relationship between the recovery state and nursing intervention nodes based on the preset historical nursing path data to obtain nursing path prediction data; perform optimization analysis based on resource requirements on the nursing path prediction data to obtain nursing path optimization data;

[0022] S3: Nursing goal parameterized evaluation module, which is used to obtain nursing rehabilitation index data; associate the nursing path optimization data with the nursing rehabilitation index data for the association between nursing goals and the patient's recovery state, and perform quantitative analysis on resource allocation and index differences through parametric modeling, so as to obtain a nursing goal evaluation model;

[0023] S4: The nursing execution dynamic iteration module is used to dynamically track the allocation and execution of neurosurgical nursing resources according to the nursing goal evaluation model, compare the actual nursing path with the nursing rehabilitation index data, and generate nursing execution deviation data; generate a feedback optimized nursing plan based on the nursing execution deviation data, and dynamically adjust nursing intervention measures and update the nursing behavior framework diagram according to the optimized nursing plan, so as to obtain neurosurgical nursing data and an updated nursing behavior framework diagram;

[0024] S5: The cloud nursing data security management module is used to hierarchically manage the neurosurgical nursing data and the updated nursing behavior framework diagram through a cloud computing-based storage architecture, and construct a multi-level data security and permission control mechanism, so as to achieve access control of the system.

[0025] In the embodiment of the present invention, refer to Figure 1 As shown, it is a schematic diagram of the step flow of a cloud computing-based neurosurgical nursing management system of the present invention. In this example, the cloud computing-based neurosurgical nursing management system includes the following modules:

[0026] S1: The neurosurgical nursing data collection module is used to obtain the nursing process data of neurosurgical patients, where the nursing process data includes the type of surgery, postoperative recovery monitoring indicators, nursing intervention measures, and nervous system medication information; perform data cleaning and standardization processing on the nursing process data, and serialize the nursing behaviors to generate a nursing behavior framework diagram with time nodes and feature relationships;

[0027] In the embodiment of the present invention, various data during the postoperative nursing process of patients are collected through intelligent monitoring devices and nursing information management systems deployed in the hospital, including the type of surgery (such as brain tumor resection, cerebrovascular repair, etc.), postoperative recovery monitoring indicators (such as heart rate, blood pressure, electroencephalogram changes, etc.), nursing intervention measures (such as wound care frequency, rehabilitation training plan), and nervous system medication information (such as analgesics, antiepileptic drugs, etc.). Data cleaning is performed on the collected data, including multiple imputation processing of missing values, elimination of outliers within the standard distribution range, and data consistency verification through a hash mapping algorithm. Standardization processing tools (such as Z-score or Min-Max standardization methods) are used to unify all data into a comparable dimension range. The nursing behavior data is serialized through a timestamp-based sorting algorithm, and a graphical tool (such as Neo4j or Graphviz) is applied to generate a nursing behavior framework diagram based on time nodes and feature relationships. The key nodes of the framework diagram represent the time points of nursing interventions, and the edges represent the sequential dependence relationships between behaviors.

[0028] S2: The nursing path intelligent prediction module is used to perform time series simulation of postoperative recovery indicators and intervention measures based on a neural network model according to the nursing behavior framework diagram, establish a mapping relationship between the recovery status and nursing intervention nodes based on the preset historical nursing path data to obtain nursing path prediction data; perform optimization analysis based on resource requirements on the nursing path prediction data to obtain nursing path optimization data;

[0029] In the embodiment of the present invention, the constructed nursing behavior framework diagram is used to build a multi-layer recurrent neural network model (LSTM) based on a deep learning framework (such as TensorFlow or PyTorch) to simulate the time series change process of key indicators (such as blood oxygen saturation, neurological function score, etc.) during postoperative recovery, and predict the effects of intervention measures (such as medication dose adjustment or rehabilitation training time arrangement). Obtain historical nursing path data from the hospital database, classify it according to the surgical type, patient age group, and underlying disease conditions, and extract eigenvalue data of typical recovery paths. Combine the recovery index prediction model, and use the Bayesian inference method to establish a mapping relationship between the recovery status and nursing intervention nodes to generate nursing path prediction data. Perform resource requirement analysis on the prediction data, and optimize the allocation of resources such as manpower, equipment utilization rate, and drug consumption based on a linear programming model, so as to obtain more efficient nursing path optimization data.

[0030] S3: The nursing goal parameterized evaluation module is used to obtain nursing rehabilitation index data; associate the nursing path optimization data with the nursing rehabilitation index data for the association between the nursing goal and the patient's recovery status, and perform quantitative analysis on the resource allocation and index differences through parametric modeling, so as to obtain a nursing goal evaluation model;

[0031] In the embodiment of the present invention, key rehabilitation index data of the patient's postoperative recovery is obtained from the intelligent monitoring system, including physiological parameters (such as blood pressure, body temperature, EEG signal frequency) and functional recovery levels (such as limb movement ability score). Align the nursing path optimization data and the rehabilitation index data through timestamps to generate an association sequence of the nursing path and the rehabilitation index based on the time axis. Extract quantitative features through the association sequence analysis, such as the influence rate of different paths on the rehabilitation speed, use principal component analysis (PCA) to screen out key feature variables, and construct a multi-objective optimization function, which includes objective functions such as resource allocation efficiency and index achievement rate. Fit the input data of the optimization model through a regression analysis tool (such as R or MATLAB), and finally generate a parameterized evaluation model for evaluating the completion of the nursing goal.

[0032] S4: The nursing execution dynamic iteration module is used to dynamically track the allocation and execution of neurosurgical nursing resources according to the nursing goal evaluation model, compare the actual nursing path with the nursing rehabilitation index data, and generate nursing execution deviation data; generate a feedback optimized nursing plan based on the nursing execution deviation data, and dynamically adjust the nursing intervention measures and update the nursing behavior framework diagram according to the optimized nursing plan, so as to obtain neurosurgical nursing data and an updated nursing behavior framework diagram;

[0033] In the embodiment of the present invention, the allocation and execution situation data of neurosurgical nursing resources are collected in real time through an embedded device, including the device operation duration, the completion situation of nursing staff tasks, etc., and real-time patient nursing path data is generated. According to the collected real-time path data, combined with the monitoring device, the current actual rehabilitation index data of the patient (such as brain wave spectrum) is generated. The actual rehabilitation index is compared with the nursing rehabilitation index data, and statistical analysis tools (such as SPSS) are used to analyze the deviation and identify the key factors causing the deviation (such as insufficient intervention frequency or unreasonable drug dosage). The deviation adjustment feature data is extracted, the effect of the optimization measure is predicted through a linear regression model, and a feedback optimized nursing plan is generated, including dynamically adjusted nursing intervention measures and resource allocation suggestions. The optimized plan is updated in real time to the nursing behavior framework diagram, and the updated execution plan is distributed through the nursing management system.

[0034] S5: The cloud nursing data security management module is used to hierarchically manage the neurosurgical nursing data and the updated nursing behavior framework diagram through a cloud computing-based storage architecture, and construct a multi-level data security and permission control mechanism, so as to realize the access control of the system.

[0035] In the embodiment of the present invention, a multi-layer cloud storage system is constructed, and the neurosurgical nursing data and the updated nursing behavior framework diagram are hierarchically stored by adopting a distributed storage architecture (such as HDFS or Ceph); the top layer stores the updated nursing behavior framework diagram, the middle layer stores the time-sensitive patient rehabilitation path data, and the bottom layer stores historical data and log records. The permission management is realized through a role-based access control (RBAC) mechanism, and different levels of medical staff (such as doctors, nurses, administrators) are assigned different permissions, and the encryption algorithm (such as AES or RSA) is used for permission management to ensure the security of data transmission. The system verifies the user permissions in real time through identity authentication (such as OAuth 2.0) and access control algorithms, effectively prevents unauthorized access, and realizes the security management and access control of neurosurgical nursing data.

[0036] Preferably, the neurosurgical nursing data collection module specifically executes the following steps:

[0037] Step S11: Obtain the nursing process data of neurosurgical patients, including the type of surgery, postoperative recovery monitoring indicators, nursing intervention measures, and neurological medication information;

[0038] Step S12: Preprocess the nursing process data to obtain preprocessed neurosurgical clinical data, where the preprocessing includes missing value filling, outlier handling, and data consistency verification;

[0039] Step S13: Use data standardization techniques to uniformly encode and standardize the measurement of the preprocessed neurosurgical clinical data to obtain standardized neurosurgical clinical data;

[0040] Step S14: Construct a nursing behavior feature vector based on time attributes according to the standardized neurosurgical clinical data, and establish a time series mapping relationship for nursing intervention measures, neurological medication information, and changes in monitoring indicators for nursing behaviors extracted in chronological order to obtain nursing behavior sequence data;

[0041] Step S15: Extract features and perform correlation analysis on the nursing behavior sequence data, and generate a nursing behavior framework diagram with time dimension and behavior relevance through a graphical method.

[0042] As an embodiment of the present invention, refer to Figure 2 shown in Figure 1 for the detailed step flow diagram of step S1 in

[0043] Step S11: Obtain the nursing process data of neurosurgical patients, including the type of surgery, postoperative recovery monitoring indicators, nursing intervention measures, and neurological medication information;

[0044] In the embodiment of the present invention, multi-source data collection devices are deployed in the neurosurgical ward, including an electronic medical record system (EMR), monitoring instruments (such as electrocardiogram monitors, electroencephalogram recorders), a drug management system, etc., to automatically collect data of patients during the postoperative nursing process. Among them, the type of surgery is determined by the surgery name and code recorded in the EMR (such as aneurysm clipping, code A001); the postoperative recovery monitoring indicators include blood pressure, heart rate, electroencephalogram frequency, etc., which are recorded by the monitoring instrument once every minute; the nursing intervention measures are recorded through the nursing management system, including the daily nursing times, rehabilitation training items, etc.; the neurological medication information is extracted by the drug management system according to the patient ID, including the drug name, dosage, medication time, etc. All data are uniformly stored through an integration platform to ensure the comprehensiveness and timeliness of data collection.

[0045] Step S12: Preprocess the nursing process data to obtain neurosurgical clinical preprocessed data, where the preprocessing includes missing value filling, outlier handling, and data consistency verification;

[0046] In the embodiment of the present invention, preprocessing operations are performed on the data collected in step S11. For the missing value problem, an imputation method based on the K-Nearest Neighbor (KNN) algorithm is used for filling. For example, if the blood pressure record of a patient is missing at a certain time period, the blood pressure data at adjacent time points can be used for estimation. For outliers (such as a heart rate exceeding 200 beats per minute or an electroencephalogram frequency lower than 0.5 Hz), the outlier points are removed by setting the upper and lower limit ranges, and at the same time, deviation correction is performed in combination with the context data. In the data consistency verification, a hash check algorithm is used to compare and verify the recorded medication information and nursing measures to ensure that the nursing process records are consistent with the reference standards in the database. Through these processes, neurosurgical clinical preprocessed data without missing values, outliers, and with consistency is obtained.

[0047] Step S13: Use data standardization technology to uniformly encode and measure-standardize the neurosurgical clinical preprocessed data to obtain neurosurgical clinical standardized data;

[0048] In the embodiment of the present invention, for the neurosurgical clinical preprocessed data obtained in S12, the Z-score standardization method is used to unify the monitoring indicators into the standardized range of zero mean and unit variance. For example, data in different dimensions such as blood pressure and heart rate are unified into a comparable numerical scale. For categorical data (such as surgical types, nursing intervention measures), the One-Hot Encoding technology is used to convert it into a binary feature vector. For example, "cerebral aneurysm clipping" is encoded as [1, 0, 0], and "intracerebral hemorrhage evacuation" is encoded as [0, 1, 0]. In addition, the standardized dictionary is used to unify the drug names (such as "carbamazepine") into the international standardized drug names (such as "Carbamazepine"). After unified encoding and measure-standardization, standardized neurosurgical clinical standardized data is obtained.

[0049] Step S14: Construct a nursing behavior feature vector based on time attributes according to the neurosurgical clinical standardized data, and establish a time series mapping relationship of nursing intervention measures, nervous system medication information, and changes in monitoring indicators for the nursing behaviors extracted based on the time sequence, so as to obtain nursing behavior sequence data;

[0050] In the embodiment of the present invention, based on the standardized clinical neurosurgery data generated according to S13, the time attributes of nursing behaviors are extracted to construct a feature vector. For example, nursing behavior features include the type of nursing intervention, the types and dosages of medications, the change rate of monitoring indicators, etc., and the time attributes include the time point and duration of the behavior occurrence. Using a time series modeling tool (such as the time series module of Pandas or MATLAB), the data is arranged in chronological order to generate a time series mapping relationship. In the mapping relationship, nodes represent behaviors (such as a single intervention measure), and edges represent the chronological order and correlation of behaviors (such as the change of indicators after a certain medication). The finally output nursing behavior sequence data is in the form of a matrix corresponding to features and time, which is convenient for subsequent analysis.

[0051] Step S15: Perform feature extraction and association analysis on the nursing behavior sequence data, and generate a nursing behavior framework diagram with time dimension and behavior correlation through a graphical method.

[0052] In the embodiment of the present invention, feature extraction is performed on the nursing behavior sequence data in step S14. The extracted features include key intervention nodes (such as the first medication time, key rehabilitation training time points) and the association characteristics of indicator changes (such as the causal relationship between heart rate changes and medications). An association analysis algorithm (such as Apriori or FP-Growth) is used to mine frequent patterns and correlation rules between different nursing behaviors. Through a graphical tool (such as Neo4j or Gephi), the extracted behavior features are plotted into a nursing behavior framework diagram according to the time dimension. In the diagram, nodes represent nursing behaviors or events, and edges represent the chronological order and association strength in the time dimension. The nursing behavior framework diagram has intuitiveness and can clearly show the overall trend of the patient's nursing path and indicator changes, providing support for subsequent path prediction and optimization.

[0053] The present invention plays a crucial role in the process of nursing data collection, processing, and analysis, and has remarkable effects on improving nursing quality, optimizing nursing pathways, and enhancing the intelligent level of the system. First, step S11 provides a comprehensive basis for subsequent data analysis and personalized nursing plans by comprehensively obtaining the nursing process data of neurosurgical patients, including surgical types, postoperative recovery monitoring indicators, nursing intervention measures, and neurological medication information. Then, step S12 ensures the integrity, accuracy, and consistency of the data by performing preprocessing operations such as missing value filling, outlier handling, and data consistency verification on the nursing process data, eliminates the biases caused by data missing or anomalies, and improves the reliability and accuracy of data analysis. Step S13 standardizes the nursing process data through data standardization techniques for unified coding and measurement standardization, ensures the compatibility of data from different sources and formats, and provides a unified standard for further analysis, enhancing the consistency and comparability of the data. Subsequently, step S14 constructs a nursing behavior feature vector based on time attributes, extracts the time series mapping relationships of nursing intervention measures, drug information, and changes in monitoring indicators, and can accurately capture the dynamic changes in the patient's nursing process, providing strong data support for future nursing path prediction and adjustment. Finally, step S15 deeply mines the nursing behavior sequence data through feature extraction and association analysis, and generates a nursing behavior framework diagram with time dimension and behavior correlation through a graphical method, providing a clear and intuitive view of the nursing process, helping medical staff understand the internal connections of nursing behaviors, thereby optimizing nursing intervention measures and improving nursing efficiency and effect. Through the close cooperation of these steps, the system can achieve intelligent decision-making and dynamic adjustment based on real-time data, thereby providing more accurate and personalized nursing services, and ultimately achieving the goal of improving the patient's recovery effect and nursing quality.

[0054] Preferably, step S15 includes the following steps:

[0055] Step S151: Perform multi-scale feature decomposition on the nursing behavior sequence data based on time features, behavior attribute features, and association strength features to obtain nursing behavior feature data;

[0056] Step S152: Deconstruct the topological structure of the nursing behavior sequence data through complex network analysis methods, and identify key nodes, association paths, and behavior propagation patterns to establish a behavior network topological model;

[0057] Step S153: Perform semantic-level similarity analysis and semantic clustering on the nursing behavior feature data, and extract potential semantic associations and implicit patterns of nursing behaviors to obtain nursing semantic feature data;

[0058] Step S154: Construct a semantic association mapping of nursing behaviors based on the knowledge graph according to the nursing semantic feature data, and define the semantic relationships and association weights between nursing behavior nodes through the ontology modeling method, so as to form a nursing semantic association network;

[0059] Step S155: Use a graphical method to transform the nursing semantic association network into a three-dimensional visualization framework diagram with time dimension and behavior relevance, so as to obtain a nursing behavior framework diagram.

[0060] Embodiments of the present invention extract time features, behavior attribute features, and association strength features from nursing behavior sequence data to support multi-scale analysis. The time features include the occurrence time point, time interval, and behavior duration of nursing behaviors. The sliding window method is used to perform time segmentation analysis on the sequence data, and the window length is set to range from 5 minutes to 1 hour; the behavior attribute features include nursing behavior types (such as medication, monitoring, rehabilitation training) and their corresponding standardized index values, and key behavior attributes are extracted through a feature dimensionality reduction method based on principal component analysis (PCA); the association strength features quantify the association degree between behaviors through the mutual information algorithm (MI), such as analyzing the dependence relationship between the medication frequency and the change of monitoring indicators. After feature decomposition, multi-dimensional nursing behavior feature data containing time, behavior attributes, and association strength is generated, providing a basis for subsequent network analysis and semantic mining. Using the nursing behavior feature data generated by S151, a nursing behavior topology model based on a complex network is constructed. First, a weighted directed graph is constructed according to the time sequence and association strength between nursing behaviors, where the nodes represent nursing behaviors (such as medication or monitoring), and the edge weights are defined by the association strength feature values. Then, complex network analysis tools (such as NetworkX or Gephi) are used to calculate the topological indicators of the network, including the degree centrality, betweenness centrality, clustering coefficient, etc. of the nodes, and key nodes (such as high-frequency intervention behaviors) and key paths (such as important behavior chains) are identified. In addition, the influence propagation process of nursing behaviors on monitoring indicators is simulated through a behavior propagation model (such as the SIR model), and the behavior propagation mode is extracted, thereby establishing a complete nursing behavior network topology model, providing a structural basis for subsequent semantic analysis. Perform semantic-level similarity analysis and clustering on the nursing behavior feature data generated by S151. First, the nursing behavior data is embedded based on a word vector model (such as Word2Vec or BERT), and the behavior description text is converted into a vector representation; then, the cosine similarity is used to measure the semantic similarity between behaviors, and the DBSCAN (density-based spatial clustering) algorithm is used to perform clustering analysis on semantically similar behaviors. For example, "postoperative monitoring of blood oxygen saturation" and "postoperative monitoring of heart rate" are classified into the "postoperative monitoring" category. In addition, latent semantic analysis (LSA) is used to extract the implicit semantic patterns between behaviors, such as identifying the implicit association between "postoperative medication" and "indicator recovery". After semantic analysis and clustering, nursing semantic feature data is obtained, laying a foundation for constructing a semantic association network. Using the nursing semantic feature data generated by S153, a nursing behavior semantic association network is established using a knowledge graph construction tool (such as Neo4j or RDF4J).First, define the semantic relationships and association weights between nursing behavior nodes through an ontology modeling tool (such as Protégé). For example, the association weight between the "postoperative monitoring" class and the "medication adjustment" class is set to 0.8, reflecting their strong causal relationship. Secondly, use the high-frequency behavior classes in the semantic clustering results as the core nodes of the knowledge graph, and establish association paths based on the time attributes and semantic relationships of the behaviors. For example, the path from the "postoperative medication" node to the "recovery monitoring" node is labeled as "causal relationship". After construction, the nursing semantic association network can accurately reflect the semantic relationships and their time evolution of nursing behaviors. Using the nursing semantic association network generated by S154, a three-dimensional visualization tool (such as Unity or ParaView) is used to generate a three-dimensional nursing behavior framework diagram with time dimension and behavior relevance. The time dimension is reflected by the Z-axis, and the behavior nodes are vertically arranged in chronological order; the behavior association is represented by the connection lines between the nodes, where the thickness of the lines represents the association weight; the node colors are used to distinguish behavior categories (such as monitoring, medication, rehabilitation). In addition, the dynamic display of node information is realized through interactive operations. For example, clicking on a certain node can display the specific nursing behavior description and semantic association information. The generated nursing behavior framework diagram provides intuitive visual support for the optimization of the neurosurgical nursing path and intervention decision-making.

[0061] This invention is a crucial part of the neurosurgical nursing management system, involving in-depth analysis, feature extraction, semantic association, and visual presentation of nursing behavior data. It has remarkable effects and can comprehensively improve the accuracy, visualization, and intelligence level of nursing management. Through multi-scale feature decomposition of nursing behavior sequence data based on time features, behavior attribute features, and association strength features, multi-dimensional information of nursing behaviors is effectively extracted. This operation can deeply explore various features in the nursing process, identify key factors and potential patterns of nursing behaviors. Through multi-scale feature analysis, changes in nursing behaviors can be accurately captured at different time points, under different nursing interventions or monitoring indicators, providing a solid foundation for subsequent nursing behavior prediction, optimization, and intervention. By using complex network analysis methods to deconstruct the topological structure of nursing behavior sequence data, the association relationships and propagation patterns between different nodes (such as nursing intervention measures, monitoring indicators, and medication information) in the nursing behavior data can be revealed. This process helps to construct a behavioral network topological model by identifying key nodes, behavior propagation paths, and association strengths in nursing behaviors, thereby helping medical staff understand the mutual influences between different nursing activities. This not only optimizes the allocation of nursing resources but also provides an important guiding basis for dynamically adjusting nursing paths and improving nursing effects. Then, through semantic-level similarity analysis and semantic clustering, in-depth mining of nursing behavior feature data is carried out to extract potential semantic associations and implicit patterns in nursing behaviors. This analysis can reveal the deep relationships behind nursing behaviors, help identify common nursing intervention patterns, monitoring trends, and medication usage rules in the nursing path, and thus provide data support for the formulation of personalized nursing plans. Through semantic analysis, the intelligence level of the nursing process can be further improved, making nursing work more in line with the individual needs of patients. By constructing a semantic association mapping of nursing behaviors based on a knowledge graph and defining semantic relationships and association weights between nursing behavior nodes through ontology modeling methods, a highly abstract, accurate, and hierarchical nursing behavior semantic association network can be constructed. This network can systematically display various elements in nursing behaviors and their mutual relationships, thus providing comprehensive support for clinical decision-making. Through ontology modeling, not only the expression ability of the nursing behavior semantic network is enhanced, but also the system's understanding and reasoning ability of the nursing process are further improved. Using graphical methods to transform the nursing semantic association network into a three-dimensional visualization framework diagram with time dimension and behavior relevance, the complex relationships, change trends, and association patterns in the nursing process can be visually presented. This visualized nursing behavior framework diagram not only facilitates real-time observation and analysis by medical staff but also helps them quickly identify potential problems or optimization spaces in the nursing process, thus making timely adjustments. The time dimension display of the visualization framework diagram also makes the dynamic adjustment and prediction of nursing paths more accurate and efficient, improving the accuracy and operability of nursing management.By deeply analyzing nursing behavior data, constructing a semantic association network of nursing behaviors, and visualizing the display, it provides strong data support and decision-making basis for the optimization of the neurosurgical nursing process. These steps not only promote the intelligent prediction and dynamic adjustment of the nursing path, but also improve the optimal allocation of nursing resources, ultimately realizing personalized and precise nursing services, thereby effectively improving the patient's rehabilitation effect and nursing quality.

[0062] Preferably, the nursing path intelligent prediction module specifically executes the following steps:

[0063] Step S21: Construct a postoperative recovery index prediction model based on a deep neural network to simulate the dynamic change process of the patient's postoperative recovery according to the nursing behavior framework diagram;

[0064] Step S22: Obtain historical nursing path data;

[0065] Step S23: Extract the typical recovery path characteristics of patients with different surgical types, age groups, and underlying diseases from the historical nursing path data to obtain typical recovery path data;

[0066] Step S24: Establish a mapping relationship between the recovery status and nursing intervention nodes according to the typical recovery path data and the postoperative recovery index prediction model to obtain nursing path prediction data; conduct an optimization analysis based on resource requirements for the nursing path prediction data to obtain nursing path optimization data.

[0067] As an embodiment of the present invention, refer to Figure 3 shown, for Figure 1 the detailed step flow diagram of step S2 in, in the embodiment of the present invention, the nursing path intelligent prediction module specifically executes the following steps:

[0068] Step S21: Construct a postoperative recovery index prediction model based on a deep neural network to simulate the dynamic change process of the patient's postoperative recovery according to the nursing behavior framework diagram;

[0069] Based on the nursing behavior framework diagram, an embodiment of the present invention designs a deep neural network (DNN) model to simulate the dynamic change process of a patient's postoperative recovery. The input of the model is nursing behavior sequence data, including postoperative key monitoring indicators (such as heart rate, blood pressure, electroencephalogram activity), nursing intervention types (such as medication adjustment, rehabilitation training), and time features (such as the occurrence time and duration of nursing behaviors); the output is the change curve of key indicators for the patient's postoperative recovery (such as the recovery trend of blood oxygen saturation). The network structure uses multiple layers of LSTM (Long Short-Term Memory) units to capture the time dependence of nursing behaviors on recovery indicators; the model is trained using a historical dataset, where the ratio of the training set to the validation set is 8:2, the optimization algorithm is Adam, and the loss function is mean squared error (MSE). In practical applications, taking a patient after cerebral aneurysm surgery as an example, the nursing behavior sequence of a certain patient is input, and the time node and trend of the postoperative blood pressure recovering from 140 / 90 mmHg to 120 / 80 mmHg are predicted, providing data support for nursing intervention.

[0070] Step S22: Obtain historical nursing path data;

[0071] An embodiment of the present invention extracts the postoperative nursing data of neurosurgery patients in the past three years from the hospital nursing information management system. The data sources include electronic medical records, medication records, monitoring data, and nursing plans, covering different surgical types (such as craniotomy for tumor resection, intracranial aneurysm clipping), patient age groups (such as young, middle-aged, elderly), and different underlying diseases (such as diabetes, hypertension). By associating various data through the patient ID, the complete nursing path of each patient is extracted, including records of changes in monitoring indicators, medication adjustments, rehabilitation plans, etc. from the first day of postoperative care to postoperative discharge. To ensure data quality, duplicate records are removed using hash verification, and the data is structurally stored using a time series-based segmented storage technology, finally forming a historical nursing path dataset containing the entire process of a patient's postoperative care.

[0072] Step S23: Extract the typical recovery path features of patients with different surgical types, age groups, and underlying diseases from the historical nursing path data, so as to obtain typical recovery path data;

[0073] In the embodiments of the present invention, the historical care path data generated by S22 is used to extract the typical recovery path characteristics of different groups according to the surgical type, patient age group, and underlying diseases. First, for each group, the dynamic time warping (DTW) algorithm is used to perform similarity analysis on the time series of the care path to identify the main path of the recovery process. Secondly, key nodes in the recovery path are extracted, such as the blood oxygen saturation reaching 90% on the 5th day after surgery and the completion of the first out-of-bed training on the 10th day after surgery. Taking patients with cerebral aneurysms (aged 50 - 60 without underlying diseases) as an example, the typical recovery path characteristics include the blood pressure stabilizing at 130 / 85 mmHg on the 3rd day after surgery, the reduction of medication dosage completed on the 7th day after surgery, and the changes in monitoring indicators and behavior records at the time of discharge on the 15th day after surgery. Finally, the typical recovery path data is output to provide input support for the optimization of the care path.

[0074] Step S24: Establish a mapping relationship between the recovery status and the nursing intervention nodes according to the typical recovery path data and the postoperative recovery index prediction model to obtain the nursing path prediction data; perform optimization analysis based on resource requirements on the nursing path prediction data to obtain the nursing path optimization data.

[0075] In the embodiments of the present invention, the typical recovery path data of S23 and the postoperative recovery index prediction model of S21 are used to establish a mapping relationship between the recovery status and the nursing intervention nodes. First, taking the recovery status output by the prediction model (such as stable blood pressure and normal electroencephalogram activity recovery) as the target variable, the Bayesian network is used to analyze the influence weights of different nursing behaviors (such as medication dosage adjustment and rehabilitation training time) on the recovery status, so as to establish a causal mapping relationship between the status and the intervention. Secondly, combined with the nursing path prediction data, the integer linear programming optimization model is used to analyze the resource requirements, with the objective function of minimizing the occupation of nursing manpower and materials, and setting constraint conditions (such as the time interval of nursing behaviors and the priority of resource allocation). Taking patients after cerebral aneurysm surgery as an example, the optimized path suggests adjusting the blood oxygen monitoring frequency to once every 4 hours, and concentrating the rehabilitation training on the 7th - 10th day after surgery to improve the resource utilization rate. Finally, the nursing path optimization data is output to provide a scientific basis for formulating an efficient nursing plan.

[0076] By constructing a prediction model for postoperative recovery indicators based on a deep neural network, the present invention can simulate the dynamic change process of a patient's postoperative recovery. Such a model can accurately predict the postoperative recovery trend of a patient according to the nursing behavior data extracted from the nursing behavior framework diagram, and identify key recovery indicators and potential health risks. Through the powerful learning ability of the deep neural network, the model can process complex multi-dimensional data, comprehensively consider various influencing factors, such as the individual differences of patients, the type of surgery, postoperative nursing interventions, etc., so as to provide a more accurate recovery assessment for doctors and help formulate personalized nursing plans. By obtaining historical nursing path data, valuable empirical data is provided for model training and verification. These historical data can reflect the actual effects of different types of surgery, patient groups, and different intervention measures on postoperative recovery, thus providing a practical basis for the analysis and model construction of subsequent steps. By fully mining and applying these data, the accuracy and reliability of the prediction model can be improved. Extracting typical recovery path features from historical nursing path data, especially for different types of surgery, age groups, and patients with underlying diseases, helps to identify the recovery patterns of different groups. This step can reveal the possible change trajectories of various patients during the postoperative recovery process through classified analysis of the data, and then form targeted nursing path data. Such cluster analysis helps to accurately formulate personalized nursing strategies for different patient groups and ensure the optimization of the recovery process. Combining the typical recovery path data with the postoperative recovery indicator prediction model, a mapping relationship between the recovery state and the nursing intervention nodes is established to obtain nursing path prediction data. This process provides a clear nursing path prediction chain for clinical practice, and nursing measures can be adjusted in a timely manner according to the predicted recovery state, thereby improving the efficiency and effect of nursing interventions. At the same time, through the optimization analysis based on resource requirements, the nursing path is further optimized to ensure the reasonable allocation and utilization of nursing resources. Through this optimization process, resource waste can be avoided, the recovery efficiency of patients can be maximally improved, and unnecessary nursing interventions can be reduced. Through precise data analysis, model prediction, and path optimization, the intelligent level of postoperative recovery management can be effectively improved. These steps not only provide personalized nursing plans for patients, but also help medical staff better understand the key factors in the postoperative recovery process, so as to make more scientific decisions. At the same time, through resource optimization, the efficient utilization of nursing resources is ensured, the overall quality of nursing work and the recovery speed of patients are improved.

[0077] Preferably, step S24 includes the following steps:

[0078] Step S241: Establish a mapping relationship matrix between the recovery state and the nursing intervention nodes according to the typical recovery path data and the postoperative recovery indicator prediction model, and quantify the conditional probability and influence weight of different nursing interventions on the patient's recovery state, so as to obtain the probability transition matrix of the mapping relationship;

[0079] Step S242: Generate nursing path prediction data according to the probability transition matrix, and conduct a multi-dimensional resource requirement assessment on the nursing path prediction data, so as to construct a comprehensive resource optimization model, where the multi-dimensional resource requirements include labor costs, utilization rate of medical equipment, and drug consumption;

[0080] Step S243: Solve the optimization of resource allocation based on resource utilization efficiency for the comprehensive resource optimization model through a constraint optimization algorithm, so as to obtain optimized resource allocation data;

[0081] Step S244: Dynamically adjust the nursing path according to the optimized resource allocation data based on the patient's real-time recovery status and resource constraints, so as to obtain nursing path optimization data.

[0082] Based on the typical recovery path data and the postoperative recovery index prediction model, the embodiments of the present invention construct a mapping relationship matrix between the patient's recovery status and the nursing intervention nodes. First, classify the key nursing behaviors and recovery statuses (such as stable blood pressure, normal electroencephalogram recovery) in the typical recovery path data, and extract the corresponding nursing interventions (such as adjusting the drug dosage, increasing rehabilitation training) and their occurrence times; then, use the Bayesian network model to calculate the conditional probabilities of the causal relationships between the nursing interventions and the recovery statuses, and obtain the quantitative influence weights of different intervention measures on the recovery status. For example, for a patient with a postoperative cerebral aneurysm, the conditional probability of adjusting the drug dosage on blood oxygen recovery is 0.7, and the weight of rehabilitation training on heart rate stability is 0.6. Finally, convert the conditional probabilities into a probability transition matrix, where the matrix elements represent the probability values from the current recovery status to the target recovery status, and output this mapping relationship in matrix form to provide data support for subsequent path prediction. According to the probability transition matrix of S241, use the Markov chain to simulate the postoperative recovery process of different patients under specific nursing paths, and generate nursing path prediction data. Taking the patient after brain tumor resection as an example, input their individual nursing behavior sequence, and output the dynamic change trends of postoperative blood pressure and electroencephalogram activities and the time nodes of the corresponding intervention measures. At the same time, evaluate the multi-dimensional resource requirements according to the prediction data, and set the evaluation dimensions to include labor costs (such as daily nursing time occupation), medical equipment utilization rates (such as the usage rate of monitors), and drug consumption (such as the dosage of anticoagulants). In the resource requirement evaluation, combine the resource occupancy records and time nodes of each stage in the nursing path, calculate the total resource consumption and decompose it into specific demand values for each dimension. For example, predict that a patient needs 2 hours of nursing per day, the usage frequency of the monitor is 60%, and the dosage of anticoagulants is 50 mg / day, and generate a comprehensive resource requirement report to provide a basis for the construction of the resource optimization model. Based on the comprehensive resource requirement evaluation results of S242, construct a resource optimization model with the maximum resource utilization efficiency as the objective function. The input of the optimization model is the multi-dimensional resource requirement data, and the constraint conditions include labor availability (such as the daily working hours of nurses ≤ 8 hours), equipment usage restrictions (such as a single monitor supporting a maximum of 4 patients), and drug supply quantities (such as the daily inventory limit of anticoagulants). Use a constraint optimization algorithm, such as the particle swarm optimization algorithm (PSO), to solve the optimal solution of the model and output the optimized resource allocation data. Taking the patient after cerebral aneurysm surgery as an example, the optimization results show that the nursing time is adjusted from 2 hours per day to 1.5 hours, the sharing rate of monitors is increased to 80%, and the dosage of anticoagulants is reduced to 40 mg / day, while ensuring the stability of the recovery indicators, and optimizing the efficiency and cost of resource allocation. According to the optimized resource allocation data of S243, dynamically monitor the patient's real-time recovery status, and adjust the nursing path in combination with resource constraints. Use the real-time monitoring data (such as blood oxygen saturation of 90%, heart rate of 75 beats per minute) as the input to determine whether the patient meets the trigger conditions for path adjustment (such as the recovery indicators not meeting the standards or resource occupancy exceeding the limit).When the trigger condition is met, call the optimized resource allocation data to adjust the nursing behavior sequence. For example, when the blood oxygen level of a patient on the 5th day after surgery does not reach the target, dynamically increase the drug dosage; when the utilization rate of the monitor exceeds 90%, postpone some non-critical monitoring tasks. In this way, the nursing path of the patient is dynamically updated to generate optimized data for the adjusted nursing path. For patients after cerebral aneurysm surgery, the path adjustment results may include adjusting the daily drug dosage to 45 mg and reducing the blood pressure monitoring frequency to once every 6 hours, so as to achieve efficient use of resources and optimization of the patient's recovery status.

[0083] The present invention establishes a mapping relationship matrix between the recovery state and the nursing intervention nodes by combining typical recovery path data and a postoperative recovery index prediction model. This step quantifies the conditional probability and influence weight of different nursing intervention measures on the patient's recovery state, providing a scientific basis for establishing accurate nursing path prediction data. By quantifying the impact of nursing interventions on the recovery state, it is possible to better identify key intervention nodes and nursing steps, improving the pertinence and effectiveness of nursing behaviors. In addition, through the construction of a probability transition matrix, the nursing path prediction becomes more scientific and systematic, capable of providing detailed decision-making support for subsequent nursing interventions and resource allocation. The probability transition matrix is used to generate nursing path prediction data, and further, a multi-dimensional resource requirement assessment is carried out on it. These multi-dimensional resource requirement assessments not only consider the labor cost but also cover factors such as the utilization rate of medical equipment and drug consumption, comprehensively reflecting the resource consumption of the nursing path. Through this comprehensive resource requirement analysis, the resource utilization status during the nursing process can be accurately evaluated, providing data support for resource optimization. This step helps nursing managers understand and master the resource consumption characteristics of the nursing path, facilitating the adoption of reasonable resource allocation strategies to ensure the efficient use of resources. The comprehensive resource optimization model is solved through a constraint optimization algorithm to obtain optimized resource allocation data. The optimization algorithm can reasonably arrange the allocation and use of resources according to the availability and utilization efficiency of resources, aiming to maximize the resource utilization efficiency. Through this step, resource waste can be avoided, and the resource allocation can be flexibly adjusted according to the actual situation of the patient to ensure that the patient receives appropriate nursing resources. The optimization of resource allocation not only helps improve nursing efficiency but also reduces unnecessary cost expenditures. After obtaining the optimized resource allocation data, the nursing path is dynamically adjusted according to the patient's real-time recovery state and resource constraints. This dynamic adjustment process ensures that the nursing path always adapts to the individual needs and actual recovery status of the patient, making the nursing plan more flexible and targeted. By adjusting the nursing path in real time, the nursing process can more accurately respond to the patient's recovery needs, further improving the patient's recovery efficiency and nursing effect. In addition, the adjustment based on real-time data also helps reduce unnecessary nursing interventions during the patient's recovery process, enhancing the utilization efficiency of nursing resources. In summary, through accurate nursing path prediction, resource requirement assessment, and the application of optimization algorithms, the personalization and intelligence level of the nursing path are effectively improved. These steps not only optimize the allocation of nursing resources, reduce unnecessary resource waste, but also ensure that patients can receive highly targeted and effective nursing interventions, ultimately improving the patient's recovery effect and nursing quality. In addition, through the dynamic adjustment of the nursing path, it is ensured that during the patient's recovery process, the nursing intervention can respond to the patient's changes in real time, further enhancing the efficiency and quality of nursing services.

[0084] Preferably, the nursing goal parametric evaluation module specifically performs the following steps:

[0085] Step S31: Obtain nursing rehabilitation index data, including key physiological parameters of the patient's postoperative recovery and the level of functional recovery.

[0086] Step S32: Perform time series matching on the nursing path optimization data and the nursing rehabilitation index data to construct a correlation sequence of the nursing path and rehabilitation index based on the time axis, thereby obtaining the nursing path-rehabilitation correlation sequence data.

[0087] Step S33: Conduct feature analysis on the nursing path-rehabilitation correlation sequence data, extract the quantitative correlation features between different nursing paths and rehabilitation effects, thereby obtaining the quantitative correlation feature data.

[0088] Step S34: Generate a multi-objective optimization function according to the quantitative correlation feature data, and quantitatively express the differences in resource allocation, intervention measures, and rehabilitation goals, thereby obtaining the input data for the optimization model.

[0089] Step S35: Conduct regression analysis based on the input data of the optimization model through parametric modeling methods, and perform nursing goal evaluation processing, thereby obtaining the nursing goal evaluation model.

[0090] In the embodiments of the present invention, key physiological parameters and functional recovery levels of postoperative recovery are obtained through the real-time monitoring devices and daily assessment records of patients. Specifically, it includes physiological indicators such as blood oxygen saturation, heart rate, and respiratory rate, as well as motor ability scores (such as walking distance or grip strength test) and neurological function scores (such as simplified ADL daily living ability assessment). For example, for patients after stroke surgery, blood oxygen saturation (normal value ≥ 95%), walking distance (daily target of 1000 meters), and electroencephalogram activity score (normal range of 10 - 15 points) are collected. By collecting and recording these index data daily, a nursing and rehabilitation index database containing time nodes is formed, providing input for subsequent correlation analysis. Optimize the time dimension of the nursing path optimization data and the nursing and rehabilitation index data, and perform a time series matching operation on the two. First, align the intervention measures (such as daily drug dosage, nursing frequency) in the nursing path optimization data with the recovery status in the rehabilitation index data along the time axis to generate a time series correlation table; then, complete the time points of the data with misaligned time through interpolation to ensure the one-to-one correspondence of the two sequences. For example, the nursing path optimization data of a certain patient after stroke surgery shows that physical therapy is increased on the 3rd day and the drug dosage is reduced on the 5th day, which is correlated with the rehabilitation index data (the walking distance increases by 200 meters on the 3rd day and the blood pressure returns to normal on the 5th day) to form complete nursing path - rehabilitation correlation sequence data. Analyze the characteristics of the nursing path - rehabilitation correlation sequence data to extract the quantitative impact of different nursing paths on the rehabilitation effect. Use correlation analysis methods (such as Pearson correlation coefficient) to quantify the relationship between intervention measures and recovery indicators. For example, evaluate the impact of daily drug dosage adjustment on blood oxygen saturation recovery. Further, extract composite intervention characteristics (such as the enhanced effect of the combined action of drugs and physical therapy on walking ability recovery) through multiple regression analysis. Taking patients after stroke surgery as an example, the results show that for every 10% increase in drug dosage, the blood oxygen saturation increases by 5%, and for every increase in physical therapy frequency by once / day, the walking distance increases by 300 meters, generating a quantitative correlation feature data set. Based on the quantitative correlation feature data, construct a multi-objective optimization function, including the optimization of resource allocation, the adjustment of intervention measures, and the quantitative expression of the difference in the achievement rate of rehabilitation goals. For example, taking blood oxygen saturation and walking distance as rehabilitation goals, construct the objective function: Maximize (rehabilitation goal achievement rate) - Minimize (resource usage). The input data includes intervention measures (drug dosage, nursing frequency), resource costs (drug costs, nursing time), and target values (blood oxygen saturation ≥ 95%, walking distance ≥ 1000 meters). By assigning weights to each target, construct a comprehensive optimization objective function to provide input for optimization solving. Use parametric modeling methods to perform regression analysis according to the optimization model input data in S34 to generate an evaluation model.A polynomial regression model is used to fit the relationship between resource allocation, intervention measures, and the rehabilitation goal achievement rate. For example, the fitting formula is: Rehabilitation achievement rate = 0.8 × Drug dose adjustment coefficient + 0.6 × Nursing frequency adjustment coefficient - 0.2 × Resource usage cost. Through model training and cross-validation, the accuracy and robustness of the model are evaluated. Taking stroke patients after surgery as an example, the regression analysis results show that the drug dose adjustment coefficient makes the greatest contribution to the rehabilitation achievement rate, and the accuracy of the model in predicting that the walking distance reaches the target is 92%. Finally, a nursing goal evaluation model is generated to provide decision-making support for real-time adjustment of the nursing path and optimization of intervention measures.

[0091] The nursing rehabilitation index data for obtaining the key physiological parameters and functional recovery levels of patients after surgery in this invention is the basis for nursing goal assessment. By collecting this data, the actual situation of patients' postoperative rehabilitation can be accurately reflected, covering the changes in key physiological parameters such as cardiovascular, respiratory, and neurological, as well as the functional recovery of patients. This step helps to ensure the timeliness and accuracy of nursing interventions, enabling nursing measures to closely follow the patient's rehabilitation progress, promptly detect problems during recovery, and thus provide a scientific basis for optimizing subsequent nursing paths. By correlating the nursing path optimization data with the nursing rehabilitation index data through time series matching, the relationship between nursing interventions and rehabilitation effects can be revealed. Time series matching ensures the consistency and synchronization of data, facilitating an understanding of the impact of nursing interventions on patient recovery at specific time points. The effect of this step is that it can clearly display the critical moments in the nursing process and the changes in rehabilitation indicators, providing a basis for adjusting the nursing path. At the same time, this data correlation on the time axis can help identify the key factors affecting rehabilitation effects, ensuring the continuous optimization of the nursing plan. Conducting feature analysis on the nursing path-rehabilitation correlation sequence data and extracting the quantitative correlation features between different nursing paths and rehabilitation effects can provide an in-depth understanding of the specific associations between various nursing measures and patient rehabilitation effects. Through quantitative analysis, the effectiveness of each nursing path can be objectively evaluated, and personalized nursing plans can be customized for different patient groups. The effect of this step is to provide a detailed analysis perspective, making the adjustment of the nursing path no longer rely on subjective experience but on data-driven analysis results, thereby improving the scientific nature and precision of nursing. Generating a multi-objective optimization function can simultaneously consider the differences among resource allocation, intervention measures, and rehabilitation goals, weighing multiple goals in the nursing process. By quantitatively expressing these goal differences, the optimization model can achieve efficient resource allocation while ensuring the maximization of patient recovery goals. This step helps to achieve the maximum benefit of nursing interventions under limited resources, avoid over-intervention or resource waste, and improve the overall efficiency of nursing services. By processing the optimization model through regression analysis, the achievement of nursing goals can be quantified, and the effectiveness of different nursing measures can be evaluated. This evaluation not only reflects the degree of achievement of nursing goals but also can identify which nursing interventions are most conducive to patient rehabilitation. Through parametric modeling, the nursing goal assessment model can be continuously optimized according to actual data, ensuring the scientific nature and precision of nursing goals. The effect of this step is to provide quantitative evaluation indicators for nursing managers, helping them to be more objective and data-driven in decision-making. Overall, the above steps provide a scientific and systematic data support and analysis framework for optimizing the neurosurgical nursing process. By obtaining patients' rehabilitation data, time series matching, feature analysis, multi-objective optimization, and parametric modeling, the relationship between nursing paths and rehabilitation effects can be accurately quantified, and the allocation of nursing resources can be optimized.This not only improves the personalization and accuracy of the care pathway, but also enhances the effectiveness of nursing intervention measures, contributing to the improvement of the postoperative recovery speed and effect of patients. In addition, the implementation of this process provides a data-based nursing goal assessment model for medical institutions, which can provide important reference basis for future nursing decisions.

[0092] Preferably, step S33 includes the following steps:

[0093] Step S331: Perform multi-level feature deconstruction processing on the care pathway-rehabilitation correlation sequence data in terms of time scale, feature intensity, and complexity dimension, so as to obtain care pathway-rehabilitation multi-dimensional feature data;

[0094] Step S332: Analyze the topological structure of the correlation network between the care pathway and rehabilitation indicators according to the care pathway-rehabilitation multi-dimensional feature data, identify key nodes, correlation paths, and propagation modes, so as to establish a network mapping model of the care pathway-rehabilitation correlation;

[0095] Step S333: Perform dimensionality reduction processing on the care pathway-rehabilitation correlation sequence data according to the network mapping model, and screen out the key feature dimensions with the highest information entropy and discrimination, so as to obtain quantitative correlation features.

[0096] In the embodiments of the present invention, for the nursing path-rehabilitation associated sequence data, multi-level feature deconstruction processing is performed using time scale decomposition, feature intensity analysis, and complexity dimension modeling methods. The specific operations include: First, the time series data is segmented and analyzed according to different time scales such as daily, weekly, and monthly, and statistical features such as the mean, extreme value, and change rate within the time window are extracted. For example, the change rate of blood oxygen saturation (unit: % / week) is calculated on a weekly basis; Second, through feature intensity analysis, the influence degree of nursing intervention measures (such as drug dose adjustment) on rehabilitation indicators (such as heart rate recovery) is normalized, and the influence degree is standardized to the 0-1 interval; Finally, through complexity dimension modeling, a complexity quantitative analysis is performed on the change trend of the patient's rehabilitation indicators. For example, the sample entropy algorithm is used to calculate the complexity value of blood pressure change. Combining these steps, nursing path-rehabilitation multi-dimensional feature data including time, intensity, and complexity dimensions is generated, providing input for subsequent network analysis. Based on the nursing path-rehabilitation multi-dimensional feature data, an association network is constructed and its topological structure is analyzed. The specific methods include: First, an association rule mining algorithm is used to identify the significant associations between intervention measures and rehabilitation indicators in the nursing path. For example, the confidence level of the increase in the frequency of physical therapy by each time / day and the improvement of the walking ability score is 0.85; Then, a network modeling tool (such as Gephi or the Python NetworkX library) is used to generate a nursing path-rehabilitation association network with nodes representing nursing intervention measures and rehabilitation indicators, and edges representing the association relationship between the two; Subsequently, the topological structure of the network is analyzed. Key nodes are identified through centrality analysis (such as the dominant influence of drug adjustment on the rehabilitation effect), key association paths are identified through the shortest path algorithm, and the propagation modes of different nursing behaviors are identified through modularity analysis. For example, the post-stroke care network shows that physical therapy and motor ability recovery form a key path, and the edge weight is 0.75, indicating the importance of this path. Finally, a network mapping model of the nursing path-rehabilitation association is established, providing a basis for subsequent optimization. According to the network mapping model, dimensionality reduction processing is performed on the nursing path-rehabilitation associated sequence data, and the key feature dimensions with the highest information entropy and discrimination are selected. The specific steps include: First, the principal component analysis (PCA) method is used to perform dimensionality reduction processing on each feature dimension in the network mapping model, calculate the contribution rate of the principal component features, and retain the principal components with a cumulative contribution rate of more than 95%; Then, based on the information entropy formula, the feature information entropy values of each principal component are calculated, and the feature dimension with the highest information entropy is selected as the key feature. For example, the feature information entropy of drug dose adjustment on heart rate recovery is 0.92, and the feature information entropy of walking ability score on blood oxygen saturation is 0.87; Finally, through discrimination analysis, the performance differences of the feature dimensions in different patient groups (such as age group, disease severity) are evaluated, and the dimension with the highest discrimination is selected as the quantitative association feature. For example, the screening results show that the discrimination between drug adjustment and blood oxygen recovery is 0.85, and finally the key quantitative association features are obtained, providing high-value data for the input of the optimization model.

[0097] Through multi-level feature deconstruction of the nursing path-rehabilitation correlation sequence data in terms of time scale, feature intensity, and complexity dimension, the present invention can comprehensively refine the complex relationship between the nursing path and rehabilitation indicators. This processing method helps to understand the connection between nursing intervention and rehabilitation effect from multiple perspectives (such as time dimension, feature change intensity, complexity, etc.), so as to obtain more detailed nursing path data. This multi-level feature deconstruction provides more valuable information, laying a solid foundation for the subsequent optimization of the nursing path. For example, it can identify which time points and which nursing measures play key roles in the rehabilitation process, and which features or interventions have greater intensity or complexity impacts. Ultimately, this provides a scientific basis for the formulation of personalized nursing plans for patients, helping to accurately predict the rehabilitation process of patients. This step can reveal the complex interaction relationship between the nursing path and rehabilitation effect by analyzing the topological structure of the correlation network between the nursing path and rehabilitation indicators. Topological structure analysis helps to identify key nodes and influencing factors in the nursing process. These key nodes may be specific nursing intervention measures, rehabilitation indicators, or time nodes, which play a decisive role in the overall nursing path. In addition, the path and pattern of the spread of nursing measures can also be identified during the analysis process, thereby helping to identify the most effective nursing intervention sequences and patterns. This analysis not only improves the scientific nature of nursing path design, but also reveals how nursing interventions dynamically spread and interact during the patient's recovery process, so as to optimize nursing plans and resource allocation and improve the treatment effect. By performing dimensionality reduction processing on the network mapping model, the core features in the nursing path-rehabilitation correlation data can be effectively extracted, and redundant information can be excluded. This process helps to simplify the data structure and focus on the most representative and influential feature dimensions, thereby improving the efficiency and accuracy of data analysis. By screening key features with the highest information entropy and discrimination, those features with a large discrimination between nursing effects and rehabilitation effects can be accurately found, which helps to further optimize nursing intervention measures. For example, the effects of certain nursing measures may vary significantly among different patients, and these differences can be quantified by the level of information entropy, helping nurses to make more accurate decisions in the diverse patient needs. Ultimately, the obtained quantitative correlation features will provide a more explicit and reliable basis for future nursing path design. The implementation of the above steps helps to deeply analyze the relationship between the nursing path and rehabilitation through multi-dimensional feature deconstruction, topological structure analysis, and data dimensionality reduction. This not only improves the optimization and personalized design of the nursing path, but also enhances the pertinence of nursing intervention measures and the scientific nature of effect evaluation. By quantifying key features, identifying effective intervention paths, and optimizing the allocation of nursing resources, nurses can make more accurate and reasonable decisions based on data, thereby improving the patient's recovery speed and effect. These steps lay a foundation for the dynamic optimization of the neurosurgical nursing process and a data-driven decision support system, ultimately helping to improve nursing quality, resource utilization efficiency, and patient rehabilitation experience.

[0098] Preferably, the nursing execution dynamic iteration module specifically performs the following steps:

[0099] Step S41: Real-time track the allocation and execution of current neurosurgical nursing resources according to the nursing goal evaluation model, so as to obtain real-time nursing path data;

[0100] Step S42: Generate patient's actual rehabilitation index data according to the real-time nursing path data;

[0101] Step S43: Compare the patient's actual rehabilitation index data with the nursing rehabilitation index data, and analyze the nursing path deviation and rehabilitation index difference, so as to generate nursing execution deviation data;

[0102] Step S44: Identify the key factors leading to the deviation based on statistical analysis according to the nursing execution deviation data, and extract deviation adjustment features, so as to obtain deviation adjustment feature data;

[0103] Step S45: Generate a feedback optimized nursing plan according to the nursing execution deviation data, and dynamically adjust nursing intervention measures and update the nursing behavior framework diagram according to the optimized nursing plan, so as to obtain neurosurgical nursing data and an updated nursing behavior framework diagram.

[0104] Based on the nursing goal assessment model, the embodiments of the present invention track the allocation and implementation of current neurosurgical nursing resources in real time. The specific operations include: collecting the time node data of the current nursing activities of patients, the records of intervention measures, and the data on resource utilization through sensors, handheld devices, and electronic health record systems. For example, the nursing system records the daily physical therapy duration of patients (unit: minutes), and the monitoring device records the number of times of drug use (unit: times / day). At the same time, these real-time data are uniformly stored and processed through a cloud computing architecture, and the nursing path data are dynamically updated in combination with the patient's recovery progress. For example, the physical therapy frequency is increased to 2 times a day starting from the 5th day after surgery, generating real-time nursing path data. The actual rehabilitation index data of the patient are extracted using the real-time nursing path data. The specific methods include: first, the key physiological parameters and functional recovery levels of the patient after surgery are collected in real time, such as the resting heart rate is recorded by a heart rate monitor (unit: beats / min), and the walking distance is recorded by a mobile device (unit: meters / day); then, these data are matched with the established time nodes in the nursing path, and the rehabilitation index data for the corresponding time period are generated. For example, by matching with the path time nodes, the heart rate recovery situation and the walking ability recovery trend data on the 7th day after surgery are generated as the actual rehabilitation index data of the patient. The actual rehabilitation index data of the patient are compared with the nursing rehabilitation index data, and the nursing path deviation and the rehabilitation index difference are analyzed. The specific operations include: first, comparing the actual and expected index values. For example, the actual value of the resting heart rate of the patient on the 5th day after surgery is 78 beats / min, while the expected value is 72 beats / min, and the difference is 6 beats / min; then, the deviation trend is evaluated based on time series analysis, and whether the deviation is significant is verified through statistical methods (such as t-test). Next, based on the deviation, factors such as the adjustment of the time nodes of the nursing path and the change in the implementation frequency of intervention measures are analyzed to generate nursing execution deviation data. For example, it is found that the physical therapy is not carried out 2 times a day as planned, but actually 1 time a day. Statistical analysis is performed on the nursing execution deviation data to identify the key factors causing the deviation and extract the deviation adjustment characteristics. The specific methods include: first, a multivariate regression model is used to analyze the key factors of the nursing execution deviation. For example, the influence degree of the drug dose adjustment on the rehabilitation index difference is analyzed; then, the correlation analysis method is used to identify the variables that significantly affect the deviation, such as a 10% increase in the drug dose corresponding to a 5-beat / min decrease in the resting heart rate difference; finally, the key adjustment characteristics, such as the postponement of the time node and the adjustment of the intervention measure frequency, are extracted to form the deviation adjustment characteristic data, providing a basis for optimizing the nursing plan. Based on the nursing execution deviation data, a feedback optimized nursing plan is generated and the intervention measures are dynamically adjusted, and finally the nursing behavior framework diagram is updated.The specific operations include: First, formulate an optimized nursing plan by combining deviation adjustment feature data, such as increasing the daily physical therapy frequency from once to twice, or adjusting the medication to reduce the dosage by 5 milligrams each time; then, optimize the feasibility of the plan through dynamic model simulation and evaluate its impact on resource consumption and patient recovery; next, implement the optimized plan and record the adjusted nursing path; finally, update the nursing behavior framework diagram, including new time nodes, newly added intervention measures, and resource allocation situations, such as adding physical therapy nodes and reallocating treatment resources. Ultimately, generate neurosurgical nursing data containing optimized adjustment data and an updated nursing behavior framework diagram for subsequent system iterative optimization.

[0105] Through real-time tracking of the allocation and execution of nursing resources, the present invention can dynamically monitor the actual implementation progress of the nursing path. The implementation of this step helps to immediately detect possible resource shortages or mismatches in the nursing process, and timely adjust the nursing path, so as to ensure that patients receive timely and adequate nursing interventions. Real-time data tracking enables nursing staff to flexibly respond to emergencies, ensure the smooth execution of the nursing plan, and effectively avoid nursing delays or resource waste. Real-time generation of actual rehabilitation index data of patients provides the latest patient recovery status for nursing staff. This step helps nurses or doctors to grasp the patient's rehabilitation progress in real time and timely evaluate the effect of nursing interventions. If a patient's rehabilitation index does not meet the expectation, nursing measures can be quickly adjusted to avoid unnecessary risks during the patient's rehabilitation process. In addition, the digital management of real-time monitoring of rehabilitation indicators helps to improve the personalized nursing effect of patients. Comparing and analyzing the differences between the actual rehabilitation indicators of patients and the expected nursing rehabilitation indicators can help the nursing team discover deviations in nursing execution, and then evaluate the effectiveness and deficiencies of the current nursing path. This analysis helps to identify potential problems or unexpected rehabilitation progress of patients during the recovery process, and provides comparison data as the basis for optimizing nursing measures. The generated nursing execution deviation data is crucial for discovering potential risk points and provides data support for subsequent intervention decisions. Identifying the key factors of nursing execution deviation based on statistical analysis can provide specific improvement directions for the nursing team. This analysis can reveal the key factors affecting the patient's rehabilitation process, such as the timing, type, intensity of nursing interventions, etc., and propose targeted adjustment measures. By extracting deviation adjustment features, nursing staff can clearly understand which nursing behaviors are the main causes of deviations, so as to prescribe the right medicine and adjust nursing measures. This process can accurately locate problems and customize appropriate nursing adjustment plans for each patient, thereby improving the accuracy and effectiveness of nursing interventions. By generating feedback to optimize the nursing plan and dynamically adjusting nursing intervention measures, continuous optimization of the nursing path can be achieved. The implementation of this step can ensure that nursing measures are adjusted according to the patient's real-time rehabilitation situation and nursing execution deviations, maximizing the effect of patient rehabilitation. By updating the nursing behavior framework diagram, the improvement and adjustment of the nursing path can be timely reflected, enabling nursing staff to clearly understand the chronological relationship and change trend of nursing behaviors and intervention measures. Nursing staff can adjust nursing strategies in real time based on this dynamically updated framework diagram to ensure that patients receive the most effective nursing interventions at different rehabilitation stages. This closed-loop feedback mechanism not only improves the adaptability of nursing effects, but also optimizes the allocation and management of nursing resources. These steps effectively enhance the dynamic management ability of the neurosurgical nursing path by combining multiple functions such as real-time tracking, deviation analysis, key factor identification, and feedback optimization. The implementation of each step can be finely adjusted according to the actual situation of patients to ensure the accuracy and personalization of the nursing plan.The timely detection and adjustment of nursing execution deviations can help reduce uncertainties and potential risks during the nursing process, and ensure the efficiency and safety of the patient's rehabilitation process. At the same time, by continuously updating the nursing behavior framework diagram and optimizing the nursing plan, the nursing team can continuously improve the quality of nursing services, and ultimately maximize the patient's rehabilitation effect. This nursing process based on data and dynamic adjustment not only helps improve the patient experience, but also improves the work efficiency of the nursing team.

[0106] Preferably, step S45 includes the following steps:

[0107] Generate optimized nursing plan data based on deviation adjustment feature data, where the optimized nursing plan includes dynamic adjustment of nursing intervention measures, resource allocation plan data, and nursing path optimization suggestions; apply the optimized nursing plan to the nursing behavior framework diagram, and update the time series and association relationships of nursing behaviors, so as to generate neurosurgical nursing data and an updated nursing behavior framework diagram.

[0108] In the embodiments of the present invention, optimized nursing plan data is generated based on nursing execution deviation data and deviation adjustment characteristic data. The specific operations include: identifying key factors affecting nursing effects through data analysis and regression models (such as linear regression or decision tree algorithms), and adjusting nursing intervention measures based on this. Taking a patient in the postoperative recovery period as an example, assume that on the 5th day after the operation, the patient's heart rate did not reach the expected recovery level, and the deviation characteristics indicate improper drug use. Based on this information, the optimization plan includes increasing the frequency of drug dose adjustment (for example, increasing the drug dose adjustment once a day), and adjusting other nursing measures according to the deviation analysis suggestions, such as strengthening the frequency of rehabilitation training. Then, combined with the patient's individual situation and resource constraints, optimize the resource allocation plan. For example, according to the prediction of drug consumption, optimize the procurement and distribution of drugs to make it conform to the new nursing plan. Finally, the optimized nursing plan data includes adjusted nursing intervention measures, resource allocation plans, and nursing path optimization suggestions, forming a comprehensive dynamic nursing intervention plan. After generating the optimized nursing plan data, apply it to the nursing behavior framework diagram, and update the time series and correlation relationships of nursing behaviors. The specific operations include: First, use the existing nursing intervention measure nodes in the nursing behavior framework diagram to insert the optimized measures into appropriate time nodes. For example, reflect the frequency changes of drug adjustment and rehabilitation treatment in the time series. Assume that the drug adjustment node in the original plan is set on the 3rd day after the operation, and after optimization, the adjustment frequency needs to be once every two days starting from the 2nd day after the operation, and this change will be clearly shown in the diagram. Then, according to the optimized nursing plan, modify the time series relationship in the nursing path and update the correlation pattern of the intervention measures. For example, if the newly adjusted drug use frequency has a significant impact on heart rate recovery, then form a new causal relationship path in the framework diagram to show the effect of drug adjustment on heart rate recovery. At the same time, update the correlation relationship to ensure that the new nursing intervention measures can be highly correlated with the changes in the patient's recovery status. Finally, generate an updated nursing behavior framework diagram, which contains all optimized nursing paths, resource allocations, and intervention measures, forming a dynamic and real-time nursing path management model. Through the cloud computing platform, this updated diagram can be synchronized to the nursing management system in real time to ensure the implementation of real-time monitoring and adjustment.

[0109] By carefully analyzing the deviations in nursing execution, the present invention can identify which links in the nursing process are insufficient and whether the unreasonable intervention measures, resource allocation, or nursing path lead to the unexpected progress of the patient's recovery. Optimizing the nursing plan can target the problem and ensure that the deviations can be corrected in the shortest time. Since the recovery situation of each patient is different, it is necessary to adjust nursing interventions and resource allocation in a timely manner according to the actual situation. Optimizing the nursing plan can improve the personalization level of nursing services and provide the most suitable nursing strategies for the unique needs of patients. The optimized nursing plan includes data on resource allocation plans, which can ensure the most reasonable utilization of medical resources (such as human resources, equipment, drugs, etc.), avoid waste of resources, and ensure the efficient execution of nursing measures and interventions. As the patient's condition changes, the nursing needs may fluctuate. By dynamically adjusting nursing intervention measures, the actual state changes of the patient can be reflected in real time, so that the nursing plan can always be highly consistent with the patient's needs and avoid excessive or insufficient interventions. Dynamic adjustment helps to reduce the uncertainty in nursing execution and ensure the consistency between the nursing plan and the actual needs. Timely adjustment of the plan can effectively reduce the risks in the nursing process and improve the recovery effect. According to the patient's real-time recovery situation and nursing intervention needs, nurses, doctors, and medical equipment are rationally allocated to ensure that patients can receive the necessary nursing services at the best time and avoid waste of human and equipment resources. By optimizing the resource allocation plan, uneven or excessive resource allocation can be avoided, unnecessary resource waste can be reduced, and the overall operation efficiency of medical institutions can be improved. The optimized nursing plan clearly shows the latest path, intervention measures, and time nodes of nursing behaviors through the update of the framework diagram. Nursing staff can intuitively see the adjustment of the nursing process through the graphical framework diagram and understand the sequence and timing of each nursing intervention. The updated time series and correlation relationships clarify the correlation between various nursing intervention measures and the patient's recovery status, helping nursing staff clearly master when and how to intervene in order to achieve the best recovery effect. The dynamically updated nursing behavior framework diagram is not only convenient for individual nursing staff to operate, but also helps the overall coordination of the nursing team. Team members can perform nursing tasks based on the unified framework diagram to ensure the smoothness and synergy of the nursing process and reduce chaos or conflicts in nursing operations. By applying the optimized nursing plan to the nursing behavior framework diagram, the generated latest nursing data and updated diagram provide a scientific and efficient work guide for the nursing team: the updated nursing data not only records the patient's current recovery status, but also reflects the execution of nursing interventions, providing a basis for subsequent nursing adjustments. The update of the nursing behavior framework diagram shows each link of the nursing path and can clearly indicate the possible bottlenecks and deficiencies in the nursing process. As the nursing path is continuously optimized, the nursing process gradually tends to be refined and efficient.The real-time and dynamically updated nursing behavior framework diagram ensures the scientificity and rationality of the nursing path. By adjusting and optimizing each nursing link, it improves the controllability of nursing quality, reduces the occurrence of nursing errors and deviations, and thus enhances the safety and rehabilitation effect of patients. Through these steps, the refined management of the nursing path can be achieved, which not only effectively improves the accuracy of nursing intervention, the efficiency of resource utilization and the nursing quality in clinical practice, but also ensures that neurosurgical patients receive the most appropriate nursing plan throughout the rehabilitation process. The implementation of the optimized nursing plan can be dynamically adjusted according to the patient's real-time recovery situation and nursing needs, so as to provide personalized and refined nursing services for patients, and ultimately improve the success rate of patient rehabilitation and the overall effect of nursing.

[0110] Preferably, the cloud nursing data security management module specifically performs the following steps:

[0111] Step S51: Construct a cloud storage system based on a multi-layer security architecture, and hierarchically store neurosurgical nursing data and the updated diagram of the nursing behavior framework through the cloud storage system, so as to obtain a neurosurgical nursing management system;

[0112] Step S52: Establish a multi-dimensional permission control mechanism based on role attributes according to the neurosurgical nursing management system, set fine-grained data access permissions for medical personnel at different levels, and perform permission management through identity authentication and access control algorithms, so as to obtain permission management data;

[0113] Step S53: Implement access control to the neurosurgical nursing management system according to the permission management data.

[0114] In an embodiment of the present invention, a cloud storage system with a multi-layer security architecture is constructed to achieve hierarchical storage of neurosurgical nursing data and updated diagrams of nursing behavior frameworks. The specific operations include: When designing the cloud storage system, distributed storage technology is adopted to ensure efficient data reading and writing capabilities. The data is hierarchically managed according to sensitivity and access frequency. For example, sensitive data such as patient personal information and treatment records are stored in an encrypted high-security storage layer, while regular nursing path data, update logs, etc. are stored in a low-security storage layer. The system sets different encryption levels and access control policies according to the different importance of the data. For example, for highly sensitive data (such as drug usage, surgical records), advanced encryption algorithms (such as AES-256) are applied, while lighter encryption measures are used for low-sensitive data. The data storage and management are uniformly managed through the API of the cloud platform to ensure the security and traceability of the stored data. Through this hierarchical storage structure, the security of neurosurgical nursing data can be guaranteed, and the response speed and processing efficiency of the system can be improved. According to the requirements of the neurosurgical nursing management system, a multi-dimensional permission control mechanism based on role attributes is established. The specific operations include: According to the organizational structure and medical processes of the hospital, medical personnel with different roles (such as attending physicians, nurses, data administrators, etc.) are assigned to different permission levels. Through fine-grained permission control, the access permissions are divided into multiple dimensions, such as read permission, edit permission, and delete permission. For example, an attending physician can access the comprehensive treatment records of patients and make modification operations; while a nurse can only view the nursing progress and daily nursing records of patients and cannot perform editing or deletion operations on medical records. Through the role management system, the user role is verified during the identity authentication stage, and the access permissions are dynamically adjusted according to the role information. The specific permission control algorithm can be based on the RBAC (Role-Based Access Control) model, combined with the user identity authentication and role authorization mechanism for fine-grained permission settings. For example, an attending physician can access all patient information, while an ordinary nurse can only access the data of the patients they are responsible for. According to the generated permission management data, access control of the neurosurgical nursing management system is further implemented. The specific operations include: In the system background, through the identity authentication mechanism, it is ensured that only authenticated medical personnel can access the system. The token-based authentication method (such as OAuth2.0) is used for identity verification to ensure that each access request is authorized. In addition, the system applies an access control list (ACL) at each permission level for access verification to ensure that each user can only access the specific data they are authorized to view. For example, a data administrator can access all nursing data and framework diagram update records, while ordinary nursing staff can only view the data related to their responsibilities. The access request is processed by the permission check engine, and any unauthorized access attempt is rejected. In addition, the system also sets a logging function to automatically record each access request and access result for later auditing and data security monitoring.Through these means, the security and data protection of the neurosurgical nursing management system are ensured.

[0115] Through a multi-layer security architecture, the present invention can adopt different security measures such as encryption, backup, and access control according to the sensitivity and importance of data. For important patient data, nursing behavior data, etc., more stringent security measures can be taken to ensure the confidentiality and integrity of the data. By storing data in a hierarchical and graded manner, different types of data can be stored in storage media at different levels according to characteristics such as the access frequency and update frequency of the data. This method can optimize storage resources, improve the operating efficiency of the system, and at the same time reduce the storage cost. The cloud storage system itself has good scalability and can increase the storage capacity at any time to cope with the growth of data volume. In addition, the high availability of the cloud platform can also ensure that data will not be lost or inaccessible due to a single hardware failure. Through the role attribute and multi-dimensional permission control mechanism, different levels and scopes of data access permissions can be set for different roles (such as doctors, nurses, administrators, etc.). Each role can access different data according to its responsibilities, avoiding the risk of data abuse or improper access. For example, nursing staff may only be able to access data related to patient care, while doctors can access more comprehensive medical records and rehabilitation information. In neurosurgical nursing management, patient data usually involves sensitive health information. Through identity authentication and access control algorithms, unauthorized personnel can be effectively prevented from accessing patient data, thereby protecting patient privacy and complying with relevant laws and regulations (such as GDPR or HIPAA). The multi-dimensional permission control mechanism combined with the identity authentication algorithm ensures that only authenticated users can access relevant data, preventing data from being maliciously tampered with or leaked. In addition, the access control algorithm can also monitor and alert abnormal access in real time, further enhancing the security of the system. Based on the permission management data generated in the first two steps, precise access control of the system can be achieved, ensuring that different medical personnel access corresponding data and functions according to their roles and responsibilities. For example, nurses can only view the nursing records of patients, while senior medical personnel such as neurosurgical experts can view detailed medical records, surgical records, and rehabilitation data. Through strict permission control, the risk of illegal operations or data leakage is avoided. The system can track the access records of each user to ensure that all operations can be traced. Through the access control mechanism, the system can generate detailed access logs for easy auditing and supervision. This provides compliance guarantee for the hospital and ensures compliance with the relevant requirements of the medical industry for data security, privacy protection, and operation transparency. By setting different permissions according to roles, medical personnel can find the information they need more quickly, avoiding complex permission settings and data redundancy. This can not only improve work efficiency but also reduce operation errors caused by improper permission configuration. These three steps, through constructing a multi-layer security architecture, refined role permission control, and strict access control mechanism, provide strong data security guarantee and efficient permission management for the neurosurgical nursing management system. The system can effectively avoid unauthorized access, protect patient privacy, and at the same time improve the work efficiency of medical personnel.This design not only provides a safe and reliable data management environment for patients, but also offers an efficient and compliant nursing data management platform for hospitals.

[0116] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0117] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A neurosurgery nursing management system based on cloud computing, characterized in that: Includes the following modules: Neurosurgery nursing data collection module is used to obtain the nursing process data of neurosurgery patients, including the type of surgery, postoperative recovery monitoring indicators, nursing intervention measures and nervous system medication information; the nursing process data is cleaned and standardized, and the nursing behavior is serialized to generate a nursing behavior framework diagram with time nodes and feature relationships; The nursing pathway intelligent prediction module is used to simulate the time series of postoperative recovery indicators and intervention measures based on the neural network model according to the nursing behavior framework diagram, and to establish a mapping relationship between recovery status and nursing intervention nodes according to the preset historical nursing pathway data to obtain nursing pathway prediction data; and to perform optimization analysis on the nursing pathway prediction data based on resource requirements to obtain nursing pathway optimization data; The nursing goal parameterized evaluation module is used to obtain nursing rehabilitation indicator data; the nursing pathway optimization data and nursing rehabilitation indicator data are used to associate nursing goals with patient recovery status, and resource allocation and indicator differences are quantitatively analyzed through parameterized modeling to obtain a nursing goal evaluation model; Nursing execution dynamic iteration module is used to dynamically track the allocation and execution of neurosurgery nursing resources according to the nursing goal evaluation model, and compare the actual nursing path with the nursing rehabilitation indicator data to generate nursing execution deviation data; Generate feedback based on nursing execution deviation data to optimize the nursing plan, dynamically adjust nursing intervention measures based on the optimized nursing plan, and update the nursing behavior framework diagram, thereby obtaining neurosurgery nursing data and nursing behavior framework update diagram; The cloud nursing data security management module is used to manage neurosurgery nursing data and nursing behavior framework update diagrams in layers through a cloud computing-based storage architecture, and to build a multi-level data security and permission control mechanism to achieve system access control.

2. The neurosurgery nursing management system based on cloud computing according to claim 1 is characterized in that: The neurosurgery nursing data collection module specifically performs the following steps: Step S11: Obtaining nursing process data of neurosurgery patients, including surgery type, postoperative recovery monitoring indicators, nursing intervention measures, and nervous system medication information; Step S12: preprocessing the nursing process data to obtain neurosurgery clinical preprocessing data, wherein the preprocessing includes missing value filling, outlier processing, and data consistency verification; Step S13: using data standardization technology to uniformly encode and measure the pre-processed data of neurosurgery clinical practice, thereby obtaining standardized data of neurosurgery clinical practice; Step S14: constructing a nursing behavior feature vector based on time attributes according to the standardized clinical data of neurosurgery, and establishing a time series mapping relationship of nursing behaviors based on time sequence extraction of nursing intervention measures, nervous system medication information, and monitoring indicator changes, thereby obtaining nursing behavior sequence data; Step S15: Perform feature extraction and correlation analysis on the nursing behavior sequence data, and generate a nursing behavior framework diagram with time dimension and behavior correlation through a graphical method.

3. The cloud computing-based neurosurgery nursing management system according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: performing multi-scale feature decomposition on the nursing behavior sequence data based on time features, behavior attribute features, and association strength features, thereby obtaining nursing behavior feature data; Step S152: Deconstruct the topological structure of the nursing behavior sequence data by complex network analysis method, and identify key nodes, associated paths and behavior propagation patterns, so as to establish a behavior network topology model; Step S153: performing semantic level similarity analysis and semantic clustering on the nursing behavior feature data, and extracting latent semantic associations and implicit patterns of the nursing behavior, thereby obtaining nursing semantic feature data; Step S154: constructing a nursing behavior semantic association mapping based on the knowledge graph according to the nursing semantic feature data, and defining the semantic relationship and association weight between nursing behavior nodes through an ontology modeling method, thereby forming a nursing semantic association network; Step S155: using a graphical method to transform the nursing semantic association network into a three-dimensional visualization framework diagram with time dimension and behavior association, thereby obtaining a nursing behavior framework diagram.

4. The cloud computing-based neurosurgery nursing management system according to claim 3 is characterized in that: The nursing pathway intelligent prediction module specifically performs the following steps: Step S21: constructing a postoperative recovery index prediction model based on a deep neural network to simulate the dynamic change process of postoperative recovery of patients according to the nursing behavior framework diagram; Step S22: Obtain historical nursing pathway data; Step S23: extracting typical recovery path features of patients with different surgery types, age groups, and underlying diseases from the historical nursing path data, thereby obtaining typical recovery path data; Step S24: Establish a mapping relationship between recovery status and nursing intervention nodes based on typical recovery path data and a postoperative recovery index prediction model to obtain nursing path prediction data; perform optimization analysis on the nursing path prediction data based on resource requirements to obtain nursing path optimization data.

5. The neurosurgery nursing management system based on cloud computing according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: Establish a mapping relationship matrix between recovery status and nursing intervention nodes based on typical recovery path data and a postoperative recovery index prediction model, and quantify the conditional probability and impact weight of different nursing interventions on the patient's recovery status, thereby obtaining a probability transfer matrix of the mapping relationship; Step S242: Generate nursing pathway prediction data according to the probability transfer matrix, and perform multi-dimensional resource demand evaluation on the nursing pathway prediction data, so as to construct a comprehensive resource optimization model, wherein the multi-dimensional resource demand includes labor cost, medical equipment utilization rate, and drug consumption; Step S243: Optimizing the resource allocation based on resource utilization efficiency of the comprehensive resource optimization model through a constraint optimization algorithm, thereby obtaining optimized resource allocation data; Step S244: Dynamically adjust the nursing pathway based on the patient's real-time recovery status and resource constraints according to the optimized resource configuration data, thereby obtaining nursing pathway optimization data.

6. The cloud computing-based neurosurgery nursing management system according to claim 5, characterized in that: The nursing goal parameterized assessment module specifically performs the following steps: Step S31: Acquire nursing rehabilitation index data, including key physiological parameters and functional recovery level of the patient after surgery; Step S32: performing time series matching on the nursing pathway optimization data and the nursing rehabilitation index data, constructing a nursing pathway and rehabilitation index association sequence based on the time axis, thereby obtaining nursing pathway-rehabilitation association sequence data; Step S33: performing feature analysis on the nursing pathway-rehabilitation association sequence data, extracting quantitative association features between different nursing pathways and rehabilitation effects, thereby obtaining quantitative association feature data; Step S34: generating a multi-objective optimization function according to the quantitative correlation feature data, and quantitatively expressing the differences in resource allocation, intervention measures and rehabilitation goals, thereby obtaining optimization model input data; Step S35: Perform regression analysis based on the optimized model input data through a parametric modeling method, and perform nursing goal evaluation processing to obtain a nursing goal evaluation model.

7. The neurosurgery nursing management system based on cloud computing according to claim 6 is characterized in that: Step S33 includes the following steps: Step S331: performing multi-level feature deconstruction processing on the nursing pathway-rehabilitation association sequence data in terms of time scale, feature intensity and complexity, thereby obtaining nursing pathway-rehabilitation multi-dimensional feature data; Step S332: performing topological structure analysis on the association network of the nursing pathway and rehabilitation indicators according to the nursing pathway-rehabilitation multidimensional feature data, identifying key nodes, association paths and propagation patterns, and thus establishing a network mapping model of the nursing pathway-rehabilitation association; Step S333: Perform dimensionality reduction processing on the nursing pathway-rehabilitation association sequence data according to the network mapping model, and select key feature dimensions with the highest information entropy and discrimination, so as to obtain quantitative association features.

8. The neurosurgery nursing management system based on cloud computing according to claim 7 is characterized in that: The dynamic iteration module of nursing execution specifically performs the following steps: Step S41: tracking the current allocation and execution of neurosurgery nursing resources in real time according to the nursing goal evaluation model, thereby obtaining real-time nursing pathway data; Step S42: generating actual rehabilitation index data of the patient according to the real-time nursing pathway data; Step S43: comparing the actual rehabilitation index data of the patient with the nursing rehabilitation index data, and performing nursing path deviation and rehabilitation index difference analysis to generate nursing execution deviation data; Step S44: identifying key factors causing the deviation based on statistical analysis according to the nursing execution deviation data, and extracting deviation adjustment features, thereby obtaining deviation adjustment feature data; Step S45: Generate a feedback optimization nursing plan based on the nursing execution deviation data, dynamically adjust nursing intervention measures and update the nursing behavior framework diagram based on the optimized nursing plan, so as to obtain neurosurgery nursing data and a nursing behavior framework update diagram.

9. The neurosurgery nursing management system based on cloud computing according to claim 8, characterized in that: Step S45 includes the following steps: Generate optimized nursing plan data based on deviation-adjusted feature data, where the optimized nursing plan includes dynamic adjustment of nursing intervention measures, resource allocation plan data, and nursing pathway optimization suggestions; apply the optimized nursing plan to the nursing behavior framework diagram, and update the time series and correlation relationship of nursing behavior to generate neurosurgery nursing data and a nursing behavior framework update diagram.

10. The neurosurgery nursing management system based on cloud computing according to claim 9, characterized in that: The cloud nursing data security management module specifically performs the following steps: Step S51: constructing a cloud storage system based on a multi-layer security architecture, and storing neurosurgery nursing data and nursing behavior framework update diagrams in a hierarchical manner through the cloud storage system, thereby obtaining a neurosurgery nursing management system; Step S52: establishing a multi-dimensional authority control mechanism based on role attributes according to the neurosurgery nursing management system, setting fine-grained data access rights for medical personnel at different levels, and performing authority management through identity authentication and access control algorithms, thereby obtaining authority management data; Step S53: Implement access control to the neurosurgery nursing management system according to the authority management data.

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