Clinical nursing potential safety hazard intelligent analysis and early warning system

Abstract

The invention discloses an intelligent analysis and early warning system for potential safety hazards of clinical nursing, and relates to the technical field of intelligent nursing monitoring. Physiological data of a patient is collected in real time and preprocessed, a physiological data set S is constructed and summarized and calculated, characteristic indexes are obtained, and a microcirculation disturbance index Wxh is calculated according to the characteristic indexes; the method comprises the following steps: evaluating the current body state of a patient, constructing a time sequence analysis model, predicting a microcirculation disturbance index Wxh of the patient in a future period of time, evaluating the health state of the patient in the future period of time, carrying out risk early warning, carrying out nursing strategy grading, and carrying out nursing intervention on the patient. The physiological data of the patient in the clinical nursing process are monitored in real time, the clinical nursing effect is optimized, and the nursing efficiency and the nursing safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent nursing monitoring, and particularly to an intelligent analysis and early warning system for clinical nursing safety hazards. Background Art

[0002] Intelligent nursing monitoring is mainly applied to scenarios such as ward nursing, intensive care unit (ICU), and postoperative rehabilitation, etc., to continuously monitor the physiological status of patients, identify nursing safety hazards, and provide intelligent early warnings, so as to reduce nursing errors and improve medical quality. In the specific application of intelligent nursing monitoring, the intelligent identification and early warning of microcirculation disorder (MCTD) is an important nursing direction. Microcirculation disorder (MCTD) is a potential hemodynamic abnormal state, which often occurs in high-risk groups such as postoperative patients, long-term bedridden patients, and severe infected patients. If not detected and intervened early, it may lead to deep vein thrombosis (DVT), multiple organ dysfunction syndrome (MODS), and even endanger life.

[0003] At present, the clinical monitoring of microcirculation disorder (MCTD) mainly relies on the empirical judgment of doctors and nursing staff. The commonly used methods usually include traditional methods such as manual measurement of capillary refill time (CRT), local temperature perception, and fingertip blood oxygen measurement, etc. However, these methods have obvious limitations. First, most of the existing monitoring of microcirculation disorder (MCTD) is intermittent monitoring, making it difficult to continuously track the microcirculation state. Second, these monitoring indicators are single and rely on manual judgment, and nursing staff may ignore the early signals of microcirculation damage due to insufficient experience or excessive workload. Third, the existing nursing monitoring means mainly focus on systemic hemodynamics rather than local microcirculation state, resulting in the early development process of microcirculation disorder (MCTD) being difficult to capture. When obvious perfusion disorders occur in patients, they often have entered a relatively severe pathological stage, increasing the treatment difficulty and medical intervention cost. Therefore, the current nursing monitoring means are insufficient in the ability to detect the early stage of microcirculation disorder.

[0004] Early identification of microcirculation disorders in MCTD is difficult, mainly due to the progressive and latent nature of its pathological changes. In the initial stage of MCTD, blood flow supply may only show a slight decrease in perfusion in local areas, resulting in a slightly lower skin temperature or a slightly prolonged capillary filling time. These changes often do not reach the alert threshold of nursing staff and are easily overlooked. At the same time, traditional monitoring methods lack the ability to integrate multiple microcirculation-related parameters, making it difficult for small changes in a single indicator to trigger early warnings. On the other hand, in a busy clinical environment, nursing staff often have difficulty conducting detailed microcirculation examinations on each patient at high frequencies, resulting in certain "blind spots" in nursing monitoring, increasing the difficulty of medical intervention and medical costs. Therefore, the deficiencies in the current situation not only affect the patient's recovery speed and postoperative complication control, but also increase the nursing workload and medical resource consumption, and may even lead to life-threatening consequences in severe cases. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent analysis and early warning system for clinical nursing safety hazards, which solves the problems in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent analysis and early warning system for clinical nursing safety hazards, including a data collection and preprocessing module, a feature extraction module, a data analysis and preliminary evaluation module, a health prediction module, and an intelligent nursing module; The data collection and preprocessing module is used to collect patient physiological data, perform preprocessing, and construct a physiological data set S based on the preprocessed patient physiological data; The feature extraction module is used to obtain the microvascular oscillation factor Zd, the blood flow shear force variation coefficient Jq, and the capillary optical transmission factor Mx based on the physiological data set S; The data analysis and preliminary evaluation module is used to obtain the microcirculation disorder index Wxh based on the microvascular oscillation factor Zd, the blood flow shear force variation coefficient Jq, and the capillary optical transmission factor Mx, and evaluate the patient's current physical state; The health prediction module is used to construct a time series analysis model, predict the microcirculation disorder index Wxh of the patient in the next period of time, evaluate the future health state of the patient, and conduct risk early warnings; The intelligent nursing module is used to classify the nursing strategy level of the patient and perform nursing interventions, and real-time monitor the patient physiological data during the clinical nursing process to optimize the clinical nursing effect.

[0007] Preferably, the data collection and preprocessing module includes a data collection unit and a preprocessing unit; The data acquisition unit is used to use the intelligent sensor group to monitor the patient's physical condition in real time under the same conditions and obtain the patient's physiological data. Among them, the intelligent sensor group includes a skin pressure sensor, a laser Doppler flowmeter, and a near-infrared spectroscopy analyzer. The patient's physiological data includes local skin pressure , capillary blood flow velocity , blood viscosity and local tissue optical transmittance ; The preprocessing unit is used to preprocess the obtained patient physiological data. Among them, the preprocessing includes data correction, outlier removal, signal smoothing, data interpolation correction, and data normalization; Based on the preprocessed patient physiological data, a physiological data set S is constructed.

[0008] Preferably, the feature extraction module is used to extract feature indicators according to the physiological data set S, including the microvascular oscillation factor Zd, the capillary optical transmittance factor Mx, and the blood flow shear force variation coefficient Jq; The acquisition method of the microvascular oscillation factor Zd is: ; In the formula, represents the time window, represents the capillary blood flow velocity of the patient at time point t, t ∈ { , }; The acquisition method of the capillary optical transmittance factor Mx is: ; In the formula, represents the local tissue optical transmittance, represents the reference optical transmittance of healthy tissue.

[0009] Preferably, according to the physiological data set S, the blood flow shear force of the patient at time point t is calculated , and the specific calculation method is: ; In the formula, represents the blood viscosity at time point t, represents the capillary blood flow velocity at time point t, represents the capillary diameter at time point t; And based on the blood flow shear force of the patient at time point t , the average value and the standard deviation of the patient's blood flow shear force during the monitoring period are obtained, and the blood flow shear force variation coefficient Jq is calculated. The specific calculation method is: ; In the formula, represents the local skin pressure , represents the historical average value of the local skin pressure of the patient, represents the healthy reference pressure, represents the local pressure adjustment factor.

[0010] Preferably, the data analysis and preliminary evaluation module includes a microcirculation disorder index calculation unit and a preliminary evaluation unit; The microcirculation disorder index calculation unit is used to perform a summary calculation based on the microvascular oscillation factor Zd, the capillary optical transmittance factor Mx, and the blood flow shear force variation coefficient Jq to obtain the microcirculation disorder index Wxh. Among them, the specific acquisition method of the microcirculation disorder index Wxh is as follows: ; In the formula, , and respectively represent the weight coefficients of the microvascular oscillation factor Zd, the capillary optical transmittance factor Mx, and the blood flow shear force variation coefficient Jq, represents the correction constant.

[0011] Preferably, the preliminary evaluation unit is used to preset the first microcirculation disorder threshold and the second microcirculation disorder threshold , and compare the first microcirculation disorder threshold and the second microcirculation disorder threshold with the microcirculation disorder index Wxh for comparative analysis to evaluate the patient's care level. The specific evaluation content is as follows: If the microcirculation disorder index Wxh is less than or equal to the first microcirculation disorder threshold , that is, Wxh ≤ , it is determined that the patient's physiological health status is normal. At this time, the patient's care level is green, maintaining daily care and continuously monitoring the patient's physiological data; If the microcirculation disorder index Wxh is greater than the first microcirculation disorder threshold , and less than the second microcirculation disorder threshold , that is, <Wxh< , then it is determined that the patient's physiological health status is a risk state. At this time, the patient's care level is yellow, strengthening nursing measures, adjusting the patient's body position, and predicting the patient's physiological health; If the microcirculation disorder index Wxh is greater than or equal to the second microcirculation disorder threshold , it is determined that the patient's physiological health status is in a dangerous state. At this time, the patient's nursing level is red, and medical intervention is required immediately, including anticoagulation and blood flow promotion treatment.

[0012] Preferably, the health prediction module includes a microcirculation disorder index prediction unit and a warning unit; The microcirculation disorder index prediction unit is used to obtain the historical physiological data of patients with green and yellow nursing levels based on the medical database, preprocess the historical physiological data of patients, and obtain the microcirculation disorder index Wxh of the patient in the past period of time according to the preprocessed historical physiological data. Calculate the change rate of the microcirculation disorder index Wxh based on the microcirculation disorder index Wxh of the patient in the past period of time , and the specific calculation method is: ; In the formula, represents the microcirculation disorder index of the patient at the current time point t, represents the microcirculation disorder index before time steps in the past, represents the time step; According to the weighted moving average method, combined with the change rate of the microcirculation disorder index Wxh , obtain the microcirculation disorder index of the patient at the future time point , and the specific acquisition method is: ; where k represents the time correction factor.

[0013] Preferably, the warning unit is used to obtain the microcirculation disorder index Wxh of the patient in the future period of time and construct a time series set H. Among them, the specific form of the time series set H is: ; Among them, represents the microcirculation disorder index at the future time point ; According to the time series set H, obtain the microcirculation disorder fluctuation coefficient Bd. Among them, the specific acquisition method of the microcirculation disorder fluctuation coefficient Bd is: ; In the formula, M represents the total number of predicted time points in the future period of time represents the average value of the microcirculation disorder index of the patient in the future period of time, ={1, 2, 3,..., M}; Preset the stability evaluation threshold \(W_d\), compare and analyze the microcirculation disorder fluctuation coefficient \(B_d\) with the stability evaluation threshold \(W_d\) to evaluate the stability of the patient's physical health status. The specific evaluation process is as follows: If the microcirculation disorder fluctuation coefficient \(B_d\) is greater than or equal to the stability evaluation threshold \(W_d\), it is determined that the patient's physical health status is unstable. At this time, an intelligent nursing report is generated and sent to the terminal devices of the attending doctor and the nursing staff, and it is highlighted until the attending doctor and the nursing staff respond. The intelligent nursing report includes the patient identification, the patient's microcirculation disorder index , the microcirculation disorder fluctuation coefficient \(B_d\), the patient's nursing level, and the evaluation result of the patient's physical health status; If the microcirculation disorder fluctuation coefficient \(B_d\) is less than the stability evaluation threshold \(W_d\), it is determined that the patient's physical health status is stable. At this time, subsequent tracking points are automatically set, that is, the stability of the patient's physical health status is evaluated every 30 minutes, and an intelligent nursing report is generated.

[0014] Preferably, the intelligent nursing module includes a nursing strategy generation and execution unit and a nursing feedback adjustment unit; The nursing strategy generation and execution unit is used to generate intelligent nursing strategies based on the intelligent nursing report. According to the patient's nursing level and the evaluation result of the patient's physical health status stability, the patients are divided into different nursing strategy levels, including routine nursing strategies, risk prevention nursing strategies, enhanced nursing strategies, and emergency nursing strategies; Routine nursing means adjusting the body position every 2 hours during the regular shift, collecting microcirculation physiological indicators every 6 hours, and maintaining fluid balance, without the need for a doctor to intervene; The risk prevention nursing strategy refers to mild intensive nursing, including adjusting the body position every hour, collecting microcirculation physiological indicators every 2 hours, and performing hemodynamic detection on the patient; The enhanced nursing strategy means setting up a microcirculation dynamic monitoring station for continuous dynamic nursing, adjusting the body position every hour, performing continuous intravenous fluid replacement according to the doctor's order, maintaining CVP > 8 cmH₂O, and locally warming the peripheral capillary area; The emergency nursing strategy means starting an intermittent pneumatic device for 10 minutes each time, rotating every 30 minutes, elevating the lower limb position by 15°, maintaining blood flow back to the heart, and setting up a dedicated nurse to regularly patrol the patient area.

[0015] Preferably, the nursing feedback adjustment unit is used to perform nursing interventions on patients according to routine nursing strategies, risk prevention nursing strategies, enhancement nursing strategies, and emergency nursing strategies, collect physiological data related to microcirculation disorders of patients after nursing interventions, store the physiological data related to microcirculation disorders of patients in the medical database, regularly analyze the changing trend of the physiological state of patients after nursing interventions, and intelligently adjust nursing measures.

[0016] The present invention provides a clinical nursing safety hazard intelligent analysis and early warning system, which has the following beneficial effects: (1) By constructing a data acquisition and preprocessing module based on real-time monitoring of multi-source sensors, physiological data such as local skin pressure, capillary blood flow velocity, blood viscosity, and optical transmittance under the microscopic circulation state of patients are collected, and the microvascular oscillation factor Zd, blood flow shear force variation coefficient Jq, and capillary optical transmittance factor Mx are calculated through the feature extraction module. Such indicators have strong microcirculation perfusion sensitivity and early abnormal amplification effects. Before clinical symptoms appear, a physiological risk score can be performed through the microcirculation disorder index Wxh constructed by the data analysis and preliminary evaluation module, realizing the early identification of microcirculation dysfunction. The system divides the current nursing levels of patients based on the microcirculation disorder threshold, including green, yellow, and red, and directly guides them into the corresponding nursing processes, ensuring early detection, early intervention, and early control, and effectively avoiding the traditional risk of "waiting for symptoms to appear before treatment".

[0017] (2) By constructing a time series analysis model through the health prediction module, the future microcirculation disorder index Wxh of patients with green and yellow nursing levels is predicted, and a time series set H is constructed. The system calculates the microcirculation disorder fluctuation coefficient Bd through volatility analysis and compares it with the preset stability evaluation threshold Wd, so as to realize the quantitative evaluation of the future state stability and change trend of patients. The system can clearly judge whether there is a risk situation of "large state fluctuations and deteriorating trends" for patients in the future. Before entering the red danger level, an intelligent early warning prompt is sent to doctors and nursing staff, and an intelligent nursing report including score prediction, volatility judgment, current level, and nursing suggestions is automatically generated. This mechanism improves the clinical judgment ability of the "risk development path" and avoids intervention delays or misjudgments caused by lagging risk awareness.

[0018] (3) The intelligent nursing module consists of two parts: a nursing strategy generation and execution unit and a nursing feedback monitoring and adjustment unit. The nursing strategy generation and execution unit automatically matches the nursing strategy level according to the patient's current level and future fluctuation status, including routine, risk defense, enhancement, and emergency, and generates a structured nursing template containing nursing goals, action content, execution frequency, and collection requirements. The nursing feedback monitoring and adjustment unit continuously collects and analyzes the physiological indicators of the patient after nursing to determine whether the nursing intervention has an improvement effect, and prompts whether it is necessary to fine-tune the nursing actions or nursing frequency according to the trend results. This mechanism avoids the passive mode of "nursing behavior is the end point of execution" and turns it into a continuous closed loop of "behavior - feedback - adjustment", improving the accuracy of individualized nursing response and effectively solving the technical problems of the disconnection between intervention and effect and the static nursing path in traditional nursing. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a block diagram of an intelligent analysis and early warning system for clinical nursing safety hazards of the present invention; Figure 2 is a schematic flow chart of the data analysis and preliminary evaluation module of the present invention; Figure 3 is a schematic flow chart of the health prediction module of the present invention; Figure 4 is a line graph of the change trend of the microcirculation disorder index of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1 Please refer to Figures 1 to 4 , the present invention provides an intelligent analysis and early warning system for clinical nursing safety hazards, including a data collection and preprocessing module, a feature extraction module, a data analysis and preliminary evaluation module, a health prediction module, and an intelligent nursing module; The data collection and preprocessing module is used to collect the physiological data of the patient, perform preprocessing, and construct a physiological data set S based on the preprocessed physiological data of the patient; The feature extraction module is used to obtain the microvascular oscillation factor Zd, the blood flow shear force variation coefficient Jq, and the capillary optical transmission factor Mx according to the physiological data set S; The data analysis and preliminary evaluation module is used to obtain the microcirculation disorder index Wxh based on the microvascular oscillation factor Zd, the coefficient of variation of blood flow shear force Jq, and the capillary optical transmission factor Mx, and to evaluate the current physical state of the patient; The health prediction module is used to construct a time series analysis model to predict the microcirculation disorder index Wxh of the patient in a future period of time, evaluate the future health state of the patient, and issue a risk warning; The intelligent nursing module is used to classify the nursing strategy level for the patient and perform nursing interventions, and to monitor the physiological data of the patient in real time during the clinical nursing process to optimize the clinical nursing effect.

[0022] In the embodiment, by constructing a data acquisition and preprocessing module, a feature extraction module, a data analysis and preliminary evaluation module, a health prediction module, and an intelligent nursing module, a set of closed-loop intelligent analysis and warning processes for clinical nursing safety hazards are formed. First, the system uses a multi-source intelligent sensor group to collect raw physiological data related to microcirculation in real time, and through preprocessing, ensures the stability and reliability of the data, and constructs a high-quality physiological data set S. Subsequently, the feature extraction module calculates three sensitive indicators: the microvascular oscillation factor Zd, the coefficient of variation of blood flow shear force Jq, and the capillary optical transmission factor Mx, providing a feature basis for subsequent evaluation. The data analysis module comprehensively constructs the microcirculation disorder index Wxh based on these three factors to accurately depict the current microcirculation state of the patient. Furthermore, the health prediction module constructs a time series analysis model to predict the physiological trend in a future period of time, identify potential deterioration risks, and achieve early warning. Finally, the intelligent nursing module generates individualized nursing strategies according to the current level and predicted trend of the patient, and continuously monitors the intervention effect, forming an intelligent nursing closed loop of "evaluation - prediction - response - feedback", solving the problems of lagging risk identification, uncontrollable response, and lack of feedback in traditional nursing, and improving the nursing efficiency and safety.

[0023] Embodiment 2 Please refer to Figure 1 , specifically: The data acquisition and preprocessing module includes a data acquisition unit and a preprocessing unit; The data acquisition unit is used to monitor the physical state of the patient in real time using an intelligent sensor group under the same conditions to obtain the physiological data of the patient. Among them, the intelligent sensor group includes a skin pressure sensor, a laser Doppler flowmeter, and a near-infrared spectroscopy analyzer, and the physiological data of the patient includes local skin pressure , capillary blood flow velocity , blood viscosity and local tissue optical transmittance ; Among them, the local skin pressure is obtained by using a skin pressure sensor; The capillary blood flow velocity and blood viscosity Obtained by using a laser Doppler flowmeter; Local tissue optical transmittance Obtained by using a near-infrared spectroscopy analyzer; The preprocessing unit is used to preprocess the obtained physiological data of the patient. Among them, the preprocessing includes data correction, outlier removal, signal smoothing, data interpolation correction, and data normalization; Construct a physiological data set S based on the preprocessed physiological data of the patient.

[0024] In the embodiment, by integrating a skin pressure sensor, a laser Doppler flowmeter, a finger clip oximeter, and a near-infrared spectroscopy analyzer, it is possible to continuously and real-time monitor the microcirculation physiological state of the patient under unified standard conditions. The key physiological data collected cover local skin pressure, capillary blood flow velocity, blood viscosity, and local tissue optical transmittance. These data are highly sensitive and can reflect the microcirculation perfusion efficiency and tissue metabolic level. To ensure the accuracy of subsequent feature extraction and model analysis, the system preprocessing unit performs data correction, outlier removal, signal smoothing, interpolation completion, and normalization on the original data, improving data stability and consistency, effectively eliminating interference caused by sensor noise, instantaneous jitter, and uneven sampling. Finally, a physiological data set S with a unified format and complete physiological indicators is constructed, providing a high-quality and highly reliable data basis for subsequent feature index extraction, microcirculation disorder index calculation, and health prediction of the system, improving the overall evaluation accuracy and nursing response efficiency of the system.

[0025] Embodiment 3 Please refer to Figure 1 , specifically: The feature extraction module is used to extract feature indicators based on the physiological data set S, including the microvascular oscillation factor Zd, the capillary optical transmittance factor Mx, and the blood flow shear force variation coefficient Jq; The acquisition method of the microvascular oscillation factor Zd is: ; In the formula, represents the time window, represents the capillary blood flow velocity of the patient at time point t, t ∈ { , }; The microvascular oscillation factor Zd is used to reflect the micro-variation amplitude of the capillary layer blood flow velocity within a short time window, and can identify the hidden decline trend of early perfusion function, making up for the defect that traditional nursing methods are insensitive to microcirculation function. Its introduction enables this system to actively identify functional perfusion disorders before obvious clinical symptoms appear, providing a reliable decision-making basis for early nursing intervention.

[0026] The capillary optical transmittance factor Mx is obtained as follows: ; In the formula, represents the local tissue optical transmittance, Indicates the baseline optical transmittance of healthy tissue, Indicates the relative ratio of transmittance. When it increases, the value of Mx will decrease steadily and continuously. Using the natural logarithm function ln can amplify the slight changes in the relative ratio of transmittance.

[0027] When microcirculation perfusion is insufficient, oxygen delivery is impaired, or cell metabolism is unbalanced, the optical transmittance decreases. The capillary optical transmittance factor Mx is highly correlated with capillary blood flow and tissue oxygenation status. It is an "optical window" of the functional status of microcirculation, and can sense the local tissue oxygen supply status and microblood flow regulation function in real time. It is an important parameter for judging the early stages of microcirculation disorders. Compared with traditional macroscopic vital signs, the capillary optical transmittance factor Mx focuses more on the "tissue-level" perfusion status, has the advantages of non-contact and continuous monitoring, and can effectively fill the technical gap in the difficulty in capturing microcirculation perfusion changes. As one of the core features of this system, it participates in the evaluation of the microcirculation disorder index, trend prediction, and nursing feedback judgment, and can build a core channel of "forward-looking warning + data-based nursing adjustment" to effectively solve the key technical problems of "difficult to identify in the early stage and no feedback in nursing".

[0028] Based on the physiological data set S, calculate the patient's blood flow shear force at time point t , the specific calculation method is: ; In the formula, represents the blood viscosity at time point t, represents the capillary blood flow velocity at time point t, It represents the capillary diameter at time point t. In circular capillaries, blood flows in a nearly laminar manner, and its velocity distribution is parabolic. The maximum velocity gradient is at the vessel wall. The number 4 comes from the ratio between the maximum velocity and the average velocity in the parabolic distribution, that is, the center is twice the average value. The blood flow shear force is derived using the basic shear force calculation formula combined with the velocity gradient expression. Calculation formula; And according to the patient's blood flow shear force at time point t , obtain the patient's blood flow shear force during the monitoring period The mean and standard deviation , and calculate the coefficient of variation of blood flow shear force Jq. The specific calculation method is: ; In the formula, represents the local skin pressure , represents the historical average value of the local skin pressure of the patient, represents the healthy reference pressure, represents the local pressure adjustment factor, represents the blood flow shear stress volatility, represents the pressure correction term. Using "1+" in the formula makes the pressure correction term partially valid. When is 0, the formula holds.

[0029] The local pressure adjustment factor is obtained by using a linear regression module and performing model fitting in combination with the patient's personal historical characteristic data.

[0030] The coefficient of variation of blood flow shear stress Jq is used to identify whether the microcirculation perfusion state is in a state of dynamic instability, shear disorder, and the boundary of the influence of nursing intervention. This characteristic index integrates multiple source factors such as capillary blood flow velocity, blood viscosity, blood vessel diameter, and local pressure, and can effectively reflect the "functional perturbation" of tissue-level perfusion. Without relying on clinical symptoms, it provides a decision-making basis for the system to quantitatively judge the risk of microcirculation disorders, and as the core component of the microcirculation disorder index Wxh, it supports various intelligent functions of the system for early identification, nursing feedback regulation, and intervention path adjustment, and is the fulcrum of predictive nursing and personalized risk intervention.

[0031] In the embodiment, through quantitative calculation of the constructed physiological data set S, three core characteristic indicators highly related to microcirculation disorders are extracted, including the microvascular oscillation factor Zd, the capillary optical transmittance factor Mx, and the coefficient of variation of blood flow shear stress Jq, which enhances the accuracy of the system's description of the patient's microcirculation state. Among them, the microvascular oscillation factor Zd is used to reflect the fluctuation amplitude of the capillary blood flow velocity per unit time, sensitively capturing abnormal microcirculation rhythms. The capillary optical transmittance factor Mx reveals changes in tissue oxygenation ability and capillary permeability by comparing the local tissue optical transmittance with the healthy reference value. The coefficient of variation of blood flow shear stress Jq calculates the shear stress based on blood viscosity, blood flow velocity, and capillary diameter, and further measures its variation degree after combining the local pressure adjustment factor, intuitively reflecting the blood flow stability and perfusion resistance state. These indicators are dynamically generated based on continuously collected data, not only having good physiological significance but also avoiding misjudgment caused by fluctuations in single-point indicators, improving the accuracy and response sensitivity of subsequent microcirculation disorder index assessments, thus providing a high-resolution parameter basis for the system to early identify microcirculation dysfunction and strengthening the quantitative risk judgment ability before nursing intervention.

[0032] Example 4 Please refer to Figure 1 、 Figure 2 and Figure 4 , specifically: the data analysis and preliminary evaluation module includes a microcirculation disorder index calculation unit and a preliminary evaluation unit; The microcirculation disorder index calculation unit is used to perform a summary calculation based on the microvascular oscillation factor Zd, the capillary optical transmission factor Mx, and the blood flow shear force variation coefficient Jq to obtain the microcirculation disorder index Wxh. Among them, the specific acquisition method of the microcirculation disorder index Wxh is: ; In the formula, 、 and respectively represent the weight coefficients of the microvascular oscillation factor Zd, the capillary optical transmission factor Mx, and the blood flow shear force variation coefficient Jq, represents a correction constant, where the weight coefficients 、 and The specific values are set by the customer according to the actual situation, 0 < <1, 0 < <1, 0 < <1, and + + = 1.

[0033] Microcirculation disorder is a precursor to various critical complications (such as DVT, MODS, organ hypoperfusion). The microcirculation disorder index Wxh can quantitatively identify it early, fill the perception blind spot of traditional nursing indicators, comprehensively characterize the microcirculation state, and is an effective compressed expression of multi-parameter information, which can be directly used for the classification of nursing levels and is convenient for clinical implementation.

[0034] The preliminary evaluation unit is used to preset a first microcirculation disorder threshold and a second microcirculation disorder threshold , and compare the first microcirculation disorder threshold and the second microcirculation disorder threshold with the microcirculation disorder index Wxh for comparative analysis to evaluate the patient's nursing level. The specific evaluation content is as follows: If the microcirculation disorder index Wxh is less than or equal to the first microcirculation disorder threshold , that is, Wxh ≤ , it is determined that the patient's physiological health status is normal, and at this time the patient's nursing level is green, maintaining daily care and continuously monitoring the patient's physiological data; If the microcirculation disorder index Wxh is greater than the first microcirculation disorder threshold , and less than the second microcirculation disorder threshold , that is <Wxh< , it is determined that the patient's physiological health status is a risk state. At this time, the patient's nursing level is yellow. Strengthen nursing measures, adjust the patient's body position, and conduct physiological health prediction for the patient; If the microcirculation disorder index Wxh is greater than or equal to the second microcirculation disorder threshold , it is determined that the patient's physiological health status is a dangerous state. At this time, the patient's nursing level is red, and immediate medical intervention is required for anticoagulation and blood flow promotion treatment.

[0035] Specific examples are as follows: Suppose there is a patient who needs nursing monitoring after surgery. Use an intelligent sensor group to collect the patient's physiological data in real time and perform feature extraction to obtain the feature indicators as shown in Table 1 below: Table 1 According to Table 1, calculate the patient's microcirculation disorder index Wxh: ; Preset the first microcirculation disorder threshold and the second microcirculation disorder threshold are 0.60 and 0.80 respectively. At this time = , which is greater than the first microcirculation disorder threshold and less than the second microcirculation disorder threshold , that is, 0.60< <0.80. It is determined that the patient's physiological health status is a risk state, and the nursing level is "yellow".

[0036] In an embodiment, by constructing a "microcirculation disorder index calculation unit" and a "preliminary evaluation unit", a structured path from multi-dimensional physiological characteristic data vectors to clinical care level determination is achieved, with significant intelligent evaluation advantages. First, the system fuses and calculates the microvascular oscillation factor Zd, the capillary optical transmittance factor Mx, and the coefficient of variation of blood flow shear force Jq to calculate the microcirculation disorder index Wxh, comprehensively reflecting the stability of microcirculation perfusion, blood flow rhythm, and tissue oxygenation ability, avoiding evaluation biases caused by relying on a single indicator. By setting two levels of risk thresholds (the first and second microcirculation disorder thresholds), the physiological state of patients can be automatically classified into three levels: green (normal), yellow (risk), and red (dangerous), achieving precise nursing stratified management. Among them, patients in the green level continue with daily care and observation, patients in the yellow level immediately enter the health prediction and nursing intervention process, while patients in the red level trigger a clinical emergency response, skipping the prediction and directly initiating anticoagulation and blood flow support treatment. This mechanism enables the nursing system to have quantitative, real-time, and interpretable grading capabilities, solving the core problems in traditional nursing such as fuzzy early evaluation of microcirculation disorders, strong subjectivity in judgment, and response delays, effectively improving the accuracy and efficiency of clinical early warning and nursing response.

[0037] Example 5 Please refer to Figure 1 and Figure 3 , specifically: The health prediction module includes a microcirculation disorder index prediction unit and a warning unit; The microcirculation disorder index prediction unit is used to obtain the historical physiological data of patients at the green and yellow care levels based on the medical database, preprocess the historical physiological data of patients, and obtain the microcirculation disorder index Wxh of the patients over a past period of time based on the preprocessed historical physiological data. Calculate the change rate of the microcirculation disorder index Wxh based on the microcirculation disorder index Wxh of the patients over a past period of time , and the specific calculation method is: ; In the formula, represents the microcirculation disorder index of the patient at the current time point t, represents the microcirculation disorder index before the past time step, and represents the time step; Based on the weighted moving average method, combined with the change rate of the microcirculation disorder index Wxh, obtain the microcirculation disorder index of the patient at the future time point Among them, k represents the time correction factor, and its specific value is obtained by constructing an exponential decay model by combining the predicted future time length and the fluctuation degree of the future microcirculation disorder index.

[0038] The warning unit is used to obtain the microcirculation disorder index Wxh of the patient within a future period of time and construct a time series set H. Among them, the specific form of the time series set H is: ; Among them, represents the future time point microcirculation disorder index; According to the time series set H, the microcirculation disorder fluctuation coefficient Bd is obtained. Among them, the specific acquisition method of the microcirculation disorder fluctuation coefficient Bd is: ; In the formula, M represents the total number of predicted time points within a future period of time, that is, from to , there are M future time points in total, represents the average value of the microcirculation disorder index of the patient within a future period of time, ={1, 2, 3,..., M}; A preset stability evaluation threshold Wd is set, and the microcirculation disorder fluctuation coefficient Bd is compared with the stability evaluation threshold Wd to evaluate the stability of the patient's physical health status. The specific evaluation process is as follows: If the microcirculation disorder fluctuation coefficient Bd is greater than or equal to the stability evaluation threshold Wd, it is determined that the patient's physical health status is unstable. At this time, an intelligent nursing report is generated and sent to the terminal devices of the attending doctor and the nursing staff, and it is highlighted until the attending doctor and the nursing staff respond. Among them, the intelligent nursing report includes the patient identification, the microcirculation disorder index of the patient , the microcirculation disorder fluctuation coefficient Bd, the nursing level of the patient, and the evaluation result of the patient's physical health status; If the microcirculation disorder fluctuation coefficient Bd is less than the stability evaluation threshold Wd, it is determined that the patient's physical health status is stable. At this time, subsequent tracking points are automatically set, that is, the stability of the patient's physical health status is evaluated every 30 minutes, and an intelligent nursing report is generated.

[0039] The specific example is as follows: Assume that the patient's physiological health status is in a risk state and the nursing level is "yellow". At this time, it is necessary to predict the patient's health, collect the patient's historical physiological data, and calculate the microcirculation disorder index Wxh of the patient within a past period of time according to the patient's historical physiological data, as shown in Table 2 below: Table 2 According to Table 2 above, calculate the change rate of the microcirculation disorder index Wxh : ; Predict the microcirculation disorder index Wxh at the next 6 time points, as shown in Table 3 below: Table 3 According to Table 3 above, obtain the time series set H: ; Calculate the microcirculation disorder fluctuation coefficient Bd, Bd = 0.015. The preset stability evaluation threshold Wd is 0.7. At this time, Bd = 0.015 is less than 0.7, so it is determined that the patient's physical health status is stable.

[0040] In the embodiment, by constructing a health prediction module, the dynamic judgment of the future health status of patients with green and yellow nursing levels is accurately realized, breaking through the limitation of "making decisions only based on the current status" in the traditional nursing path. First, the system extracts the recent microcirculation disorder index Wxh from the patient's historical physiological data through the microcirculation disorder index prediction unit, and calculates its change rate. The weighted moving average model is used to combine with the time correction factor to predict the scores at future time points, forming a predicted time series set. Subsequently, the warning unit calculates the microcirculation disorder fluctuation coefficient Bd based on this sequence to judge the stability of the score trend, and establishes a risk fluctuation model for the patient in the future period of time. This mechanism improves the system's recognition ability of "whether the risk state is controllable" and "whether there is a potential for deterioration", making up for the technical shortcoming of insufficient perception of the future state in the traditional nursing path. When the system identifies that the patient's state is unstable, it can actively push an intelligent nursing report, highlighting and prompting doctors and nurses to intervene in a timely manner; if the patient's state is stable, the subsequent follow-up evaluation points are automatically set to achieve rhythmic allocation and precise regulation of nursing resources, comprehensively improving the timeliness of early warning response, the forward-looking nature of nursing intervention, and the intelligent level of nursing management.

[0041] Example 6 Please refer to Figure 1 , specifically: The intelligent nursing module includes a nursing strategy generation and execution unit and a nursing feedback and adjustment unit; The nursing strategy generation and execution unit is used to generate intelligent nursing strategies according to the intelligent nursing report. According to the patient's nursing level and the evaluation result of the patient's physical health status stability, the patients are divided into different nursing strategy levels, including routine nursing strategies, risk defense nursing strategies, enhanced nursing strategies, and emergency nursing strategies; Routine care means making a body position adjustment every 2 hours, collecting microcirculation physiological indicators every 6 hours, and maintaining fluid balance and controlling the infusion rate within the regular shifts, without the need for doctor intervention; The risk prevention and defense nursing strategy refers to mild intensive care, including making a body position adjustment every hour, collecting microcirculation physiological indicators every 2 hours, and performing hemodynamic detection on the patient; The enhanced nursing strategy means setting up a dynamic microcirculation monitoring station for continuous dynamic care, making a body position adjustment every hour, performing continuous intravenous fluid replacement according to the doctor's orders, maintaining CVP > 8 cmH2O, and locally warming the peripheral capillary area, where CVP represents central venous pressure, and CVP > 8 cmH2O represents that the specific parameter of central venous pressure is greater than the normal reference value; The emergency nursing strategy means starting an intermittent pneumatic device for 10 minutes each time, alternating every 30 minutes, elevating the lower limb position by 15°, maintaining blood flow back to the heart, increasing the oxygen inhalation concentration, and arranging a dedicated nurse to regularly patrol the patient area.

[0042] The nursing feedback and adjustment unit is used to perform nursing interventions on the patient according to the routine nursing strategy, risk prevention and defense nursing strategy, enhanced nursing strategy and emergency nursing strategy, collect the physiological data related to the patient's microcirculation disorder after the nursing intervention, store the physiological data related to the patient's microcirculation disorder in the medical database, regularly analyze the change trend of the patient's physiological state after the nursing intervention, and intelligently adjust the nursing measures.

[0043] In the embodiment, by constructing an intelligent nursing module, a closed-loop response mechanism from microcirculation risk identification to the implementation of individualized nursing strategies is realized. The nursing strategy generation and execution unit automatically matches the nursing strategy level according to the patient's nursing level and the evaluation result of the future health state stability, including four categories: routine care, risk prevention and defense care, enhanced care, and emergency care, corresponding to different intensities of body position management, monitoring frequency, fluid regulation, and use of auxiliary devices, ensuring the precise matching of nursing measures with the patient's risk state. After the nursing implementation, the nursing feedback and adjustment unit real-time collects the patient's key microcirculation physiological parameters, such as PPI, CRT, temperature gradient, etc., judges the nursing response effect through trend analysis, and automatically prompts to fine-tune the nursing action or frequency when the patient's state fluctuates. The physiological data is automatically uploaded to the medical database for continuously optimizing the nursing path and subsequent intervention plan. This module effectively solves the problems of "static intervention strategy, lagging response, and lack of data feedback on nursing behavior" in traditional nursing, realizes the intelligence of nursing decision-making driven by data, the dynamicization of nursing path, and the closed-loop of nursing execution, improves the perfusion stability of the patient and the microcirculation management efficiency, and has clinical application value.

[0044] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent analysis and early warning system for clinical nursing safety hazards, characterized in that: It includes a data acquisition and preprocessing module, a feature extraction module, a data analysis and preliminary evaluation module, a health prediction module, and an intelligent nursing module; The data acquisition and preprocessing module is used to collect patients' physiological data, perform preprocessing, and construct a physiological data set S based on the preprocessed patients' physiological data; The feature extraction module is used to obtain the microvascular oscillation factor Zd, the blood flow shear force variation coefficient Jq, and the capillary optical transmission factor Mx according to the physiological data set S; The data analysis and preliminary evaluation module is used to obtain the microcirculation disorder index Wxh based on the microvascular oscillation factor Zd, the blood flow shear force variation coefficient Jq, and the capillary optical transmission factor Mx, and evaluate the current physical state of the patient; The health prediction module is used to construct a time series analysis model, predict the microcirculation disorder index Wxh of the patient in the next period of time, evaluate the future health status of the patient, and issue a risk warning; The intelligent nursing module is used to classify the nursing strategy level of the patient and perform nursing interventions, and real-time monitor the physiological data of the patient during the clinical nursing process to optimize the clinical nursing effect.

2. The intelligent analysis and early warning system for clinical nursing safety hazards according to claim 1, wherein: The data acquisition and preprocessing module includes a data acquisition unit and a preprocessing unit; The data acquisition unit is used to use an intelligent sensor group to monitor the patient's physical condition in real time under the same conditions and obtain the patient's physiological data. Among them, the intelligent sensor group includes a skin pressure sensor, a laser Doppler flowmeter, and a near-infrared spectroscopy analyzer. The patient's physiological data includes local skin pressure , capillary blood flow velocity , blood viscosity , and local tissue optical transmittance ; The preprocessing unit is used to preprocess the obtained patients' physiological data. Among them, the preprocessing includes data correction, outlier removal, signal smoothing, data interpolation correction, and data normalization; Based on the preprocessed patients' physiological data, a physiological data set S is constructed.

3. An intelligent analysis and early warning system for clinical nursing safety hazards according to claim 2, characterized in that: The feature extraction module is used to extract feature indicators according to the physiological data set S, including the microvascular oscillation factor Zd, the capillary optical transmission factor Mx, and the blood flow shear force variation coefficient Jq; The acquisition method of the microvascular oscillation factor Zd is: ； In the formula, represents the time window, represents the capillary blood flow velocity of the patient at time point t, where t ∈ { , }; The acquisition method of the capillary optical transmission factor Mx is: ； In the formula, represents the local tissue optical transmittance, represents the reference optical transmittance of healthy tissue.

4. An intelligent analysis and early warning system for potential safety hazards in clinical nursing according to claim 2, characterized in that: Calculate the blood flow shear force of the patient at time point t based on the physiological data set S , and the specific calculation method is as follows: ； In the formula, represents the blood viscosity at time point t, represents the capillary blood flow velocity at time point t, represents the capillary diameter at time point t; and based on the blood flow shear force of the patient at time point t , obtain the blood flow shear force of the patient during the monitoring period mean value and standard deviation , and calculate the coefficient of variation Jq of the blood flow shear force. The specific calculation method is as follows: ； In the formula, represents the local skin pressure , represents the historical mean value of the local skin pressure of the patient, represents the healthy reference pressure, represents the local pressure adjustment factor.

5. An intelligent analysis and early warning system for potential safety hazards in clinical nursing according to claim 4, characterized in that: The data analysis and preliminary evaluation module includes a microcirculation disorder index calculation unit and a preliminary evaluation unit; The microcirculation disorder index calculation unit is used to perform a summary calculation based on the microvascular oscillation factor Zd, the capillary optical transmission factor Mx, and the blood flow shear force variation coefficient Jq to obtain the microcirculation disorder index Wxh. Among them, the specific acquisition method of the microcirculation disorder index Wxh is: ； In the formula, , and respectively represent the weight coefficients of the microvascular oscillation factor Zd, the capillary optical transmission factor Mx, and the blood flow shear force variation coefficient Jq, represents the correction constant.

6. The intelligent analysis and early warning system for clinical nursing safety hazards according to claim 5, wherein: The preliminary evaluation unit is used to preset a first microcirculation disorder threshold and a second microcirculation disorder threshold , and compare and analyze the first microcirculation disorder threshold and the second microcirculation disorder threshold with the microcirculation disorder index Wxh to evaluate the patient's nursing level. The specific evaluation content is as follows: If the microcirculation disorder index Wxh is less than or equal to the first microcirculation disorder threshold , that is, Wxh ≤ , it is determined that the physiological health status of the patient is in a normal state. At this time, the nursing level of the patient is green, maintain daily care, and continuously monitor the physiological data of the patient; If the microcirculation disorder index Wxh is greater than the first microcirculation disorder threshold , and less than the second microcirculation disorder threshold , that is <Wxh< , then it is determined that the physiological health status of the patient is a risk state. At this time, the nursing level of the patient is yellow. Strengthen nursing measures, adjust the patient's body position, and conduct physiological health prediction of the patient; If the microcirculation disorder index Wxh is greater than or equal to the second microcirculation disorder threshold , it is determined that the physiological health state of the patient is in a dangerous state. At this time, the nursing level of the patient is red, and medical intervention is required immediately for anticoagulation and blood flow promotion treatment.

7. An intelligent analysis and early warning system for clinical nursing safety hazards according to claim 6, characterized in that: The health prediction module includes a microcirculation disorder index prediction unit and a warning unit; The microcirculation disorder index prediction unit is used to obtain the historical physiological data of patients with green and yellow nursing levels according to the medical database, preprocess the historical physiological data of the patients, and obtain the microcirculation disorder index Wxh of the patients in the past period of time based on the preprocessed historical physiological data; Calculate the change rate of the microcirculation disorder index Wxh based on the microcirculation disorder index Wxh of the patient over a past period of time , and the specific calculation method is as follows: ； In the formula, represents the microcirculation disorder index of the patient at the current time point t, represents the past microcirculation disorder index before the time step, represents the time step; According to the weighted moving average method and in combination with the change rate of the microcirculation disorder index Wxh , the microcirculation disorder index of the patient at a future time point is obtained. The specific obtaining method is as follows: ​ ； Among them, k represents a time correction factor.

8. An intelligent analysis and early warning system for potential clinical nursing safety hazards according to claim 7, characterized in that: The warning unit is used to obtain the microcirculation disorder index Wxh of the patient in the next period of time and construct a time series set H. Among them, the specific manifestation form of the time series set H is: ； Among them, represents the microcirculation disorder index of the patient at a future time point; Based on the time series set H, the microcirculation disorder fluctuation coefficient Bd is obtained. Among them, the specific acquisition method of the microcirculation disorder fluctuation coefficient Bd is: ； Where M represents the total number of predicted time points in a future period of time, represents the average value of the microcirculation disorder index of the patient in a future period of time, ={1, 2, 3,..., M}; A preset stability evaluation threshold Wd is set, and the microcirculation disorder fluctuation coefficient Bd is compared and analyzed with the stability evaluation threshold Wd to evaluate the stability of the patient's physical health status. The specific evaluation process is as follows: If the microcirculation disorder fluctuation coefficient Bd is greater than or equal to the stability assessment threshold Wd, it is determined that the patient's physical health status is unstable. At this time, an intelligent nursing report is generated, and the intelligent nursing report is sent to the terminal devices of the attending doctor and the nursing staff and highlighted until the attending doctor and the nursing staff respond. Among them, the intelligent nursing report includes the patient identification, the patient's microcirculation disorder index , the microcirculation disorder fluctuation coefficient Bd, the patient's nursing level, and the assessment result of the patient's physical health status; If the microcirculation disorder fluctuation coefficient Bd is less than the stability assessment threshold Wd, it is determined that the patient's physical health status is stable. At this time, subsequent tracking points are automatically set, that is, the stability of the patient's physical health status is evaluated every 30 minutes, and an intelligent nursing report is generated.

9. An intelligent analysis and early warning system for potential safety hazards in clinical nursing according to claim 8, characterized in that: The intelligent nursing module includes a nursing strategy generation and execution unit and a nursing feedback and adjustment unit; The nursing strategy generation and execution unit is used to generate intelligent nursing strategies based on the intelligent nursing report. According to the patient's nursing level and the evaluation results of the patient's physical health status stability, the patients are divided into different nursing strategy levels, including routine nursing strategies, risk defense nursing strategies, enhanced nursing strategies, and emergency nursing strategies; Routine nursing means that the body position is adjusted every 2 hours within the regular shift, the microcirculation physiological indexes are collected every 6 hours, and the fluid balance is maintained, and no doctor intervention is required; The risk defense nursing strategy refers to mild intensive nursing, including body position adjustment every hour, collection of microcirculation physiological indexes every 2 hours, and hemodynamic detection of the patient; The enhanced nursing strategy means setting up a microcirculation dynamic monitoring station for continuous dynamic nursing, adjusting the body position every hour, performing continuous intravenous fluid replacement according to the doctor's order, maintaining CVP > 8 cmH2O, and locally warming the peripheral capillary area; The emergency nursing strategy means starting an intermittent pneumatic device for 10 minutes each time, alternating every 30 minutes, elevating the lower limb position by 15°, maintaining blood return to the heart, and arranging a dedicated nurse to regularly patrol the patient area.

10. An intelligent analysis and early warning system for clinical nursing safety hazards according to claim 9, characterized in that: The nursing feedback and adjustment unit is used to perform nursing interventions on the patient according to the routine nursing strategy, risk defense nursing strategy, enhanced nursing strategy, and emergency nursing strategy, collect the physiological data related to the patient's microcirculation disorder after the nursing intervention, store the physiological data related to the patient's microcirculation disorder in the medical database, regularly analyze the change trend of the patient's physiological state after the nursing intervention, and intelligently adjust the nursing measures.

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